modeFRONTIER vs OpenMDAO

modeFRONTIER automates engineering simulation workflows and design optimisation by connecting third-party CAD/CAE software with in-house solvers. It combines multiple engineering disciplines in unified workflows and allows several optimisation strategies in one project file. Design exploration includes design of experiments, response surface models, multi-objective optimisation, robust design, reliability analysis and uncertainty quantification. Surrogate models, machine-learning algorithms and reduced-order models support data-driven modelling; ESTECO identifies pilOPT as its proprietary self-adaptive optimisation algorithm. Integrations include parametric solvers and custom connectors for Abaqus, Adams, ANSYS Workbench, CATIA and ANSA. pyFRONTIER and pyCONSOLE connect Python workflows, including scikit-learn and SciPy optimize libraries. Workflows can run in parallel, on local grid computing, or through remote HPC and cloud resources. VOLTA adds collaboration, distributed execution and result traceability. Pricing is on request, with academic licences limited to non-commercial or academic use.

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.