diff --git a/doc/notebooks/03_tutorial_lbm_formulation.ipynb b/doc/notebooks/03_tutorial_lbm_formulation.ipynb
index 6bee62ef94e6f09c70d962bd14cc9022e2c0f357..c3263a4d02dcd91db01ee341fd5a2d11653f1372 100644
--- a/doc/notebooks/03_tutorial_lbm_formulation.ipynb
+++ b/doc/notebooks/03_tutorial_lbm_formulation.ipynb
@@ -140,7 +140,7 @@
" "
],
"text/plain": [
- "<lbmpy.methods.momentbased.momentbasedmethod.MomentBasedLbMethod at 0x7f1be00e30d0>"
+ "<lbmpy.methods.momentbased.momentbasedmethod.MomentBasedLbMethod at 0x7f6ca1147f40>"
]
},
"execution_count": 2,
@@ -233,7 +233,7 @@
},
{
"data": {
- "image/png": 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\n",
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\n",
"text/plain": [
"<Figure size 1152x432 with 1 Axes>"
]
@@ -326,7 +326,7 @@
" "
],
"text/plain": [
- "<lbmpy.methods.momentbased.momentbasedmethod.MomentBasedLbMethod at 0x7f1b8f8e0400>"
+ "<lbmpy.methods.momentbased.momentbasedmethod.MomentBasedLbMethod at 0x7f6c98ef28b0>"
]
},
"execution_count": 5,
@@ -404,7 +404,7 @@
" "
],
"text/plain": [
- "<lbmpy.methods.momentbased.momentbasedmethod.MomentBasedLbMethod at 0x7f1b8763a4f0>"
+ "<lbmpy.methods.momentbased.momentbasedmethod.MomentBasedLbMethod at 0x7f6c98f10af0>"
]
},
"execution_count": 6,
@@ -444,6 +444,151 @@
"ortho_mrt.is_orthogonal, weighted_ortho_mrt.is_weighted_orthogonal"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Central moment lattice Boltzmann methods\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Another popular method is the cascaded lattice Boltzmann method. The cascaded LBM increases the numerical stability by shifting the collision step to the central moment space. Thus it is applied in the non-moving frame and achieves a better Galilean invariance. Typically the central moment collision operator is derived for compressible LB methods, and a higher-order equilibrium is used. Although incompressible LB methods with a second-order equilibrium can be derived with lbmpy, it violates the Galilean invariance and thus reduces the advantages of the method."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " <table style=\"border:none; width: 100%\">\n",
+ " <tr style=\"border:none\">\n",
+ " <th style=\"border:none\" >Central Moment</th>\n",
+ " <th style=\"border:none\" >Eq. Value </th>\n",
+ " <th style=\"border:none\" >Relaxation Rate</th>\n",
+ " </tr>\n",
+ " <tr style=\"border:none\">\n",
+ " <td style=\"border:none\">$1$</td>\n",
+ " <td style=\"border:none\">$\\rho$</td>\n",
+ " <td style=\"border:none\">$\\omega_{0}$</td>\n",
+ " </tr>\n",
+ "<tr style=\"border:none\">\n",
+ " <td style=\"border:none\">$x$</td>\n",
+ " <td style=\"border:none\">$0$</td>\n",
+ " <td style=\"border:none\">$\\omega_{1}$</td>\n",
+ " </tr>\n",
+ "<tr style=\"border:none\">\n",
+ " <td style=\"border:none\">$y$</td>\n",
+ " <td style=\"border:none\">$0$</td>\n",
+ " <td style=\"border:none\">$\\omega_{2}$</td>\n",
+ " </tr>\n",
+ "<tr style=\"border:none\">\n",
+ " <td style=\"border:none\">$x^{2}$</td>\n",
+ " <td style=\"border:none\">$\\frac{\\rho}{3}$</td>\n",
+ " <td style=\"border:none\">$\\omega_{3}$</td>\n",
+ " </tr>\n",
+ "<tr style=\"border:none\">\n",
+ " <td style=\"border:none\">$y^{2}$</td>\n",
+ " <td style=\"border:none\">$\\frac{\\rho}{3}$</td>\n",
+ " <td style=\"border:none\">$\\omega_{4}$</td>\n",
+ " </tr>\n",
+ "<tr style=\"border:none\">\n",
+ " <td style=\"border:none\">$x y$</td>\n",
+ " <td style=\"border:none\">$0$</td>\n",
+ " <td style=\"border:none\">$\\omega_{5}$</td>\n",
+ " </tr>\n",
+ "<tr style=\"border:none\">\n",
+ " <td style=\"border:none\">$x^{2} y$</td>\n",
+ " <td style=\"border:none\">$0$</td>\n",
+ " <td style=\"border:none\">$\\omega_{6}$</td>\n",
+ " </tr>\n",
+ "<tr style=\"border:none\">\n",
+ " <td style=\"border:none\">$x y^{2}$</td>\n",
+ " <td style=\"border:none\">$0$</td>\n",
+ " <td style=\"border:none\">$\\omega_{7}$</td>\n",
+ " </tr>\n",
+ "<tr style=\"border:none\">\n",
+ " <td style=\"border:none\">$x^{2} y^{2}$</td>\n",
+ " <td style=\"border:none\">$\\frac{\\rho}{9}$</td>\n",
+ " <td style=\"border:none\">$\\omega_{8}$</td>\n",
+ " </tr>\n",
+ "\n",
+ " </table>\n",
+ " "
+ ],
+ "text/plain": [
+ "<lbmpy.methods.momentbased.centralmomentbasedmethod.CentralMomentBasedLbMethod at 0x7f6c98f21790>"
+ ]
+ },
+ "execution_count": 19,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "central_moment_method = create_lb_method(stencil=\"D2Q9\", method=\"central_moment\",\n",
+ " equilibrium_order=4, compressible=True)\n",
+ "central_moment_method"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The shift to the central moment space is done by applying a so-called shift matrix. Usually, this introduces a high numerical overhead. This problem is solved with lbmpy because each transformation stage can be specifically optimised individually. Therefore, it is possible to derive a CLBM with only a little numerical overhead."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/latex": [
+ "$\\displaystyle \\left[\\begin{matrix}1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\- u_{0} & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\- u_{1} & 0 & 1 & 0 & 0 & 0 & 0 & 0 & 0\\\\u_{0}^{2} & - 2 u_{0} & 0 & 1 & 0 & 0 & 0 & 0 & 0\\\\u_{1}^{2} & 0 & - 2 u_{1} & 0 & 1 & 0 & 0 & 0 & 0\\\\u_{0} u_{1} & - u_{1} & - u_{0} & 0 & 0 & 1 & 0 & 0 & 0\\\\- u_{0}^{2} u_{1} & 2 u_{0} u_{1} & u_{0}^{2} & - u_{1} & 0 & - 2 u_{0} & 1 & 0 & 0\\\\- u_{0} u_{1}^{2} & u_{1}^{2} & 2 u_{0} u_{1} & 0 & - u_{0} & - 2 u_{1} & 0 & 1 & 0\\\\u_{0}^{2} u_{1}^{2} & - 2 u_{0} u_{1}^{2} & - 2 u_{0}^{2} u_{1} & u_{1}^{2} & u_{0}^{2} & 4 u_{0} u_{1} & - 2 u_{1} & - 2 u_{0} & 1\\end{matrix}\\right]$"
+ ],
+ "text/plain": [
+ "⎡ 1 0 0 0 0 0 0 0 0⎤\n",
+ "⎢ ⎥\n",
+ "⎢ -u₀ 1 0 0 0 0 0 0 0⎥\n",
+ "⎢ ⎥\n",
+ "⎢ -u₠0 1 0 0 0 0 0 0⎥\n",
+ "⎢ ⎥\n",
+ "⎢ 2 ⎥\n",
+ "⎢ u₀ -2⋅u₀ 0 1 0 0 0 0 0⎥\n",
+ "⎢ ⎥\n",
+ "⎢ 2 ⎥\n",
+ "⎢ u₠0 -2⋅u₠0 1 0 0 0 0⎥\n",
+ "⎢ ⎥\n",
+ "⎢ u₀⋅u₠-u₠-u₀ 0 0 1 0 0 0⎥\n",
+ "⎢ ⎥\n",
+ "⎢ 2 2 ⎥\n",
+ "⎢-u₀ ⋅u₠2⋅u₀⋅u₠u₀ -u₠0 -2⋅u₀ 1 0 0⎥\n",
+ "⎢ ⎥\n",
+ "⎢ 2 2 ⎥\n",
+ "⎢-u₀⋅u₠u₠2⋅u₀⋅u₠0 -u₀ -2⋅u₠0 1 0⎥\n",
+ "⎢ ⎥\n",
+ "⎢ 2 2 2 2 2 2 ⎥\n",
+ "⎣u₀ ⋅u₠-2⋅u₀⋅u₠-2⋅u₀ ⋅u₠u₠u₀ 4⋅u₀⋅u₠-2⋅u₠-2⋅u₀ 1⎦"
+ ]
+ },
+ "execution_count": 20,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "central_moment_method.shift_matrix"
+ ]
+ },
{
"cell_type": "markdown",
"metadata": {},
@@ -458,7 +603,7 @@
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": 9,
"metadata": {},
"outputs": [
{
@@ -477,7 +622,7 @@
" 3â‹…y - 4⎠⎦, ⎣- 15â‹…x - 15â‹…y + 9â‹…âŽx + y ⎠+ 2⎦⎦"
]
},
- "execution_count": 8,
+ "execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
@@ -502,7 +647,7 @@
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": 10,
"metadata": {},
"outputs": [
{
@@ -565,10 +710,10 @@
" "
],
"text/plain": [
- "<lbmpy.methods.momentbased.momentbasedmethod.MomentBasedLbMethod at 0x7f1b874f5e20>"
+ "<lbmpy.methods.momentbased.momentbasedmethod.MomentBasedLbMethod at 0x7f6c98e48d90>"
]
},
- "execution_count": 9,
+ "execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
@@ -586,7 +731,7 @@
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": 11,
"metadata": {},
"outputs": [
{
@@ -649,10 +794,10 @@
" "
],
"text/plain": [
- "<lbmpy.methods.momentbased.momentbasedmethod.MomentBasedLbMethod at 0x7f1b873e9cd0>"
+ "<lbmpy.methods.momentbased.momentbasedmethod.MomentBasedLbMethod at 0x7f6c98e4dac0>"
]
},
- "execution_count": 10,
+ "execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
@@ -696,7 +841,7 @@
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": 12,
"metadata": {},
"outputs": [
{
@@ -759,10 +904,10 @@
" "
],
"text/plain": [
- "<lbmpy.methods.momentbased.momentbasedmethod.MomentBasedLbMethod at 0x7f1b8766a520>"
+ "<lbmpy.methods.momentbased.momentbasedmethod.MomentBasedLbMethod at 0x7f6c98cac970>"
]
},
- "execution_count": 11,
+ "execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
@@ -787,12 +932,12 @@
},
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
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mLSAgQDt16qTvvfeeJQq5bTabNmjQINvcItdH9ujTp49lRk3LqijAueXNm1djYmIsMzJgSkqK1qpVy6XsNWvW1P/+97/6ww8/WGJfmNWNDecWGhpqqae+z549m27UxYyac5GXFUZdzmj0q8yO9Z06ddL333/fMh3Ud911V7a57SMdWekJUVfOYw3D0GbNmunbb7+tf/zxhyXOZdOOuphZu/3223XgwIH6+++/m57bZrNpy5YtXc795ptvWiK3q/tw5+1k06ZNpufO6qGnzD6X27ZtMz13RqMuZrYf7NChg2VGZko7sltmLTAwUCMjI3XMmDG6f/9+s2NnOBpqZrmjo6MtdVPowQcfzDa3c6GUFda3quqYMWNc+ly2atVKhw8fbpkb6X/88YdL+2+rPZzo6r6wTJky+sQTT+jSpUstcd2jqvrss89mmztfvnyamJiokyZNskzBa9qRZTJr9hHRN2zYYIlt5erVq5kWFju3kJAQffDBBy010szLL7/s8n7FSufhWd1Mdm72Yt25c+dappA+7aiLmbX69evr66+/bpl+5eweTnQ+z7IXdFmlmCKjURczO/bbr5V3795tdmyHzApX0jYrXQfZLVq0yKXs9iKoWbNmWerhkawePHdu9iI0q814kVWRt3Oz6iikWRUdO7eKFStacmaDnTt3Zjpwi3NzHs3TStu/zWbTpk2burTvtBfCWuE+l7Nhw4a5fG7Zv39/y822k9VD6s7NPhqm1YrRsip+dW6NGzfWN9980zLn9nbOs0Zm1goVKqR33XWX5R7uHTFiRLbZQ0ND9eGHH7bcQ20TJkzIMre/v7+l6hisgMLFf2kTChdvaa7syJ1bWFiYPvPMM5YYzeu3335zOXeBAgW0Z8+eOnfuXNOLGDdt2uTWOq9ataq+/PLLunbtWlNPYPr37+9W7rJly+pTTz1l6hMYV65c0bCwMLdyN2zYUAcOHGjqjfaxY8e6lblIkSJ677336uzZs00bleTKlStatmxZt3LXq1dP33jjDVM7l7IaFSijZr/p+/7775t6UerKTZm0J7Zt27Y1fWqG5557zq3cIten4LN3hJl13Ek71YsrLTg4WBMTE/WTTz4x7Sk6VzvZ07ZatWqZXgz45Zdfup27SJEi2qNHD506dappI2RkNNV1ds0+cqfZxSRvvfWW2+v89ttv10GDBpk6ZURWo19l1mrXrq19+/bVNWvWmHZDbPPmzW7nrlq1qv7nP/8x9cZAVqNFZtYqVKhg+lO5WY26mFmzwlTYWY26mFmzj5K6cOFC0657Zs2a5XZuK0yFndWoi5k1+2jXZt4wymrUxcxajRo19L///a+pU0p/9tlnbueuV6+e9uvXz9QR37755hu3c9sLjsy87kk7dZsrrUGDBqbfzHL1gdu02/crr7xi6nFeVbVbt25uH3esMOp/ViPpZdScH/Qzc9SxAwcOaGBgoMu5rTQVtisP4ji38uXLa58+fUzvm81qJp+MWt68eTUuLs70h6COHTum+fPndyu7VUaRdOVmsnNzHl3JzNF9Dh8+7NKDrM6tVKlS+uijj+qCBQtMvbHs6sOJzq169eqmn2epul4A5dxq1qypL7/8sunbelajLmbWrDJbRHajLmbU7AMrWOEhblcfPHduRYoU0bvvvltnzJhh+sPzrhZ5OzcrjULqatGxcytUqJB2797dMlNKZzXqYmbbv5Ueslu6dKnb+x/7LDtmX3+oXh91MSQkxCf3n6quP6Se9jNslVkbXC1+dW7h4eH61FNP6ddff236Q2F79+7VPHnyuJzdeSRMsz+/WY04mlHLly+fJiQk6IQJE0yfPejq1atarlw5l7M3atRIBw0aZJnBr8xwMwoXDb1eGAcLMwwjTEQOiIgcOHBAwsLCTE4ET6xbt06WLVsm/v7+kidPHsfXK1euyEsvvSSefBaLFi0q8fHx0rVrV+ncubPkz58/13Nv3rxZli5dKnnz5k3XgoKC5P/+7/9k586dbi2zQIECEh8fL927d5eoqCjJly9frufetGmTLFmyRAICAtK1wMBA6d+/v+zatcvt5YaHh0vXrl3ljjvukJYtW4q/v3+u5t6wYYMsWbJE/P39UzU/Pz85d+6c9OvXz6PlFi5cWGJjY6VLly4SFRUlBQsWzNXcGzdulEWLFqXKa/+6ePFiWbZsmUfLrVixonTp0kUSExOlZcuWkidPnlzNvWnTJlm0aJEjr70lJyfLK6+8IikpKW4vMygoSDp27ChdunSRhIQEKVmyZK5mFhH5448/ZMGCBRmu6+XLl3u0zHLlykliYqIkJiZKmzZtJCAgIJdTX9+fzJ8/X/z8/MTPz08MwxBVlQEDBsjVq1c9WmbdunUlMTFREhISpFGjRmIYRi6nvr6+58+fLyLiWP6RI0fkww8/9HiZ1apVk/j4eElISJDmzZvn+rYtcj33vHnzREQcJ35Hjx6VcePGebzMIkWKSFRUlMTGxkp0dLQUL148t+I62HPbbDZRVbHZbGKz2eS9996Ty5cve7zc+vXrS2xsrMTGxkqTJk1yff+9efNmmTt3riOvPf/q1avlp59+8ni5hQsXls6dOzvWeYkSJXIxtcjWrVvlyy+/dKxr+7aSkpIiI0eOlKSkJI+WaxiGNGvWzLHO69Wrl6ufzx07dsisWbNERBznUPavq1atku+//97jZVevXl3i4uIkLi4u1489f/31l8ycOTPVv9nXy7lz52T48OEeLzs8PFzi4+MlPj5e2rdvL0FBQTnK6mz37t0yY8YMR1bDMBxNROSdd96Rs2fPerTskiVLSlxcnMTHx0unTp0kODg413Lv2bNHpk+fni6zPffHH38sBw4c8GjZRYsWldjYWImPj5eoqCgpVKhQruXeu3evTJs2zZE77dexY8d6nLtgwYISGRkp8fHxEhMTIyEhIbkTWkT27dsnn332Waavf/jhh3L06FGPlp0/f36JiIhwfDZLlSrlacx09u/fL59++qnjZ+frMlWVUaNGyblz5zxadr58+aRjx44SHx8vsbGxUrZs2Rzntfv7779lypQpjpzOX202m7zzzjse78Od13dsbKyULl06VzKLiBw8eFAmTZrk+Nl5fV+7dk2GDh3q0Tm4SOrtOzo6WkJDQ3Oc1+7QoUMyceLEDF+7du2avP3222Kz2TxadrFixSQmJkbi4+MlMjJSChcunJOoqRw+fFgmTJjg+Nl5X5KcnCxvvfWWJCcne7TsUqVKpdp/52Z/xNGjR+WTTz5Jtc923ocPGTLE489lWFiYJCQkSEJCgrRr1y5Xj5cnTpyQjz/+2HGtY/9q/37s2LGye/duy+U+deqUfPTRR47rNOf8fn5+snDhQlmxYoVHyy5ZsqTEx8dLYmKidOzYMVf7f06fPi0ffvihI6f9utjeduzYIWPHjvVo2YUKFZLo6Gjp0qWLREdH5+rn8uzZszJ69OgMc9v7I15++WWPPpt58uSRdu3aOa7tw8PDcy33uXPn5IMPPki1jtOu808++US2bNni0fJr1qzp6P9p3Lix+Pn55Uru8+fPy/vvv58qZ9q2bt06x7mXu4oUKSKxsbGSmJgokZGRuXZOeOHCBRk1alS6z6TzZzMpKUn69u3r0fHHMAxp2rSpJCQkSGJiotSoUSNXrtcuXrwoI0eOTLXvc95/23+eNm2abNy40aP3CAkJkdjYWElISJDOnTtLgQIFci23SMbXDfbvDxw4kKO+oIYNGzr25fXr18/xOr906ZKMGDHC8bPz8tJeR8yYMUO2bt3q0fvkzZtXOnXqJPHx8RIXFydlypTJQWqRy5cvy7vvvuvS76akpMjgwYPl2rVrHr1Xbp5nXb16Vd555x2Xf//y5csydOhQj9+vePHiEhMTI3FxcTnKnpSUlGmOrO5JjRo1yuPr/Hz58kmnTp0c12+ebDPJyckyePDgTF/PKvvatWtlyZIlbr+nXY0aNRzbu7v9tikpKfL2229n+TvZ3Qv8/PPP3b7nZufv7y+tWrVy9A/ddtttLv2dqsqgQYM8es+0PvvsM4/uvYmIBAQESNu2bR35K1asmO3fuJPblf3u0qVL5eeff3Z5mc78/PykefPmjvxZHV/feuutdNtCRr+b9t+y+509e/akugZ0l337j4+Pl+bNm6frQx88eLDjmj2j444r32f1uqrKW2+9JefPn/cof4kSJRz9dREREY7zhKFDhzqOJWmP8bn9de7cubJy5UqP8tv7YxISEiQ2NlamTJkiSUlJ6c5JnL/P6jVPvl+3bl2qfjJ3BAYGSocOHSRv3rxSuXJlKVKkSLpzq6zOtVxp2f3+6tWrU/U7uZu/atWqUqNGDalWrZoEBwdn+B5pz3Fz83feffdd2bBhg0f5Q0NDpWbNmlKrVi0pX758qus15+uItD9n9r27r02aNCndvQ9XlS9fXmrXri316tWTMmXKpKuTsDd7jU1ut4kTJ0rfvn3dzl25cmXp0qWLdO3aVZo1a5br9x2t6uDBg879DeGqejDHC82N6kcaIy4ie548YedOy5cvn3bp0kWnTJmSq0MDezLqhTutQIECevfdd+u8efNydWQPd59Q96SFhoZq79699euvv861p/HdHenPkxYYGKgxMTH6ySef5NoT1p6MMuJuK168uGN6m9x60nfSpEk3NbNhGNqiRQsdNmxYro6yN2XKlJua+2aNIDl16tSbmrts2bL65JNP5vp0WTc7d7FixbRXr146a9asXH2q9Gbn9vPz05YtW+qQIUNy9ckiT0b/cbeFhIRor1699PPPP8+1Y6YnowC42wzD0CZNmuiAAQN03bp1ufIEqSejcnnSwsLC9LHHHtP58+fnyhRlc+fO9Upu+1Pr06dPz5VtxZPRQz1pwcHB2rVrV508ebIePXo0x7k9edrZk5Y3b16NjY3VcePG6T///JPj3J6MyuVJy+0pajwZxdKTZp+C75133tHt27fneD/u6nQyudFyc0ppV6eIzI3WsGFDfeONN3JlH75y5Uqv5W7cuLEOGDAgV0Z9+/nnn72S2X4ePnjw4Fw5T1m7dq1Xcuf2aBfuzJKQk2YfxSu39t8bN270Su4CBQroHXfckWujo2zdutUruQsWLKjdu3fXadOm5cqoOjt37vRK7vz582vXrl11ypQpubK+//rrL6/kdp7COzemhdq7d69XcotcP+68+eabuTI69/79+72Wu3Tp0rk2FfbBgwe9ljswMFAjIyP1o48+yvHoeocOHfJabhHRKlWq6AsvvKA//vhjjkaoO3LkiFdz20c0HDt2bI7W+bFjx7yaW+T6NfKTTz6Zo9GuT5w44fXcIv+bXtHTc/FTp06Zkts+qp6n51lnz541JXdOs1+4cMG03PbWqFEj7d+/v65fv97l66DLly+bnlsk9ZS6roxsnJSUZHpm51a1alV94YUXdPny5VmOEpySkmJ61oyafRTSrKajNTtjVq1SpUqZTint7mjsZjTnKdXt9y3cHVHTzBYUFKTR0dH60UcfuT2SqRWaYRg+sZ3QaLTrrUSJEvroo4/qokWLTJvNxluYKvpf2oTCxVvCyJEjvbZj9Pf31w4dOugHH3yQ407VDz/80Gu5CxQooPfcc0+uFDF6M7eIaOHChbVXr145Lqrzdu7cKqzzRsGlc7MPIT1p0iQ9duyYx7m9UXDp3GrUqKGvvvpqjqdWu9kFl87NXug6bty4HBe63uyCS+dmv5n32Wef5bjY6NNPP/Va7oCAAO3UqZO+//77umfPnhzl9kYBoHMrX768Pv300zmeYnL69OlezW0v3BkyZEiOpqifOXOmV3OLiJYsWVIfeughnT17tp45c8aj3LNnz/Z67qCgII2KitLRo0d7vJ3PmzfP67n9/f21TZs2OmzYMN22bZtH24q3Chedm2EY2rx58xwV7yxZssTruUX+d0Nsw4YNHuX++uuvTcldp04dfe211/SXX37x6HjvyfTEudGqVKmizz//fLY3NDLjzcJF55bTKaW9Wbjo3OzT73311VceFXR7s3DRuZUpU0Z79+7t8bSB3ipcTNvsU6Z7OvWPtwoX07acTnXoyTTtudFyWtCwYcMGr2e2T1s7fPhw3bFjh9uZVVU3b97s9dz+/v7avn17HTVqlO7evduj3Nu2bTNlfbdp0yZH0wR7q+AybbMXA27atMmj7Xv37t2m5K5QoYL26dNHly9f7tH+xJsFl87Nfm3v6VTYf//9tym5Ra7vCwcNGuTRdaY3Cy7TtuLFi+v999+vc+bM0fPnz7uV+/Dhw6bltq/zgQMHul2se/ToUVNzBwcHa7du3XTy5Mlu9XOaVbjo3DwpwDSrcDFtq1Gjhr700ksuT8tsZuFiRtldPUe0QuGicytdurRL1xNWKVx0bnny5NH27dvryJEjM72XYrXCRedWpEgR7dmzp06bNi1df7lVCxedW0ZFdKrWLlx0bvYppadOnaonTpzwuYI0+30Lf39/07PQaDSa1VtwcLDeeeedOm3aNI+uY62OwsV/aRMKF28Jw4YNM23n2KhRI33rrbd0+/btbuceMWKEKZkLFiyo99xzj3711Vce3YB87733TFvf+fPn1zvuuENnzJjh9uhpo0ePNvVAWrNmTe3bt69u3brVrdwfffSRaZntN5hGjhzp9k1fbxdcOrcyZcroE088oWvWrHErs6r3Cy7tzTAMbdasmQ4fPtyjG9XeLLh0bvabed9++63bmVW9W7iYtkVERHi071ZVnTZtmimZ/fz8NCoqSrds2eJR7s8//9yU3Hny5NFOnTrp/PnzPboB6a2RCzNqt912m7722mseXQB9+eWXpuW277+XLFnidm4zChedW1BQkD777LNur/MFCxaYmltEtGnTprp+/Xq3ci9evNj03JUqVdI5c+a49fk0q3DRuZUpU0YnT57sVgGjWYWLzi00NFQnTpzoVm6zChedW6lSpXTatGlubSdmFS46twoVKri9fZtVuOjcqlevrkuXLnU5s6p5hYvOrU6dOrpixQq3cptVuOjcateurT/++KNbuc0qXHRuNWrU0GXLlrmV24zCxYy273nz5rn1ufzjjz9Mz127dm0dN26cSwUYdlu2bDE9d82aNXXQoEFuXdNv377d9NwVKlTQ5557zq2Hhv/880/TcxctWlR79eqlP/30k8u5zSq4dG72EcfGjx/vcvHlvn37TM8tcv1c9j//+Y/Lo6GbWXDp3OwjGrq6rXh7pMisWrly5XTYsGEuzZLj7ZEis2qGYWhsbKxLD5gfP37c9LzOrVChQjpixIhsH4KySuGic6tWrVq2/YZWKlx0bg0aNNDVq1dnmttqhYvOrVy5cjp79uwMz7esWLjo3Pz8/LR3797pRpG2cuGicytcuLCOGjXKcTz1hcJF51akSBEdM2aMXrt2zfQsnrTAwEA1DMP0HDQajUa7+S1PnjwaERGhH3744S1T63UzChfzCACvSElJMeV9y5YtKw0aNJAaNWpI6dKl3f77K1eu3IRU2Tt//rzMmDFDZsyYIWXKlJGRI0fKXXfdJYZhuPT3165du8kJM3fp0iX55ZdfpHDhwpI/f35JSEhwObdZ24mISO3atSUmJkYiIiKkSpUqbv2tWbmLFCkikZGREhMTI5GRkRIcHOzW35uVu27duhITEyMxMTHSqFEjt//eZrPdhFRZCwkJkejoaImJiZHOnTtL/vz53V6GXi/G95qgoCDp0KGDxMbGSkxMjFSsWNGj5Xg7d8OGDSUuLk7i4uKkUaNG4ufn59X390SBAgUkKipKEhISJDo6WkJCQjxeljf/ewsVKiQxMTGO3EWKFPF4Wd7MbRiGNG/eXBISEiQxMVGqV6/u8bK8vX0FBwdLZGSkJCYmSkxMjMfbiqvH1dxUokQJiYuLk/j4eImIiHD7mGOmunXrSnx8vMTFxUmTJk3c/v/u7f2gXUhIiMTExEhcXJx07txZChcu7Nbfm5W7UKFCEhUVJXFxcRIVFSWhoaFu/b1ZuYsXLy6xsbEer2+zVKhQQeLj4yU+Pl7atm0rgYGBbv29GfsTEZF69eo5cjdu3Njtz6UZuQ3DkKZNmzpy165d2+0cZuVu1qyZI3etWrVM+//urgYNGjhyN2zY0Gf2386fyzZt2khQUJBbf29W7sKFC6faf7t7nmLGdZrI9fO5Fi1aSGxsrMTGxrr92TQrt4hI+fLlHbnbt28v+fLlc/lvzcwdHBwsnTp1clzXh4WFufy3ZuY2DENuv/12iYmJkdjYWGnYsKHLf2tmbpH/bSsxMTHSvn17yZPHtdsaZucuWrSoREVFSUxMjFv7FbP2g3Z58+aV9u3bOz6fFSpUcOnvzF7ffn5+0rx5c8c5rav7Q7PXt4hI9erVHX1BLVq0kICAALMjuSSnx04z1ahRw3G+0rx5c/H39zc7kksMw5AmTZo4rvHr1q3rM+e2IiK1atVyrPemTZv6zHoX+d+1fnx8vERHR0vx4sXNjuSW8PBwR79W+/btJW/evGZHckvZsmUd2467541WkHb7KVGihCWOP66yX1evXbvW1PuYAOBrkpOTZdmyZXLs2DE5ePCg/N///Z+ULFnS7FiWQ+Ei4CXJycleeZ88efJIq1atJDo6WqKjoz26meTMjMJFf39/adq0qUREREinTp2kadOmbnfWeLtwMSgoSNq0aSORkZESGRnp8c0wb57wO3e0R0VFSbly5Txeljc7JuvVq+e4OdCsWTOXO6sz4q3cBQoUkIiICMfn0p2bGhnxVm77DY2YmBiPbqin5Y3c4eHhjo71Dh06eFRg6W358uWTTp06SVxcnMTGxkrZsmVzZbk3u9MyPDxcEhISJD4+Xtq1a+f2DenM3OxCuvDwcElMTJSEhASPClwyc7Nz582bVyIiIiQxMVHi4uJy7cLGG53bpUuXdhRZ5lbHqLc65WvXru3Yzj0p+DOLvXA7Pj5eYmNjc3SM97a6des6btg1adIkRzcxvNkBfNtttzlyt2rVymduNNauXdtxw8tXbhrZi7ftNyxq1qzpEzfqAgMDHZ/LuLg4n/lc5s+fXzp37uzYn/hKx1pwcHCq3CVKlDA7kkvy5s0rHTt2dOTO6XWDt9gL5+w3QmvUqJGjz6U399/2QpHY2Fhp2bJljvbf3sxdrFgxR6FIZGSkFCtWzONlefN63t/fX1q1auW4ZsvJtuLtAqmqVas6Cv48Kci183Zu5wc+o6KiPN4fejt3njx5pHXr1o4+CU+3FTMK6erWrevYxps2bepRv5UZuXOjP8WMAowiRYqkKpzzlUKigIAAadu2reMY5O5D5GaqWrWq45zWl6598uTJI23btnVkr1y5stmRXOar5+Qi/9vW7ddvnj5YbpZKlSo5srdu3TrX+hG95fbbb3fkr1evnk9cOzu7/fbbHdcY9evX97n8lStXTrX9+Mr+0q5gwYISGRkpcXFxEhMTI6GhoRIYGOhThYv2QSKGDh0qSUlJZscB8C9SsGBBiYiIcFyL59a931uV4UvV/P9WhmGEicgBEZEDBw74TMc1Ulu9erX8+OOPkpycLCkpKZKSkiLJycly5swZGTduXI6WHRYW5iiI6tixoxQqVCiXUousWLFCfvrpJ7ly5YpcvXpVrly5kqotXbo0V4oba9SoIZ06dZKIiAhp27Ztjv8bVq9eLStWrJBr165l2GbNmiVnz57N0XtUq1ZNoqKiJDIyUtq2bZsrBVJr166V7777zrGNOLfjx4/L5MmTc7T8GjVqOEbNa9WqVa4VGq1bt86R22azpfq6bNkyWb9+vcfLthf92Q/subkPXL9+vSxbtizdur506ZK89957OVp2jRo1HB3srVq1ytVOjd9//12++eabdOt76dKl8ttvv3m83LQjWOZ2J9imTZtk6dKlYrPZRFXFZrPJtWvXZMiQIR4Xd+d0dBFXbN68WZYsWSIi/+uEP3jwoHz44YceLzMsLMxR4NKhQ4eb8oTo9u3bZeHChY71YRiGHDlyREaMGOHxMhs3bizx8fGSkJBw0zq7du7cKQsWLBA/Pz8xDMPx9Y033pBz5855tMwGDRo4CuduVifXX3/9JV999ZX4+fmlyr5ixQr56quvPFpmSEiIxMXFSWJi4k0b4e+vv/6SefPmOTLbc6uqvPbaa3L58mWPllu3bl3HOvdkhKjs7Nq1S+bOnSsikmobNwxDli9f7vjMusveoZ6QkCBxcXG53qG+a9cumTNnjuNn52uxs2fPytChQz1edqlSpRz7lU6dOuXq9rJ792758ssvHcP2i0iqYfyHDRsm58+f92jZ9iJL+w278uXL51ruPXv2yOzZszPMLSLy0UcfyaFDhzxadp48eaRNmzaO3Lfddluu5d67d6/MmjXLkdf5q8j13AcPHvRo2YGBgdK+fXvHDS9XR89xxf79+2XmzJmZvj5q1Cg5evSoR8u2d5bbRyZwdxTLrBw4cEA+//zzVP9m36+oqrz99tseH39CQ0MlNjbWMVJrwYIFc5zX7uDBgzJjxgzHz877wpSUFOnfv79cvXrVo2U7j2TRoUOHXB2J49ChQzJt2rRU/2bPnpSUJP379/f4Jkh4eLgjd7t27XI19+HDh2Xq1KkZvpaUlCQDBgzwuMjEvv+Oj4+Xjh075ur+2zl32v1JcnKyDBw40OPzcOcRlnJ7xJkjR47IlClTMj3uvPPOO3Lx4kWPlh0QECDt2rVzXD/kZqHI0aNHZfLkyY5rnbRfJ0+eLAcOHPB4+c7FUM2aNcu1YnN7bpvNlq6pqnz99dc5uqYvUaKEREdHS2xsrERERORolHNnx44dk0mTJqXKa782ttlssn37dpk3b57Hyw8MDJR27do5ruurVq2aK7lPnDghEyZMSJfX/v2VK1dk1KhROXoP51kemjdvnqMHPu1Onjwp48ePT5fbOf8XX3wh+/bt8/g9SpUq5cjdqVOnXBn9+fTp0zJu3LgMt29727Bhg3z99dcev0f+/PmlU6dOjhEhc6Pf6syZMzJ27FjH/iOjfcrly5dzdH1/M/pTzp07Jx999FGGU3/Zs6uqzJ49W7Zv3+7x+9SsWdNxHt6iRYscb+MXLlyQDz/8MF1mkdTHn7///lsmTpzo8fvYzw3j4uIkIiIix/3fly5dktGjRzty2mV0DTFt2jSP17m/v7+0bt3acb6S02ufK1euyPvvv+/S7yYnJ8ubb77p8SAFxYsXl5iYGImPj8/xqPJXr151ObeIyOXLl2XAgAEev19YWFiq0eU8Pbe9du1alseVjD73qipDhgyRM2fOePSezus9MjLSo209OTlZRo4cme7fM8qb9t9WrVol8+fPd/s9Rf43cqt93btbOJ+SkuLSvjmrZU6ePNnjz2v+/PklIiLCcc1fqlQpl/5OVWX48OEevWdaEyZMkL/++sujv82XL1+q/K7MIvfOO+949F6ZWbRokaxatcqjv/Xz85OWLVs6tp9q1apl+v962LBh6Qr+M6vZcOffd+/eLZMmTXIz+f9UrFjRUeCd0UP/w4cPd1z7ZnTs8eR7559VVUaMGOFxP2Nmg0S8++67jmvftMfJ3P767bffyrp16zzKL3L9IWN7H+/PP//s6CPJ7Bwlt7/fvn27LFq0yOP8oaGhUr58ebntttukSpUqEhQUlO05lruvZ/U7O3fulG+//dbj/IGBgVK1alWpVq2aVK1aVYKDg7PN4nye62rL7G++++472bt3r8f5g4ODpWrVqlK1alWpWLGiBAYGprueyOhnV1/L6vf+/PPPHJ3ji1y/JqxYsaJUqFBBQkJCHNeaN7tduHBBTpw4kaPsIv+b3TI6OjrHD8da2cGDByU8PNz+Y7iqenazwpm7HyKa95uIhMmNOcJvlXnP8T+DBg3Kct77jFqePHm0ffv2OmzYMN28ebPabDZTsq9evdrt7PZWqlQp7dWrl3766ad68OBBr+Zeu3atR5kLFSqkXbt21XHjxum+ffu8mllV9ZVXXnE7c758+TQuLk4//PBD3bNnj9czX7x4UUuVKuV27ho1auh//vMf/f777/Xq1atez/3ee+95vK4/+ugj3bt3r9czX7hwQUuUKOF27rp16+orr7yiP/30k167ds3ruT/99FO3MxcvXlzvvfdenTFjhp48edLrmVVVn3jiCbdzN2nSRAcNGqQbNmwwbb/93HPPuZU5KChIY2NjdezYsV7fVztbvHix28fJiIgIHT16tO7fv9+03CkpKVqzZk23sletWlVffPFF/emnnzQ5Odm07F9++aXb67xjx476/vvvm7IPtEtOTtZq1aq5lb1YsWLaq1cvnTVrlp49e9a07G+++abb+5WGDRvqG2+8oevWrdOUlBRTcn/zzTdu5y5durT27t1b58+frxcuXDAl98aNG93OHRISovfff7/OmjVLz5w5Y0ruzZs3u527ZMmS+vDDD+u8efP0/PnzpuT+9ddf3c5dsWJF7dOnj3777bemnA+qqn799ddu565du7a++uqr+vPPP5u2H585c6bbuRs1aqQDBgzQ33//3bTzlPHjx7ud+/bbb9eBAweaen41duxYt3M3aNBAX3/9dV27dq1p++8pU6a4nfu2227TF154QZcvX65JSUmm5Hb3vNC+H3zooYd0zpw5eu7cOVNyb9++Xf38/NzKbb/G/Pjjj007p7148aKWLFnS7XXeuHFj7d+/v6nbeEJCgtu5w8LC9LHHHtP58+ebdsz86KOP3M4dHBysiYmJOm7cOP37779Nyb1v3z4NCAhwK7dhGNqsWTMdOHCg/vbbb6ZsKzabTVu2bOn2Oq9SpYr+3//9n37zzTd65coVr+dWVR0zZozbuYsVK2Z6f8qRI0c0f/78buUOCgrSqKgoHT16tCn9m3aPPvqo2+u8fv362q9fP/3ll19M2x/+888/GhQU5FbuokWL6r333quff/65nj592pTcqqqfffaZ2+u8Vq1a+sorr+iqVatM7VcZNWqU29mtcG6rqvrdd9+5nb1GjRr60ksv6cqVK01d7zabTevXr+9W9oIFC+qdd96pn376qR4/fty07KqqBw4c0MDAQLfyly1bVp944gldvHixXrp0ydT8e/bs0Tx58rid//HHH9dFixaZnv/SpUtu388qXLiw9ujRQ6dNm6YnTpwwNb+q6n333edWfj8/P23ZsqUOHTpUt27dauq+R9WzfkbnbejixYum5vdkGwoMDNTIyEgdPXq0qf3qdtHR0W7/P6hXr56+9tprumbNGlOPAZ7mr1Klij7//PP63XffmdYHqap68uRJLViwoNv5GzZsqK+//rr++uuvpp1v2mw2bdy4sdvZCxQooN26ddOJEyfqoUOHTMmu6tk9X3v+Ll26mHotboYDBw44r4cwzY2auNxYCI3CRXjm1KlTWrhwYZd2fGFhYdq7d2+dN2+eaR3tzmw2m7Zq1crlHXdwcLDGxsbqqFGjdMuWLaae/EZERLiU2TAMbdy4sb722mu6cuVK027MqKoePXrU5U69qlWrOjpPL1++bFpmVdV3333Xpcz58uXT2NhY0wosnV24cMHlGzNVqlTRPn366Ndff236uh46dKjLn8UuXbroJ598YvoxJSkpSStXruxS7vr16+trr72mq1evNv3CZ//+/S7dkLGfcE+aNEmPHDliamZV1cOHD2vevHmzzR0aGqoPPfSQzps3z7SCImc2m02bNm2abe7ChQvr3XffrZ9//rlpBUVpzZ4926VjTbNmzXTIkCG6bds20zuHVK8XXNatWzfb7IUKFdKePXvqjBkzTL2R4Wz69Oku7VOqVavmKBA1o2g7rdOnT2uRIkWyzZ03b16Ni4vTcePGmVpMbGez2bRZs2YurfPbb79d33zzTf3tt98ssZ137drVpdx169bVvn37mlqE5uzOO+90KbcViqKcxcTEuLQ/bNGihQ4ZMsT06wXV69t38+bNs80dEBCgERER+sEHH5h+HqvqetF83rx5NTY21jL7k6SkJK1QoUK2ufPly6cJCQk6fvx4Uzs07a5evarh4eHZ5g4KCtKYmBj9+OOPTT8HV1W9du2aS+fh/v7+2r59ex0xYoTu3LnT7NhudYTbi/utsh+8++67Xcpdvnx5ffrpp3XJkiWm37hVdf2avlChQnrnnXfq5MmTLXHt4+oDq/7+/tq6dWsdMmSIbtq0yfRjz+XLl7Vs2bIuZa9atao+99xz+u2335pWOOfM1YKuokWL6t13362fffaZ6cUhqq7fHA8ICNBOnTrpqFGjLLE/vHTpkpYpU8al7PXq1dO+fftaoj9FVfWFF15wKXeZMmVMf9jJmavFOPZrtrFjx1rimK+q2qdPH5fWeY0aNfS///2vZa6Tr127prfddptLn0/7+fju3bvNjq2qrheuWO3cVtX1+z558uTRDh066KhRo3TXrl1mx3b46quvXD7nevbZZy1zHLV7+umnXcrfuHFjffPNN0198Cwjrp4PWDW/qwNa2Iucvv/+e1PvG6a1c+dOlx6WKlSokN511106depUS5yP2bnaDyNyfZCIgQMHWm4bcrVo3QoPGWdkzZo1LuW39yl9/PHHlirW+vnnn13Kbx8gyip9HXZ9+/Z1KX/+/Pk1MTFRx48fr//884/ZsVVVdf78+S5lF7n+UKwVCkXt3C26r1mzpqmDL1kBhYv/0iYULt6ysjoABQQEWGJUxcwsWLAgy522v7+/tmjRQt944w1duXKlZXbcy5cvz/Zk8f7779fp06frsWPHzI7r8Pzzz2d5ghgdHa0ffPCB/vXXX2ZHdTh//ryGhIRkmrtSpUr67LPP6tKlSy1xQ8bunXfeyTRzUFCQRkZG6vvvv69//vmn2VEdzp49q8WKFcs0d7Vq1fT555/XZcuWWaojZuLEiZlmto8Y8cknn1jiZrqzxx9/PNPcFSpU0GeffdbUURcyk9XNgZo1a+orr7ximcIcZ99++22mucuVK6fPPvusLlu2zDLHGbusiv/sI1l+8sknevjwYbOjpjNv3rxM17m9Y9eK6zw5OVmrV6+e6XlJ27Zt9d1337VUZ4RdVqM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"text/plain": [
"<Figure size 3200x1200 with 1 Axes>"
]
@@ -821,7 +966,7 @@
},
{
"cell_type": "code",
- "execution_count": 13,
+ "execution_count": 14,
"metadata": {},
"outputs": [
{
@@ -836,7 +981,7 @@
" 6⋅ω₀ "
]
},
- "execution_count": 13,
+ "execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
@@ -849,7 +994,7 @@
},
{
"cell_type": "code",
- "execution_count": 14,
+ "execution_count": 15,
"metadata": {},
"outputs": [
{
@@ -864,7 +1009,7 @@
" 9 3⋅ω₠9⋅ω₀"
]
},
- "execution_count": 14,
+ "execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
diff --git a/doc/notebooks/10_tutorial_conservative_allen_cahn_two_phase.ipynb b/doc/notebooks/10_tutorial_conservative_allen_cahn_two_phase.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..47ff269287c06b97bf44e27451057f31432a3e75
--- /dev/null
+++ b/doc/notebooks/10_tutorial_conservative_allen_cahn_two_phase.ipynb
@@ -0,0 +1,832 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# The conservative Allen-Cahn model for high Reynolds number, two phase flow with large-density and viscosity constrast"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from pystencils.session import *\n",
+ "from lbmpy.session import *\n",
+ "\n",
+ "from pystencils.simp import sympy_cse\n",
+ "from pystencils.boundaries import BoundaryHandling\n",
+ "\n",
+ "from lbmpy.phasefield_allen_cahn.contact_angle import ContactAngle\n",
+ "from lbmpy.phasefield_allen_cahn.force_model import MultiphaseForceModel, CentralMomentMultiphaseForceModel\n",
+ "from lbmpy.phasefield_allen_cahn.kernel_equations import *\n",
+ "from lbmpy.phasefield_allen_cahn.parameter_calculation import calculate_parameters_rti\n",
+ "\n",
+ "from lbmpy.advanced_streaming import LBMPeriodicityHandling\n",
+ "from lbmpy.boundaries import NoSlip, LatticeBoltzmannBoundaryHandling"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "If `pycuda` is installed the simulation automatically runs on GPU"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "try:\n",
+ " import pycuda\n",
+ "except ImportError:\n",
+ " pycuda = None\n",
+ " gpu = False\n",
+ " target = 'cpu'\n",
+ " print('No pycuda installed')\n",
+ "\n",
+ "if pycuda:\n",
+ " gpu = True\n",
+ " target = 'gpu'"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The conservative Allen-Cahn model (CACM) for two-phase flow is based on the work of Fakhari et al. (2017) [Improved locality of the phase-field lattice-Boltzmann model for immiscible fluids at high density ratios](http://dx.doi.org/10.1103/PhysRevE.96.053301). The model can be created for two-dimensional problems as well as three-dimensional problems, which have been described by Mitchell et al. (2018) [Development of a three-dimensional\n",
+ "phase-field lattice Boltzmann method for the study of immiscible fluids at high density ratios](http://dx.doi.org/10.1103/PhysRevE.96.053301). Furthermore, cascaded lattice Boltzmann methods can be combined with the model which was described in [A cascaded phase-field lattice Boltzmann model for the simulation of incompressible, immiscible fluids with high density contrast](http://dx.doi.org/10.1016/j.camwa.2019.08.018)\n",
+ "\n",
+ "\n",
+ "The CACM is suitable for simulating highly complex two phase flow problems with high-density ratios and high Reynolds numbers. In this tutorial, an overview is provided on how to derive the model with lbmpy. For this, the model is defined with two LBM populations. One for the interface tracking, which we call the phase-field LB step and one for recovering the hydrodynamic properties. The latter is called the hydrodynamic LB step."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Geometry Setup\n",
+ "\n",
+ "First of all, the stencils for the phase-field LB step as well as the stencil for the hydrodynamic LB step are defined. According to the stencils, the simulation can be performed in either 2D- or 3D-space. For 2D simulations, only the D2Q9 stencil is supported. For 3D simulations, the D3Q15, D3Q19 and the D3Q27 stencil are supported. Note here that the cascaded LBM can not be derived for D3Q15 stencils."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "stencil_phase = get_stencil(\"D2Q9\")\n",
+ "stencil_hydro = get_stencil(\"D2Q9\")\n",
+ "assert(len(stencil_phase[0]) == len(stencil_hydro[0]))\n",
+ "\n",
+ "dimensions = len(stencil_phase[0])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Definition of the domain size"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# domain \n",
+ "L0 = 256\n",
+ "domain_size = (L0, 4 * L0)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Parameter definition\n",
+ "\n",
+ "The next step is to calculate all parameters which are needed for the simulation. In this example, a Rayleigh-Taylor instability test case is set up. The parameter calculation for this setup is already implemented in lbmpy and can be used with the dimensionless parameters which describe the problem."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# time step\n",
+ "timesteps = 8000\n",
+ "\n",
+ "# reference time\n",
+ "reference_time = 4000\n",
+ "# density of the heavier fluid\n",
+ "rho_H = 1.0\n",
+ "\n",
+ "# calculate the parameters for the RTI\n",
+ "parameters = calculate_parameters_rti(reference_length=L0,\n",
+ " reference_time=reference_time,\n",
+ " density_heavy=rho_H,\n",
+ " capillary_number=0.44,\n",
+ " reynolds_number=3000,\n",
+ " atwood_number=0.998,\n",
+ " peclet_number=1000,\n",
+ " density_ratio=1000,\n",
+ " viscosity_ratio=100)\n",
+ "# get the parameters\n",
+ "rho_L = parameters.get(\"density_light\")\n",
+ "\n",
+ "mu_H = parameters.get(\"dynamic_viscosity_heavy\")\n",
+ "mu_L = parameters.get(\"dynamic_viscosity_light\")\n",
+ "\n",
+ "tau_H = parameters.get(\"relaxation_time_heavy\")\n",
+ "tau_L = parameters.get(\"relaxation_time_light\")\n",
+ "\n",
+ "sigma = parameters.get(\"surface_tension\")\n",
+ "M = parameters.get(\"mobility\")\n",
+ "gravitational_acceleration = parameters.get(\"gravitational_acceleration\")\n",
+ "\n",
+ "\n",
+ "drho3 = (rho_H - rho_L)/3\n",
+ "# interface thickness\n",
+ "W = 5\n",
+ "# coeffcient related to surface tension\n",
+ "beta = 12.0 * (sigma/W)\n",
+ "# coeffcient related to surface tension\n",
+ "kappa = 1.5 * sigma*W\n",
+ "# relaxation rate allen cahn (h)\n",
+ "w_c = 1.0/(0.5 + (3.0 * M))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Fields\n",
+ "\n",
+ "As a next step all fields which are needed get defined. To do so, we create a `datahandling` object. More details about it can be found in the third tutorial of the [pystencils framework]( http://pycodegen.pages.walberla.net/pystencils/). This object holds all fields and manages the kernel runs."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# create a datahandling object\n",
+ "dh = ps.create_data_handling((domain_size), periodicity=(True, False), parallel=False, default_target=target)\n",
+ "\n",
+ "# pdf fields. g is used for hydrodynamics and h for the interface tracking \n",
+ "g = dh.add_array(\"g\", values_per_cell=len(stencil_hydro))\n",
+ "dh.fill(\"g\", 0.0, ghost_layers=True)\n",
+ "h = dh.add_array(\"h\",values_per_cell=len(stencil_phase))\n",
+ "dh.fill(\"h\", 0.0, ghost_layers=True)\n",
+ "\n",
+ "g_tmp = dh.add_array(\"g_tmp\", values_per_cell=len(stencil_hydro))\n",
+ "dh.fill(\"g_tmp\", 0.0, ghost_layers=True)\n",
+ "h_tmp = dh.add_array(\"h_tmp\",values_per_cell=len(stencil_phase))\n",
+ "dh.fill(\"h_tmp\", 0.0, ghost_layers=True)\n",
+ "\n",
+ "# velocity field\n",
+ "u = dh.add_array(\"u\", values_per_cell=dh.dim)\n",
+ "dh.fill(\"u\", 0.0, ghost_layers=True)\n",
+ "\n",
+ "# phase-field\n",
+ "C = dh.add_array(\"C\")\n",
+ "dh.fill(\"C\", 0.0, ghost_layers=True)\n",
+ "C_tmp = dh.add_array(\"C_tmp\")\n",
+ "dh.fill(\"C_tmp\", 0.0, ghost_layers=True)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "As a next step the relaxation time is stated in a symbolic form. It is calculated via interpolation."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# relaxation time and rate\n",
+ "tau = 0.5 + tau_L + (C.center) * (tau_H - tau_L)\n",
+ "s8 = 1/(tau)\n",
+ "\n",
+ "# density for the whole domain\n",
+ "rho = rho_L + (C.center) * (rho_H - rho_L)\n",
+ "\n",
+ "# body force\n",
+ "body_force = [0, 0, 0]\n",
+ "body_force[1] = gravitational_acceleration * rho"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Definition of the lattice Boltzmann methods"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "For both LB steps, a weighted orthogonal MRT (WMRT) method is used. It is also possible to change the method to a simpler SRT scheme or a more complicated CLBM scheme. The CLBM scheme can be obtained by commenting in the python snippets in the notebook cells below. Note here that the hydrodynamic LB step is formulated as an incompressible velocity-based LBM. Thus, the velocity terms can not be removed from the equilibrium in the central moment space."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " <table style=\"border:none; width: 100%\">\n",
+ " <tr style=\"border:none\">\n",
+ " <th style=\"border:none\" >Moment</th>\n",
+ " <th style=\"border:none\" >Eq. Value </th>\n",
+ " <th style=\"border:none\" >Relaxation Rate</th>\n",
+ " </tr>\n",
+ " <tr style=\"border:none\">\n",
+ " <td style=\"border:none\">$1$</td>\n",
+ " <td style=\"border:none\">$\\rho$</td>\n",
+ " <td style=\"border:none\">$0$</td>\n",
+ " </tr>\n",
+ "<tr style=\"border:none\">\n",
+ " <td style=\"border:none\">$x$</td>\n",
+ " <td style=\"border:none\">$\\rho u_{0}$</td>\n",
+ " <td style=\"border:none\">$1.82082623441035$</td>\n",
+ " </tr>\n",
+ "<tr style=\"border:none\">\n",
+ " <td style=\"border:none\">$y$</td>\n",
+ " <td style=\"border:none\">$\\rho u_{1}$</td>\n",
+ " <td style=\"border:none\">$1.82082623441035$</td>\n",
+ " </tr>\n",
+ "<tr style=\"border:none\">\n",
+ " <td style=\"border:none\">$x^{2} - y^{2}$</td>\n",
+ " <td style=\"border:none\">$\\rho u_{0}^{2} - \\rho u_{1}^{2}$</td>\n",
+ " <td style=\"border:none\">$1$</td>\n",
+ " </tr>\n",
+ "<tr style=\"border:none\">\n",
+ " <td style=\"border:none\">$x y$</td>\n",
+ " <td style=\"border:none\">$\\rho u_{0} u_{1}$</td>\n",
+ " <td style=\"border:none\">$1$</td>\n",
+ " </tr>\n",
+ "<tr style=\"border:none\">\n",
+ " <td style=\"border:none\">$3 x^{2} + 3 y^{2} - 2$</td>\n",
+ " <td style=\"border:none\">$3 \\rho u_{0}^{2} + 3 \\rho u_{1}^{2}$</td>\n",
+ " <td style=\"border:none\">$1$</td>\n",
+ " </tr>\n",
+ "<tr style=\"border:none\">\n",
+ " <td style=\"border:none\">$3 x^{2} y - y$</td>\n",
+ " <td style=\"border:none\">$0$</td>\n",
+ " <td style=\"border:none\">$1$</td>\n",
+ " </tr>\n",
+ "<tr style=\"border:none\">\n",
+ " <td style=\"border:none\">$3 x y^{2} - x$</td>\n",
+ " <td style=\"border:none\">$0$</td>\n",
+ " <td style=\"border:none\">$1$</td>\n",
+ " </tr>\n",
+ "<tr style=\"border:none\">\n",
+ " <td style=\"border:none\">$9 x^{2} y^{2} - 3 x^{2} - 3 y^{2} + 1$</td>\n",
+ " <td style=\"border:none\">$0$</td>\n",
+ " <td style=\"border:none\">$1$</td>\n",
+ " </tr>\n",
+ "\n",
+ " </table>\n",
+ " "
+ ],
+ "text/plain": [
+ "<lbmpy.methods.momentbased.momentbasedmethod.MomentBasedLbMethod at 0x7f8d7a8b3dc0>"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "method_phase = create_lb_method(stencil=stencil_phase, method=\"mrt\", compressible=True, weighted=True,\n",
+ " relaxation_rates=[1, 1, 1, 1])\n",
+ "\n",
+ "method_phase.set_first_moment_relaxation_rate(w_c)\n",
+ "\n",
+ "# method_phase = create_lb_method(stencil=stencil_phase, method=\"central_moment\", compressible=True,\n",
+ "# relaxation_rates=[0, w_c, 1, 1, 1], equilibrium_order=4)\n",
+ "method_phase"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " <table style=\"border:none; width: 100%\">\n",
+ " <tr style=\"border:none\">\n",
+ " <th style=\"border:none\" >Moment</th>\n",
+ " <th style=\"border:none\" >Eq. Value </th>\n",
+ " <th style=\"border:none\" >Relaxation Rate</th>\n",
+ " </tr>\n",
+ " <tr style=\"border:none\">\n",
+ " <td style=\"border:none\">$1$</td>\n",
+ " <td style=\"border:none\">$\\rho$</td>\n",
+ " <td style=\"border:none\">$0$</td>\n",
+ " </tr>\n",
+ "<tr style=\"border:none\">\n",
+ " <td style=\"border:none\">$x$</td>\n",
+ " <td style=\"border:none\">$u_{0}$</td>\n",
+ " <td style=\"border:none\">$0$</td>\n",
+ " </tr>\n",
+ "<tr style=\"border:none\">\n",
+ " <td style=\"border:none\">$y$</td>\n",
+ " <td style=\"border:none\">$u_{1}$</td>\n",
+ " <td style=\"border:none\">$0$</td>\n",
+ " </tr>\n",
+ "<tr style=\"border:none\">\n",
+ " <td style=\"border:none\">$x^{2} - y^{2}$</td>\n",
+ " <td style=\"border:none\">$u_{0}^{2} - u_{1}^{2}$</td>\n",
+ " <td style=\"border:none\">$\\frac{1}{0.664004086170318 - 0.147603677553286 {{C}_{(0,0)}}}$</td>\n",
+ " </tr>\n",
+ "<tr style=\"border:none\">\n",
+ " <td style=\"border:none\">$x y$</td>\n",
+ " <td style=\"border:none\">$u_{0} u_{1}$</td>\n",
+ " <td style=\"border:none\">$\\frac{1}{0.664004086170318 - 0.147603677553286 {{C}_{(0,0)}}}$</td>\n",
+ " </tr>\n",
+ "<tr style=\"border:none\">\n",
+ " <td style=\"border:none\">$3 x^{2} + 3 y^{2} - 2$</td>\n",
+ " <td style=\"border:none\">$3 u_{0}^{2} + 3 u_{1}^{2}$</td>\n",
+ " <td style=\"border:none\">$1$</td>\n",
+ " </tr>\n",
+ "<tr style=\"border:none\">\n",
+ " <td style=\"border:none\">$3 x^{2} y - y$</td>\n",
+ " <td style=\"border:none\">$0$</td>\n",
+ " <td style=\"border:none\">$1$</td>\n",
+ " </tr>\n",
+ "<tr style=\"border:none\">\n",
+ " <td style=\"border:none\">$3 x y^{2} - x$</td>\n",
+ " <td style=\"border:none\">$0$</td>\n",
+ " <td style=\"border:none\">$1$</td>\n",
+ " </tr>\n",
+ "<tr style=\"border:none\">\n",
+ " <td style=\"border:none\">$9 x^{2} y^{2} - 3 x^{2} - 3 y^{2} + 1$</td>\n",
+ " <td style=\"border:none\">$0$</td>\n",
+ " <td style=\"border:none\">$1$</td>\n",
+ " </tr>\n",
+ "\n",
+ " </table>\n",
+ " "
+ ],
+ "text/plain": [
+ "<lbmpy.methods.momentbased.momentbasedmethod.MomentBasedLbMethod at 0x7f8d61b4a640>"
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "method_hydro = create_lb_method(stencil=stencil_phase, method=\"mrt\", compressible=False, weighted=True,\n",
+ " relaxation_rates=[s8, 1, 1, 1])\n",
+ "\n",
+ "# method_hydro = create_lb_method(stencil=stencil_phase, method=\"central_moment\", compressible=False,\n",
+ "# relaxation_rates=[s8, 1, 1], equilibrium_order=4)\n",
+ "\n",
+ "method_hydro"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Initialization"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The probability distribution functions (pdfs) are initialised with the equilibrium distribution for the LB methods."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "h_updates = initializer_kernel_phase_field_lb(h, C, u, method_phase, W)\n",
+ "g_updates = initializer_kernel_hydro_lb(g, u, method_hydro)\n",
+ "\n",
+ "h_init = ps.create_kernel(h_updates, target=dh.default_target, cpu_openmp=True).compile()\n",
+ "g_init = ps.create_kernel(g_updates, target=dh.default_target, cpu_openmp=True).compile()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Following this, the phase field is initialised directly in python."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# initialize the domain\n",
+ "def Initialize_distributions():\n",
+ " Nx = domain_size[0]\n",
+ " Ny = domain_size[1]\n",
+ " \n",
+ " for block in dh.iterate(ghost_layers=True, inner_ghost_layers=False):\n",
+ " x = np.zeros_like(block.midpoint_arrays[0])\n",
+ " x[:, :] = block.midpoint_arrays[0]\n",
+ " \n",
+ " y = np.zeros_like(block.midpoint_arrays[1])\n",
+ " y[:, :] = block.midpoint_arrays[1]\n",
+ "\n",
+ " y -= 2 * L0\n",
+ " tmp = 0.1 * Nx * np.cos((2 * np.pi * x) / Nx)\n",
+ " init_values = 0.5 + 0.5 * np.tanh((y - tmp) / (W / 2))\n",
+ " block[\"C\"][:, :] = init_values\n",
+ " block[\"C_tmp\"][:, :] = init_values\n",
+ " \n",
+ " if gpu:\n",
+ " dh.all_to_gpu() \n",
+ " \n",
+ " dh.run_kernel(h_init)\n",
+ " dh.run_kernel(g_init)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<matplotlib.image.AxesImage at 0x7f8d617272e0>"
+ ]
+ },
+ "execution_count": 12,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 1152x432 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "Initialize_distributions()\n",
+ "plt.scalar_field(dh.gather_array(C.name))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Source Terms"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "For the Allen-Cahn LB step, the Allen-Cahn equation needs to be applied as a source term. Here, a simple forcing model is used which is directly applied in the moment space: \n",
+ "$$\n",
+ "F_i^\\phi (\\boldsymbol{x}, t) = \\Delta t \\frac{\\left[1 - 4 \\left(\\phi - \\phi_0\\right)^2\\right]}{\\xi} w_i \\boldsymbol{c}_i \\cdot \\frac{\\nabla \\phi}{|{\\nabla \\phi}|},\n",
+ "$$\n",
+ "where $\\phi$ is the phase-field, $\\phi_0$ is the interface location, $\\Delta t$ it the timestep size $\\xi$ is the interface width, $\\boldsymbol{c}_i$ is the discrete direction from stencil_phase and $w_i$ are the weights. Furthermore, the equilibrium needs to be shifted:\n",
+ "\n",
+ "$$\n",
+ "\\bar{h}^{eq}_\\alpha = h^{eq}_\\alpha - \\frac{1}{2} F^\\phi_\\alpha\n",
+ "$$\n",
+ "\n",
+ "The hydrodynamic force is given by:\n",
+ "$$\n",
+ "F_i (\\boldsymbol{x}, t) = \\Delta t w_i \\frac{\\boldsymbol{c}_i \\boldsymbol{F}}{\\rho c_s^2},\n",
+ "$$\n",
+ "where $\\rho$ is the interpolated density and $\\boldsymbol{F}$ is the source term which consists of the pressure force \n",
+ "$$\n",
+ "\\boldsymbol{F}_p = -p^* c_s^2 \\nabla \\rho,\n",
+ "$$\n",
+ "the surface tension force:\n",
+ "$$\n",
+ "\\boldsymbol{F}_s = \\mu_\\phi \\nabla \\phi\n",
+ "$$\n",
+ "and the viscous force term:\n",
+ "$$\n",
+ "F_{\\mu, i}^{\\mathrm{MRT}} = - \\frac{\\nu}{c_s^2 \\Delta t} \\left[\\sum_{\\beta} c_{\\beta i} c_{\\beta j} \\times \\sum_{\\alpha} \\Omega_{\\beta \\alpha}(g_\\alpha - g_\\alpha^{\\mathrm{eq}})\\right] \\frac{\\partial \\rho}{\\partial x_j}.\n",
+ "$$\n",
+ "\n",
+ "In the above equations $p^*$ is the normalised pressure which can be obtained from the zeroth order moment of the hydrodynamic distribution function $g$. The lattice speed of sound is given with $c_s$ and the chemical potential is $\\mu_\\phi$. Furthermore, the viscosity is $\\nu$ and $\\Omega$ is the moment-based collision operator. Note here that the hydrodynamic equilibrium is also adjusted as shown above for the phase-field distribution functions.\n",
+ "\n",
+ "\n",
+ "For CLBM methods the forcing is applied directly in the central moment space. This is done with the `CentralMomentMultiphaseForceModel`. Furthermore, the GUO force model is applied here to be consistent with [A cascaded phase-field lattice Boltzmann model for the simulation of incompressible, immiscible fluids with high density contrast](http://dx.doi.org/10.1016/j.camwa.2019.08.018). Here we refer to equation D.7 which can be derived for 3D stencils automatically with lbmpy."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "force_h = [f / 3 for f in interface_tracking_force(C, stencil_phase, W)]\n",
+ "force_g = hydrodynamic_force(g, C, method_hydro, tau, rho_H, rho_L, kappa, beta, body_force)\n",
+ "\n",
+ "\n",
+ "if isinstance(method_phase, CentralMomentBasedLbMethod):\n",
+ " force_model_h = CentralMomentMultiphaseForceModel(force=force_h)\n",
+ "else:\n",
+ " force_model_h = MultiphaseForceModel(force=force_h)\n",
+ "\n",
+ "\n",
+ "if isinstance(method_hydro, CentralMomentBasedLbMethod):\n",
+ " force_model_g = CentralMomentMultiphaseForceModel(force=force_g, rho=rho)\n",
+ "else:\n",
+ " force_model_g = MultiphaseForceModel(force=force_g, rho=rho)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Definition of the LB update rules"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The update rule for the phase-field LB step is defined as:\n",
+ "\n",
+ "$$\n",
+ "h_i (\\boldsymbol{x} + \\boldsymbol{c}_i \\Delta t, t + \\Delta t) = h_i(\\boldsymbol{x}, t) + \\Omega_{ij}^h(\\bar{h_j}^{eq} - h_j)|_{(\\boldsymbol{x}, t)} + F_i^\\phi(\\boldsymbol{x}, t).\n",
+ "$$\n",
+ "In our framework the pull scheme is applied as streaming step. Furthermore, the update of the phase-field is directly integrated into the kernel. As a result of this, a second temporary phase-field is needed."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "allen_cahn_lb = get_collision_assignments_phase(lb_method=method_phase,\n",
+ " velocity_input=u,\n",
+ " output={'density': C_tmp},\n",
+ " force_model=force_model_h,\n",
+ " symbolic_fields={\"symbolic_field\": h,\n",
+ " \"symbolic_temporary_field\": h_tmp},\n",
+ " kernel_type='stream_pull_collide')\n",
+ "\n",
+ "# allen_cahn_lb = sympy_cse(allen_cahn_lb)\n",
+ "\n",
+ "ast_allen_cahn_lb = ps.create_kernel(allen_cahn_lb, target=dh.default_target, cpu_openmp=True)\n",
+ "kernel_allen_cahn_lb = ast_allen_cahn_lb.compile()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The update rule for the hydrodynmaic LB step is defined as:\n",
+ "\n",
+ "$$\n",
+ "g_i (\\boldsymbol{x} + \\boldsymbol{c}_i \\Delta t, t + \\Delta t) = g_i(\\boldsymbol{x}, t) + \\Omega_{ij}^g(\\bar{g_j}^{eq} - g_j)|_{(\\boldsymbol{x}, t)} + F_i(\\boldsymbol{x}, t).\n",
+ "$$\n",
+ "\n",
+ "Here, the push scheme is applied which is easier due to the data access required for the viscous force term. Furthermore, the velocity update is directly done in the kernel."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {
+ "scrolled": false
+ },
+ "outputs": [],
+ "source": [
+ "hydro_lb_update_rule = get_collision_assignments_hydro(lb_method=method_hydro,\n",
+ " density=rho,\n",
+ " velocity_input=u,\n",
+ " force_model=force_model_g,\n",
+ " sub_iterations=2,\n",
+ " symbolic_fields={\"symbolic_field\": g,\n",
+ " \"symbolic_temporary_field\": g_tmp},\n",
+ " kernel_type='collide_stream_push')\n",
+ "\n",
+ "# hydro_lb_update_rule = sympy_cse(hydro_lb_update_rule)\n",
+ "\n",
+ "ast_hydro_lb = ps.create_kernel(hydro_lb_update_rule, target=dh.default_target, cpu_openmp=True)\n",
+ "kernel_hydro_lb = ast_hydro_lb.compile()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Boundary Conditions"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "As a last step suitable boundary conditions are applied"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# periodic Boundarys for g, h and C\n",
+ "periodic_BC_C = dh.synchronization_function(C.name, target=dh.default_target, optimization = {\"openmp\": True})\n",
+ "\n",
+ "periodic_BC_g = LBMPeriodicityHandling(stencil=stencil_hydro, data_handling=dh, pdf_field_name=g.name,\n",
+ " streaming_pattern='push')\n",
+ "periodic_BC_h = LBMPeriodicityHandling(stencil=stencil_phase, data_handling=dh, pdf_field_name=h.name,\n",
+ " streaming_pattern='pull')\n",
+ "\n",
+ "# No slip boundary for the phasefield lbm\n",
+ "bh_allen_cahn = LatticeBoltzmannBoundaryHandling(method_phase, dh, 'h',\n",
+ " target=dh.default_target, name='boundary_handling_h',\n",
+ " streaming_pattern='pull')\n",
+ "\n",
+ "# No slip boundary for the velocityfield lbm\n",
+ "bh_hydro = LatticeBoltzmannBoundaryHandling(method_hydro, dh, 'g' ,\n",
+ " target=dh.default_target, name='boundary_handling_g',\n",
+ " streaming_pattern='push')\n",
+ "\n",
+ "contact_angle = BoundaryHandling(dh, C.name, stencil_hydro, target=dh.default_target)\n",
+ "contact = ContactAngle(90, W)\n",
+ "\n",
+ "wall = NoSlip()\n",
+ "if dimensions == 2:\n",
+ " bh_allen_cahn.set_boundary(wall, make_slice[:, 0])\n",
+ " bh_allen_cahn.set_boundary(wall, make_slice[:, -1])\n",
+ "\n",
+ " bh_hydro.set_boundary(wall, make_slice[:, 0])\n",
+ " bh_hydro.set_boundary(wall, make_slice[:, -1])\n",
+ " \n",
+ " contact_angle.set_boundary(contact, make_slice[:, 0])\n",
+ " contact_angle.set_boundary(contact, make_slice[:, -1])\n",
+ "else:\n",
+ " bh_allen_cahn.set_boundary(wall, make_slice[:, 0, :])\n",
+ " bh_allen_cahn.set_boundary(wall, make_slice[:, -1, :])\n",
+ "\n",
+ " bh_hydro.set_boundary(wall, make_slice[:, 0, :])\n",
+ " bh_hydro.set_boundary(wall, make_slice[:, -1, :])\n",
+ " \n",
+ " contact_angle.set_boundary(contact, make_slice[:, 0, :])\n",
+ " contact_angle.set_boundary(contact, make_slice[:, -1, :])\n",
+ "\n",
+ "\n",
+ "bh_allen_cahn.prepare()\n",
+ "bh_hydro.prepare()\n",
+ "contact_angle.prepare()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Full timestep"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# definition of the timestep for the immiscible fluids model\n",
+ "def timeloop():\n",
+ " # Solve the interface tracking LB step with boundary conditions\n",
+ " periodic_BC_h()\n",
+ " bh_allen_cahn() \n",
+ " dh.run_kernel(kernel_allen_cahn_lb)\n",
+ " dh.swap(\"C\", \"C_tmp\")\n",
+ " \n",
+ " # apply the three phase-phase contact angle\n",
+ " contact_angle()\n",
+ " # periodic BC of the phase-field\n",
+ " periodic_BC_C()\n",
+ " \n",
+ " # solve the hydro LB step with boundary conditions\n",
+ " dh.run_kernel(kernel_hydro_lb)\n",
+ " periodic_BC_g()\n",
+ " bh_hydro()\n",
+ "\n",
+ " \n",
+ " # field swaps\n",
+ " dh.swap(\"h\", \"h_tmp\")\n",
+ " dh.swap(\"g\", \"g_tmp\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {
+ "scrolled": false
+ },
+ "outputs": [
+ {
+ "data": {
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type=\"video/mp4\">\n",
+ " Your browser does not support the video tag.\n",
+ "</video>"
+ ],
+ "text/plain": [
+ "<IPython.core.display.HTML object>"
+ ]
+ },
+ "execution_count": 18,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "Initialize_distributions()\n",
+ "\n",
+ "frames = 300\n",
+ "steps_per_frame = (timesteps//frames) + 1\n",
+ "\n",
+ "if 'is_test_run' not in globals():\n",
+ " def run():\n",
+ " for i in range(steps_per_frame):\n",
+ " timeloop()\n",
+ " \n",
+ " if gpu:\n",
+ " dh.to_cpu(\"C\")\n",
+ " return dh.gather_array(C.name)\n",
+ "\n",
+ " animation = plt.scalar_field_animation(run, frames=frames, rescale=True)\n",
+ " set_display_mode('video')\n",
+ " res = display_animation(animation)\n",
+ "else:\n",
+ " timeloop()\n",
+ " res = None\n",
+ "res"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Note that the video is played for 10 seconds while the simulation time is only 2 seconds!"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.8.2"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/doc/notebooks/demo_create_method_from_scratch.ipynb b/doc/notebooks/demo_create_method_from_scratch.ipynb
index b99a251bf099b08823cdc60a48313bc7b53a4e00..8bb9db5c89fff53ac848f40a76c2445c2d8c15bf 100644
--- a/doc/notebooks/demo_create_method_from_scratch.ipynb
+++ b/doc/notebooks/demo_create_method_from_scratch.ipynb
@@ -174,10 +174,11 @@
}
],
"source": [
- "from lbmpy.maxwellian_equilibrium import get_moments_of_continuous_maxwellian_equilibrium\n",
+ "from lbmpy.maxwellian_equilibrium import get_equilibrium_values_of_maxwell_boltzmann_function\n",
"\n",
- "eq_moments = get_moments_of_continuous_maxwellian_equilibrium(moments, order=2, dim=2, \n",
- " c_s_sq=sp.Rational(1, 3))\n",
+ "eq_moments = get_equilibrium_values_of_maxwell_boltzmann_function(moments, order=2, dim=2, \n",
+ " c_s_sq=sp.Rational(1, 3),\n",
+ " space=\"moment\")\n",
"omega = sp.symbols(\"omega\")\n",
"relaxation_info = [(moment, eq_value, omega) for moment, eq_value in zip(moments, eq_moments)]\n",
"relaxation_info"
@@ -248,7 +249,7 @@
" "
],
"text/plain": [
- "<lbmpy.methods.momentbased.momentbasedmethod.MomentBasedLbMethod at 0x7fc9d1ff5d60>"
+ "<lbmpy.methods.momentbased.momentbasedmethod.MomentBasedLbMethod at 0x7f3aca401520>"
]
},
"execution_count": 7,
@@ -282,7 +283,7 @@
"<div>Subexpressions:</div><table style=\"border:none; width: 100%; \"><tr style=\"border:none\"> <td style=\"border:none\">$$vel0Term \\leftarrow f_{4} + f_{6} + f_{8}$$</td> </tr> <tr style=\"border:none\"> <td style=\"border:none\">$$vel1Term \\leftarrow f_{1} + f_{5}$$</td> </tr> <tr style=\"border:none\"> <td style=\"border:none\">$$\\rho \\leftarrow f_{0} + f_{2} + f_{3} + f_{7} + vel0Term + vel1Term$$</td> </tr> <tr style=\"border:none\"> <td style=\"border:none\">$$u_{0} \\leftarrow \\frac{F_{0}}{2} - f_{3} - f_{5} - f_{7} + vel0Term$$</td> </tr> <tr style=\"border:none\"> <td style=\"border:none\">$$u_{1} \\leftarrow \\frac{F_{1}}{2} - f_{2} + f_{6} - f_{7} - f_{8} + vel1Term$$</td> </tr> <tr style=\"border:none\"> <td style=\"border:none\">$$forceTerm_{0} \\leftarrow \\left(1 - \\frac{\\omega}{2}\\right) \\left(- \\frac{4 F_{0} u_{0}}{3} - \\frac{4 F_{1} u_{1}}{3}\\right)$$</td> </tr> <tr style=\"border:none\"> <td style=\"border:none\">$$forceTerm_{1} \\leftarrow \\left(1 - \\frac{\\omega}{2}\\right) \\left(- \\frac{F_{0} u_{0}}{3} + \\frac{F_{1} \\left(2 u_{1} + 1\\right)}{3}\\right)$$</td> </tr> <tr style=\"border:none\"> <td style=\"border:none\">$$forceTerm_{2} \\leftarrow \\left(1 - \\frac{\\omega}{2}\\right) \\left(- \\frac{F_{0} u_{0}}{3} + \\frac{F_{1} \\left(2 u_{1} - 1\\right)}{3}\\right)$$</td> </tr> <tr style=\"border:none\"> <td style=\"border:none\">$$forceTerm_{3} \\leftarrow \\left(1 - \\frac{\\omega}{2}\\right) \\left(\\frac{F_{0} \\left(2 u_{0} - 1\\right)}{3} - \\frac{F_{1} u_{1}}{3}\\right)$$</td> </tr> <tr style=\"border:none\"> <td style=\"border:none\">$$forceTerm_{4} \\leftarrow \\left(1 - \\frac{\\omega}{2}\\right) \\left(\\frac{F_{0} \\left(2 u_{0} + 1\\right)}{3} - \\frac{F_{1} u_{1}}{3}\\right)$$</td> </tr> <tr style=\"border:none\"> <td style=\"border:none\">$$forceTerm_{5} \\leftarrow \\left(1 - \\frac{\\omega}{2}\\right) \\left(\\frac{F_{0} \\left(2 u_{0} - 3 u_{1} - 1\\right)}{12} + \\frac{F_{1} \\left(- 3 u_{0} + 2 u_{1} + 1\\right)}{12}\\right)$$</td> </tr> <tr style=\"border:none\"> <td style=\"border:none\">$$forceTerm_{6} \\leftarrow \\left(1 - \\frac{\\omega}{2}\\right) \\left(\\frac{F_{0} \\left(2 u_{0} + 3 u_{1} + 1\\right)}{12} + \\frac{F_{1} \\left(3 u_{0} + 2 u_{1} + 1\\right)}{12}\\right)$$</td> </tr> <tr style=\"border:none\"> <td style=\"border:none\">$$forceTerm_{7} \\leftarrow \\left(1 - \\frac{\\omega}{2}\\right) \\left(\\frac{F_{0} \\left(2 u_{0} + 3 u_{1} - 1\\right)}{12} + \\frac{F_{1} \\left(3 u_{0} + 2 u_{1} - 1\\right)}{12}\\right)$$</td> </tr> <tr style=\"border:none\"> <td style=\"border:none\">$$forceTerm_{8} \\leftarrow \\left(1 - \\frac{\\omega}{2}\\right) \\left(\\frac{F_{0} \\left(2 u_{0} - 3 u_{1} + 1\\right)}{12} + \\frac{F_{1} \\left(- 3 u_{0} + 2 u_{1} - 1\\right)}{12}\\right)$$</td> </tr> </table><div>Main Assignments:</div><table style=\"border:none; width: 100%; \"><tr style=\"border:none\"> <td style=\"border:none\">$$d_{0} \\leftarrow f_{0} + forceTerm_{0} + \\omega \\left(- f_{5} - f_{6} - f_{7} - f_{8} + \\frac{\\rho u_{0}^{2}}{3} + \\frac{\\rho u_{1}^{2}}{3} + \\frac{\\rho}{9}\\right) - \\omega \\left(- f_{1} - f_{2} - f_{5} - f_{6} - f_{7} - f_{8} + \\rho u_{1}^{2} + \\frac{\\rho}{3}\\right) - \\omega \\left(- f_{3} - f_{4} - f_{5} - f_{6} - f_{7} - f_{8} + \\rho u_{0}^{2} + \\frac{\\rho}{3}\\right) + \\omega \\left(- f_{0} - f_{1} - f_{2} - f_{3} - f_{4} - f_{5} - f_{6} - f_{7} - f_{8} + \\rho\\right)$$</td> </tr> <tr style=\"border:none\"> <td style=\"border:none\">$$d_{1} \\leftarrow f_{1} + forceTerm_{1} - \\frac{\\omega \\left(- f_{5} - f_{6} + f_{7} + f_{8} + \\frac{\\rho u_{1}}{3}\\right)}{2} + \\frac{\\omega \\left(- f_{1} + f_{2} - f_{5} - f_{6} + f_{7} + f_{8} + \\rho u_{1}\\right)}{2} - \\frac{\\omega \\left(- f_{5} - f_{6} - f_{7} - f_{8} + \\frac{\\rho u_{0}^{2}}{3} + \\frac{\\rho u_{1}^{2}}{3} + \\frac{\\rho}{9}\\right)}{2} + \\frac{\\omega \\left(- f_{1} - f_{2} - f_{5} - f_{6} - f_{7} - f_{8} + \\rho u_{1}^{2} + \\frac{\\rho}{3}\\right)}{2}$$</td> </tr> <tr style=\"border:none\"> <td style=\"border:none\">$$d_{2} \\leftarrow f_{2} + forceTerm_{2} + \\frac{\\omega \\left(- f_{5} - f_{6} + f_{7} + f_{8} + \\frac{\\rho u_{1}}{3}\\right)}{2} - \\frac{\\omega \\left(- f_{1} + f_{2} - f_{5} - f_{6} + f_{7} + f_{8} + \\rho u_{1}\\right)}{2} - \\frac{\\omega \\left(- f_{5} - f_{6} - f_{7} - f_{8} + \\frac{\\rho u_{0}^{2}}{3} + \\frac{\\rho u_{1}^{2}}{3} + \\frac{\\rho}{9}\\right)}{2} + \\frac{\\omega \\left(- f_{1} - f_{2} - f_{5} - f_{6} - f_{7} - f_{8} + \\rho u_{1}^{2} + \\frac{\\rho}{3}\\right)}{2}$$</td> </tr> <tr style=\"border:none\"> <td style=\"border:none\">$$d_{3} \\leftarrow f_{3} + forceTerm_{3} + \\frac{\\omega \\left(f_{5} - f_{6} + f_{7} - f_{8} + \\frac{\\rho u_{0}}{3}\\right)}{2} - \\frac{\\omega \\left(f_{3} - f_{4} + f_{5} - f_{6} + f_{7} - f_{8} + \\rho u_{0}\\right)}{2} - \\frac{\\omega \\left(- f_{5} - f_{6} - f_{7} - f_{8} + \\frac{\\rho u_{0}^{2}}{3} + \\frac{\\rho u_{1}^{2}}{3} + \\frac{\\rho}{9}\\right)}{2} + \\frac{\\omega \\left(- f_{3} - f_{4} - f_{5} - f_{6} - f_{7} - f_{8} + \\rho u_{0}^{2} + \\frac{\\rho}{3}\\right)}{2}$$</td> </tr> <tr style=\"border:none\"> <td style=\"border:none\">$$d_{4} \\leftarrow f_{4} + forceTerm_{4} - \\frac{\\omega \\left(f_{5} - f_{6} + f_{7} - f_{8} + \\frac{\\rho u_{0}}{3}\\right)}{2} + \\frac{\\omega \\left(f_{3} - f_{4} + f_{5} - f_{6} + f_{7} - f_{8} + \\rho u_{0}\\right)}{2} - \\frac{\\omega \\left(- f_{5} - f_{6} - f_{7} - f_{8} + \\frac{\\rho u_{0}^{2}}{3} + \\frac{\\rho u_{1}^{2}}{3} + \\frac{\\rho}{9}\\right)}{2} + \\frac{\\omega \\left(- f_{3} - f_{4} - f_{5} - f_{6} - f_{7} - f_{8} + \\rho u_{0}^{2} + \\frac{\\rho}{3}\\right)}{2}$$</td> </tr> <tr style=\"border:none\"> <td style=\"border:none\">$$d_{5} \\leftarrow f_{5} + forceTerm_{5} + \\frac{\\omega \\left(- f_{5} - f_{6} + f_{7} + f_{8} + \\frac{\\rho u_{1}}{3}\\right)}{4} - \\frac{\\omega \\left(f_{5} - f_{6} - f_{7} + f_{8} + \\rho u_{0} u_{1}\\right)}{4} - \\frac{\\omega \\left(f_{5} - f_{6} + f_{7} - f_{8} + \\frac{\\rho u_{0}}{3}\\right)}{4} + \\frac{\\omega \\left(- f_{5} - f_{6} - f_{7} - f_{8} + \\frac{\\rho u_{0}^{2}}{3} + \\frac{\\rho u_{1}^{2}}{3} + \\frac{\\rho}{9}\\right)}{4}$$</td> </tr> <tr style=\"border:none\"> <td style=\"border:none\">$$d_{6} \\leftarrow f_{6} + forceTerm_{6} + \\frac{\\omega \\left(- f_{5} - f_{6} + f_{7} + f_{8} + \\frac{\\rho u_{1}}{3}\\right)}{4} + \\frac{\\omega \\left(f_{5} - f_{6} - f_{7} + f_{8} + \\rho u_{0} u_{1}\\right)}{4} + \\frac{\\omega \\left(f_{5} - f_{6} + f_{7} - f_{8} + \\frac{\\rho u_{0}}{3}\\right)}{4} + \\frac{\\omega \\left(- f_{5} - f_{6} - f_{7} - f_{8} + \\frac{\\rho u_{0}^{2}}{3} + \\frac{\\rho u_{1}^{2}}{3} + \\frac{\\rho}{9}\\right)}{4}$$</td> </tr> <tr style=\"border:none\"> <td style=\"border:none\">$$d_{7} \\leftarrow f_{7} + forceTerm_{7} - \\frac{\\omega \\left(- f_{5} - f_{6} + f_{7} + f_{8} + \\frac{\\rho u_{1}}{3}\\right)}{4} + \\frac{\\omega \\left(f_{5} - f_{6} - f_{7} + f_{8} + \\rho u_{0} u_{1}\\right)}{4} - \\frac{\\omega \\left(f_{5} - f_{6} + f_{7} - f_{8} + \\frac{\\rho u_{0}}{3}\\right)}{4} + \\frac{\\omega \\left(- f_{5} - f_{6} - f_{7} - f_{8} + \\frac{\\rho u_{0}^{2}}{3} + \\frac{\\rho u_{1}^{2}}{3} + \\frac{\\rho}{9}\\right)}{4}$$</td> </tr> <tr style=\"border:none\"> <td style=\"border:none\">$$d_{8} \\leftarrow f_{8} + forceTerm_{8} - \\frac{\\omega \\left(- f_{5} - f_{6} + f_{7} + f_{8} + \\frac{\\rho u_{1}}{3}\\right)}{4} - \\frac{\\omega \\left(f_{5} - f_{6} - f_{7} + f_{8} + \\rho u_{0} u_{1}\\right)}{4} + \\frac{\\omega \\left(f_{5} - f_{6} + f_{7} - f_{8} + \\frac{\\rho u_{0}}{3}\\right)}{4} + \\frac{\\omega \\left(- f_{5} - f_{6} - f_{7} - f_{8} + \\frac{\\rho u_{0}^{2}}{3} + \\frac{\\rho u_{1}^{2}}{3} + \\frac{\\rho}{9}\\right)}{4}$$</td> </tr> </table>"
],
"text/plain": [
- "AssignmentCollection: d_6, d_5, d_8, d_2, d_7, d_1, d_4, d_0, d_3 <- f(f_8, f_5, f_4, f_7, f_2, f_1, f_3, F_0, omega, F_1, f_0, f_6)"
+ "AssignmentCollection: d_1, d_2, d_7, d_4, d_3, d_0, d_8, d_5, d_6 <- f(omega, F_1, F_0, f_5, f_4, f_0, f_7, f_3, f_2, f_1, f_8, f_6)"
]
},
"execution_count": 8,
@@ -310,10 +311,10 @@
{
"data": {
"text/html": [
- "<table style=\"border:none\"><tr><th>Name</th><th>Runtime</th><th>Adds</th><th>Muls</th><th>Divs</th><th>Total</th></tr><tr><td>OriginalTerm</td><td>-</td> <td>293</td> <td>261</td> <td>0</td> <td>554</td> </tr><tr><td>sympy_cse</td><td>44.95 ms</td> <td>114</td> <td>67</td> <td>0</td> <td>181</td> </tr></table>"
+ "<table style=\"border:none\"><tr><th>Name</th><th>Runtime</th><th>Adds</th><th>Muls</th><th>Divs</th><th>Total</th></tr><tr><td>OriginalTerm</td><td>-</td> <td>293</td> <td>261</td> <td>0</td> <td>554</td> </tr><tr><td>sympy_cse</td><td>45.13 ms</td> <td>114</td> <td>67</td> <td>0</td> <td>181</td> </tr></table>"
],
"text/plain": [
- "<pystencils.simp.simplificationstrategy.SimplificationStrategy.create_simplification_report.<locals>.Report at 0x7fc9d1dd6610>"
+ "<pystencils.simp.simplificationstrategy.SimplificationStrategy.create_simplification_report.<locals>.Report at 0x7f3aca401190>"
]
},
"execution_count": 9,
@@ -363,7 +364,7 @@
"<h5 style=\"padding-bottom:10px\">Initial Version</h5> <div style=\"padding-left:20px;\">$$d_{7} \\leftarrow f_{7} + forceTerm_{7} - \\frac{\\omega \\left(- f_{5} - f_{6} + f_{7} + f_{8} + \\frac{\\rho u_{1}}{3}\\right)}{4} + \\frac{\\omega \\left(f_{5} - f_{6} - f_{7} + f_{8} + \\rho u_{0} u_{1}\\right)}{4} - \\frac{\\omega \\left(f_{5} - f_{6} + f_{7} - f_{8} + \\frac{\\rho u_{0}}{3}\\right)}{4} + \\frac{\\omega \\left(- f_{5} - f_{6} - f_{7} - f_{8} + \\frac{\\rho u_{0}^{2}}{3} + \\frac{\\rho u_{1}^{2}}{3} + \\frac{\\rho}{9}\\right)}{4}$$</div><h5 style=\"padding-bottom:10px\">expand</h5> <div style=\"padding-left:20px;\">$$d_{7} \\leftarrow - f_{7} \\omega + f_{7} + forceTerm_{7} + \\frac{\\omega \\rho u_{0}^{2}}{12} + \\frac{\\omega \\rho u_{0} u_{1}}{4} - \\frac{\\omega \\rho u_{0}}{12} + \\frac{\\omega \\rho u_{1}^{2}}{12} - \\frac{\\omega \\rho u_{1}}{12} + \\frac{\\omega \\rho}{36}$$</div><h5 style=\"padding-bottom:10px\">replace_second_order_velocity_products</h5> <div style=\"padding-left:20px;\">$$d_{7} \\leftarrow - f_{7} \\omega + f_{7} + forceTerm_{7} + \\frac{\\omega \\rho u_{0}^{2}}{12} - \\frac{\\omega \\rho u_{0}}{12} + \\frac{\\omega \\rho u_{1}^{2}}{12} - \\frac{\\omega \\rho u_{1}}{12} + \\frac{\\omega \\rho \\left(u0Pu1^{2} - u_{0}^{2} - u_{1}^{2}\\right)}{8} + \\frac{\\omega \\rho}{36}$$</div><h5 style=\"padding-bottom:10px\">expand</h5> <div style=\"padding-left:20px;\">$$d_{7} \\leftarrow - f_{7} \\omega + f_{7} + forceTerm_{7} + \\frac{\\omega \\rho u0Pu1^{2}}{8} - \\frac{\\omega \\rho u_{0}^{2}}{24} - \\frac{\\omega \\rho u_{0}}{12} - \\frac{\\omega \\rho u_{1}^{2}}{24} - \\frac{\\omega \\rho u_{1}}{12} + \\frac{\\omega \\rho}{36}$$</div><h5 style=\"padding-bottom:10px\">factor_relaxation_rates</h5> <div style=\"padding-left:20px;\">$$d_{7} \\leftarrow f_{7} + forceTerm_{7} + \\omega \\left(- f_{7} + \\frac{\\rho u0Pu1^{2}}{8} - \\frac{\\rho u_{0}^{2}}{24} - \\frac{\\rho u_{0}}{12} - \\frac{\\rho u_{1}^{2}}{24} - \\frac{\\rho u_{1}}{12} + \\frac{\\rho}{36}\\right)$$</div><h5 style=\"padding-bottom:10px\">replace_density_and_velocity</h5> <div style=\"padding-left:20px;\">$$d_{7} \\leftarrow f_{7} + forceTerm_{7} + \\omega \\left(- f_{7} + \\frac{\\rho u0Pu1^{2}}{8} - \\frac{\\rho u_{0}^{2}}{24} - \\frac{\\rho u_{0}}{12} - \\frac{\\rho u_{1}^{2}}{24} - \\frac{\\rho u_{1}}{12} + \\frac{\\rho}{36}\\right)$$</div><h5 style=\"padding-bottom:10px\">replace_common_quadratic_and_constant_term</h5> <div style=\"padding-left:20px;\">$$d_{7} \\leftarrow f_{7} + forceTerm_{7} + \\omega \\left(- f_{7} + \\frac{f_{eq common}}{36} + \\frac{\\rho u0Pu1^{2}}{8} - \\frac{\\rho u_{0}}{12} - \\frac{\\rho u_{1}}{12}\\right)$$</div><h5 style=\"padding-bottom:10px\">factor_density_after_factoring_relaxation_times</h5> <div style=\"padding-left:20px;\">$$d_{7} \\leftarrow f_{7} + forceTerm_{7} + \\omega \\left(- f_{7} + \\frac{f_{eq common}}{36} + \\rho \\left(\\frac{u0Pu1^{2}}{8} - \\frac{u_{0}}{12} - \\frac{u_{1}}{12}\\right)\\right)$$</div><h5 style=\"padding-bottom:10px\">subexpression_substitution_in_main_assignments</h5> <div style=\"padding-left:20px;\">$$d_{7} \\leftarrow f_{7} + forceTerm_{7} + \\omega \\left(- f_{7} + \\frac{f_{eq common}}{36} + \\rho \\left(\\frac{u0Pu1^{2}}{8} - \\frac{u0Pu1}{12}\\right)\\right)$$</div><h5 style=\"padding-bottom:10px\">add_subexpressions_for_divisions</h5> <div style=\"padding-left:20px;\">$$d_{7} \\leftarrow f_{7} + forceTerm_{7} + \\omega \\left(- f_{7} + \\frac{f_{eq common}}{36} + \\rho \\left(\\frac{u0Pu1^{2}}{8} - \\frac{u0Pu1}{12}\\right)\\right)$$</div><h5 style=\"padding-bottom:10px\">sympy_cse</h5> <div style=\"padding-left:20px;\">$$d_{7} \\leftarrow f_{7} + forceTerm_{7} + \\omega \\left(\\rho \\left(- \\xi_{95} + \\xi_{96}\\right) + \\xi_{64} + \\xi_{92}\\right)$$</div>"
],
"text/plain": [
- "<pystencils.simp.simplificationstrategy.SimplificationStrategy.show_intermediate_results.<locals>.IntermediateResults at 0x7fc9d1da2880>"
+ "<pystencils.simp.simplificationstrategy.SimplificationStrategy.show_intermediate_results.<locals>.IntermediateResults at 0x7f3aca39b160>"
]
},
"execution_count": 11,
diff --git a/doc/sphinx/lbmpy.bib b/doc/sphinx/lbmpy.bib
index 55a756d145e174d616bbcea53726923c49379eaf..83163c4d84fb34cfe45c9a6132fee546ba92a663 100644
--- a/doc/sphinx/lbmpy.bib
+++ b/doc/sphinx/lbmpy.bib
@@ -73,7 +73,6 @@ keywords = {lbm,multiphase,phasefield},
mendeley-tags = {lbm,multiphase,phasefield},
pages = {1--11},
title = {{Ternary free-energy lattice Boltzmann model with tunable surface tensions and contact angles}},
-volume = {033305},
year = {2016}
}
@@ -81,21 +80,27 @@ year = {2016}
author = {Geier, Martin and Sch{\"{o}}nherr, Martin and Pasquali, Andrea and Krafczyk, Manfred},
title = {{The cumulant lattice Boltzmann equation in three dimensions: Theory and validation}},
journal = {Computers \& Mathematics with Applications},
+volume = {70},
+number = {4},
+pages = {507-547},
year = {2015},
-doi = {10.1016/j.camwa.2015.05.001}
+issn = {0898-1221},
+doi = {10.1016/j.camwa.2015.05.001},
}
@Article{Coreixas2019,
- author = {Christophe Coreixas and Bastien Chopard and Jonas Latt},
- journal = {Physical Review E},
- title = {Comprehensive comparison of collision models in the lattice Boltzmann framework: Theoretical investigations},
- year = {2019},
- month = {sep},
- number = {3},
- pages = {033305},
- volume = {100},
- doi = {10.1103/physreve.100.033305},
- publisher = {American Physical Society ({APS})},
+ title = {Comprehensive comparison of collision models in the lattice Boltzmann framework: Theoretical investigations},
+ author = {Coreixas, Christophe and Chopard, Bastien and Latt, Jonas},
+ journal = {Phys. Rev. E},
+ volume = {100},
+ issue = {3},
+ pages = {033305},
+ numpages = {46},
+ year = {2019},
+ month = {Sep},
+ publisher = {American Physical Society},
+ doi = {10.1103/PhysRevE.100.033305},
+ url = {https://link.aps.org/doi/10.1103/PhysRevE.100.033305}
}
@PhdThesis{Geier2006,
@@ -104,3 +109,14 @@ doi = {10.1016/j.camwa.2015.05.001}
title = {Ab inito derivation of the cascaded lattice Boltzmann automaton},
year = {2006},
}
+
+@article{Fakhari2018,
+title = {A phase-field lattice {Boltzmann} model for simulating multiphase flows in porous media: Application and comparison to experiments of {CO2} sequestration at pore scale},
+journal = {Advances in Water Resources},
+volume = {114},
+pages = {119-134},
+year = {2018},
+issn = {0309-1708},
+doi = {10.1016/j.advwatres.2018.02.005},
+author = {Fakhari, A. and Li, Y. and Bolster, D. and Christensen, K. T.},
+}
diff --git a/doc/sphinx/maxwellian_equilibrium.rst b/doc/sphinx/maxwellian_equilibrium.rst
index b2656cb6eb62b11eafa6e60ce82a617904a15d2d..3d9a3b2d98a5ede09cd145babd71d1ded6f375ee 100644
--- a/doc/sphinx/maxwellian_equilibrium.rst
+++ b/doc/sphinx/maxwellian_equilibrium.rst
@@ -11,7 +11,7 @@ Maxwellian Equilibrium
.. autofunction:: lbmpy.maxwellian_equilibrium.continuous_maxwellian_equilibrium
- .. autofunction:: lbmpy.maxwellian_equilibrium.get_moments_of_continuous_maxwellian_equilibrium
+ .. autofunction:: lbmpy.maxwellian_equilibrium.get_equilibrium_values_of_maxwell_boltzmann_function
.. autofunction:: lbmpy.maxwellian_equilibrium.get_moments_of_discrete_maxwellian_equilibrium
diff --git a/doc/sphinx/tutorials.rst b/doc/sphinx/tutorials.rst
index 0a8f6e3c429fa5c905d7ec779612b3e695b0c309..4d7f260b31982cb241b3785be4d6fa90998fb679 100644
--- a/doc/sphinx/tutorials.rst
+++ b/doc/sphinx/tutorials.rst
@@ -17,6 +17,7 @@ You can open the notebooks directly to play around with the code examples.
/notebooks/07_tutorial_thermal_lbm.ipynb
/notebooks/08_tutorial_shanchen_twophase.ipynb
/notebooks/09_tutorial_shanchen_twocomponent.ipynb
+ /notebooks/10_tutorial_conservative_allen_cahn_two_phase.ipynb
/notebooks/demo_stencils.ipynb
/notebooks/demo_create_method_from_scratch.ipynb
/notebooks/demo_moments_cumulants_and_maxwellian_equilibrium.ipynb
diff --git a/lbmpy/continuous_distribution_measures.py b/lbmpy/continuous_distribution_measures.py
index c89960fa8935cd317291780fbc2ca84c22c989b7..461967bbcfbe68db2930e480a092c8465e5b1582 100644
--- a/lbmpy/continuous_distribution_measures.py
+++ b/lbmpy/continuous_distribution_measures.py
@@ -6,11 +6,11 @@ import sympy as sp
from lbmpy.moments import polynomial_to_exponent_representation
from pystencils.cache import disk_cache, memorycache
-from pystencils.sympyextensions import complete_the_squares_in_exp
+from pystencils.sympyextensions import complete_the_squares_in_exp, scalar_product
@memorycache()
-def moment_generating_function(generating_function, symbols, symbols_in_result):
+def moment_generating_function(generating_function, symbols, symbols_in_result, velocity=None):
r"""
Computes the moment generating function of a probability distribution. It is defined as:
@@ -21,6 +21,8 @@ def moment_generating_function(generating_function, symbols, symbols_in_result):
generating_function: sympy expression
symbols: a sequence of symbols forming the vector x
symbols_in_result: a sequence forming the vector t
+ velocity: if the generating function generates central moments, the velocity needs to be substracted. Thus the
+ velocity symbols need to be passed. All generating functions need to have the same parameters.
Returns:
transformation result F: an expression that depends now on symbols_in_result
@@ -55,9 +57,27 @@ def moment_generating_function(generating_function, symbols, symbols_in_result):
return sp.simplify(result)
-def cumulant_generating_function(func, symbols, symbols_in_result):
+def central_moment_generating_function(func, symbols, symbols_in_result, velocity=sp.symbols("u_:3")):
+ r"""
+ Computes central moment generating func, which is defined as:
+
+ .. math ::
+ K( \vec{\Xi} ) = \exp ( - \vec{\Xi} \cdot \vec{u} ) M( \vec{\Xi}.
+
+ For parameter description see :func:`moment_generating_function`.
"""
- Computes cumulant generating func, which is the logarithm of the moment generating func.
+ argument = - scalar_product(symbols_in_result, velocity)
+
+ return sp.exp(argument) * moment_generating_function(func, symbols, symbols_in_result)
+
+
+def cumulant_generating_function(func, symbols, symbols_in_result, velocity=None):
+ r"""
+ Computes cumulant generating func, which is the logarithm of the moment generating func:
+
+ .. math ::
+ C(\vec{\Xi}) = \log M(\vec{\Xi})
+
For parameter description see :func:`moment_generating_function`.
"""
return sp.ln(moment_generating_function(func, symbols, symbols_in_result))
@@ -93,16 +113,16 @@ def multi_differentiation(generating_function, index, symbols):
@memorycache(maxsize=512)
-def __continuous_moment_or_cumulant(func, moment, symbols, generating_function):
+def __continuous_moment_or_cumulant(func, moment, symbols, generating_function, velocity=sp.symbols("u_:3")):
if type(moment) is tuple and not symbols:
symbols = sp.symbols("xvar yvar zvar")
dim = len(moment) if type(moment) is tuple else len(symbols)
# not using sp.Dummy here - since it prohibits caching
- t = tuple([sp.Symbol("tmpvar_%d" % i, ) for i in range(dim)])
+ t = sp.symbols(f"tmpvar_:{dim}")
symbols = symbols[:dim]
- generating_function = generating_function(func, symbols, t)
+ generating_function = generating_function(func, symbols, t, velocity=velocity)
if type(moment) is tuple:
return multi_differentiation(generating_function, moment, t)
@@ -128,6 +148,18 @@ def continuous_moment(func, moment, symbols=None):
return __continuous_moment_or_cumulant(func, moment, symbols, moment_generating_function)
+def continuous_central_moment(func, moment, symbols=None, velocity=sp.symbols("u_:3")):
+ """Computes central moment of given function.
+
+ Args:
+ func: function to compute moments of
+ moment: tuple or polynomial describing the moment
+ symbols: if moment is given as polynomial, pass the moment symbols, i.e. the dof of the polynomial
+ """
+ return __continuous_moment_or_cumulant(func, moment, symbols, central_moment_generating_function,
+ velocity=velocity)
+
+
def continuous_cumulant(func, moment, symbols=None):
"""Computes cumulant of continuous function.
diff --git a/lbmpy/creationfunctions.py b/lbmpy/creationfunctions.py
index 78d48ad416a208c2f7bd028c3f88d9614698706c..a08f9ad9d7713002c9797aea24b816e090faf6ac 100644
--- a/lbmpy/creationfunctions.py
+++ b/lbmpy/creationfunctions.py
@@ -198,7 +198,8 @@ import lbmpy.forcemodels as forcemodels
import lbmpy.methods.centeredcumulant.force_model as cumulant_force_model
from lbmpy.fieldaccess import CollideOnlyInplaceAccessor, PdfFieldAccessor, PeriodicTwoFieldsAccessor
from lbmpy.fluctuatinglb import add_fluctuations_to_collision_rule
-from lbmpy.methods import (create_mrt_orthogonal, create_mrt_raw, create_srt, create_trt, create_trt_kbc)
+from lbmpy.methods import (create_mrt_orthogonal, create_mrt_raw, create_central_moment,
+ create_srt, create_trt, create_trt_kbc)
from lbmpy.methods.abstractlbmethod import RelaxationInfo
from lbmpy.methods.centeredcumulant import CenteredCumulantBasedLbMethod
from lbmpy.methods.momentbased.moment_transforms import PdfsToCentralMomentsByShiftMatrix
@@ -417,7 +418,7 @@ def create_lb_method(**params):
'equilibrium_order': params['equilibrium_order'],
'force_model': force_model,
'maxwellian_moments': params['maxwellian_moments'],
- 'c_s_sq': params['c_s_sq'],
+ 'c_s_sq': params['c_s_sq']
}
cumulant_params = {
@@ -454,6 +455,8 @@ def create_lb_method(**params):
nested_moments = params['nested_moments'] if 'nested_moments' in params else None
method = create_mrt_orthogonal(stencil_entries, relaxation_rate_getter, weighted=weighted,
nested_moments=nested_moments, **common_params)
+ elif method_name.lower() == 'central_moment':
+ method = create_central_moment(stencil_entries, relaxation_rates, **common_params)
elif method_name.lower() == 'mrt_raw':
method = create_mrt_raw(stencil_entries, relaxation_rates, **common_params)
elif method_name.lower().startswith('trt-kbc-n'):
@@ -529,7 +532,7 @@ def force_model_from_string(force_model_name, force_values):
'silva': forcemodels.Buick,
'edm': forcemodels.EDM,
'schiller': forcemodels.Schiller,
- 'cumulant': cumulant_force_model.CenteredCumulantForceModel
+ 'cumulant': cumulant_force_model.CenteredCumulantForceModel,
}
if force_model_name.lower() not in force_model_dict:
raise ValueError("Unknown force model %s" % (force_model_name,))
@@ -682,6 +685,6 @@ def update_with_default_parameters(params, opt_params=None, fail_on_unknown_para
stencil_entries = stencil_param
else:
stencil_entries = get_stencil(params_result['stencil'])
- params_result['relaxation_rates'] = sp.symbols("omega_:%d" % len(stencil_entries))
+ params_result['relaxation_rates'] = sp.symbols(f"omega_:{len(stencil_entries)}")
return params_result, opt_params_result
diff --git a/lbmpy/cumulants.py b/lbmpy/cumulants.py
index 954463609df654f5a8b1c44632ee59f6f50ac7ab..e4c084f78a1deaa1aa95ce648727acd8a800b185 100644
--- a/lbmpy/cumulants.py
+++ b/lbmpy/cumulants.py
@@ -124,7 +124,7 @@ def discrete_cumulant(func, cumulant, stencil):
assert len(stencil) == len(func)
dim = len(stencil[0])
- wave_numbers = tuple([sp.Symbol("Xi_%d" % (i,)) for i in range(dim)])
+ wave_numbers = sp.symbols(f"Xi_:{dim}")
generating_function = __get_discrete_cumulant_generating_function(func, stencil, wave_numbers)
if type(cumulant) is tuple:
diff --git a/lbmpy/maxwellian_equilibrium.py b/lbmpy/maxwellian_equilibrium.py
index a210ceeb9f25807f04d81feb4c498937e24bbb05..e719e7c53898f851b9d8c26bfaa951a2fb9451b8 100644
--- a/lbmpy/maxwellian_equilibrium.py
+++ b/lbmpy/maxwellian_equilibrium.py
@@ -10,6 +10,10 @@ import sympy as sp
from sympy import Rational as R
from pystencils.cache import disk_cache
+from pystencils.sympyextensions import remove_higher_order_terms
+
+from lbmpy.moments import MOMENT_SYMBOLS
+from lbmpy.continuous_distribution_measures import continuous_moment, continuous_central_moment, continuous_cumulant
def get_weights(stencil, c_s_sq=sp.Rational(1, 3)):
@@ -50,7 +54,7 @@ get_weights.weights = {
@disk_cache
-def discrete_maxwellian_equilibrium(stencil, rho=sp.Symbol("rho"), u=tuple(sp.symbols("u_0 u_1 u_2")), order=2,
+def discrete_maxwellian_equilibrium(stencil, rho=sp.Symbol("rho"), u=sp.symbols("u_:3"), order=2,
c_s_sq=sp.Symbol("c_s") ** 2, compressible=True):
"""
Returns the common discrete LBM equilibrium as a list of sympy expressions
@@ -101,18 +105,19 @@ def discrete_maxwellian_equilibrium(stencil, rho=sp.Symbol("rho"), u=tuple(sp.sy
@disk_cache
-def generate_equilibrium_by_matching_moments(stencil, moments, rho=sp.Symbol("rho"), u=tuple(sp.symbols("u_0 u_1 u_2")),
+def generate_equilibrium_by_matching_moments(stencil, moments, rho=sp.Symbol("rho"), u=sp.symbols("u_:3"),
c_s_sq=sp.Symbol("c_s") ** 2, order=None):
"""
Computes discrete equilibrium, by setting the discrete moments to values taken from the continuous Maxwellian.
The number of moments has to match the number of directions in the stencil. For documentation of other parameters
- see :func:`get_moments_of_continuous_maxwellian_equilibrium`
+ see :func:`get_equilibrium_values_of_maxwell_boltzmann_function`
"""
from lbmpy.moments import moment_matrix
dim = len(stencil[0])
Q = len(stencil)
assert len(moments) == Q, "Moment count(%d) does not match stencil size(%d)" % (len(moments), Q)
- continuous_moments_vector = get_moments_of_continuous_maxwellian_equilibrium(moments, dim, rho, u, c_s_sq, order)
+ continuous_moments_vector = get_equilibrium_values_of_maxwell_boltzmann_function(moments, dim, rho, u, c_s_sq,
+ order, space="moment")
continuous_moments_vector = sp.Matrix(continuous_moments_vector)
M = moment_matrix(moments, stencil)
assert M.rank() == Q, "Rank of moment matrix (%d) does not match stencil size (%d)" % (M.rank(), Q)
@@ -121,8 +126,8 @@ def generate_equilibrium_by_matching_moments(stencil, moments, rho=sp.Symbol("rh
@disk_cache
def continuous_maxwellian_equilibrium(dim=3, rho=sp.Symbol("rho"),
- u=tuple(sp.symbols("u_0 u_1 u_2")),
- v=tuple(sp.symbols("v_0 v_1 v_2")),
+ u=sp.symbols("u_:3"),
+ v=sp.symbols("v_:3"),
c_s_sq=sp.Symbol("c_s") ** 2):
"""
Returns sympy expression of Maxwell Boltzmann distribution
@@ -141,15 +146,14 @@ def continuous_maxwellian_equilibrium(dim=3, rho=sp.Symbol("rho"),
return rho / (2 * sp.pi * c_s_sq) ** (sp.Rational(dim, 2)) * sp.exp(- vel_term / (2 * c_s_sq))
-# -------------------------------- Equilibrium moments/cumulants ------------------------------------------------------
-
-
+# -------------------------------- Equilibrium moments ----------------------------------------------------------------
@disk_cache
-def get_moments_of_continuous_maxwellian_equilibrium(moments, dim, rho=sp.Symbol("rho"),
- u=tuple(sp.symbols("u_0 u_1 u_2")),
- c_s_sq=sp.Symbol("c_s") ** 2, order=None):
+def get_equilibrium_values_of_maxwell_boltzmann_function(moments, dim, rho=sp.Symbol("rho"),
+ u=sp.symbols("u_:3"),
+ c_s_sq=sp.Symbol("c_s") ** 2, order=None,
+ space="moment"):
"""
- Computes moments of the continuous Maxwell Boltzmann equilibrium distribution
+ Computes equilibrium values from the continuous Maxwell Boltzmann equilibrium.
Args:
moments: moments to compute, either in polynomial or exponent-tuple form
@@ -159,19 +163,27 @@ def get_moments_of_continuous_maxwellian_equilibrium(moments, dim, rho=sp.Symbol
c_s_sq: symbol for speed of sound squared, defaults to symbol c_s**2
order: if this parameter is not None, terms that have a higher polynomial order in the macroscopic velocity
are removed
+ space: function space of the equilibrium values. Either moment, central moment or cumulant space are supported.
- >>> get_moments_of_continuous_maxwellian_equilibrium( ( (0,0,0), (1,0,0), (0,1,0), (0,0,1), (2,0,0) ), dim=3 )
+ >>> get_equilibrium_values_of_maxwell_boltzmann_function( ( (0,0,0), (1,0,0), (0,1,0), (0,0,1), (2,0,0) ), dim=3 )
[rho, rho*u_0, rho*u_1, rho*u_2, rho*(c_s**2 + u_0**2)]
"""
- from pystencils.sympyextensions import remove_higher_order_terms
- from lbmpy.moments import MOMENT_SYMBOLS
- from lbmpy.continuous_distribution_measures import continuous_moment
-
# trick to speed up sympy integration (otherwise it takes multiple minutes, or aborts):
# use a positive, real symbol to represent c_s_sq -> then replace this symbol afterwards with the real c_s_sq
c_s_sq_helper = sp.Symbol("csq_helper", positive=True, real=True)
mb = continuous_maxwellian_equilibrium(dim, rho, u, MOMENT_SYMBOLS[:dim], c_s_sq_helper)
- result = [continuous_moment(mb, moment, MOMENT_SYMBOLS[:dim]).subs(c_s_sq_helper, c_s_sq) for moment in moments]
+ if space == "moment":
+ result = [continuous_moment(mb, moment, MOMENT_SYMBOLS[:dim]).subs(c_s_sq_helper, c_s_sq)
+ for moment in moments]
+ elif space == "central moment":
+ result = [continuous_central_moment(mb, moment, MOMENT_SYMBOLS[:dim], velocity=u).subs(c_s_sq_helper, c_s_sq)
+ for moment in moments]
+ elif space == "cumulant":
+ result = [continuous_cumulant(mb, moment, MOMENT_SYMBOLS[:dim]).subs(c_s_sq_helper, c_s_sq)
+ for moment in moments]
+ else:
+ raise ValueError("Only moment, central moment or cumulant space are supported")
+
if order is not None:
result = [remove_higher_order_terms(r, order=order, symbols=u) for r in result]
@@ -180,7 +192,7 @@ def get_moments_of_continuous_maxwellian_equilibrium(moments, dim, rho=sp.Symbol
@disk_cache
def get_moments_of_discrete_maxwellian_equilibrium(stencil, moments,
- rho=sp.Symbol("rho"), u=tuple(sp.symbols("u_0 u_1 u_2")),
+ rho=sp.Symbol("rho"), u=sp.symbols("u_:3"),
c_s_sq=sp.Symbol("c_s") ** 2, order=None, compressible=True):
"""Compute moments of discrete maxwellian equilibrium.
@@ -235,32 +247,12 @@ def compressible_to_incompressible_moment_value(term, rho, u):
res += t
return res
-
-# -------------------------------- Equilibrium moments -----------------------------------------------------------------
-
-
-def get_cumulants_of_continuous_maxwellian_equilibrium(cumulants, dim, rho=sp.Symbol("rho"),
- u=tuple(sp.symbols("u_0 u_1 u_2")), c_s_sq=sp.Symbol("c_s") ** 2,
- order=None):
- from lbmpy.moments import MOMENT_SYMBOLS
- from lbmpy.continuous_distribution_measures import continuous_cumulant
- from pystencils.sympyextensions import remove_higher_order_terms
-
- # trick to speed up sympy integration (otherwise it takes multiple minutes, or aborts):
- # use a positive, real symbol to represent c_s_sq -> then replace this symbol afterwards with the real c_s_sq
- c_s_sq_helper = sp.Symbol("csq_helper", positive=True, real=True)
- mb = continuous_maxwellian_equilibrium(dim, rho, u, MOMENT_SYMBOLS[:dim], c_s_sq_helper)
- result = [continuous_cumulant(mb, cumulant, MOMENT_SYMBOLS[:dim]).subs(c_s_sq_helper, c_s_sq)
- for cumulant in cumulants]
- if order is not None:
- result = [remove_higher_order_terms(r, order=order, symbols=u) for r in result]
-
- return result
+# -------------------------------- Equilibrium cumulants ---------------------------------------------------------------
@disk_cache
def get_cumulants_of_discrete_maxwellian_equilibrium(stencil, cumulants,
- rho=sp.Symbol("rho"), u=tuple(sp.symbols("u_0 u_1 u_2")),
+ rho=sp.Symbol("rho"), u=sp.symbols("u_:3"),
c_s_sq=sp.Symbol("c_s") ** 2, order=None, compressible=True):
from lbmpy.cumulants import discrete_cumulant
if order is None:
diff --git a/lbmpy/methods/__init__.py b/lbmpy/methods/__init__.py
index 7583b3390208198375ab2173591140d8a2ccc20c..d73e0aca929fb4a0c72f9ce705636bd5a0b1ad7d 100644
--- a/lbmpy/methods/__init__.py
+++ b/lbmpy/methods/__init__.py
@@ -1,5 +1,5 @@
from lbmpy.methods.creationfunctions import (
- create_mrt_orthogonal, create_mrt_raw, create_srt, create_trt, create_trt_kbc,
+ create_mrt_orthogonal, create_mrt_raw, create_central_moment, create_srt, create_trt, create_trt_kbc,
create_trt_with_magic_number, create_with_continuous_maxwellian_eq_moments,
create_with_discrete_maxwellian_eq_moments, mrt_orthogonal_modes_literature,
create_centered_cumulant_model, create_with_default_polynomial_cumulants,
@@ -13,7 +13,7 @@ from .conservedquantitycomputation import DensityVelocityComputation
__all__ = ['RelaxationInfo', 'AbstractLbMethod',
'AbstractConservedQuantityComputation', 'DensityVelocityComputation',
'create_srt', 'create_trt', 'create_trt_with_magic_number', 'create_trt_kbc',
- 'create_mrt_orthogonal', 'create_mrt_raw',
+ 'create_mrt_orthogonal', 'create_mrt_raw', 'create_central_moment',
'create_with_continuous_maxwellian_eq_moments', 'create_with_discrete_maxwellian_eq_moments',
'mrt_orthogonal_modes_literature', 'create_centered_cumulant_model',
'create_with_default_polynomial_cumulants', 'create_with_polynomial_cumulants',
diff --git a/lbmpy/methods/abstractlbmethod.py b/lbmpy/methods/abstractlbmethod.py
index 8bdd0606bb5e42126217a9f7f6946229b54945f6..07f36344c4d7299cc7d530b473e801256d7cbf75 100644
--- a/lbmpy/methods/abstractlbmethod.py
+++ b/lbmpy/methods/abstractlbmethod.py
@@ -32,12 +32,12 @@ class AbstractLbMethod(abc.ABC):
@property
def pre_collision_pdf_symbols(self):
"""Tuple of symbols representing the pdf values before collision"""
- return sp.symbols("f_:%d" % (len(self.stencil),))
+ return sp.symbols(f"f_:{len(self.stencil)}")
@property
def post_collision_pdf_symbols(self):
"""Tuple of symbols representing the pdf values after collision"""
- return sp.symbols("d_:%d" % (len(self.stencil),))
+ return sp.symbols(f"d_:{len(self.stencil)}")
# ------------------------- Abstract Methods & Properties ----------------------------------------------------------
diff --git a/lbmpy/methods/centeredcumulant/centered_cumulants.py b/lbmpy/methods/centeredcumulant/centered_cumulants.py
index 7c7d2db04a608a0dd48bc63c04065d0738c8a6a3..8572db1c28c97d9160f99e5aa065fd8d204fa73b 100644
--- a/lbmpy/methods/centeredcumulant/centered_cumulants.py
+++ b/lbmpy/methods/centeredcumulant/centered_cumulants.py
@@ -6,10 +6,6 @@ from pystencils.stencil import have_same_entries
from lbmpy.moments import MOMENT_SYMBOLS, moment_sort_key, exponent_to_polynomial_representation
-def statistical_quantity_symbol(name, exponents):
- return sp.Symbol(f'{name}_{"".join(str(i) for i in exponents)}')
-
-
def exponent_tuple_sort_key(x):
return moment_sort_key(exponent_to_polynomial_representation(x))
diff --git a/lbmpy/methods/centeredcumulant/centeredcumulantmethod.py b/lbmpy/methods/centeredcumulant/centeredcumulantmethod.py
index 27f2202ed7b6bc6e711b3c4439abeca4f341ce7b..099f8ee0c9bfe829803108b4d9fe5073fec0d54b 100644
--- a/lbmpy/methods/centeredcumulant/centeredcumulantmethod.py
+++ b/lbmpy/methods/centeredcumulant/centeredcumulantmethod.py
@@ -14,12 +14,12 @@ from lbmpy.methods.conservedquantitycomputation import AbstractConservedQuantity
from lbmpy.moments import (
moments_up_to_order, get_order,
monomial_to_polynomial_transformation_matrix,
- moment_sort_key, exponent_to_polynomial_representation, extract_monomials, MOMENT_SYMBOLS)
+ moment_sort_key, exponent_to_polynomial_representation, extract_monomials, MOMENT_SYMBOLS,
+ statistical_quantity_symbol)
# Local Imports
-from lbmpy.methods.centeredcumulant.centered_cumulants import (
- statistical_quantity_symbol, exponent_tuple_sort_key)
+from lbmpy.methods.centeredcumulant.centered_cumulants import exponent_tuple_sort_key
from lbmpy.methods.centeredcumulant.cumulant_transform import (
PRE_COLLISION_CUMULANT, POST_COLLISION_CUMULANT,
@@ -429,7 +429,7 @@ class CenteredCumulantBasedLbMethod(AbstractLbMethod):
if self._force_model is not None and \
not isinstance(self._force_model, CenteredCumulantForceModel) and include_force_terms:
force_model_terms = self._force_model(self)
- force_term_symbols = sp.symbols("forceTerm_:%d" % (len(force_model_terms, )))
+ force_term_symbols = sp.symbols(f"forceTerm_:{len(force_model_terms)}")
force_subexpressions = [Assignment(sym, force_model_term)
for sym, force_model_term in zip(force_term_symbols, force_model_terms)]
subexpressions += force_subexpressions
diff --git a/lbmpy/methods/centeredcumulant/cumulant_transform.py b/lbmpy/methods/centeredcumulant/cumulant_transform.py
index f054856680b9d6d2db5118d87857133d2c77c43b..6ec8d95520c91a03e6578bccb708cf9d4eab73cf 100644
--- a/lbmpy/methods/centeredcumulant/cumulant_transform.py
+++ b/lbmpy/methods/centeredcumulant/cumulant_transform.py
@@ -5,13 +5,11 @@ from pystencils import Assignment, AssignmentCollection
from pystencils.simp import SimplificationStrategy, add_subexpressions_for_divisions
from pystencils.simp.assignment_collection import SymbolGen
-from lbmpy.moments import moments_up_to_order, get_order
+from lbmpy.moments import moments_up_to_order, get_order, statistical_quantity_symbol
from itertools import product, chain
-from lbmpy.methods.centeredcumulant.centered_cumulants import (
- statistical_quantity_symbol, exponent_tuple_sort_key
-)
+from lbmpy.methods.centeredcumulant.centered_cumulants import exponent_tuple_sort_key
from lbmpy.methods.momentbased.moment_transforms import (
AbstractMomentTransform, PRE_COLLISION_CENTRAL_MOMENT, POST_COLLISION_CENTRAL_MOMENT
)
diff --git a/lbmpy/methods/centeredcumulant/galilean_correction.py b/lbmpy/methods/centeredcumulant/galilean_correction.py
index 788289ab148610a6d3336118e462a71cb6f38a27..c5550536cb5ecff4417753735ca7586d4abac730 100644
--- a/lbmpy/methods/centeredcumulant/galilean_correction.py
+++ b/lbmpy/methods/centeredcumulant/galilean_correction.py
@@ -2,8 +2,7 @@ from pystencils.simp.assignment_collection import AssignmentCollection
import sympy as sp
from pystencils import Assignment
-from lbmpy.moments import MOMENT_SYMBOLS
-from lbmpy.methods.centeredcumulant.centered_cumulants import statistical_quantity_symbol
+from lbmpy.moments import MOMENT_SYMBOLS, statistical_quantity_symbol
from lbmpy.methods.centeredcumulant.cumulant_transform import PRE_COLLISION_CUMULANT
x, y, z = MOMENT_SYMBOLS
diff --git a/lbmpy/methods/creationfunctions.py b/lbmpy/methods/creationfunctions.py
index 55aede9b11d236d07f3325dd5571815893505fe1..0c9234bc74fa04dfbab142be8ed95bc2ea651817 100644
--- a/lbmpy/methods/creationfunctions.py
+++ b/lbmpy/methods/creationfunctions.py
@@ -6,8 +6,7 @@ from functools import reduce
import sympy as sp
from lbmpy.maxwellian_equilibrium import (
- compressible_to_incompressible_moment_value, get_cumulants_of_continuous_maxwellian_equilibrium,
- get_moments_of_continuous_maxwellian_equilibrium,
+ compressible_to_incompressible_moment_value, get_equilibrium_values_of_maxwell_boltzmann_function,
get_moments_of_discrete_maxwellian_equilibrium, get_weights)
from lbmpy.methods.abstractlbmethod import RelaxationInfo
@@ -19,13 +18,14 @@ from lbmpy.methods.centeredcumulant.cumulant_transform import CentralMomentsToCu
from lbmpy.methods.conservedquantitycomputation import DensityVelocityComputation
from lbmpy.methods.momentbased.momentbasedmethod import MomentBasedLbMethod
-from lbmpy.methods.momentbased.moment_transforms import PdfsToCentralMomentsByShiftMatrix
+from lbmpy.methods.momentbased.centralmomentbasedmethod import CentralMomentBasedLbMethod
+from lbmpy.methods.momentbased.moment_transforms import FastCentralMomentTransform
from lbmpy.moments import (
MOMENT_SYMBOLS, discrete_moment, exponents_to_polynomial_representations,
get_default_moment_set_for_stencil, gram_schmidt, is_even, moments_of_order,
moments_up_to_component_order, sort_moments_into_groups_of_same_order,
- is_bulk_moment, is_shear_moment, get_order)
+ is_bulk_moment, is_shear_moment, get_order, set_up_shift_matrix)
from lbmpy.relaxationrates import relaxation_rate_from_magic_number
from lbmpy.stencils import get_stencil
@@ -34,7 +34,9 @@ from pystencils.sympyextensions import common_denominator
def create_with_discrete_maxwellian_eq_moments(stencil, moment_to_relaxation_rate_dict, compressible=False,
- force_model=None, equilibrium_order=2, c_s_sq=sp.Rational(1, 3)):
+ force_model=None, equilibrium_order=2, c_s_sq=sp.Rational(1, 3),
+ central_moment_space=False,
+ central_moment_transform_class=FastCentralMomentTransform):
r"""Creates a moment-based LBM by taking a list of moments with corresponding relaxation rate.
These moments are relaxed against the moments of the discrete Maxwellian distribution.
@@ -51,6 +53,10 @@ def create_with_discrete_maxwellian_eq_moments(stencil, moment_to_relaxation_rat
force_model: force model instance, or None if no external forces
equilibrium_order: approximation order of macroscopic velocity :math:`\mathbf{u}` in the equilibrium
c_s_sq: Speed of sound squared
+ central_moment_space: If set to True and instance of
+ `lbmpy.methods.momentbased.centralmomentbasedmethod.CentralMomentBasedLbMethod` is returned.
+ Thus the collision will be performed in the central moment space.
+ central_moment_transform_class: class to transform PDFs to the central moment space.
Returns:
`lbmpy.methods.momentbased.MomentBasedLbMethod` instance
@@ -62,18 +68,29 @@ def create_with_discrete_maxwellian_eq_moments(stencil, moment_to_relaxation_rat
"The number of moments has to be the same as the number of stencil entries"
density_velocity_computation = DensityVelocityComputation(stencil, compressible, force_model)
- eq_values = get_moments_of_discrete_maxwellian_equilibrium(stencil, tuple(mom_to_rr_dict.keys()),
+
+ moments = tuple(mom_to_rr_dict.keys())
+ eq_values = get_moments_of_discrete_maxwellian_equilibrium(stencil, moments,
c_s_sq=c_s_sq, compressible=compressible,
order=equilibrium_order)
+ if central_moment_space:
+ N = set_up_shift_matrix(moments, stencil)
+ eq_values = sp.simplify(N * sp.Matrix(eq_values))
rr_dict = OrderedDict([(mom, RelaxationInfo(eq_mom, rr))
for mom, rr, eq_mom in zip(mom_to_rr_dict.keys(), mom_to_rr_dict.values(), eq_values)])
- return MomentBasedLbMethod(stencil, rr_dict, density_velocity_computation, force_model)
+ if central_moment_space:
+ return CentralMomentBasedLbMethod(stencil, rr_dict, density_velocity_computation,
+ force_model, central_moment_transform_class)
+ else:
+ return MomentBasedLbMethod(stencil, rr_dict, density_velocity_computation, force_model)
def create_with_continuous_maxwellian_eq_moments(stencil, moment_to_relaxation_rate_dict, compressible=False,
- force_model=None, equilibrium_order=2, c_s_sq=sp.Rational(1, 3)):
+ force_model=None, equilibrium_order=2, c_s_sq=sp.Rational(1, 3),
+ central_moment_space=False,
+ central_moment_transform_class=FastCentralMomentTransform):
r"""
Creates a moment-based LBM by taking a list of moments with corresponding relaxation rate. These moments are
relaxed against the moments of the continuous Maxwellian distribution.
@@ -92,6 +109,10 @@ def create_with_continuous_maxwellian_eq_moments(stencil, moment_to_relaxation_r
force_model: force model instance, or None if no external forces
equilibrium_order: approximation order of macroscopic velocity :math:`\mathbf{u}` in the equilibrium
c_s_sq: Speed of sound squared
+ central_moment_space: If set to True and instance of
+ `lbmpy.methods.momentbased.centralmomentbasedmethod.CentralMomentBasedLbMethod` is returned.
+ Thus the collision will be performend in the central moment space.
+ central_moment_transform_class: class to transform PDFs to the central moment space.
Returns:
`lbmpy.methods.momentbased.MomentBasedLbMethod` instance
@@ -102,18 +123,32 @@ def create_with_continuous_maxwellian_eq_moments(stencil, moment_to_relaxation_r
assert len(mom_to_rr_dict) == len(stencil), "The number of moments has to be equal to the number of stencil entries"
dim = len(stencil[0])
density_velocity_computation = DensityVelocityComputation(stencil, compressible, force_model)
- eq_values = get_moments_of_continuous_maxwellian_equilibrium(tuple(mom_to_rr_dict.keys()), dim, c_s_sq=c_s_sq,
- order=equilibrium_order)
+ moments = tuple(mom_to_rr_dict.keys())
+
+ if compressible and central_moment_space:
+ eq_values = get_equilibrium_values_of_maxwell_boltzmann_function(moments, dim, c_s_sq=c_s_sq,
+ order=equilibrium_order,
+ space="central moment")
+ else:
+ eq_values = get_equilibrium_values_of_maxwell_boltzmann_function(moments, dim, c_s_sq=c_s_sq,
+ order=equilibrium_order, space="moment")
if not compressible:
rho = density_velocity_computation.defined_symbols(order=0)[1]
u = density_velocity_computation.defined_symbols(order=1)[1]
eq_values = [compressible_to_incompressible_moment_value(em, rho, u) for em in eq_values]
+ if central_moment_space:
+ N = set_up_shift_matrix(moments, stencil)
+ eq_values = sp.simplify(N * sp.Matrix(eq_values))
rr_dict = OrderedDict([(mom, RelaxationInfo(eq_mom, rr))
for mom, rr, eq_mom in zip(mom_to_rr_dict.keys(), mom_to_rr_dict.values(), eq_values)])
- return MomentBasedLbMethod(stencil, rr_dict, density_velocity_computation, force_model)
+ if central_moment_space:
+ return CentralMomentBasedLbMethod(stencil, rr_dict, density_velocity_computation,
+ force_model, central_moment_transform_class)
+ else:
+ return MomentBasedLbMethod(stencil, rr_dict, density_velocity_computation, force_model)
def create_generic_mrt(stencil, moment_eq_value_relaxation_rate_tuples, compressible=False,
@@ -254,6 +289,45 @@ def create_mrt_raw(stencil, relaxation_rates, maxwellian_moments=False, **kwargs
return create_with_discrete_maxwellian_eq_moments(stencil, rr_dict, **kwargs)
+def create_central_moment(stencil, relaxation_rates, maxwellian_moments=False, **kwargs):
+ r"""
+ Creates moment based LB method where the collision takes place in the central moment space.
+
+ Args:
+ stencil: nested tuple defining the discrete velocity space. See :func:`lbmpy.stencils.get_stencil`
+ relaxation_rates: relaxation rates (inverse of the relaxation times) for each moment
+ maxwellian_moments: determines if the discrete or continuous maxwellian equilibrium is
+ used to compute the equilibrium moments.
+ Returns:
+ :class:`lbmpy.methods.momentbased.CentralMomentBasedLbMethod` instance
+ """
+ if isinstance(stencil, str):
+ stencil = get_stencil(stencil)
+ moments = get_default_moment_set_for_stencil(stencil)
+ sorted_moments = sort_moments_into_groups_of_same_order(moments)
+ if len(relaxation_rates) == len(sorted_moments) - 2:
+ relaxation_rates = [0, 0, *relaxation_rates]
+
+ if len(relaxation_rates) == len(moments):
+ rr_dict = OrderedDict(zip(moments, relaxation_rates))
+ elif len(relaxation_rates) == len(sorted_moments):
+ full_relaxation_rates_list = list()
+ for i in sorted_moments:
+ full_relaxation_rates_list.extend([relaxation_rates[i]] * len(sorted_moments[i]))
+ rr_dict = OrderedDict(zip(moments, full_relaxation_rates_list))
+ else:
+ raise ValueError(f"The number of relaxation rates does not fit to the method. "
+ f"You can either choose {len(moments)} relaxation rates to relax every central moment with "
+ f"a specific value or {len(sorted_moments)} relaxation rates to relax each order of "
+ f"central moments or {len(sorted_moments) - 2} relaxation rates to relax the conserved "
+ f"moments with zero and the higher order moments with the defined values.")
+
+ if maxwellian_moments:
+ return create_with_continuous_maxwellian_eq_moments(stencil, rr_dict, central_moment_space=True, **kwargs)
+ else:
+ return create_with_discrete_maxwellian_eq_moments(stencil, rr_dict, central_moment_space=True, **kwargs)
+
+
def create_trt_kbc(dim, shear_relaxation_rate, higher_order_relaxation_rate, method_name='KBC-N4',
maxwellian_moments=False, **kwargs):
"""
@@ -480,7 +554,7 @@ def mrt_orthogonal_modes_literature(stencil, is_weighted):
def create_centered_cumulant_model(stencil, cumulant_to_rr_dict, force_model=None,
equilibrium_order=None, c_s_sq=sp.Rational(1, 3),
galilean_correction=False,
- central_moment_transform_class=PdfsToCentralMomentsByShiftMatrix,
+ central_moment_transform_class=FastCentralMomentTransform,
cumulant_transform_class=CentralMomentsToCumulantsByGeneratingFunc):
r"""Creates a cumulant lattice Boltzmann model.
@@ -526,8 +600,9 @@ def create_centered_cumulant_model(stencil, cumulant_to_rr_dict, force_model=Non
cumulants_to_relaxation_info_dict[d] = RelaxationInfo(0, cumulant_to_rr_dict[d])
# Polynomial Cumulant Equilibria
- polynomial_equilibria = get_cumulants_of_continuous_maxwellian_equilibrium(
- higher_order_polynomials, dim, rho=density_symbol, u=velocity_symbols, c_s_sq=c_s_sq, order=equilibrium_order)
+ polynomial_equilibria = get_equilibrium_values_of_maxwell_boltzmann_function(
+ higher_order_polynomials, dim, rho=density_symbol, u=velocity_symbols,
+ c_s_sq=c_s_sq, order=equilibrium_order, space="cumulant")
polynomial_equilibria = [density_symbol * v for v in polynomial_equilibria]
for i, c in enumerate(higher_order_polynomials):
diff --git a/lbmpy/methods/momentbased/centralmomentbasedmethod.py b/lbmpy/methods/momentbased/centralmomentbasedmethod.py
new file mode 100644
index 0000000000000000000000000000000000000000..4e2835844e06e44cf9043aa553739199800d19a5
--- /dev/null
+++ b/lbmpy/methods/momentbased/centralmomentbasedmethod.py
@@ -0,0 +1,294 @@
+import sympy as sp
+from collections import OrderedDict
+
+from pystencils import Assignment, AssignmentCollection
+
+from lbmpy.methods.abstractlbmethod import AbstractLbMethod, LbmCollisionRule, RelaxationInfo
+from lbmpy.methods.conservedquantitycomputation import AbstractConservedQuantityComputation
+from lbmpy.methods.momentbased.moment_transforms import (FastCentralMomentTransform,
+ PRE_COLLISION_CENTRAL_MOMENT, POST_COLLISION_CENTRAL_MOMENT)
+
+from lbmpy.moments import (polynomial_to_exponent_representation, MOMENT_SYMBOLS, moment_matrix, set_up_shift_matrix,
+ statistical_quantity_symbol)
+
+
+def relax_central_moments(moment_indices, pre_collision_values,
+ relaxation_rates, equilibrium_values,
+ post_collision_base=POST_COLLISION_CENTRAL_MOMENT):
+
+ post_collision_symbols = [sp.Symbol(f'{post_collision_base}_{"".join(str(i) for i in m)}') for m in moment_indices]
+ equilibrium_vec = sp.Matrix(equilibrium_values)
+ moment_vec = sp.Matrix(pre_collision_values)
+ relaxation_matrix = sp.diag(*relaxation_rates)
+ moment_vec = moment_vec + relaxation_matrix * (equilibrium_vec - moment_vec)
+ main_assignments = [Assignment(s, eq) for s, eq in zip(post_collision_symbols, moment_vec)]
+
+ return AssignmentCollection(main_assignments)
+
+# =============================== LB Method Implementation ===========================================================
+
+
+class CentralMomentBasedLbMethod(AbstractLbMethod):
+ """
+ Central Moment based LBM is a class to represent the single (SRT), two (TRT) and multi relaxation time (MRT)
+ methods, where the collision is performed in the central moment space.
+ These methods work by transforming the pdfs into moment space using a linear transformation and then shiftig
+ them into the central moment space. In the central moment space each component (moment) is relaxed to an
+ equilibrium moment by a certain relaxation rate. These equilibrium moments can e.g. be determined by taking the
+ equilibrium moments of the continuous Maxwellian.
+
+ Args:
+ stencil: see :func:`lbmpy.stencils.get_stencil`
+ moment_to_relaxation_info_dict: a dictionary mapping moments in either tuple or polynomial formulation
+ to a RelaxationInfo, which consists of the corresponding equilibrium moment
+ and a relaxation rate
+ conserved_quantity_computation: instance of :class:`lbmpy.methods.AbstractConservedQuantityComputation`.
+ This determines how conserved quantities are computed, and defines
+ the symbols used in the equilibrium moments like e.g. density and velocity
+ force_model: force model instance, or None if no forcing terms are required
+ central_moment_transform_class: class to transform PDFs to the central moment space.
+ """
+
+ def __init__(self, stencil, moment_to_relaxation_info_dict, conserved_quantity_computation=None, force_model=None,
+ central_moment_transform_class=FastCentralMomentTransform):
+ assert isinstance(conserved_quantity_computation, AbstractConservedQuantityComputation)
+ super(CentralMomentBasedLbMethod, self).__init__(stencil)
+
+ self._force_model = force_model
+ self._moment_to_relaxation_info_dict = OrderedDict(moment_to_relaxation_info_dict.items())
+ self._conserved_quantity_computation = conserved_quantity_computation
+ self._weights = None
+ self._central_moment_transform_class = central_moment_transform_class
+
+ @property
+ def central_moment_transform_class(self):
+ return self._central_moment_transform_class
+
+ @property
+ def moments(self):
+ return tuple(self._moment_to_relaxation_info_dict.keys())
+
+ @property
+ def moment_equilibrium_values(self):
+ return tuple([e.equilibrium_value for e in self._moment_to_relaxation_info_dict.values()])
+
+ @property
+ def first_order_equilibrium_moment_symbols(self, ):
+ return self._conserved_quantity_computation.first_order_moment_symbols
+
+ @property
+ def force_model(self):
+ return self._force_model
+
+ @property
+ def relaxation_info_dict(self):
+ return self._moment_to_relaxation_info_dict
+
+ @property
+ def relaxation_rates(self):
+ return tuple([e.relaxation_rate for e in self._moment_to_relaxation_info_dict.values()])
+
+ @property
+ def zeroth_order_equilibrium_moment_symbol(self, ):
+ return self._conserved_quantity_computation.zeroth_order_moment_symbol
+
+ def set_zeroth_moment_relaxation_rate(self, relaxation_rate):
+ e = sp.Rational(1, 1)
+ prev_entry = self._moment_to_relaxation_info_dict[e]
+ new_entry = RelaxationInfo(prev_entry[0], relaxation_rate)
+ self._moment_to_relaxation_info_dict[e] = new_entry
+
+ def set_first_moment_relaxation_rate(self, relaxation_rate):
+ for e in MOMENT_SYMBOLS[:self.dim]:
+ assert e in self._moment_to_relaxation_info_dict, "First moments are not relaxed separately by this method"
+ for e in MOMENT_SYMBOLS[:self.dim]:
+ prev_entry = self._moment_to_relaxation_info_dict[e]
+ new_entry = RelaxationInfo(prev_entry[0], relaxation_rate)
+ self._moment_to_relaxation_info_dict[e] = new_entry
+
+ def set_conserved_moments_relaxation_rate(self, relaxation_rate):
+ self.set_zeroth_moment_relaxation_rate(relaxation_rate)
+ self.set_first_moment_relaxation_rate(relaxation_rate)
+
+ def set_force_model(self, force_model):
+ self._force_model = force_model
+
+ @property
+ def moment_matrix(self):
+ return moment_matrix(self.moments, self.stencil)
+
+ @property
+ def shift_matrix(self):
+ return set_up_shift_matrix(self.moments, self.stencil)
+
+ @property
+ def relaxation_matrix(self):
+ d = sp.zeros(len(self.relaxation_rates))
+ for i in range(0, len(self.relaxation_rates)):
+ d[i, i] = self.relaxation_rates[i]
+ return d
+
+ def __getstate__(self):
+ # Workaround for a bug in joblib
+ self._moment_to_relaxation_info_dict_to_pickle = [i for i in self._moment_to_relaxation_info_dict.items()]
+ return self.__dict__
+
+ def _repr_html_(self):
+ table = """
+ <table style="border:none; width: 100%">
+ <tr {nb}>
+ <th {nb} >Central Moment</th>
+ <th {nb} >Eq. Value </th>
+ <th {nb} >Relaxation Rate</th>
+ </tr>
+ {content}
+ </table>
+ """
+ content = ""
+ for moment, (eq_value, rr) in self._moment_to_relaxation_info_dict.items():
+ vals = {
+ 'rr': f"${sp.latex(rr)}$",
+ 'cumulant': f"${sp.latex(moment)}$",
+ 'eq_value': f"${sp.latex(eq_value)}$",
+ 'nb': 'style="border:none"',
+ }
+ content += """<tr {nb}>
+ <td {nb}>{cumulant}</td>
+ <td {nb}>{eq_value}</td>
+ <td {nb}>{rr}</td>
+ </tr>\n""".format(**vals)
+ return table.format(content=content, nb='style="border:none"')
+
+ # ----------------------- Overridden Abstract Members --------------------------
+
+ @property
+ def conserved_quantity_computation(self):
+ """Returns an instance of class :class:`lbmpy.methods.AbstractConservedQuantityComputation`"""
+ return self._conserved_quantity_computation
+
+ @property
+ def weights(self):
+ """Returns a sequence of weights, one for each lattice direction"""
+ if self._weights is None:
+ self._weights = self._compute_weights()
+ return self._weights
+
+ def get_equilibrium(self, conserved_quantity_equations=None, subexpressions=False, pre_simplification=False,
+ keep_cqc_subexpressions=True):
+ """Returns equation collection, to compute equilibrium values.
+ The equations have the post collision symbols as left hand sides and are
+ functions of the conserved quantities
+
+ Args:
+ conserved_quantity_equations: equations to compute conserved quantities.
+ subexpressions: if set to false all subexpressions of the equilibrium assignments are plugged
+ into the main assignments
+ pre_simplification: with or without pre_simplifications for the calculation of the collision
+ keep_cqc_subexpressions: if equilibrium is returned without subexpressions keep_cqc_subexpressions
+ determines if also subexpressions to calculate conserved quantities should be
+ plugged into the main assignments
+ """
+ r_info_dict = {c: RelaxationInfo(info.equilibrium_value, 1)
+ for c, info in self._moment_to_relaxation_info_dict.items()}
+ ac = self._central_moment_collision_rule(
+ r_info_dict, conserved_quantity_equations, pre_simplification)
+ if not subexpressions:
+ if keep_cqc_subexpressions:
+ bs = self._bound_symbols_cqc(conserved_quantity_equations)
+ return ac.new_without_subexpressions(subexpressions_to_keep=bs)
+ else:
+ return ac.new_without_subexpressions()
+ else:
+ return ac
+
+ def get_equilibrium_terms(self):
+ equilibrium = self.get_equilibrium()
+ return sp.Matrix([eq.rhs for eq in equilibrium.main_assignments])
+
+ def get_collision_rule(self, conserved_quantity_equations=None, pre_simplification=False):
+ """Returns an LbmCollisionRule i.e. an equation collection with a reference to the method.
+ This collision rule defines the collision operator."""
+ return self._central_moment_collision_rule(
+ self._moment_to_relaxation_info_dict, conserved_quantity_equations, pre_simplification, True)
+
+ # ------------------------------- Internals --------------------------------------------
+
+ def _bound_symbols_cqc(self, conserved_quantity_equations=None):
+ f = self.pre_collision_pdf_symbols
+ cqe = conserved_quantity_equations
+
+ if cqe is None:
+ cqe = self._conserved_quantity_computation.equilibrium_input_equations_from_pdfs(f)
+
+ return cqe.bound_symbols
+
+ def _compute_weights(self):
+ defaults = self._conserved_quantity_computation.default_values
+ cqe = AssignmentCollection([Assignment(s, e) for s, e in defaults.items()])
+ eq_ac = self.get_equilibrium(cqe, subexpressions=False, keep_cqc_subexpressions=False)
+
+ weights = []
+ for eq in eq_ac.main_assignments:
+ value = eq.rhs.expand()
+ assert len(value.atoms(sp.Symbol)) == 0, "Failed to compute weights"
+ weights.append(value)
+ return weights
+
+ def _central_moment_collision_rule(self, moment_to_relaxation_info_dict,
+ conserved_quantity_equations=None,
+ pre_simplification=False,
+ include_force_terms=False):
+ stencil = self.stencil
+ dim = len(self.stencil[0])
+ f = self.pre_collision_pdf_symbols
+ density = self.zeroth_order_equilibrium_moment_symbol
+ velocity = self.first_order_equilibrium_moment_symbols
+ cqe = conserved_quantity_equations
+
+ if cqe is None:
+ cqe = self._conserved_quantity_computation.equilibrium_input_equations_from_pdfs(f)
+
+ moments_as_exponents = list()
+ for moment in self.moments:
+ _, exponent_tuple = polynomial_to_exponent_representation(moment)[0]
+ moments_as_exponents.append(exponent_tuple[:dim])
+
+ # 1) Get Forward Transformation from PDFs to central moments
+ pdfs_to_k_transform = self._central_moment_transform_class(
+ stencil, moments_as_exponents, density, velocity, conserved_quantity_equations=cqe)
+ pdfs_to_k_eqs = pdfs_to_k_transform.forward_transform(f, simplification=pre_simplification)
+
+ # 2) Collision
+ moment_symbols = [statistical_quantity_symbol(PRE_COLLISION_CENTRAL_MOMENT, exp)
+ for exp in moments_as_exponents]
+
+ relaxation_infos = [moment_to_relaxation_info_dict[m] for m in self.moments]
+ relaxation_rates = [info.relaxation_rate for info in relaxation_infos]
+ equilibrium_value = [info.equilibrium_value for info in relaxation_infos]
+
+ collision_eqs = relax_central_moments(
+ moments_as_exponents, tuple(moment_symbols),
+ tuple(relaxation_rates), tuple(equilibrium_value))
+
+ # 3) Get backward transformation from central moments to PDFs
+ d = self.post_collision_pdf_symbols
+ k_post_to_pdfs_eqs = pdfs_to_k_transform.backward_transform(d, simplification=pre_simplification)
+
+ # 4) Now, put it all together.
+ all_acs = [] if pdfs_to_k_transform.absorbs_conserved_quantity_equations else [cqe]
+ all_acs += [pdfs_to_k_eqs, collision_eqs]
+ subexpressions = [ac.all_assignments for ac in all_acs]
+ subexpressions += k_post_to_pdfs_eqs.subexpressions
+ main_assignments = k_post_to_pdfs_eqs.main_assignments
+
+ # 5) Maybe add forcing terms.
+ if self._force_model is not None and include_force_terms:
+ force_model_terms = self._force_model(self)
+ force_term_symbols = sp.symbols(f"forceTerm_:{len(force_model_terms)}")
+ force_subexpressions = [Assignment(sym, force_model_term)
+ for sym, force_model_term in zip(force_term_symbols, force_model_terms)]
+ subexpressions += force_subexpressions
+ main_assignments = [Assignment(eq.lhs, eq.rhs + force_term_symbol)
+ for eq, force_term_symbol in zip(main_assignments, force_term_symbols)]
+
+ return LbmCollisionRule(self, main_assignments, subexpressions)
diff --git a/lbmpy/methods/momentbased/moment_transforms.py b/lbmpy/methods/momentbased/moment_transforms.py
index e31d2e6e440f09258e3d7321c04e4fcf867d4040..1a13d8ef068bc326a41694c67600d00597f5fcc2 100644
--- a/lbmpy/methods/momentbased/moment_transforms.py
+++ b/lbmpy/methods/momentbased/moment_transforms.py
@@ -8,11 +8,11 @@ from pystencils.simp.assignment_collection import SymbolGen
from pystencils.sympyextensions import subs_additive, fast_subs
from lbmpy.moments import moment_matrix, set_up_shift_matrix, contained_moments, moments_up_to_order
+from lbmpy.moments import statistical_quantity_symbol as sq_sym
from lbmpy.methods.momentbased.momentbasedsimplifications import (
substitute_moments_in_conserved_quantity_equations,
split_pdf_main_assignments_by_symmetry)
-from lbmpy.methods.centeredcumulant.centered_cumulants import statistical_quantity_symbol as sq_sym
# ============================ PDFs <-> Central Moments ==============================================================
diff --git a/lbmpy/methods/momentbased/momentbasedmethod.py b/lbmpy/methods/momentbased/momentbasedmethod.py
index 299faa02370f5143b03a3b59f3ca2b41c639b739..70b05a633aa385d7a6dd3b3450b3ffd57cac485a 100644
--- a/lbmpy/methods/momentbased/momentbasedmethod.py
+++ b/lbmpy/methods/momentbased/momentbasedmethod.py
@@ -218,7 +218,7 @@ class MomentBasedLbMethod(AbstractLbMethod):
if self._forceModel is not None and include_force_terms:
force_model_terms = self._forceModel(self)
- force_term_symbols = sp.symbols("forceTerm_:%d" % (len(force_model_terms,)))
+ force_term_symbols = sp.symbols(f"forceTerm_:{len(force_model_terms)}")
force_subexpressions = [Assignment(sym, force_model_term)
for sym, force_model_term in zip(force_term_symbols, force_model_terms)]
all_subexpressions += force_subexpressions
@@ -243,7 +243,7 @@ class MomentBasedLbMethod(AbstractLbMethod):
for rt in unique_relaxation_rates:
rt = sp.sympify(rt)
if not isinstance(rt, sp.Symbol):
- rt_symbol = sp.Symbol("rr_%d" % (len(subexpressions),))
+ rt_symbol = sp.Symbol(f"rr_{len(subexpressions)}")
subexpressions[rt] = rt_symbol
new_rr = [subexpressions[sp.sympify(e)] if sp.sympify(e) in subexpressions else e
diff --git a/lbmpy/moments.py b/lbmpy/moments.py
index 09a1f56ab129edeb8d9a8f78af690a9527040b7c..db3590bd1fe47d247d24ffdda629d8a76a17e4c5 100644
--- a/lbmpy/moments.py
+++ b/lbmpy/moments.py
@@ -191,6 +191,10 @@ def sort_moments_into_groups_of_same_order(moments):
# -------------------- Common Function working with exponent tuples and polynomial moments -----------------------------
+def statistical_quantity_symbol(name, exponents):
+ return sp.Symbol(f'{name}_{"".join(str(i) for i in exponents)}')
+
+
def is_even(moment):
"""
A moment is considered even when under sign reversal nothing changes i.e. :math:`m(-x,-y,-z) = m(x,y,z)`
@@ -377,7 +381,7 @@ def moment_matrix(moments, stencil, shift_velocity=None):
return sp.Matrix(len(moments), len(stencil), generator)
-def set_up_shift_matrix(moments, stencil, velocity_symbols=None):
+def set_up_shift_matrix(moments, stencil, velocity_symbols=sp.symbols("u_:3")):
"""
Sets up a shift matrix to shift raw moments to central moment space.
@@ -387,13 +391,14 @@ def set_up_shift_matrix(moments, stencil, velocity_symbols=None):
- stencil: Nested tuple of lattice velocities
- velocity_symbols: Sequence of symbols corresponding to the shift velocity
"""
+ # TODO: this function takes quite some time for D3Q27. Needs to be optimised
x, y, z = MOMENT_SYMBOLS
dim = len(stencil[0])
nr_directions = len(stencil)
directions = np.asarray(stencil)
- u = velocity_symbols if velocity_symbols is not None else sp.symbols(f"u_:{dim}")
+ u = velocity_symbols[:dim]
f = sp.symbols(f"f_:{nr_directions}")
m = sp.symbols(f"m_:{nr_directions}")
diff --git a/lbmpy/phasefield_allen_cahn/contact_angle.py b/lbmpy/phasefield_allen_cahn/contact_angle.py
new file mode 100644
index 0000000000000000000000000000000000000000..c3195e4caa7fce7ed98b9bc3ed7f9d18a3d1b5af
--- /dev/null
+++ b/lbmpy/phasefield_allen_cahn/contact_angle.py
@@ -0,0 +1,67 @@
+import math
+import sympy as sp
+
+from pystencils import Assignment
+
+from pystencils.boundaries.boundaryhandling import BoundaryOffsetInfo
+from pystencils.boundaries.boundaryconditions import Boundary
+
+from pystencils.data_types import TypedSymbol
+
+
+class ContactAngle(Boundary):
+ r"""
+ Wettability condition on solid boundaries according to equation 25 in :cite:`Fakhari2018`.
+
+ Args:
+ contact_angle: contact angle in degrees which is applied between the fluid and the solid boundary.
+ interface_width: interface width of the phase field model.
+ name: optional name of the boundary
+ data_type: data type for temporary variables which are used.
+ """
+
+ inner_or_boundary = False
+ single_link = True
+
+ def __init__(self, contact_angle, interface_width, name=None, data_type='double'):
+ self._contact_angle = contact_angle
+ self._interface_width = interface_width
+ self._data_type = data_type
+
+ super(ContactAngle, self).__init__(name)
+
+ def __call__(self, field, direction_symbol, **kwargs):
+
+ neighbor = BoundaryOffsetInfo.offset_from_dir(direction_symbol, field.spatial_dimensions)
+
+ if field.index_dimensions == 0:
+ if math.isclose(90, self._contact_angle, abs_tol=1e-5):
+ return [Assignment(field.center, field[neighbor])]
+
+ dist = TypedSymbol("h", self._data_type)
+ angle = TypedSymbol("a", self._data_type)
+ tmp = TypedSymbol("tmp", self._data_type)
+
+ result = []
+ result.append(Assignment(tmp, sum([x * x for x in neighbor])))
+ result.append(Assignment(dist, 0.5 * sp.sqrt(tmp)))
+ result.append(Assignment(angle, math.cos(math.radians(self._contact_angle))))
+
+ var = - dist * (4.0 / self._interface_width) * angle
+ tmp = 1 + var
+ else_branch = (tmp - sp.sqrt(tmp * tmp - 4 * var * field[neighbor])) / var - field[neighbor]
+ update = sp.Piecewise((field[neighbor], dist < 0.001), (else_branch, True))
+
+ result.append(Assignment(field.center, update))
+ return result
+ else:
+ raise NotImplementedError("Contact angle only implemented for phase-fields which have a single "
+ "value for each cell")
+
+ def __hash__(self):
+ return hash("ContactAngle")
+
+ def __eq__(self, other):
+ if not isinstance(other, ContactAngle):
+ return False
+ return self.__dict__ == other.__dict__
diff --git a/lbmpy/phasefield_allen_cahn/force_model.py b/lbmpy/phasefield_allen_cahn/force_model.py
index 85b405116a6145561e70cc96936f22eee09f280e..c3ff81f3aff504609ae9542ed72417125806c019 100644
--- a/lbmpy/phasefield_allen_cahn/force_model.py
+++ b/lbmpy/phasefield_allen_cahn/force_model.py
@@ -1,7 +1,7 @@
import sympy as sp
-import numpy as np
-from lbmpy.forcemodels import Simple
+from pystencils import Assignment
+from lbmpy.forcemodels import Simple, Luo
class MultiphaseForceModel:
@@ -12,26 +12,34 @@ class MultiphaseForceModel:
def __init__(self, force, rho=1):
self._force = force
self._rho = rho
+ self.force_symp = sp.symbols(f"F_:{len(force)}")
+ self.subs_terms = [Assignment(rhs, lhs) for rhs, lhs in zip(self.force_symp, force)]
def __call__(self, lb_method):
- stencil = lb_method.stencil
-
- force_symp = sp.symbols("force_:{}".format(lb_method.dim))
- simple = Simple(force_symp)
- force = [f / self._rho for f in simple(lb_method)]
+ simple = Simple(self.force_symp)
+ force = sp.Matrix(simple(lb_method))
moment_matrix = lb_method.moment_matrix
- relaxation_rates = sp.Matrix(np.diag(lb_method.relaxation_rates))
- mrt_collision_op = moment_matrix.inv() * relaxation_rates * moment_matrix
- result = -0.5 * mrt_collision_op * sp.Matrix(force) + sp.Matrix(force)
+ return sp.simplify(moment_matrix * force) / self._rho
+
- for i in range(0, len(stencil)):
- result[i] = result[i].simplify()
+class CentralMomentMultiphaseForceModel:
+ r"""
+ A simple force model in the central moment space.
+ """
+ def __init__(self, force, rho=1):
+ self._force = force
+ self._rho = rho
+ self.force_symp = sp.symbols(f"F_:{len(force)}")
+ self.subs_terms = [Assignment(rhs, lhs) for rhs, lhs in zip(self.force_symp, force)]
- subs_dict = dict(zip(force_symp, self._force))
+ def __call__(self, lb_method, **kwargs):
+ luo = Luo(self.force_symp)
+ force = sp.Matrix(luo(lb_method))
- for i in range(0, len(stencil)):
- result[i] = result[i].subs(subs_dict)
+ M = lb_method.moment_matrix
+ N = lb_method.shift_matrix
- return result
+ result = sp.simplify(M * force)
+ return sp.simplify(N * result) / self._rho
diff --git a/lbmpy/phasefield_allen_cahn/kernel_equations.py b/lbmpy/phasefield_allen_cahn/kernel_equations.py
index b81271a97dcd171c2b2e20e9bb24268f0d8e843e..eb6d5db16299be78da63a4da975f02a29e365b51 100644
--- a/lbmpy/phasefield_allen_cahn/kernel_equations.py
+++ b/lbmpy/phasefield_allen_cahn/kernel_equations.py
@@ -1,11 +1,16 @@
from pystencils.fd.derivation import FiniteDifferenceStencilDerivation
-from pystencils import Assignment, AssignmentCollection
+from pystencils import Assignment
+from lbmpy.methods.momentbased.centralmomentbasedmethod import CentralMomentBasedLbMethod
+from lbmpy.moments import get_order
from lbmpy.maxwellian_equilibrium import get_weights
-from lbmpy.fieldaccess import StreamPushTwoFieldsAccessor, CollideOnlyInplaceAccessor
+from lbmpy.fieldaccess import StreamPullTwoFieldsAccessor, StreamPushTwoFieldsAccessor, CollideOnlyInplaceAccessor
+from lbmpy.methods.abstractlbmethod import LbmCollisionRule
+
+from lbmpy.phasefield_allen_cahn.phasefield_simplifications import create_phasefield_simplification_strategy
+from lbmpy.phasefield_allen_cahn.force_model import CentralMomentMultiphaseForceModel
import sympy as sp
-import numpy as np
def chemical_potential_symbolic(phi_field, stencil, beta, kappa):
@@ -43,8 +48,7 @@ def chemical_potential_symbolic(phi_field, stencil, beta, kappa):
lap += res.apply(phi_field.center)
# get the chemical potential
- mu = 4.0 * beta * phi_field.center * (phi_field.center - 1.0) * (phi_field.center - 0.5) - \
- kappa * lap
+ mu = 4.0 * beta * phi_field.center * (phi_field.center - 1.0) * (phi_field.center - 0.5) - kappa * lap
return mu
@@ -260,37 +264,41 @@ def interface_tracking_force(phi_field, stencil, interface_thickness, fd_stencil
return result
-def get_update_rules_velocity(src_field, u_in, lb_method, force, density, sub_iterations=2):
+def get_update_rules_velocity(src_field, u_in, lb_method, force_model, density, sub_iterations=2):
r"""
Get assignments to update the velocity with a force shift
Args:
src_field: the source field of the hydrodynamic distribution function
u_in: velocity field
lb_method: mrt lattice boltzmann method used for hydrodynamics
- force: force acting on the hydrodynamic lb step
+ force_model: one of the phase_field force models which are applied in the collision space
density: the interpolated density of the simulation
sub_iterations: number of updates of the velocity field
"""
stencil = lb_method.stencil
dimensions = len(stencil[0])
+ rho = lb_method.conserved_quantity_computation.zeroth_order_moment_symbol
+ u_symp = lb_method.conserved_quantity_computation.first_order_moment_symbols
+
+ force = force_model._force
+ force_symp = force_model.force_symp
+
moment_matrix = lb_method.moment_matrix
- eq = lb_method.moment_equilibrium_values
- first_eqs = lb_method.first_order_equilibrium_moment_symbols
+ moments = lb_method.moments
indices = list()
- for i in range(dimensions):
- indices.append(eq.index(first_eqs[i]))
+ for i in range(len(moments)):
+ if get_order(moments[i]) == 1:
+ indices.append(i)
- src = [src_field.center(i) for i, _ in enumerate(stencil)]
- m0 = np.dot(moment_matrix.tolist(), src)
+ m0 = moment_matrix * sp.Matrix(src_field.center_vector)
update_u = list()
- update_u.append(Assignment(sp.symbols("rho"), m0[0]))
+ update_u.append(Assignment(rho, m0[0]))
index = 0
- u_symp = sp.symbols("u_:{}".format(dimensions))
- aleph = sp.symbols("aleph_:{}".format(dimensions * sub_iterations))
+ aleph = sp.symbols(f"aleph_:{dimensions * sub_iterations}")
for i in range(dimensions):
update_u.append(Assignment(aleph[i], u_in.center_vector[i]))
@@ -303,14 +311,17 @@ def get_update_rules_velocity(src_field, u_in, lb_method, force, density, sub_it
index += 1
subs_dict = dict(zip(u_symp, aleph[index - dimensions:index]))
+
+ for i in range(dimensions):
+ update_u.append(Assignment(force_symp[i], force[i].subs(subs_dict)))
+
for i in range(dimensions):
- update_u.append(Assignment(u_symp[i], m0[indices[i]] + force[i].subs(subs_dict) / density / 2))
- # update_u.append(Assignment(u_in.center_vector[i], m0[indices[i]] + force[i].subs(subs_dict) / density / 2))
+ update_u.append(Assignment(u_symp[i], m0[indices[i]] + force_symp[i] / density / 2))
return update_u
-def get_collision_assignments_hydro(lb_method, density, velocity_input, force, sub_iterations, symbolic_fields,
+def get_collision_assignments_hydro(lb_method, density, velocity_input, force_model, sub_iterations, symbolic_fields,
kernel_type):
r"""
Get collision assignments for the hydrodynamic lattice Boltzmann step. Here the force gets applied in the moment
@@ -319,64 +330,169 @@ def get_collision_assignments_hydro(lb_method, density, velocity_input, force, s
lb_method: moment based lattice Boltzmann method
density: the interpolated density of the simulation
velocity_input: velocity field for the hydrodynamic and Allen-Chan LB step
- force: force vector containing a summation of the surface tension-, pressure-, viscous- and bodyforce vector
+ force_model: one of the phase_field force models which are applied in the collision space
sub_iterations: number of updates of the velocity field
symbolic_fields: PDF fields for source and destination
kernel_type: collide_stream_push or collide_only
"""
+ if isinstance(lb_method, CentralMomentBasedLbMethod) and not \
+ isinstance(force_model, CentralMomentMultiphaseForceModel):
+ raise ValueError("For central moment lb methods a central moment force model needs the be applied")
+
stencil = lb_method.stencil
dimensions = len(stencil[0])
+ rho = lb_method.conserved_quantity_computation.zeroth_order_moment_symbol
+
src_field = symbolic_fields['symbolic_field']
dst_field = symbolic_fields['symbolic_temporary_field']
+ if kernel_type == 'collide_stream_push':
+ accessor = StreamPushTwoFieldsAccessor()
+ else:
+ accessor = CollideOnlyInplaceAccessor()
+
+ u_symp = lb_method.conserved_quantity_computation.first_order_moment_symbols
+
moment_matrix = lb_method.moment_matrix
- rel = lb_method.relaxation_rates
- eq = lb_method.moment_equilibrium_values
+ rel = sp.diag(*lb_method.relaxation_rates)
+ eq = sp.Matrix(lb_method.moment_equilibrium_values)
- first_eqs = lb_method.first_order_equilibrium_moment_symbols
- indices = list()
- for i in range(dimensions):
- indices.append(eq.index(first_eqs[i]))
+ force_terms = force_model(lb_method)
+ eq = eq - sp.Rational(1, 2) * force_terms
- eq = np.array(eq)
+ pre = sp.symbols(f"pre_:{len(stencil)}")
+ post = sp.symbols(f"post_:{len(stencil)}")
- g_vals = [src_field.center(i) for i, _ in enumerate(stencil)]
- m0 = np.dot(moment_matrix.tolist(), g_vals)
+ to_moment_space = moment_matrix * sp.Matrix(accessor.read(src_field, stencil))
+ to_moment_space[0] = rho
- mf = np.zeros(len(stencil), dtype=object)
- for i in range(dimensions):
- mf[indices[i]] = force[i] / density
+ main_assignments = list()
+ subexpressions = get_update_rules_velocity(src_field, velocity_input, lb_method, force_model,
+ density, sub_iterations=sub_iterations)
- m = sp.symbols("m_:{}".format(len(stencil)))
+ for i in range(0, len(stencil)):
+ subexpressions.append(Assignment(pre[i], to_moment_space[i]))
- update_m = get_update_rules_velocity(src_field, velocity_input, lb_method, force,
- density, sub_iterations=sub_iterations)
- u_symp = sp.symbols("u_:{}".format(dimensions))
+ if isinstance(lb_method, CentralMomentBasedLbMethod):
+ n0 = lb_method.shift_matrix * sp.Matrix(pre)
+ to_central = sp.Matrix(sp.symbols(f"kappa_:{len(stencil)}"))
+ for i in range(0, len(stencil)):
+ subexpressions.append(Assignment(to_central[i], n0[i]))
+ pre = to_central
+
+ collision = sp.Matrix(pre) - rel * (sp.Matrix(pre) - eq) + force_terms
for i in range(0, len(stencil)):
- update_m.append(Assignment(m[i], m0[i] - (m0[i] - eq[i] + mf[i] / 2) * rel[i] + mf[i]))
+ subexpressions.append(Assignment(post[i], collision[i]))
- update_g = list()
- var = np.dot(moment_matrix.inv().tolist(), m)
- if kernel_type == 'collide_stream_push':
- push_accessor = StreamPushTwoFieldsAccessor()
- post_collision_accesses = push_accessor.write(dst_field, stencil)
- else:
- collide_accessor = CollideOnlyInplaceAccessor()
- post_collision_accesses = collide_accessor.write(src_field, stencil)
+ if isinstance(lb_method, CentralMomentBasedLbMethod):
+ n0_back = lb_method.shift_matrix.inv() * sp.Matrix(post)
+ from_central = sp.Matrix(sp.symbols(f"kappa_post:{len(stencil)}"))
+ for i in range(0, len(stencil)):
+ subexpressions.append(Assignment(from_central[i], n0_back[i]))
+ post = from_central
+
+ to_pdf_space = moment_matrix.inv() * sp.Matrix(post)
for i in range(0, len(stencil)):
- update_g.append(Assignment(post_collision_accesses[i], var[i]))
+ main_assignments.append(Assignment(accessor.write(dst_field, stencil)[i], to_pdf_space[i]))
for i in range(dimensions):
- update_g.append(Assignment(velocity_input.center_vector[i], u_symp[i]))
+ main_assignments.append(Assignment(velocity_input.center_vector[i], u_symp[i]))
+
+ collision_rule = LbmCollisionRule(lb_method, main_assignments, subexpressions)
+
+ simplification = create_phasefield_simplification_strategy(lb_method)
+ collision_rule = simplification(collision_rule)
+
+ return collision_rule
+
+
+def get_collision_assignments_phase(lb_method, velocity_input, output, force_model, symbolic_fields, kernel_type):
+ r"""
+ Get collision assignments for the phasefield lattice Boltzmann step. Here the force gets applied in the moment
+ space. Afterwards the transformation back to the pdf space happens.
+ Args:
+ lb_method: moment based lattice Boltzmann method
+ velocity_input: velocity field for the hydrodynamic and Allen-Chan LB step
+ output: output field for the phasefield (calles density as for normal LB update rules)
+ force_model: one of the phase_field force models which are applied in the collision space
+ symbolic_fields: PDF fields for source and destination
+ kernel_type: stream_pull_collide or collide_only
+ """
+
+ stencil = lb_method.stencil
+
+ src_field = symbolic_fields['symbolic_field']
+ dst_field = symbolic_fields['symbolic_temporary_field']
+ output_phase_field = output['density']
+
+ if kernel_type == 'stream_pull_collide':
+ accessor = StreamPullTwoFieldsAccessor()
+ else:
+ accessor = CollideOnlyInplaceAccessor()
+
+ subexpressions = list()
+ main_assignments = list()
+
+ rho = lb_method.conserved_quantity_computation.zeroth_order_moment_symbol
+ u_symp = lb_method.conserved_quantity_computation.first_order_moment_symbols
+
+ moment_matrix = lb_method.moment_matrix
+ rel = sp.diag(*lb_method.relaxation_rates)
+ eq = sp.Matrix(lb_method.moment_equilibrium_values)
+
+ force_terms = force_model(lb_method)
+ eq = eq - sp.Rational(1, 2) * force_terms
+
+ pre = sp.symbols(f"pre_:{len(stencil)}")
+ post = sp.symbols(f"post_:{len(stencil)}")
+
+ to_moment_space = moment_matrix * sp.Matrix(accessor.read(src_field, stencil))
+ to_moment_space[0] = rho
+
+ subexpressions.append(Assignment(rho, sum(accessor.read(src_field, stencil))))
+ for i in range(lb_method.dim):
+ subexpressions.append(Assignment(u_symp[i], velocity_input.center_vector[i]))
+ subexpressions.extend(force_model.subs_terms)
+
+ for i in range(len(stencil)):
+ subexpressions.append(Assignment(pre[i], to_moment_space[i]))
+
+ if isinstance(lb_method, CentralMomentBasedLbMethod):
+ n0 = lb_method.shift_matrix * sp.Matrix(pre)
+ to_central = sp.Matrix(sp.symbols(f"kappa_:{len(stencil)}"))
+ for i in range(0, len(stencil)):
+ subexpressions.append(Assignment(to_central[i], n0[i]))
+ pre = to_central
+
+ collision = sp.Matrix(pre) - rel * (sp.Matrix(pre) - eq) + force_terms
+
+ for i in range(len(stencil)):
+ subexpressions.append(Assignment(post[i], collision[i]))
+
+ if isinstance(lb_method, CentralMomentBasedLbMethod):
+ n0_back = lb_method.shift_matrix.inv() * sp.Matrix(post)
+ from_central = sp.Matrix(sp.symbols(f"kappa_post:{len(stencil)}"))
+ for i in range(0, len(stencil)):
+ subexpressions.append(Assignment(from_central[i], n0_back[i]))
+ post = from_central
+
+ to_pdf_space = moment_matrix.inv() * sp.Matrix(post)
+
+ for i in range(len(stencil)):
+ main_assignments.append(Assignment(accessor.write(dst_field, stencil)[i], to_pdf_space[i]))
+
+ main_assignments.append(Assignment(output_phase_field.center, sum(accessor.write(dst_field, stencil))))
+
+ collision_rule = LbmCollisionRule(lb_method, main_assignments, subexpressions)
- hydro_lb_update_rule = AssignmentCollection(main_assignments=update_g,
- subexpressions=update_m)
+ simplification = create_phasefield_simplification_strategy(lb_method)
+ collision_rule = simplification(collision_rule)
- return hydro_lb_update_rule
+ return collision_rule
def initializer_kernel_phase_field_lb(lb_phase_field, phi_field, velocity_field, mrt_method, interface_thickness,
diff --git a/lbmpy/phasefield_allen_cahn/phasefield_simplifications.py b/lbmpy/phasefield_allen_cahn/phasefield_simplifications.py
new file mode 100644
index 0000000000000000000000000000000000000000..f1806a0ccc03156f4267d14eaa773eea79412514
--- /dev/null
+++ b/lbmpy/phasefield_allen_cahn/phasefield_simplifications.py
@@ -0,0 +1,19 @@
+import sympy as sp
+
+from pystencils.simp import SimplificationStrategy, apply_to_all_assignments
+
+from lbmpy.methods.centeredcumulant.simplification import insert_aliases, insert_zeros, insert_constants
+
+
+def create_phasefield_simplification_strategy(lb_method):
+ s = SimplificationStrategy()
+ expand = apply_to_all_assignments(sp.expand)
+
+ s.add(expand)
+
+ s.add(insert_zeros)
+ s.add(insert_aliases)
+ s.add(insert_constants)
+ s.add(lambda ac: ac.new_without_unused_subexpressions())
+
+ return s
diff --git a/lbmpy/quadratic_equilibrium_construction.py b/lbmpy/quadratic_equilibrium_construction.py
index 207534a26206978e7a94de5b8581bdedda453ae2..40e531576be6616e246557cde47ef1d503d7e305 100644
--- a/lbmpy/quadratic_equilibrium_construction.py
+++ b/lbmpy/quadratic_equilibrium_construction.py
@@ -94,12 +94,13 @@ def moment_constraint_equations(stencil, equilibrium, moment_to_value_dict, u=sp
def hydrodynamic_moment_values(up_to_order=3, dim=3, compressible=True):
"""Returns the values of moments that are required to approximate Navier Stokes (if up_to_order=3)"""
- from lbmpy.maxwellian_equilibrium import get_moments_of_continuous_maxwellian_equilibrium
+ from lbmpy.maxwellian_equilibrium import get_equilibrium_values_of_maxwell_boltzmann_function
from lbmpy.moments import moments_up_to_order
moms = moments_up_to_order(up_to_order, dim)
c_s_sq = sp.Symbol("p") / sp.Symbol("rho")
- moment_values = get_moments_of_continuous_maxwellian_equilibrium(moms, dim=dim, c_s_sq=c_s_sq, order=2)
+ moment_values = get_equilibrium_values_of_maxwell_boltzmann_function(moms, dim=dim, c_s_sq=c_s_sq, order=2,
+ space="moment")
if not compressible:
moment_values = [compressible_to_incompressible_moment_value(m, sp.Symbol("rho"), sp.symbols("u_:3")[:dim])
for m in moment_values]
diff --git a/lbmpy_tests/centeredcumulant/test_central_moment_transform.py b/lbmpy_tests/centeredcumulant/test_central_moment_transform.py
index 533911a87db6c239bae11abab73ce2e05f1cec5f..c7c4ec2705c2f6120aedc274e8677ba44f25730e 100644
--- a/lbmpy_tests/centeredcumulant/test_central_moment_transform.py
+++ b/lbmpy_tests/centeredcumulant/test_central_moment_transform.py
@@ -1,9 +1,8 @@
import sympy as sp
-from lbmpy.moments import get_default_moment_set_for_stencil, extract_monomials
+from lbmpy.moments import get_default_moment_set_for_stencil, extract_monomials, statistical_quantity_symbol
import pytest
from lbmpy.stencils import get_stencil
-from lbmpy.methods.centeredcumulant.centered_cumulants import statistical_quantity_symbol
from lbmpy.methods.momentbased.moment_transforms import (
PdfsToCentralMomentsByMatrix, FastCentralMomentTransform, PdfsToCentralMomentsByShiftMatrix,
PRE_COLLISION_CENTRAL_MOMENT, POST_COLLISION_CENTRAL_MOMENT
diff --git a/lbmpy_tests/phasefield_allen_cahn/test_analytical.py b/lbmpy_tests/phasefield_allen_cahn/test_analytical.py
new file mode 100644
index 0000000000000000000000000000000000000000..36f854b800d7d30f4c9329d08a5f0572b33a6b0f
--- /dev/null
+++ b/lbmpy_tests/phasefield_allen_cahn/test_analytical.py
@@ -0,0 +1,37 @@
+import numpy as np
+
+from lbmpy.phasefield_allen_cahn.parameter_calculation import calculate_dimensionless_rising_bubble, \
+ calculate_parameters_rti
+
+from lbmpy.phasefield_allen_cahn.analytical import analytic_rising_speed
+
+
+
+def test_analytical():
+ parameters = calculate_dimensionless_rising_bubble(reference_time=18000,
+ density_heavy=1.0,
+ bubble_radius=16,
+ bond_number=30,
+ reynolds_number=420,
+ density_ratio=1000,
+ viscosity_ratio=100)
+
+ np.isclose(parameters["density_light"], 0.001, rtol=1e-05, atol=1e-08, equal_nan=False)
+ np.isclose(parameters["gravitational_acceleration"], -9.876543209876543e-08, rtol=1e-05, atol=1e-08, equal_nan=False)
+
+ parameters = calculate_parameters_rti(reference_length=128,
+ reference_time=18000,
+ density_heavy=1.0,
+ capillary_number=9.1,
+ reynolds_number=128,
+ atwood_number=1.0,
+ peclet_number=744,
+ density_ratio=3,
+ viscosity_ratio=3)
+
+ np.isclose(parameters["density_light"], 1/3, rtol=1e-05, atol=1e-08, equal_nan=False)
+ np.isclose(parameters["gravitational_acceleration"], -3.9506172839506174e-07, rtol=1e-05, atol=1e-08, equal_nan=False)
+ np.isclose(parameters["mobility"], 0.0012234169653524492, rtol=1e-05, atol=1e-08, equal_nan=False)
+
+ rs = analytic_rising_speed(1-6, 20, 0.01)
+ np.isclose(rs, 16666.666666666668, rtol=1e-05, atol=1e-08, equal_nan=False)
\ No newline at end of file
diff --git a/lbmpy_tests/phasefield_allen_cahn/test_codegen_3d.py b/lbmpy_tests/phasefield_allen_cahn/test_codegen_3d.py
index 6ca2a1dd35d0a1d3aa483dad67aaf2e9983bfc02..b03428fd2b18173a94d90ad8bf84712cbd43571c 100644
--- a/lbmpy_tests/phasefield_allen_cahn/test_codegen_3d.py
+++ b/lbmpy_tests/phasefield_allen_cahn/test_codegen_3d.py
@@ -1,123 +1,96 @@
-import numpy as np
-
-from lbmpy.creationfunctions import create_lb_method, create_lb_update_rule
-from lbmpy.phasefield_allen_cahn.analytical import analytic_rising_speed
+from lbmpy.creationfunctions import create_lb_method
from lbmpy.phasefield_allen_cahn.force_model import MultiphaseForceModel
-from lbmpy.phasefield_allen_cahn.kernel_equations import (
+from lbmpy.phasefield_allen_cahn.kernel_equations import ( get_collision_assignments_phase,
get_collision_assignments_hydro, hydrodynamic_force, initializer_kernel_hydro_lb, initializer_kernel_phase_field_lb,
interface_tracking_force)
-from lbmpy.phasefield_allen_cahn.parameter_calculation import (
- calculate_dimensionless_rising_bubble, calculate_parameters_rti)
from lbmpy.stencils import get_stencil
-from pystencils import AssignmentCollection, fields
+from pystencils import fields
-def test_codegen_3d():
+def test_allen_cahn_lb():
stencil_phase = get_stencil("D3Q15")
+ dimensions = len(stencil_phase[0])
+ # fields
+ u = fields("vel_field(" + str(dimensions) + "): [" + str(dimensions) + "D]", layout='fzyx')
+ C = fields("phase_field: [" + str(dimensions) + "D]", layout='fzyx')
+ C_tmp = fields("phase_field_tmp: [" + str(dimensions) + "D]", layout='fzyx')
+
+ h = fields("lb_phase_field(" + str(len(stencil_phase)) + "): [" + str(dimensions) + "D]", layout='fzyx')
+ h_tmp = fields("lb_phase_field_tmp(" + str(len(stencil_phase)) + "): [" + str(dimensions) + "D]", layout='fzyx')
+
+ M = 0.02
+ W = 5
+ w_c = 1.0 / (0.5 + (3.0 * M))
+
+ method_phase = create_lb_method(stencil=stencil_phase, method='srt', relaxation_rate=w_c, compressible=True)
+
+ h_updates = initializer_kernel_phase_field_lb(h, C, u, method_phase, W)
+
+ force_h = [f / 3 for f in interface_tracking_force(C, stencil_phase, W)]
+ force_model_h = MultiphaseForceModel(force=force_h)
+
+ allen_cahn_lb = get_collision_assignments_phase(lb_method=method_phase,
+ velocity_input=u,
+ output={'density': C_tmp},
+ force_model=force_model_h,
+ symbolic_fields={"symbolic_field": h,
+ "symbolic_temporary_field": h_tmp},
+ kernel_type='stream_pull_collide')
+
+ allen_cahn_lb = get_collision_assignments_phase(lb_method=method_phase,
+ velocity_input=u,
+ output={'density': C_tmp},
+ force_model=force_model_h,
+ symbolic_fields={"symbolic_field": h,
+ "symbolic_temporary_field": h_tmp},
+ kernel_type='collide_only')
+
+def test_hydro_lb():
+
stencil_hydro = get_stencil("D3Q27")
- assert (len(stencil_phase[0]) == len(stencil_hydro[0]))
dimensions = len(stencil_hydro[0])
- parameters = calculate_dimensionless_rising_bubble(reference_time=18000,
- density_heavy=1.0,
- bubble_radius=16,
- bond_number=30,
- reynolds_number=420,
- density_ratio=1000,
- viscosity_ratio=100)
-
- np.isclose(parameters["density_light"], 0.001, rtol=1e-05, atol=1e-08, equal_nan=False)
- np.isclose(parameters["gravitational_acceleration"], -9.876543209876543e-08, rtol=1e-05, atol=1e-08, equal_nan=False)
-
- parameters = calculate_parameters_rti(reference_length=128,
- reference_time=18000,
- density_heavy=1.0,
- capillary_number=9.1,
- reynolds_number=128,
- atwood_number=1.0,
- peclet_number=744,
- density_ratio=3,
- viscosity_ratio=3)
-
- np.isclose(parameters["density_light"], 1/3, rtol=1e-05, atol=1e-08, equal_nan=False)
- np.isclose(parameters["gravitational_acceleration"], -3.9506172839506174e-07, rtol=1e-05, atol=1e-08, equal_nan=False)
- np.isclose(parameters["mobility"], 0.0012234169653524492, rtol=1e-05, atol=1e-08, equal_nan=False)
-
- rs = analytic_rising_speed(1-6, 20, 0.01)
- np.isclose(rs, 16666.666666666668, rtol=1e-05, atol=1e-08, equal_nan=False)
-
density_liquid = 1.0
density_gas = 0.001
surface_tension = 0.0001
- M = 0.02
+ W = 5
# phase-field parameter
drho3 = (density_liquid - density_gas) / 3
- # interface thickness
- W = 5
# coefficient related to surface tension
beta = 12.0 * (surface_tension / W)
# coefficient related to surface tension
kappa = 1.5 * surface_tension * W
- # relaxation rate allen cahn (h)
- w_c = 1.0 / (0.5 + (3.0 * M))
- # fields
u = fields("vel_field(" + str(dimensions) + "): [" + str(dimensions) + "D]", layout='fzyx')
C = fields("phase_field: [" + str(dimensions) + "D]", layout='fzyx')
- h = fields("lb_phase_field(" + str(len(stencil_phase)) + "): [" + str(dimensions) + "D]", layout='fzyx')
- h_tmp = fields("lb_phase_field_tmp(" + str(len(stencil_phase)) + "): [" + str(dimensions) + "D]", layout='fzyx')
-
g = fields("lb_velocity_field(" + str(len(stencil_hydro)) + "): [" + str(dimensions) + "D]", layout='fzyx')
g_tmp = fields("lb_velocity_field_tmp(" + str(len(stencil_hydro)) + "): [" + str(dimensions) + "D]", layout='fzyx')
# calculate the relaxation rate for the hydro lb as well as the body force
density = density_gas + C.center * (density_liquid - density_gas)
# force acting on all phases of the model
- body_force = np.array([0, 1e-6, 0])
+ body_force = [0, 0, 0]
relaxation_time = 0.03 + 0.5
relaxation_rate = 1.0 / relaxation_time
- method_phase = create_lb_method(stencil=stencil_phase, method='srt', relaxation_rate=w_c, compressible=True)
method_hydro = create_lb_method(stencil=stencil_hydro, method="mrt", weighted=True,
- relaxation_rates=[relaxation_rate, 1, 1, 1, 1, 1],
- maxwellian_moments=True, entropic=False)
+ relaxation_rates=[relaxation_rate, 1, 1, 1, 1, 1])
# create the kernels for the initialization of the g and h field
- h_updates = initializer_kernel_phase_field_lb(h, C, u, method_phase, W)
g_updates = initializer_kernel_hydro_lb(g, u, method_hydro)
- force_h = [f / 3 for f in interface_tracking_force(C, stencil_phase, W)]
- force_model_h = MultiphaseForceModel(force=force_h)
-
force_g = hydrodynamic_force(g, C, method_hydro,
relaxation_time, density_liquid, density_gas, kappa, beta, body_force)
force_model_g = MultiphaseForceModel(force=force_g, rho=density)
- h_tmp_symbol_list = [h_tmp.center(i) for i, _ in enumerate(stencil_phase)]
- sum_h = np.sum(h_tmp_symbol_list[:])
-
- method_phase.set_force_model(force_model_h)
-
- allen_cahn_lb = create_lb_update_rule(lb_method=method_phase,
- velocity_input=u,
- compressible=True,
- optimization={"symbolic_field": h,
- "symbolic_temporary_field": h_tmp},
- kernel_type='stream_pull_collide')
-
- allen_cahn_lb.set_main_assignments_from_dict({**allen_cahn_lb.main_assignments_dict, **{C.center: sum_h}})
- allen_cahn_update_rule = AssignmentCollection(main_assignments=allen_cahn_lb.main_assignments,
- subexpressions=allen_cahn_lb.subexpressions)
- # ---------------------------------------------------------------------------------------------------------
-
hydro_lb_update_rule_normal = get_collision_assignments_hydro(lb_method=method_hydro,
density=density,
velocity_input=u,
- force=force_g,
+ force_model=force_model_g,
sub_iterations=2,
symbolic_fields={"symbolic_field": g,
"symbolic_temporary_field": g_tmp},
@@ -126,7 +99,7 @@ def test_codegen_3d():
hydro_lb_update_rule_push = get_collision_assignments_hydro(lb_method=method_hydro,
density=density,
velocity_input=u,
- force=force_g,
+ force_model=force_model_g,
sub_iterations=2,
symbolic_fields={"symbolic_field": g,
"symbolic_temporary_field": g_tmp},
diff --git a/lbmpy_tests/phasefield_allen_cahn/test_contact_angle.py b/lbmpy_tests/phasefield_allen_cahn/test_contact_angle.py
new file mode 100644
index 0000000000000000000000000000000000000000..cba55b41d264243651848d8d08bf885dc3700409
--- /dev/null
+++ b/lbmpy_tests/phasefield_allen_cahn/test_contact_angle.py
@@ -0,0 +1,38 @@
+import math
+
+import pystencils as ps
+from pystencils.boundaries.boundaryconditions import Neumann
+from pystencils.boundaries.boundaryhandling import BoundaryHandling
+
+from lbmpy.phasefield_allen_cahn.contact_angle import ContactAngle
+from lbmpy.stencils import get_stencil
+
+import numpy as np
+
+
+def test_contact_angle():
+ stencil = get_stencil("D2Q9")
+ contact_angle = 45
+ phase_value = 0.5
+
+ domain_size = (9, 9)
+
+ dh = ps.create_data_handling(domain_size, periodicity=(False, False))
+
+ C = dh.add_array("C", values_per_cell=1)
+ dh.fill("C", 0.0, ghost_layers=True)
+ dh.fill("C", phase_value, ghost_layers=False)
+
+ bh = BoundaryHandling(dh, C.name, stencil, target='cpu')
+ bh.set_boundary(ContactAngle(45, 5), ps.make_slice[:, 0])
+ bh()
+
+ h = 1.0
+ myA = 1.0 - 0.5 * h * (4.0 / 5) * math.cos(math.radians(contact_angle))
+
+ phase_on_boundary = (myA - np.sqrt(myA * myA - 4.0 * (myA - 1.0) * phase_value)) / (myA - 1.0) - phase_value
+
+ np.testing.assert_almost_equal(dh.cpu_arrays["C"][5, 0], phase_on_boundary)
+
+ assert ContactAngle(45, 5) == ContactAngle(45, 5)
+ assert ContactAngle(46, 5) != ContactAngle(45, 5)
diff --git a/lbmpy_tests/phasefield_allen_cahn/test_phase_field_allen_cahn_2d.ipynb b/lbmpy_tests/phasefield_allen_cahn/test_phase_field_allen_cahn_2d.ipynb
index cdf0127472986eb98f54cc26afb02050fb7e91e1..d368fee0bf4890e26aa65a858424dc073a72a330 100644
--- a/lbmpy_tests/phasefield_allen_cahn/test_phase_field_allen_cahn_2d.ipynb
+++ b/lbmpy_tests/phasefield_allen_cahn/test_phase_field_allen_cahn_2d.ipynb
@@ -6,19 +6,22 @@
"metadata": {},
"outputs": [],
"source": [
+ "from collections import OrderedDict\n",
"import math\n",
"\n",
- "from lbmpy.session import *\n",
"from pystencils.session import *\n",
+ "from lbmpy.session import *\n",
+ "\n",
+ "from pystencils.boundaries.boundaryhandling import BoundaryHandling\n",
"\n",
"from lbmpy.moments import MOMENT_SYMBOLS\n",
- "from collections import OrderedDict\n",
"\n",
"from lbmpy.methods.creationfunctions import create_with_discrete_maxwellian_eq_moments\n",
"\n",
"from lbmpy.phasefield_allen_cahn.parameter_calculation import calculate_parameters_rti\n",
"from lbmpy.phasefield_allen_cahn.kernel_equations import *\n",
- "from lbmpy.phasefield_allen_cahn.force_model import MultiphaseForceModel"
+ "from lbmpy.phasefield_allen_cahn.force_model import CentralMomentMultiphaseForceModel\n",
+ "from lbmpy.phasefield_allen_cahn.contact_angle import ContactAngle"
]
},
{
@@ -48,6 +51,7 @@
"source": [
"stencil_phase = get_stencil(\"D2Q9\")\n",
"stencil_hydro = get_stencil(\"D2Q9\")\n",
+ "fd_stencil = get_stencil(\"D2Q9\")\n",
"assert(len(stencil_phase[0]) == len(stencil_hydro[0]))\n",
"\n",
"dimensions = len(stencil_phase[0])"
@@ -127,7 +131,9 @@
"dh.fill(\"u\", 0.0, ghost_layers=True)\n",
"\n",
"C = dh.add_array(\"C\")\n",
- "dh.fill(\"C\", 0.0, ghost_layers=True)"
+ "dh.fill(\"C\", 0.0, ghost_layers=True)\n",
+ "C_tmp = dh.add_array(\"C_tmp\")\n",
+ "dh.fill(\"C_tmp\", 0.0, ghost_layers=True)"
]
},
{
@@ -151,10 +157,11 @@
"metadata": {},
"outputs": [],
"source": [
- "method_phase = create_lb_method(stencil=stencil_phase, method='srt', relaxation_rate=w_c, compressible = True)\n",
+ "method_phase = create_lb_method(stencil=stencil_phase, method=\"central_moment\", compressible=True,\n",
+ " relaxation_rates=[0, w_c, 1, 1, 1], equilibrium_order=4)\n",
"\n",
- "method_hydro = create_lb_method(stencil=stencil_hydro, method=\"mrt\", weighted=True,\n",
- " relaxation_rates=[s8, 1, 1, 1, 1, 1], maxwellian_moments=True, entropic=False)"
+ "method_hydro = create_lb_method(stencil=stencil_phase, method=\"central_moment\", compressible=False,\n",
+ " relaxation_rates=[s8, 1, 1], equilibrium_order=4)"
]
},
{
@@ -193,7 +200,7 @@
"metadata": {},
"outputs": [],
"source": [
- "h_updates = initializer_kernel_phase_field_lb(h, C, u, method_phase, W)\n",
+ "h_updates = initializer_kernel_phase_field_lb(h, C, u, method_phase, W, fd_stencil=fd_stencil)\n",
"g_updates = initializer_kernel_hydro_lb(g, u, method_hydro)\n",
"\n",
"h_init = ps.create_kernel(h_updates, target=dh.default_target, cpu_openmp=True).compile()\n",
@@ -206,10 +213,11 @@
"metadata": {},
"outputs": [],
"source": [
- "force_h = [f / 3 for f in interface_tracking_force(C, stencil_phase, W)]\n",
- "force_model_h = MultiphaseForceModel(force=force_h)\n",
+ "force_h = [f / 3 for f in interface_tracking_force(C, stencil_phase, W, fd_stencil=stencil_phase)]\n",
+ "force_model_h = CentralMomentMultiphaseForceModel(force=force_h)\n",
"\n",
- "force_g = hydrodynamic_force(g, C, method_hydro, tau, rho_H, rho_L, kappa, beta, body_force)"
+ "force_g = hydrodynamic_force(g, C, method_hydro, tau, rho_H, rho_L, kappa, beta, body_force)\n",
+ "force_model_g = CentralMomentMultiphaseForceModel(force=force_g, rho=rho)"
]
},
{
@@ -220,19 +228,14 @@
},
"outputs": [],
"source": [
- "h_tmp_symbol_list = [h_tmp.center(i) for i, _ in enumerate(stencil_phase)]\n",
- "sum_h = np.sum(h_tmp_symbol_list[:])\n",
+ "allen_cahn_lb = get_collision_assignments_phase(lb_method=method_phase,\n",
+ " velocity_input=u,\n",
+ " output={'density': C_tmp},\n",
+ " force_model=force_model_h,\n",
+ " symbolic_fields={\"symbolic_field\": h,\n",
+ " \"symbolic_temporary_field\": h_tmp},\n",
+ " kernel_type='stream_pull_collide')\n",
"\n",
- "method_phase.set_force_model(force_model_h)\n",
- "\n",
- "allen_cahn_lb = create_lb_update_rule(lb_method=method_phase,\n",
- " velocity_input = u, \n",
- " compressible = True,\n",
- " optimization = {\"symbolic_field\": h,\n",
- " \"symbolic_temporary_field\": h_tmp},\n",
- " kernel_type = 'stream_pull_collide')\n",
- "\n",
- "allen_cahn_lb.set_main_assignments_from_dict({**allen_cahn_lb.main_assignments_dict, **{C.center: sum_h}})\n",
"allen_cahn_lb = sympy_cse(allen_cahn_lb)\n",
"\n",
"ast_allen_cahn_lb = ps.create_kernel(allen_cahn_lb, target=dh.default_target, cpu_openmp=True)\n",
@@ -248,11 +251,11 @@
"hydro_lb_update_rule = get_collision_assignments_hydro(lb_method=method_hydro,\n",
" density=rho,\n",
" velocity_input=u,\n",
- " force = force_g,\n",
+ " force_model=force_model_g,\n",
" sub_iterations=2,\n",
" symbolic_fields={\"symbolic_field\": g,\n",
" \"symbolic_temporary_field\": g_tmp},\n",
- " kernel_type='collide_only')\n",
+ " kernel_type='collide_stream_push')\n",
"\n",
"hydro_lb_update_rule = sympy_cse(hydro_lb_update_rule)\n",
"\n",
@@ -267,31 +270,46 @@
"outputs": [],
"source": [
"# periodic Boundarys for g, h and C\n",
- "periodic_BC_g = dh.synchronization_function(g.name, target=dh.default_target, optimization = {\"openmp\": True})\n",
- "periodic_BC_h = dh.synchronization_function(h.name, target=dh.default_target, optimization = {\"openmp\": True})\n",
"periodic_BC_C = dh.synchronization_function(C.name, target=dh.default_target, optimization = {\"openmp\": True})\n",
"\n",
+ "periodic_BC_g = LBMPeriodicityHandling(stencil=stencil_hydro, data_handling=dh, pdf_field_name=g.name,\n",
+ " streaming_pattern='push')\n",
+ "periodic_BC_h = LBMPeriodicityHandling(stencil=stencil_phase, data_handling=dh, pdf_field_name=h.name,\n",
+ " streaming_pattern='pull')\n",
+ "\n",
"# No slip boundary for the phasefield lbm\n",
"bh_allen_cahn = LatticeBoltzmannBoundaryHandling(method_phase, dh, 'h',\n",
- " target=dh.default_target, name='boundary_handling_h')\n",
+ " target=dh.default_target, name='boundary_handling_h',\n",
+ " streaming_pattern='pull')\n",
"\n",
"# No slip boundary for the velocityfield lbm\n",
"bh_hydro = LatticeBoltzmannBoundaryHandling(method_hydro, dh, 'g' ,\n",
- " target=dh.default_target, name='boundary_handling_g')\n",
+ " target=dh.default_target, name='boundary_handling_g',\n",
+ " streaming_pattern='push')\n",
+ "\n",
+ "contact_angle = BoundaryHandling(dh, C.name, fd_stencil, target=dh.default_target)\n",
"\n",
+ "contact = ContactAngle(45, W)\n",
"wall = NoSlip()\n",
+ "\n",
"if dimensions == 2:\n",
" bh_allen_cahn.set_boundary(wall, make_slice[:, 0])\n",
" bh_allen_cahn.set_boundary(wall, make_slice[:, -1])\n",
"\n",
" bh_hydro.set_boundary(wall, make_slice[:, 0])\n",
" bh_hydro.set_boundary(wall, make_slice[:, -1])\n",
+ " \n",
+ " contact_angle.set_boundary(contact, make_slice[:, 0])\n",
+ " contact_angle.set_boundary(contact, make_slice[:, -1])\n",
"else:\n",
" bh_allen_cahn.set_boundary(wall, make_slice[:, 0, :])\n",
" bh_allen_cahn.set_boundary(wall, make_slice[:, -1, :])\n",
"\n",
" bh_hydro.set_boundary(wall, make_slice[:, 0, :])\n",
" bh_hydro.set_boundary(wall, make_slice[:, -1, :])\n",
+ " \n",
+ " contact_angle.set_boundary(contact, make_slice[:, 0, :])\n",
+ " contact_angle.set_boundary(contact, make_slice[:, -1, :])\n",
"\n",
"\n",
"bh_allen_cahn.prepare()\n",
@@ -303,41 +321,31 @@
"execution_count": 14,
"metadata": {},
"outputs": [],
- "source": [
- "ac_g = create_lb_update_rule(stencil = stencil_hydro,\n",
- " optimization={\"symbolic_field\": g,\n",
- " \"symbolic_temporary_field\": g_tmp},\n",
- " kernel_type='stream_pull_only')\n",
- "ast_g = ps.create_kernel(ac_g, target=dh.default_target, cpu_openmp=True)\n",
- "stream_g = ast_g.compile()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 15,
- "metadata": {},
- "outputs": [],
"source": [
"# definition of the timestep for the immiscible fluids model\n",
"def Improved_PhaseField_h_g():\n",
- " bh_allen_cahn()\n",
" periodic_BC_h()\n",
+ " bh_allen_cahn()\n",
" \n",
+ " # run the phase-field LB\n",
" dh.run_kernel(kernel_allen_cahn_lb)\n",
+ " dh.swap(\"C\", \"C_tmp\")\n",
+ " contact_angle()\n",
+ " # periodic BC of the phase-field\n",
" periodic_BC_C()\n",
+ " \n",
" dh.run_kernel(kernel_hydro_lb)\n",
- "\n",
- " bh_hydro()\n",
" periodic_BC_g()\n",
- " \n",
- " dh.run_kernel(stream_g)\n",
+ " bh_hydro()\n",
+ "\n",
+ " # field swaps\n",
" dh.swap(\"h\", \"h_tmp\")\n",
" dh.swap(\"g\", \"g_tmp\")"
]
},
{
"cell_type": "code",
- "execution_count": 16,
+ "execution_count": 15,
"metadata": {
"scrolled": false
},
@@ -351,7 +359,7 @@
},
{
"cell_type": "code",
- "execution_count": 17,
+ "execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
@@ -366,7 +374,7 @@
},
{
"cell_type": "code",
- "execution_count": 18,
+ "execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
diff --git a/lbmpy_tests/phasefield_allen_cahn/test_phase_field_derivatives.ipynb b/lbmpy_tests/phasefield_allen_cahn/test_phase_field_derivatives.ipynb
index 8fcb02a38ae833e6126867ab106f04773777a0cb..c1cf2fd56df787299e0d7b229f6eb9ac65ef1270 100644
--- a/lbmpy_tests/phasefield_allen_cahn/test_phase_field_derivatives.ipynb
+++ b/lbmpy_tests/phasefield_allen_cahn/test_phase_field_derivatives.ipynb
@@ -203,6 +203,19 @@
"assert a[0] == b"
]
},
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ng = normalized_isotropic_gradient_symbolic(C, stencil)\n",
+ "\n",
+ "tmp = (sum(map(lambda x: x * x, a)) + 1.e-32) ** 0.5 \n",
+ "\n",
+ "assert ng[0] == a[0] / tmp"
+ ]
+ },
{
"cell_type": "markdown",
"metadata": {},
@@ -212,7 +225,7 @@
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
@@ -228,7 +241,7 @@
},
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
@@ -240,7 +253,22 @@
},
{
"cell_type": "code",
- "execution_count": 13,
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "pf = pressure_force(C, stencil, 1, 0.1)\n",
+ "vf = viscous_force(g, C, lb_method, tau, 1, 0.1)\n",
+ "sf = surface_tension_force(C, stencil, 0, 1)\n",
+ "\n",
+ "assert sp.simplify(pf[0] + vf[0] + sf[0] - b[0]) == 0\n",
+ "assert sp.simplify(pf[1] + vf[1] + sf[1] - b[1]) == 0\n",
+ "assert sp.simplify(pf[2] + vf[2] + sf[2] - b[2]) == 0"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
@@ -259,7 +287,7 @@
},
{
"cell_type": "code",
- "execution_count": 14,
+ "execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
@@ -281,7 +309,7 @@
},
{
"cell_type": "code",
- "execution_count": 15,
+ "execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
@@ -316,7 +344,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.7.4"
+ "version": "3.8.2"
}
},
"nbformat": 4,
diff --git a/lbmpy_tests/test_central_moment.py b/lbmpy_tests/test_central_moment.py
new file mode 100644
index 0000000000000000000000000000000000000000..e73194209bb0a6c37692a4faa511c45e7c9d4378
--- /dev/null
+++ b/lbmpy_tests/test_central_moment.py
@@ -0,0 +1,100 @@
+import numpy as np
+import sympy as sp
+
+from lbmpy.creationfunctions import create_lb_method
+from lbmpy.forcemodels import Luo
+from lbmpy.maxwellian_equilibrium import get_weights
+from lbmpy.moments import get_default_moment_set_for_stencil, moment_matrix, set_up_shift_matrix
+from lbmpy.scenarios import create_lid_driven_cavity
+from lbmpy.stencils import get_stencil
+
+from lbmpy.methods.momentbased.moment_transforms import FastCentralMomentTransform
+
+
+def test_central_moment_ldc():
+ sc_central_moment = create_lid_driven_cavity((20, 20), method='central_moment',
+ relaxation_rates=[1.8, 1, 1], equilibrium_order=4,
+ compressible=True, force=(-1e-10, 0))
+
+ sc_central_mometn_3D = create_lid_driven_cavity((20, 20, 3), method='central_moment',
+ relaxation_rates=[1.8, 1, 1, 1, 1], equilibrium_order=4,
+ compressible=True, force=(-1e-10, 0, 0))
+
+ sc_central_moment.run(1000)
+ sc_central_mometn_3D.run(1000)
+ assert np.isfinite(np.max(sc_central_moment.velocity[:, :]))
+ assert np.isfinite(np.max(sc_central_mometn_3D.velocity[:, :, :]))
+
+
+def test_central_moment_class():
+ stencil = get_stencil("D2Q9")
+
+ method = create_lb_method(stencil=stencil, method='central_moment',
+ relaxation_rates=[1.2, 1.3, 1.4], equilibrium_order=4, compressible=True)
+
+ srt = create_lb_method(stencil=stencil, method='srt',
+ relaxation_rate=1.2, equilibrium_order=4, compressible=True)
+
+ rho = method.zeroth_order_equilibrium_moment_symbol
+ u = method.first_order_equilibrium_moment_symbols
+ cs_sq = sp.Rational(1, 3)
+
+ force_model = Luo(force=sp.symbols(f"F_:{2}"))
+
+ eq = (rho, 0, 0, rho * cs_sq, rho * cs_sq, 0, 0, 0, rho * cs_sq ** 2)
+
+ default_moments = get_default_moment_set_for_stencil(stencil)
+
+ assert method.central_moment_transform_class == FastCentralMomentTransform
+ assert method.conserved_quantity_computation.zeroth_order_moment_symbol == rho
+ assert method.conserved_quantity_computation.first_order_moment_symbols == u
+ assert method.moment_equilibrium_values == eq
+
+ assert method.force_model == None
+ method.set_force_model(force_model)
+ assert method.force_model == force_model
+
+ assert method.relaxation_matrix[0, 0] == 0
+ assert method.relaxation_matrix[1, 1] == 0
+ assert method.relaxation_matrix[2, 2] == 0
+
+ method.set_conserved_moments_relaxation_rate(1.9)
+
+ assert method.relaxation_matrix[0, 0] == 1.9
+ assert method.relaxation_matrix[1, 1] == 1.9
+ assert method.relaxation_matrix[2, 2] == 1.9
+
+ moments = list()
+ for i in method.relaxation_info_dict:
+ moments.append(i)
+
+ assert moments == default_moments
+
+ for i in range(len(stencil)):
+ assert method.relaxation_rates[i] == method.relaxation_matrix[i, i]
+
+ M = method.moment_matrix
+ N = method.shift_matrix
+
+ assert M == moment_matrix(moments, stencil=stencil)
+ assert N == set_up_shift_matrix(moments, stencil=stencil)
+
+ assert get_weights(stencil) == method.weights
+
+ eq_terms_central = method.get_equilibrium_terms()
+ eq_terms_srt = srt.get_equilibrium_terms()
+
+ for i in range(len(stencil)):
+ assert sp.simplify(eq_terms_central[i] - eq_terms_srt[i]) == 0
+
+ method = create_lb_method(stencil="D2Q9", method='central_moment',
+ relaxation_rates=[1.7, 1.8, 1.2, 1.3, 1.4], equilibrium_order=4, compressible=True)
+
+ assert method.relaxation_matrix[0, 0] == 1.7
+ assert method.relaxation_matrix[1, 1] == 1.8
+ assert method.relaxation_matrix[2, 2] == 1.8
+
+ method = create_lb_method(stencil="D2Q9", method='central_moment',
+ relaxation_rates=[1.3] * 9, equilibrium_order=4, compressible=True)
+
+ assert sum(method.relaxation_rates) == 1.3 * 9
\ No newline at end of file
diff --git a/lbmpy_tests/test_maxwellian_equilibrium.py b/lbmpy_tests/test_maxwellian_equilibrium.py
index 4410f76dc6eeaa6b7618a7eee10ef35b05c6210f..022f481d6e91e3b7719579be213c7777705a96eb 100644
--- a/lbmpy_tests/test_maxwellian_equilibrium.py
+++ b/lbmpy_tests/test_maxwellian_equilibrium.py
@@ -12,15 +12,17 @@ def test_maxwellian_moments():
rho = sp.Symbol("rho")
u = sp.symbols("u_0 u_1 u_2")
c_s = sp.Symbol("c_s")
- eq_moments = get_moments_of_continuous_maxwellian_equilibrium(((0, 0, 0), (0, 0, 1)),
- dim=3, rho=rho, u=u, c_s_sq=c_s ** 2)
+ eq_moments = get_equilibrium_values_of_maxwell_boltzmann_function(((0, 0, 0), (0, 0, 1)),
+ dim=3, rho=rho, u=u, c_s_sq=c_s ** 2,
+ space="moment")
assert eq_moments[0] == rho
assert eq_moments[1] == rho * u[2]
x, y, z = MOMENT_SYMBOLS
one = sp.Rational(1, 1)
- eq_moments = get_moments_of_continuous_maxwellian_equilibrium((one, x, x ** 2, x * y),
- dim=2, rho=rho, u=u[:2], c_s_sq=c_s ** 2)
+ eq_moments = get_equilibrium_values_of_maxwell_boltzmann_function((one, x, x ** 2, x * y),
+ dim=2, rho=rho, u=u[:2], c_s_sq=c_s ** 2,
+ space="moment")
assert eq_moments[0] == rho
assert eq_moments[1] == rho * u[0]
assert eq_moments[2] == rho * (c_s ** 2 + u[0] ** 2)
@@ -33,7 +35,8 @@ def test_continuous_discrete_moment_equivalence():
dim = len(stencil[0])
c_s_sq = sp.Rational(1, 3)
moments = tuple(moments_up_to_order(3, dim=dim, include_permutations=False))
- cm = sp.Matrix(get_moments_of_continuous_maxwellian_equilibrium(moments, order=2, dim=dim, c_s_sq=c_s_sq))
+ cm = sp.Matrix(get_equilibrium_values_of_maxwell_boltzmann_function(moments, order=2, dim=dim, c_s_sq=c_s_sq,
+ space="moment"))
dm = sp.Matrix(get_moments_of_discrete_maxwellian_equilibrium(stencil, moments, order=2,
compressible=True, c_s_sq=c_s_sq))
@@ -55,8 +58,10 @@ def test_moment_cumulant_continuous_equivalence():
u = sp.symbols("u_:{dim}".format(dim=dim))
indices = tuple(moments_up_to_component_order(2, dim=dim))
c_s_sq = sp.Rational(1, 3)
- eq_moments1 = get_moments_of_continuous_maxwellian_equilibrium(indices, dim=dim, u=u, c_s_sq=c_s_sq)
- eq_cumulants = get_cumulants_of_continuous_maxwellian_equilibrium(indices, dim=dim, u=u, c_s_sq=c_s_sq)
+ eq_moments1 = get_equilibrium_values_of_maxwell_boltzmann_function(indices, dim=dim, u=u, c_s_sq=c_s_sq,
+ space="moment")
+ eq_cumulants = get_equilibrium_values_of_maxwell_boltzmann_function(indices, dim=dim, u=u, c_s_sq=c_s_sq,
+ space="cumulant")
eq_cumulants = {idx: c for idx, c in zip(indices, eq_cumulants)}
eq_moments2 = [raw_moment_as_function_of_cumulants(idx, eq_cumulants) for idx in indices]
pdfs_to_moments = moment_matrix(indices, stencil)
@@ -82,8 +87,10 @@ def test_moment_cumulant_continuous_equivalence_polynomial_formulation():
index_tuples = tuple(moments_up_to_component_order(2, dim=dim))
indices = exponents_to_polynomial_representations(index_tuples)
c_s_sq = sp.Rational(1, 3)
- eq_moments1 = get_moments_of_continuous_maxwellian_equilibrium(indices, dim=dim, u=u, c_s_sq=c_s_sq)
- eq_cumulants = get_cumulants_of_continuous_maxwellian_equilibrium(indices, dim=dim, u=u, c_s_sq=c_s_sq)
+ eq_moments1 = get_equilibrium_values_of_maxwell_boltzmann_function(indices, dim=dim, u=u, c_s_sq=c_s_sq,
+ space="moment")
+ eq_cumulants = get_equilibrium_values_of_maxwell_boltzmann_function(indices, dim=dim, u=u, c_s_sq=c_s_sq,
+ space="cumulant")
eq_cumulants = {idx: c for idx, c in zip(index_tuples, eq_cumulants)}
eq_moments2 = [raw_moment_as_function_of_cumulants(idx, eq_cumulants) for idx in index_tuples]
pdfs_to_moments = moment_matrix(indices, stencil)