Robustness Evaluation

optiSLang quantifies the robustness of designs by generating a set of possible design realizations on the basis of scattering input variables. Optimized Latin Hypercube Sampling and the quantification of the prognosis quality of the result variation by the Coefficient of Prognosis (CoP) ensure the reliability of the variation and correlation values with a minimum of design calculations.

Best Practice

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Extended matrix of correlations

  • Definition of uncertainties as the crucial input of a robustness analysis
  • Predefined distribution function types and an input correlation matrix to support the definition of scattering input variables
  • Automated generation of optimized Latin Hypercube Samples to scan the robustness space
  • Quantification of robustness by the histogram of result values including fitting of distribution function and approximation of the violation probability
  • Identification of the most affecting input scatter using the MOP/CoP workflow


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Histogram with exceedance probability and Sigma margins

  • Stochastic input variables with distribution types and input correlation
  • Optimized Latin Hypercube Sampling
  • Fitting of distribution function in the histogram of result values
  • Approximation of Sigma margins
  • Approximation of the violation probability

Post Processing & Visualization

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Traffic light plot

  • Histograms to illustrate scatter of result values
  • Correlation matrix, MOP-based CoPs for statistical evaluation
  • Distribution fitting, Sigma values, violation probabilities
  • Traffic light plot to check the violation of limit values of critical responses

Practical Application Examples



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