#*------------------------------------------------------------------------------*
#* 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.time_integration.time_integrator import TimeIntegrator
[docs]
class RungeKutta2(TimeIntegrator):
"""2nd-order TVD RK2 scheme
"""
def __init__(self, nh: int, inactive_axis: List) -> None:
super(RungeKutta2, self).__init__(nh, inactive_axis)
self.no_stages = 2
self.timestep_multiplier = (1.0, 0.5)
self.timestep_increment_factor = (1.0, 1.0)
[docs]
def prepare_buffer_for_integration(self, cons: jnp.ndarray, init: jnp.ndarray, stage: int) -> jnp.ndarray:
""" u_cons = 0.5 u^n + 0.5 u^* """
return 0.5*cons + 0.5*init
[docs]
def integrate(self, cons: jnp.ndarray, rhs: jnp.ndarray, timestep: float, stage: int) -> jnp.ndarray:
timestep = timestep * self.timestep_multiplier[stage]
cons = self.integrate_conservatives(cons, rhs, timestep)
return cons