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Multidisciplinary Optimization

optiSLang provides powerful optimization algorithms and automated workflows for an efficient determination of optimal design parameters regarding various multidisciplinary, nonlinear and multicriteria optimization tasks.

Best Practice

 

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Approximation of the objective function

 

  • Identification of the most relevant input parameters and response values with the help of a sensitivity analysis and CoP/MOP
  • Pre-optimizaton of parameter sets using the MOP with only one additional solver call
  • Optimization wizzard for automatic selection of the most fitting algorithms for design optimization
  • Easy definition of parameter range, objectives and constraints

Methods

 

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Optimization of a centrifugal compressor impeller

 

  • Gradient-based methods (NLPQL)
  • Nature-inspired Optimization Algorithms (NOA) incl. Genetic Algorithms (GA), Evolutionary Strategies (ES) and Particle Swarm Optimization (PSO)
  • Automatic Adaptive Response Surface Method in case of less than 20 important optimization variables

Postprocessing & Visualization

 

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  • Interactive post processing adapted to the optimization algorithm
  • Fast investigation of optimization performance using different visualization options
  • Visualization of all parameters and responses histories
  • Selection of individual designs

 

 

Practical Application Examples

 

practical application examples

 

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