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Optimization

LS-OPT is designed to meet all requirements to solve arbitrary non-linear optimization tasks.
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Successive Response Surface Method (SRSM)

  • Very effective algorithm for highly nonlinear problems such as crashworthiness or metal forming applications

Genetic Optimization Algorithm (GA)

  • suitable for arbitrary problems in particular for complex performance functions
    (e.g. many local minima)

Multidisciplinary Optimization (MDO)

  • More than one load case and more than one CAE-Discipline
    Parallel execution of multiple load cases with different analyzing types and possibly different variable definitions
  • Discipline-specific job control
  • Discipline specific point selection schemes (experimental design)

Multi-Objective Optimization

  • Simultaneous optimization of more than one objective function
  • Pareto Front Solutions

Reliability Based Design Optimization (RBDO)

  • Optimization that directly accounts for the variability and the probability of failure

Robust Design Optimization (RDO)

  • Optimizing design and robustness simultaneously

Optimization variables

  • Continuous and discrete variables
  • Mixed discrete-continuous optimization
  • Dependent (linked) variables

Identification of system-/material parameters

  • Calibration of models to experimental data

Shape optimization

  • Process of optimizing the geometrical dimensions of a structural part
    Interface to parametric pre-processors: ANSA, HyperMorph, TrueGrid, User-Defined

 

 

 

Examples: 

 

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