#*------------------------------------------------------------------------------*
#* 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 abc import ABC, abstractmethod
from typing import Callable
import jax
import jax.numpy as jnp
from jaxfluids.materials.material_manager import MaterialManager
[docs]
class RiemannSolver(ABC):
"""Abstract base class for Riemann solvers.
RiemannSolver has two fundamental attributes: a material manager and a signal speed.
The solve_riemann_problem_xi method solves the one-dimensional Riemann problem.
"""
eps = jnp.finfo(jnp.float64).eps
def __init__(self, material_manager: MaterialManager, signal_speed: Callable) -> None:
self.material_manager = material_manager
self.signal_speed = signal_speed
[docs]
@abstractmethod
def solve_riemann_problem_xi(self, primes_L: jnp.ndarray, primes_R: jnp.ndarray,
cons_L: jnp.ndarray, cons_R: jnp.ndarray, axis: int, **kwargs) -> jnp.ndarray:
"""Solves one-dimensional Riemann problem in the direction as specified
by the axis argument.
:param primes_L: primtive variable buffer left of cell face
:type primes_L: jnp.ndarray
:param primes_R: primtive variable buffer right of cell face
:type primes_R: jnp.ndarray
:param cons_L: conservative variable buffer left of cell face
:type cons_L: jnp.ndarray
:param cons_R: conservative variable buffer right of cell face
:type cons_R: jnp.ndarray
:param axis: Spatial direction along which Riemann problem is solved.
:type axis: int
:return: buffer of fluxes in xi direction
:rtype: jnp.ndarray
"""
pass