Developer API

class atomics.pde_problem.PDEProblem(mesh)

PDEProblem is a class containing the mesh and the dictionaries of the boundary conditions, inputs, states, and outputs.

class atomics.atomics_group.AtomicsGroup(**kwargs)

The AtomicsGroup is a OpenMDAO Group object that wraps the assembly of OpenMDAO components. Within AtomicsGroup, the users can choose the pde_problem, linear_solver_ (for the solution total derivatives), the`` problem_type``, and whether to turn on the visualization option. problem. Parameters ———- linear_solver_ solver for the total derivatives default=’petsc_cg_ilu’, values=[‘fenics_direct’, ‘scipy_splu’, ‘fenics_krylov’, ‘petsc_gmres_ilu’, ‘scipy_cg’,’petsc_cg_ilu’]

problem_type solver for the FEA problem default=’nonlinear_problem’, values=[‘linear_problem’, ‘nonlinear_problem’, ‘nonlinear_problem_load_stepping’]

visualization whether to save the iteration histories default=’False’, values=[‘True’, ‘False’],

initialize()

Perform any one-time initialization run at instantiation.

setup()

Build this group.

This method should be overidden by your Group’s method. The reason for using this method to add subsystem is to save memory and setup time when using your Group while running under MPI. This avoids the creation of systems that will not be used in the current process.

You may call ‘add_subsystem’ to add systems to this group. You may also issue connections, and set the linear and nonlinear solvers for this group level. You cannot safely change anything on children systems; use the ‘configure’ method instead.

Available attributes:

name pathname comm options

class atomics.states_comp.StatesComp(**kwargs)

StatesComp is a OpenMDAO implicit component, which wraps the FEniCS PDE solver. The total derivatives are also calculated in StatesComp. problem. The users do not need to modify StatesComp ideally. The same settings can be modified in AtomicsGroup() from the run file. Parameters ———- linear_solver_ solver for the total derivatives values=[‘fenics_direct’, ‘scipy_splu’, ‘fenics_krylov’, ‘petsc_gmres_ilu’, ‘scipy_cg’,’petsc_cg_ilu’] problem_type solver for the FEA problem values=[‘linear_problem’, ‘nonlinear_problem’, ‘nonlinear_problem_load_stepping’] visualization whether to save the iteration histories values=[‘True’, ‘False’], Returns ——- outputs[‘state_name’] : numpy array

states

apply_nonlinear(inputs, outputs, residuals)

Compute residuals given inputs and outputs.

The model is assumed to be in an unscaled state.

Parameters

inputsVector

unscaled, dimensional input variables read via inputs[key]

outputsVector

unscaled, dimensional output variables read via outputs[key]

residualsVector

unscaled, dimensional residuals written to via residuals[key]

discrete_inputsdict or None

If not None, dict containing discrete input values.

discrete_outputsdict or None

If not None, dict containing discrete output values.

initialize()

Perform any one-time initialization run at instantiation.

linearize(inputs, outputs, partials)

Compute sub-jacobian parts and any applicable matrix factorizations.

The model is assumed to be in an unscaled state.

Parameters

inputsVector

unscaled, dimensional input variables read via inputs[key]

outputsVector

unscaled, dimensional output variables read via outputs[key]

jacobianJacobian

sub-jac components written to jacobian[output_name, input_name]

discrete_inputsdict or None

If not None, dict containing discrete input values.

discrete_outputsdict or None

If not None, dict containing discrete output values.

setup()

Declare inputs and outputs.

Available attributes:

name pathname comm options

solve_linear(d_outputs, d_residuals, mode)

Apply inverse jac product. The model is assumed to be in an unscaled state.

If mode is:

‘fwd’: d_residuals |-> d_outputs

‘rev’: d_outputs |-> d_residuals

Note : this is not the linear solution for the implicit component. We use identity so that simple implicit components can function in a preconditioner under linear gauss-seidel. To correctly solve this component, you should slot a solver in linear_solver or override this method.

Parameters

d_outputsVector

unscaled, dimensional quantities read via d_outputs[key]

d_residualsVector

unscaled, dimensional quantities read via d_residuals[key]

modestr

either ‘fwd’ or ‘rev’

solve_nonlinear(inputs, outputs)

Compute outputs given inputs. The model is assumed to be in an unscaled state.

Parameters

inputsVector

unscaled, dimensional input variables read via inputs[key]

outputsVector

unscaled, dimensional output variables read via outputs[key]