About this Webinar
Engineers make trade-offs between size, performance, and reliability in engineering product development. Simulation provides informed decisions early in the design cycle.
This episode starts with a structural finite element model in Ansys Mechanical, involving multiple design parameters and focusing on a multi-objective optimization problem. Instead of manually tuning parameters and running tedious iterations in a trial-and-error approach, we will demonstrate how optiSLang parametric workflow directly from Ansys Workbench helps to automate and streamline the optimization process. Optimization and sensitivity studies utilize the latest AI and machine learning methods in optiSLang and include linear, nonlinear, and thermal FEA problems. We’ll cover advanced topics, such as expanding native parametrization using Python scripting. We’ll demonstrate the workflow on a representative issue aligned with typical industrial problems.
What You Will Learn
- Explore the design space with advanced sensitivity analysis
- Make the most use of the simulation Design of Experiment and using it as the building block for:
- Optimization with a minimal learning curve
- Creating a metamodel of optimal prognosis (surrogate model) and finding optimal design faster
- Accelerating innovation by minimizing non-value-added and repetitive tasks, and fully automating the simulation workflows
Who Should Attend
Mechanical (Simulation) Engineers & Analysts in all industries + Academia
Speakers
Saeed Jahangirian, Principal Application Engineer at Ansys
Adarsh Chaurasia, Lead Application Engineer at Ansys
