#*------------------------------------------------------------------------------*
#* 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 DerivativeSecondOrderFace(SpatialDerivative):
"""2nd order stencil for 1st derivative at the cell face
x
| | |
| i | i+1 |
| | |
"""
def __init__(self, nh: int, inactive_axis: List, offset: int = 0) -> None:
super(DerivativeSecondOrderFace, self).__init__(nh=nh, inactive_axis=inactive_axis, offset=offset)
self.s_ = [
[ jnp.s_[..., self.n-1:-self.n , self.nhy, self.nhz], # i
jnp.s_[..., jnp.s_[self.n :-self.n+1] if self.n != 1 else jnp.s_[self.n:None], self.nhy, self.nhz], ], # i+1
[ jnp.s_[..., self.nhx, self.n-1:-self.n , self.nhz],
jnp.s_[..., self.nhx, jnp.s_[self.n :-self.n+1] if self.n != 1 else jnp.s_[self.n:None], self.nhz], ],
[ jnp.s_[..., self.nhx, self.nhy, self.n-1:-self.n ],
jnp.s_[..., self.nhx, self.nhy, jnp.s_[self.n :-self.n+1] if self.n != 1 else jnp.s_[self.n:None]], ],
]
[docs]
def derivative_xi(self, buffer: jnp.ndarray, dxi: jnp.ndarray, axis: int) -> jnp.ndarray:
s1_ = self.s_[axis]
deriv_xi = (1.0 / dxi) * (-buffer[s1_[0]] + buffer[s1_[1]])
return deriv_xi