CAESES vs OpenMDAO

CAESES is a parametric engineering platform for simulation-driven design, geometry variation, and shape optimization. Its NURBS modeling tools support geometry creation and variation, while morphing tools can alter imported geometries. Software connectors can automate workflows spanning geometry generation, meshing, simulation, post-processing, and evaluation. Available methods include parameter studies, sensitivity analysis, single- and multi-objective optimization, and surrogate modeling. Listed integrations include ANSYS Workbench and optimization tools such as HEEDS, Dakota, optiSLang, Optimus, and modeFRONTIER. CAESES does not include simulation solvers, so users connect external CFD or other simulation software. The Standard Edition includes geometry modeling, software connections, design studies, variation and optimization strategies, morphing, design variants, and batch mode. The Batch Mode License adds non-GUI operation and HPC cluster optimization; the Power Edition includes advanced optimization and a maritime, turbo, or propeller add-on. Pricing is available on request, with no free plan. Windows and Linux downloads include a free two-week trial; listed systems include Windows 11 and Linux tested on Debian 12 and Ubuntu 24.04.

OpenMDAO is a free, open-source Python framework for systems analysis and multidisciplinary optimization, available for Linux and self-hosted use. It focuses on gradient-based optimization with analytic derivatives, including design spaces with hundreds or thousands of variables. Models combine components and groups, while drivers run optimization algorithms or design of experiments. Nonlinear and linear solvers can be positioned at different levels of the model hierarchy. Derivative tools include analytic partial derivatives, finite-difference and complex-step approximations, derivative checking, and forward or reverse total derivative calculations. MPI features support parallel subsystems and distributed variables across processes. Parallel computing can also support gradient-free optimization, mixed-integer nonlinear programming and design space exploration. Users can visualize models and optimization progress, and record or read model and solver cases. Dymos for transient modeling and optimal control, and pyCycle for turbine engine cycle analysis, are identified as related libraries. Installation is documented through PyPI or a cloned GitHub repository. The supported OpenMDAO 3.x line requires Python 3.10 or later; its API may change periodically during active development.