optiSLang

optiSLang is one of the most efficient software tools for tasks regarding sensitivity analysis, multidisciplinary optimization, reliability and robustness analysis as well as robust design optimization.

Possibilities in optiSLang

Because of the worldwide market, products have to be optimized in early stages of the product development. To fulfil these requirements, optimization and stochastic analysis have to be used in virtual product development. Thus, the key to success is the assurance of optimal product features following all sensitivity and robustness requirements.

Efficient methods and an informative result visualization secure the successful solving of several problems regarding CAE-based optimization and stochastic analysis.

optiSLang allows the user to use sensitivity analysis, multidisciplinary optimization, robustness and reliability analysis through a predefined graphical user interface.

Existing simulation processes from any user-defined CAE program, pre and post processors can be connected through a graphical editor and are accessible to a parametric sensitivity study, optimization or stochastic analysis. An important innovation of optiSLang are predefined interactive post processing flows. This allows even 'non-optimization-specialists' to use successfully multidisciplinary optimization and stochastic analysis. Gradient-based methods, genetic algorithms, evolutionary strategies and adaptive response surface methods can be used for optimization in optiSLang.

For an optimal component construction have to be considered appropriately to avoid undesired scattering of product features. In optiSLang, variance or probability-based robustness evaluations of designs can be done. A large range of statistical parameters (e.g. variation, correlation, coefficient of determination and probabilities) is available for robustness analysis. Regarding reliability analysis, optiSLang offers efficient algorithms of stochastic analysis (FORM, several sampling methods) which can be combined with adaptive response surface methods. Different methods of optimization and stochastic analysis can be combined with each other for tasks of robust design optimization.