| name | openmdao-sim |
| description | Use when driving OpenMDAO (NASA's open-source multidisciplinary design, analysis, and optimization framework) via Python scripts — coupled systems with MDA, gradient-based / gradient-free optimization, derivative computation, surrogate models — through sim runtime one-shot execution. |
openmdao-sim
You are connected to OpenMDAO via sim-cli.
OpenMDAO is the canonical open-source MDAO framework, originally from
NASA Glenn. Pure Python (numpy, scipy, networkx). Pip-installable
(pip install openmdao).
Scripts are plain .py:
import openmdao.api as om
prob = om.Problem()
prob.model.add_subsystem('comp', MyComp(), promotes=['*'])
prob.driver = om.ScipyOptimizeDriver()
prob.model.add_design_var('x', lower=-10, upper=10)
prob.model.add_objective('y')
prob.setup()
prob.run_driver()
print(prob['x'], prob['y'])
Same subprocess driver mode as PyBaMM / SfePy.
base/ — always relevant
| Path | What's there |
|---|
base/reference/workflow.md | Problem → model → components → solvers/drivers → setup → run. |
base/reference/components.md | ExplicitComponent, ImplicitComponent, ExecComp, IndepVarComp. |
base/reference/solvers.md | NonlinearBlockGS, NewtonSolver, DirectSolver, LinearBlockGS. |
base/reference/optimization.md | ScipyOptimizeDriver, design vars / objective / constraints. |
base/snippets/01_sellar.py | Verified Sellar coupled MDA E2E. |
base/known_issues.md | Cyclic groups need NL solver, set_input_defaults vs setup. |
sdk/3/ — OpenMDAO 3.x
sdk/3/notes.md — version-specific surface notes.
Hard constraints
- Cyclic groups MUST set a nonlinear solver. Sellar-style
coupled disciplines (D1 → D2 → D1) need
group.nonlinear_solver = om.NonlinearBlockGS() (or NewtonSolver).
Without it the residual loop is open and run_model() errors.
setup() is mandatory before run_model() or run_driver().
It builds the data passing graph and allocates arrays.- Use
set_input_defaults for promoted inputs, set after
add_subsystem and before setup. Setting inputs after setup
uses prob['x'] = ....
- Acceptance != "ran without error". Always validate against a
known reference (Sellar has y1=25.588 / y2=12.058 by convention;
gradient-based runs should report objective improvement).
- Print results as JSON on the last stdout line.
Required protocol
- Gather inputs:
- Category A: disciplines / equations, design variables, objectives,
constraints, acceptance criterion.
- Category B: solver / driver tolerances, max iterations.
sim check openmdao.- Write
.py per base/reference/workflow.md.
sim lint script.py.sim run script.py --solver openmdao.- Validate JSON.
