Robust Design Optimization often means to explore the limits of product performance, which might be associated with risks. What would be your benefit if you knew to what extent your product development comes close to a reliable economic optimum? How robust is your design in reality?

Because of the need to further optimize product performance and resource consumption while reducing development time, RDO using CAE-based multi-objective and multi-disciplinary optimization and stochastic analysis becomes more and more a key component in virtual product development. In order to consider a large amount of multi-disciplinary, non-linear optimization variables with different priorities, an efficient and meaningful sensitivity analysis and robustness evaluation is inevitable.

optiSLang identifies automatically the relevant input and output parameters and quantifies the forecast quality with the help of  the Coefficient of Prognosis ( CoP) and the Metamodel of Optimal Prognosis (MOP). A predictable forecast quality is the key to an efficient optimization. Thus, a "no run too much" philosophy can be implemented to minimize solver calls. 

optiSLang helps you to quantify risks, to identify optimization potentials and to adjust safety margins without limitation of input parameters. Thus, you can safely meet the requirements under consideration of all potentially effecting uncertainties and tolerances.