Source code for opentidalfarm.solvers.coupled_sw_solver

import os.path

from dolfin import *
from dolfin_adjoint import *

from .solver import Solver
from ..problems import SWProblem
from ..problems import SteadySWProblem
from ..problems import MultiSteadySWProblem
from ..helpers import StateWriter, FrozenClass

[docs]class CoupledSWSolverParameters(FrozenClass): """ A set of parameters for a :class:`CoupledSWSolver`. Following parameters are available: :ivar dolfin_solver: The dictionary with parameters for the dolfin Newton solver. A list of valid entries can be printed with: .. code-block:: python info(NonlinearVariationalSolver.default_parameters(), True) By default, the MUMPS direct solver is used for the linear system. If not available, the default solver and preconditioner of FEniCS is used. :ivar dump_period: Specifies how often the solution should be dumped to disk. Use a negative value to disable it. Default 1. :ivar cache_forward_state: If True, the shallow water solutions are stored for every timestep and are used as initial guesses for the next solve. If False, the solution of the previous timestep is used as an initial guess. Default: True :ivar print_individual_turbine_power: Print out the turbine power for each turbine. Default: False :ivar quadrature_degree: The quadrature degree for the matrix assembly. Default: -1 (automatic) :ivar cpp_flags: A list of cpp compiler options for the code generation. Default: ["-O3", "-ffast-math", "-march=native"] :ivar revolve_parameters: The adjoint checkpointing settings as a set of the form (strategy, snaps_on_disk, snaps_in_ram, verbose). Default: None :ivar output_dir: The base directory in which to store the file ouputs. Default: `os.curdir` :ivar output_turbine_power: Output the power generation of the individual turbines. Default: False :ivar output_j: Output the evaluation of the choosen functional (e.g power) for each iteration and store it in a numpy textfile. (This is the same j that is printed each iteration, when the FEniCS log level is INFO or above.) Default: False :ivar output_temporal_breakdown_of_j: Output the j at each timestep and at iteration. The output is stored in a numpy textfile. (This is the same temporal breakdown which is shown when the FEniCS log level is INFO or above.) Default: False :ivar output_abs_u_at_turbine_positions: Output the absolute value of the velocity at each turbine position. Default: False :ivar output_control_array: Output a numpy textfile containing the control array from each optimisation and search iteration. Default: False :ivar callback: A callback function that is executed for every time-level. The callback function must take a single parameter which contains the dictionary with the solution variables. """ dolfin_solver = {"newton_solver": {}} dump_period = 1 print_individual_turbine_power = False # If we're printing individual turbine information, the solver needs # the helper functional instantiated in the reduced_functional which will live here output_writer = None # Output settings output_dir = os.curdir output_turbine_power = False output_j = False output_temporal_breakdown_of_j = False output_control_array = False output_abs_u_at_turbine_positions = False # Performance settings cache_forward_state = True quadrature_degree = -1 cpp_flags = ["-O3", "-ffast-math", "-march=native"] revolve_parameters = None # (strategy, # snaps_on_disk, # snaps_in_ram, # verbose) # Callback function callback = lambda self, sol: None def __init__(self): linear_solver = 'mumps' if 'mumps' in linear_solver_methods() else 'default' preconditioner = 'default' self.dolfin_solver["newton_solver"]["linear_solver"] = linear_solver self.dolfin_solver["newton_solver"]["preconditioner"] = preconditioner self.dolfin_solver["newton_solver"]["maximum_iterations"] = 20 self.dolfin_solver["newton_solver"]["convergence_criterion"] = "incremental"
[docs]class CoupledSWSolver(Solver): r""" The coupled solver solves the shallow water equations as a fully coupled system with a :math:`\theta`-timestepping discretization. For a :class:`opentidalfarm.problems.steady_sw.SteadySWProblem`, it solves :math:`u` and :math:`\eta` such that: .. math:: u \cdot \nabla u - \nu \Delta u + g \nabla \eta + \frac{c_b + c_t}{H} \| u\| u & = f_u, \\ \nabla \cdot \left(H u\right) & = 0. For a :class:`opentidalfarm.problems.multi_steady_sw.MultiSteadySWProblem`, it solves :math:`u^{n+1}` and :math:`\eta^{n+1}` for each timelevel (including the initial time) such that: .. math:: u^{n+1} \cdot \nabla u^{n+1} - \nu \Delta u^{n+1} + g \nabla \eta^{n+1} + \frac{c_b + c_t}{H^{n+1}} \| u^{n+1}\| u^{n+1} & = f_u^{n+1}, \\ \nabla \cdot \left(H^{n+1} u^{n+1}\right) & = 0. For a :class:`opentidalfarm.problems.sw.SWProblem`, and given an initial condition :math:`u^{0}` and :math:`\eta^{0}` it solves :math:`u^{n+1}` and :math:`\eta^{n+1}` for each timelevel such that: .. math:: \frac{1}{\Delta t} \left(u^{n+1} - u^{n}\right) + u^{n+\theta} \cdot \nabla u^{n+\theta} - \nu \Delta u^{n+\theta} + g \nabla \eta^{n+\theta} + \frac{c_b + c_t}{H^{n+\theta}} \| u^{n+\theta}\| u^{n+\theta} & = f_u^{n+\theta}, \\ \frac{1}{\Delta t} \left(\eta^{n+1} - \eta^{n}\right) + \nabla \cdot \left(H^{n+\theta} u^{n+\theta}\right) & = 0. with :math:`\theta \in [0, 1]` and :math:`u^{n+\theta} := \theta u^{n+1} + (1-\theta) u^n` and :math:`\eta^{n+\theta} := \theta \eta^{n+1} + (1-\theta) \eta^n`. Furthermore: - :math:`u` is the velocity, - :math:`\eta` is the free-surface displacement, - :math:`H=\eta + h` is the total water depth where :math:`h` is the water depth at rest, - :math:`f_u` is the velocity forcing term, - :math:`c_b` is the (quadratic) natural bottom friction coefficient, - :math:`c_t` is the (quadratic) friction coefficient due to the turbine farm, - :math:`\nu` is the viscosity coefficient, - :math:`g` is the gravitational constant, - :math:`\Delta t` is the time step. Some terms, such as the advection or the diffusion term, may be deactivated in the problem specification. The resulting equations are solved with Newton-iterations and the linear problems solved as specified in the `dolfin_solver` setting in :class:`CoupledSWSolverParameters`. """ def __init__(self, problem, solver_params): if not isinstance(problem, (SWProblem, SteadySWProblem)): raise TypeError("problem must be of type Problem") if not isinstance(solver_params, CoupledSWSolverParameters): raise TypeError("solver_params must be of type \ CoupledSWSolverParameters.") super(CoupledSWSolver, self).__init__() self.problem = problem self.parameters = solver_params # If cache_for_nonlinear_initial_guess is true, then we store all # intermediate state variables in this dictionary to be used for the # next solve self.state_cache = {} self.state = None self.mesh = problem.parameters.domain.mesh elements = self.problem.parameters.finite_element() self.function_space = FunctionSpace(self.mesh, MixedElement(elements))
[docs] @staticmethod def default_parameters(): """ Return the default parameters for the :class:`CoupledSWSolver`. """ return CoupledSWSolverParameters()
def _generate_strong_bcs(self): bcs = self.problem.parameters.bcs facet_ids = self.problem.parameters.domain.facet_ids fs = self.function_space # Generate velocity boundary conditions bcs_u = [] for _, expr, facet_id, _ in bcs.filter("u", "strong_dirichlet"): bc = DirichletBC(fs.sub(0), expr, facet_ids, facet_id) bcs_u.append(bc) # Generate free-surface boundary conditions bcs_eta = [] for _, expr, facet_id, _ in bcs.filter("eta", "strong_dirichlet"): bc = DirichletBC(fs.sub(1), expr, facet_ids, facet_id) bcs_eta.append(bc) return bcs_u + bcs_eta def get_optimisation_and_search_directory(self): dir = os.path.join(self.parameters.output_dir, "iter_{}".format(self.optimisation_iteration), "search_{}".format(self.search_iteration)) try: os.makedirs(dir) except OSError: pass # directory already exists return dir # For dynamic friction the problem parameters need to use the same # finished funciton as in the solver to avoid machine precision error. def _finished(self, current_time, finish_time): if (hasattr(self.problem.parameters, 'finished')): return self.problem.parameters.finished(current_time) else: return float(current_time - finish_time) >= - 1e3*DOLFIN_EPS
[docs] def solve(self, annotate=True): ''' Returns an iterator for solving the shallow water equations. ''' ############################### Setting up the equations ########################### # Get parameters problem_params = self.problem.parameters solver_params = self.parameters farm = problem_params.tidal_farm # Performance settings parameters['form_compiler']['quadrature_degree'] = \ solver_params.quadrature_degree parameters['form_compiler']['cpp_optimize_flags'] = \ " ".join(solver_params.cpp_flags) parameters['form_compiler']['cpp_optimize'] = True parameters['form_compiler']['optimize'] = True # Get domain measures ds = problem_params.domain.ds # Initialise solver settings if type(self.problem) == SWProblem: log(INFO, "Solve a transient shallow water problem") theta = Constant(problem_params.theta) dt = Constant(problem_params.dt) finish_time = Constant(problem_params.finish_time) t = Constant(problem_params.start_time) include_time_term = True elif type(self.problem) == MultiSteadySWProblem: log(INFO, "Solve a multi steady-state shallow water problem") theta = Constant(1) dt = Constant(problem_params.dt) finish_time = Constant(problem_params.finish_time) # The multi steady-state case solves the steady-state equation also # for the start time t = Constant(problem_params.start_time - dt) include_time_term = False elif type(self.problem) == SteadySWProblem: log(INFO, "Solve a steady-state shallow water problem") theta = Constant(1.) dt = Constant(1.) finish_time = Constant(0.5) t = Constant(0.) include_time_term = False else: raise TypeError("Do not know how to solve problem of type %s." % type(self.problem)) g = problem_params.g depth = problem_params.depth include_advection = problem_params.include_advection include_viscosity = problem_params.include_viscosity viscosity = problem_params.viscosity bcs = problem_params.bcs linear_divergence = problem_params.linear_divergence cache_forward_state = solver_params.cache_forward_state f_u = problem_params.f_u u_dg = "Discontinuous" in str(self.function_space.split()[0]) # Define test functions v, q = TestFunctions(self.function_space) # Define functions state = Function(self.function_space, name="Current_state") self.state = state state_new = Function(self.function_space, name="New_state") # Load initial condition (or initial guess for stady problems) # Projection is necessary to obtain 2nd order convergence ic = project(problem_params.initial_condition, self.function_space) state.assign(ic, annotate=False) # Split mixed functions u, h = split(state_new) u0, h0 = split(state) # Define the water depth if linear_divergence: H = depth else: H = h + depth # Create initial conditions and interpolate state_new.assign(state, annotate=annotate) # u_(n+theta) and h_(n+theta) u_mid = (1.0 - theta) * u0 + theta * u h_mid = (1.0 - theta) * h0 + theta * h # The normal direction n = FacetNormal(self.mesh) # Mass matrix contributions M = inner(v, u) * dx M += inner(q, h) * dx M0 = inner(v, u0) * dx M0 += inner(q, h0) * dx # Divergence term. Ct_mid = -H * inner(u_mid, grad(q)) * dx #+inner(avg(u_mid),jump(q,n))*dS # This term is only needed for dg # element pairs which is not supported # The surface integral contribution from the divergence term bc_contr = -H * dot(u_mid, n) * q * ds for function_name, u_expr, facet_id, bctype in bcs.filter(function_name='u'): if bctype in ('weak_dirichlet', 'free_slip'): # Subtract the divergence integral again bc_contr -= -H * dot(u_mid, n) * q * ds(facet_id) # for weak Dirichlet we replace it with the bc expression if bctype=='weak_dirichlet': bc_contr -= H * dot(u_expr, n) * q * ds(facet_id) elif bctype == 'flather': # The Flather boundary condition requires two facet_ids. assert len(facet_id) == 2 # Subtract the divergence integrals again bc_contr -= -H * dot(u_mid, n) * q * ds(facet_id[0]) bc_contr -= -H * dot(u_mid, n) * q * ds(facet_id[1]) # The Flather boundary condition on the left hand side bc_contr -= H * dot(u_expr, n) * q * ds(facet_id[0]) Ct_mid += sqrt(g * H) * inner(h_mid, q) * ds(facet_id[0]) # The contributions of the Flather boundary condition on the right hand side Ct_mid += sqrt(g * H) * inner(h_mid, q) * ds(facet_id[1]) # Pressure gradient term weak_eta_bcs = bcs.filter(function_name='eta', bctype='weak_dirichlet') # don't integrate pressure gradient by parts (a.o. allows dg test function v) C_mid = g * inner(v, grad(h_mid)) * dx for function_name, eta_expr, facet_id, bctype in weak_eta_bcs: # Apply the eta boundary conditions weakly on boundary IDs 1 and 2 C_mid += g * inner(dot(v, n), (eta_expr-h_mid)) * ds(facet_id) # Bottom friction friction = problem_params.friction if not farm: tf = Constant(0) elif type(farm.friction_function) == list: tf = farm.friction_function[0].copy(deepcopy=True, name="turbine_friction", annotate=annotate) tf.assign(theta*farm.friction_function[1]+(1.-float(theta))*\ farm.friction_function[0], annotate=annotate) else: tf = farm.friction_function.copy(deepcopy=True, name="turbine_friction", annotate=annotate) # FIXME: FEniCS fails on assembling the below form for u_mid = 0, even # though it is differentiable. Even this potential fix does not help: #norm_u_mid = conditional(inner(u_mid, u_mid)**0.5 < DOLFIN_EPS, Constant(0), # inner(u_mid, u_mid)**0.5) norm_u_mid = inner(u_mid, u_mid)**0.5 R_mid = friction / H * norm_u_mid * inner(u_mid, v) * dx(self.mesh) if farm: u_mag = dot(u_mid, u_mid)**0.5 if not farm.turbine_specification.thrust: R_mid += tf/H*u_mag*inner(u_mid, v)*farm.site_dx else: C_t = farm.turbine_specification.compute_C_t(u_mag) R_mid += (tf * farm.turbine_specification.turbine_parametrisation_constant * \ C_t / H) * u_mag * inner(u_mid, v) * farm.site_dx # Advection term if include_advection: Ad_mid = inner(dot(grad(u_mid), u_mid), v) * dx # un is |u.n| on the down-side (incoming), and 0 on the up-side of a facet (outgoing) un = (abs(dot(u, n))-dot(u,n))/2.0 if u_dg: # at the downside, we want inner(|u.n|*(u_down-u_up),v_down) - u_down-u_up being the gradient along the flow Ad_mid += (inner(un('+')*(u_mid('+')-u_mid('-')), v('+')) + inner(un('-')*(u_mid('-')-u_mid('+')), v('-')))*dS # apply weak_diricihlet bc for the advection term at the incoming boundaries only for function_name, u_expr, facet_id, bctype in bcs: if function_name =="u" and bctype == 'weak_dirichlet': # this is the same thing as for DG above, except u_up is the boundary value and u_down is u+ or u- (they are the same) # the fact that these are the same also means that the DG term above vanishes at the boundary and is replaced here Ad_mid += inner(un*(u_mid-u_expr), v)*ds(facet_id) if include_viscosity: # Check that we are not using a DG velocity function space, as the facet integrals are not implemented. if u_dg: raise NotImplementedError("The viscosity term for \ discontinuous elements is not supported.") D_mid = viscosity * inner(2*sym(grad(u_mid)), grad(v)) * dx # Create the final form G_mid = C_mid + Ct_mid + R_mid # Add the advection term if include_advection: G_mid += Ad_mid # Add the viscosity term if include_viscosity: G_mid += D_mid # Add the source term G_mid -= inner(f_u, v) * dx F = dt * G_mid - dt * bc_contr # Add the time term if include_time_term: F += M - M0 # Generate the scheme specific strong boundary conditions strong_bcs = self._generate_strong_bcs() ############################### Perform the simulation ########################### if solver_params.dump_period > 0: writer = StateWriter(solver=self) if type(self.problem) == SWProblem: log(INFO, "Writing state to disk...") writer.write(state) result = {"time": float(t), "u": state.split()[0], "eta": state.split()[1], "tf": tf, "state": state, "is_final": self._finished(t, finish_time)} solver_params.callback(result) yield(result) log(INFO, "Start of time loop") adjointer.time.start(t) timestep = 0 while not self._finished(t, finish_time): # Update timestep timestep += 1 t = Constant(t + dt) # Update bc's t_theta = Constant(t - (1.0 - theta) * dt) bcs.update_time(t, only_type=["strong_dirichlet"]) bcs.update_time(t_theta, exclude_type=["strong_dirichlet"]) # Update source term f_u.t = Constant(t_theta) # Set the control function for the upcoming timestep. if farm: if type(farm.friction_function) == list: tf.assign(theta*farm.friction_function[timestep]+(1.\ -float(theta))*farm.friction_function[timestep-1], annotate=annotate) else: tf.assign(farm.friction_function) # Set the initial guess for the solve if cache_forward_state and float(t) in self.state_cache: log(INFO, "Read initial guess from cache for t=%f." % t) # Load initial guess for solver from cache state_new.assign(self.state_cache[float(t)], annotate=False) elif not include_time_term: log(INFO, "Set the initial guess for the nonlinear solver to the initial condition.") # Reset the initial guess after each timestep ic = problem_params.initial_condition state_new.assign(ic, annotate=False) # Solve non-linear system with a Newton solver if self.problem._is_transient: log(INFO, "Solve shallow water equations at time %s" % float(t)) else: log(INFO, "Solve shallow water equations.") solve(F == 0, state_new, bcs=strong_bcs, solver_parameters=solver_params.dolfin_solver, annotate=annotate, J=derivative(F, state_new)) # After the timestep solve, update state state.assign(state_new) if cache_forward_state: # Save state for initial guess cache log(INFO, "Cache solution t=%f as next initial guess." % t) if float(t) not in self.state_cache: self.state_cache[float(t)] = Function(self.function_space) self.state_cache[float(t)].assign(state_new, annotate=False) if (solver_params.dump_period > 0 and timestep % solver_params.dump_period == 0): log(INFO, "Write state to disk...") writer.write(state) # Return the results result = {"time": float(t), "u": state.split()[0], "eta": state.split()[1], "tf": tf, "state": state, "is_final": self._finished(t, finish_time)} solver_params.callback(result) yield(result) # Increase the adjoint timestep adj_inc_timestep(time=float(t), finished=self._finished(t, finish_time)) # If we're outputting the individual turbine power if (self.parameters.print_individual_turbine_power or ((solver_params.dump_period > 0) and self.parameters.output_turbine_power)): self.parameters.output_writer.individual_turbine_power(self) log(INFO, "End of time loop.")