Energy solver

energy_solver

This module provides a fine-tuned solver class for the energy conservation equation. Users instantiate the EnergySolver class by providing relevant parameters and call the solve method to request a solver update.

iterative_energy_solver_parameters: dict[str, Any] = {'mat_type': 'aij', 'snes_type': 'ksponly', 'ksp_type': 'gmres', 'ksp_rtol': 1e-05, 'pc_type': 'sor'} module-attribute

Default iterative solver parameters for solution of energy equation. Configured to use the GMRES Krylov scheme with Successive Over Relaxation (SOR) preconditioning. Note that default energy solver parameters can be augmented or adjusted by accessing the solver_parameter dictionary, for example: energy_solver.solver_parameters['ksp_converged_reason'] = None energy_solver.solver_parameters['ksp_rtol'] = 1e-4 G-ADOPT defaults to iterative solvers in 3-D.

direct_energy_solver_parameters: dict[str, Any] = {'mat_type': 'aij', 'snes_type': 'ksponly', 'ksp_type': 'preonly', 'pc_type': 'lu', 'pc_factor_mat_solver_type': 'mumps'} module-attribute

Default direct solver parameters for solution of energy equation. Configured to use LU factorisation, using the MUMPS library. G-ADOPT defaults to direct solvers in 2-D.

EnergySolver(T, u, approximation, delta_t, timestepper, bcs=None, solver_parameters=None, su_advection=False)

Timestepper and solver for the energy equation. The temperature, T, is updated in place.

Parameters:

Name	Type	Description	Default
T	Function	Firedrake function for temperature	required
u	Function	Firedrake function for velocity	required
approximation	BaseApproximation	G-ADOPT base approximation describing the system of equations	required
delta_t	Constant	Simulation time step	required
timestepper	RungeKuttaTimeIntegrator	Runge-Kutta time integrator implementing an explicit or implicit numerical scheme	required
bcs	Optional[dict[int, dict[str, Number]]]	Dictionary of identifier-value pairs specifying boundary conditions	None
solver_parameters	Optional[dict[str, Any]]	Solver parameters provided to PETSc	None
su_advection	bool	Boolean specifying whether or not to use the streamline-upwind stabilisation scheme	False

Source code in g-adopt/gadopt/energy_solver.py

def __init__(
    self,
    T: Function,
    u: Function,
    approximation: BaseApproximation,
    delta_t: Constant,
    timestepper: RungeKuttaTimeIntegrator,
    bcs: Optional[dict[int, dict[str, Number]]] = None,
    solver_parameters: Optional[dict[str, Any]] = None,
    su_advection: bool = False,
):
    self.T = T
    self.Q = T.function_space()
    self.mesh = self.Q.mesh()
    self.delta_t = delta_t
    rhocp = approximation.rhocp()
    self.eq = EnergyEquation(self.Q, self.Q, rhocp=rhocp)
    self.fields = {
        'diffusivity': ensure_constant(approximation.kappa()),
        'reference_for_diffusion': approximation.Tbar,
        'source': approximation.energy_source(u),
        'velocity': u,
        'advective_velocity_scaling': rhocp
    }
    sink = approximation.linearized_energy_sink(u)
    if sink:
        self.fields['absorption_coefficient'] = sink

    if su_advection:
        if not is_continuous(self.Q):
            raise TypeError("SU advection requires a continuous function space.")

        log("Using SU advection")
        # SU(PG) ala Donea & Huerta:
        # Columns of Jacobian J are the vectors that span the quad/hex
        # which can be seen as unit-vectors scaled with the dx/dy/dz in that direction (assuming physical coordinates x,y,z aligned with local coordinates)
        # thus u^T J is (dx * u , dy * v)
        # and following (2.44c) Pe = u^T J / 2 kappa
        # beta(Pe) is the xibar vector in (2.44a)
        # then we get artifical viscosity nubar from (2.49)

        J = Function(TensorFunctionSpace(self.mesh, 'DQ', 1), name='Jacobian').interpolate(Jacobian(self.mesh))
        kappa = self.fields['diffusivity'] + 1e-12  # Set lower bound for diffusivity in case zero diffusivity specified for pure advection.
        vel = self.fields['velocity']
        Pe = absv(dot(vel, J)) / (2*kappa)  # Calculate grid peclet number
        nubar = su_nubar(vel, J, Pe)  # Calculate SU artifical diffusion

        self.fields['su_nubar'] = nubar

    if solver_parameters is None:
        if self.mesh.topological_dimension() == 2:
            self.solver_parameters = direct_energy_solver_parameters.copy()
            if INFO >= log_level:
                # not really "informative", but at least we get a 1-line message saying we've passed the energy solve
                self.solver_parameters['ksp_converged_reason'] = None
        else:
            self.solver_parameters = iterative_energy_solver_parameters.copy()
            if DEBUG >= log_level:
                self.solver_parameters['ksp_monitor'] = None
            elif INFO >= log_level:
                self.solver_parameters['ksp_converged_reason'] = None
    else:
        self.solver_parameters = solver_parameters
    apply_strongly = is_continuous(T)
    self.strong_bcs = []
    self.weak_bcs = {}
    bcs = bcs or {}
    for id, bc in bcs.items():
        weak_bc = {}
        for type, value in bc.items():
            if type == 'T':
                if apply_strongly:
                    self.strong_bcs.append(DirichletBC(self.Q, value, id))
                else:
                    weak_bc['q'] = value
            else:
                weak_bc[type] = value
        self.weak_bcs[id] = weak_bc

    self.timestepper = timestepper
    self.T_old = Function(self.Q)
    # solver is setup only at the end, so users
    # can overwrite or augment default parameters specified above
    self._solver_setup = False

setup_solver()

Sets up timestepper and associated solver, using specified solver parameters

Source code in g-adopt/gadopt/energy_solver.py

def setup_solver(self):
    """Sets up timestepper and associated solver, using specified solver parameters"""
    self.ts = self.timestepper(self.eq, self.T, self.fields, self.delta_t,
                               bnd_conditions=self.weak_bcs, solution_old=self.T_old,
                               strong_bcs=self.strong_bcs,
                               solver_parameters=self.solver_parameters)
    self._solver_setup = True

solve(t=0, update_forcings=None)

Advances solver in time.

Source code in g-adopt/gadopt/energy_solver.py

def solve(self, t=0, update_forcings=None):
    """Advances solver in time."""
    if not self._solver_setup:
        self.setup_solver()
    self.ts.advance(t, update_forcings)