Python-based Workflow for Enhanced Parametric Optimization in Ansys Mechanical

This abstract presents a novel procedure for performing parametric optimization, sensitivity analysis, and robustness analysis in Ansys Mechanical. The approach is not limited to the native capabilities of Ansys Mechanical but extends its functionality through a Python-based workflow using optiSLang and PyMechanical. This methodology enables accessing, modifying, setting parameters, and solving any Ansys Mechanical model, including Ansys Motion, Workbench LS-DYNA, and standard Mechanical systems for APDL solver. The advantages of this approach are manifold. It provides a more robust optimization workflow outside the Workbench environment, resulting in shorter optimization times. Moreover, it offers broader availability for output/input parameters, even for those not currently exposed natively in Ansys Mechanical. The workflow leverages the support of multi-solver systems in Mechanical, allowing flexibility with the choice of solvers. Additionally, the use of High-Performance Computing (HPC) resources is demonstrated to accelerate the optimization process. An intriguing aspect of this approach is its potential for generating data for Artificial Intelligence/Machine Learning (AI/ML) applications. By leveraging the synthetic data obtained from Finite Element Simulation, it becomes possible to train Machine Learning agents using AI/ML techniques. Overall, this Python-based workflow enhances the capabilities of Ansys Mechanical, enabling comprehensive parametric optimization and analysis, while also opening doors for advanced AI/ML applications in engineering simulations.