The run_problem function#

In the Brachistochrone example we used two methods on the OpenMDAO Problem class to execute the model.

run_model Takes the current model design variables and runs a single execution of the Problem’s model.
Any iterative systems are converged, but no optimization is performed. When using dymos with an optimizer-driven implicit transcription, run_model will not produce a physically valid trajectory on output. If using a solver-driven transcription, the collocation defects will be satisfied (if possible) and the resulting outputs will provide a physically valid trajectory (to the extent possible given the collocation grid).

run_driver runs a driver wrapped around the model (typically done for optimization) and repeatedly executes run_model until the associated optimization problem is satisfied. This approach will provide a physically valid trajectory, to the extent that the grid is sufficient to accurately model the dynamics.

But commonly, we want to do the following when we run dymos

  • Automatically record states, controls, and parameters at the arrived-upon “final” solution.

  • Automatically provide explicit simulation of the solution to verify the accuracy of the collocation.

  • Automatically load in the results from a previous case as the initial guess for an optimization.

  • Iteratively re-optimize the problem with different grid settings to attempt to minimize grid error (i.e. grid refinement).

To remove the need to repeatedly setup the code to do this, dymos provides a function called run_problem.

dymos.run_problem(problem, refine_method='hp', refine_iteration_limit=0, run_driver=True, simulate=False, restart=None, solution_record_file='dymos_solution.db', simulation_record_file='dymos_simulation.db', make_plots=False, plot_dir='plots', case_prefix=None, reset_iter_counts=True, simulate_kwargs=None)[source]

A Dymos-specific interface to execute an OpenMDAO problem containing Dymos Trajectories or Phases. This function can iteratively call run_driver to perform grid refinement, and automatically call simulate following a run to check the validity of a result.

Parameters
problemom.Problem

The OpenMDAO problem object to be run, presumed to contain one or more dymos phases.

refine_methodString

The choice of refinement algorithm to use for grid refinement. Current options are ‘hp’ for h-then-p adaptive or ‘ph’ for ‘p-then-h’ adaptive.

refine_iteration_limitint

The maximum number of passes through the grid refinement algorithm to be made.

run_driverbool

If True, run the driver attached to the problem, otherwise just run the model one time.

simulatebool

If True, perform a simulation of any Trajectories found in the Problem model after the driver has been run and grid refinement is complete.

restartstr, Case, or None

If given as a dict returned by om.CaseReader.get_case, automatically load the states, controls, and parameters as given in the provided case as the initial guess for the next run. If given as a string, assume the user is providing the path to a CaseRecorder file that contains a case named “final” that the user wants to use as the guess for the case.

make_plotsbool

If True, automatically generate plots of all timeseries outputs. These are stored in the reports subdirectory generated by OpenMDAO.

solution_record_filestr

Path to case recorder file use to store results from solution.

simulation_record_filestr

Path to case recorder file use to store results from simulation.

plot_dirstr

Name of the plot directory to be created.

simulate_kwargsdict

A dictionary of argument: value pairs to be passed to simulate. These are ignored when simulate=False.

case_prefixstr or None

Prefix to prepend to coordinates when recording.

reset_iter_countsbool

If True and model has been run previously, reset all iteration counters.