#*------------------------------------------------------------------------------*
#* 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_reconstruction import SpatialReconstruction
[docs]
class WENO1(SpatialReconstruction):
def __init__(self, nh: int, inactive_axis: List) -> None:
super(WENO1, self).__init__(nh=nh, inactive_axis=inactive_axis)
self._stencil_size = 2
self._slices = [
[
[jnp.s_[..., self.n-1+j:-self.n+j, self.nhy, self.nhz], ],
[jnp.s_[..., self.nhx, self.n-1+j:-self.n+j, self.nhz], ],
[jnp.s_[..., self.nhx, self.nhy, self.n-1+j:-self.n+j], ],
] for j in range(2)]
[docs]
def set_slices_stencil(self) -> None:
self._slices = [
[
[ jnp.s_[..., 0+j, None:None, None:None], ],
[ jnp.s_[..., None:None, 0+j, None:None], ],
[ jnp.s_[..., None:None, None:None, 0+j], ],
] for j in range(2)]
[docs]
def reconstruct_xi(self, buffer: jnp.ndarray, axis: int, j: int, dx = None, **kwargs) -> jnp.ndarray:
s1_ = self._slices[j][axis]
cell_state_xi_j = buffer[s1_[0]]
return cell_state_xi_j