#*------------------------------------------------------------------------------*
#* JAX-FLUIDS - *
#* *
#* A fully-differentiable CFD solver for compressible two-phase flows. *
#* Copyright (C) 2022 Deniz A. Bezgin, Aaron B. Buhendwa, Nikolaus A. Adams *
#* *
#* This program is free software: you can redistribute it and/or modify *
#* it under the terms of the GNU General Public License as published by *
#* the Free Software Foundation, either version 3 of the License, or *
#* (at your option) any later version. *
#* *
#* This program is distributed in the hope that it will be useful, *
#* but WITHOUT ANY WARRANTY; without even the implied warranty of *
#* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the *
#* GNU General Public License for more details. *
#* *
#* You should have received a copy of the GNU General Public License *
#* along with this program. If not, see <https://www.gnu.org/licenses/>. *
#* *
#*------------------------------------------------------------------------------*
#* *
#* CONTACT *
#* *
#* deniz.bezgin@tum.de // aaron.buhendwa@tum.de // nikolaus.adams@tum.de *
#* *
#*------------------------------------------------------------------------------*
#* *
#* Munich, April 15th, 2022 *
#* *
#*------------------------------------------------------------------------------*
from typing import List
import jax.numpy as jnp
from jaxfluids.stencils.spatial_derivative import SpatialDerivative
[docs]
class HOUC3(SpatialDerivative):
def __init__(self, nh: int, inactive_axis: List):
super(HOUC3, self).__init__(nh=nh, inactive_axis=inactive_axis)
self.coeff = [1.0/6.0, -1.0, 1.0/2.0, 1.0/3.0]
self.sign = [1, -1]
self._slices = [
[
[ jnp.s_[..., jnp.s_[self.n-2*j:-self.n-2*j] if -self.n-2*j != 0 else jnp.s_[self.n-2*j:None], self.nhy, self.nhz],
jnp.s_[..., self.n-1*j:-self.n-1*j, self.nhy, self.nhz],
jnp.s_[..., self.n+0*j:-self.n+0*j, self.nhy, self.nhz],
jnp.s_[..., self.n+1*j:-self.n+1*j, self.nhy, self.nhz] ],
[ jnp.s_[..., self.nhx, jnp.s_[self.n-2*j:-self.n-2*j] if -self.n-2*j != 0 else jnp.s_[self.n-2*j:None], self.nhz],
jnp.s_[..., self.nhx, self.n-1*j:-self.n-1*j, self.nhz],
jnp.s_[..., self.nhx, self.n+0*j:-self.n+0*j, self.nhz],
jnp.s_[..., self.nhx, self.n+1*j:-self.n+1*j, self.nhz] ],
[ jnp.s_[..., self.nhx, self.nhy, jnp.s_[self.n-2*j:-self.n-2*j] if -self.n-2*j != 0 else jnp.s_[self.n-2*j:None]],
jnp.s_[..., self.nhx, self.nhy, self.n-1*j:-self.n-1*j],
jnp.s_[..., self.nhx, self.nhy, self.n+0*j:-self.n+0*j],
jnp.s_[..., self.nhx, self.nhy, self.n+1*j:-self.n+1*j] ]
] for j in self.sign ]
[docs]
def derivative_xi(self, levelset: jnp.ndarray, dxi:float, i: int, j: int, *args) -> jnp.ndarray:
s1_ = self._slices[j][i]
cell_state_xi_j = sum(levelset[s1_[k]]*self.coeff[k] for k in range(len(self.coeff)))
cell_state_xi_j *= self.sign[j]
cell_state_xi_j *= (1.0 / dxi)
return cell_state_xi_j