Design optimization has become an imperative in order to achieve optimum product performance and save costs, thereby producing smarter designs in less time. This relentless application of optimization technologies and increasing market competition is pushing the limits of existing engineering capabilities. Traditional optimization methods offer single-physics deterministic optimization which many not suffice in case of multiple complex physical and stochastic environments. Therefore, it is important to further engineer product designs using a software tool that offers a framework for multidisciplinary, multi-objective robust design optimization in contrast to single-physics deterministic optimization.
With Ansys optiSLang’s unique capabilities, you can perform product development with high economic efficiency by conducting sensitivity analysis, multidisciplinary optimization, robustness evaluation, reliability analysis and signal calibration using various standard and in-house solvers. optiSLang helps engineers reach an optimal design in stochastic and multiphysics environments to produce a robust, competitive product.
Sensitivity analysis and reducing unimportant parameters
Unique response surface generation — metamodel of optimal prognosis
Optimization methods, robustness and reliability evaluation
Robust design optimization performance
Process integration, signal calibration, demo and case studies
Dinesh Kumar has been a senior application engineer at Ansys for more than five years. His total simulation experience, spanning more than eight years, includes expertise in nonlinear FEM, NVH, optimizations and Six Sigma analysis. He has done significant work in the field of design optimization, ranging from non-parametric optimization like topology and free shape optimization to parametric multidisciplinary multiobjective optimization. Dinesh has been working closely with various industry teams at Ansys and has vast experience in solving engineering problems for the automotive, off-road hydraulics, transport and power, and aviation industries.