Ansys LS-OPT is a standalone design optimization and probabilistic analysis package with an interface to Ansys LS-DYNA. It is difficult to achieve an optimal design because design objectives are often in conflict. LS-OPT uses a systematic approach involving an inverse process for design optimization: First you specify the criteria and then you compute the best design according to a mathematical framework.

Probabilistic analysis is necessary when a design is subjected to structural and environmental input variations that cause a variation in response that may lead to undesirable behavior or failure. A probabilistic analysis, using multiple simulations, assesses the effect of the input variation on the response variation and determines the probability of failure.

Together, design optimization and probabilistic analysis help you to reach an optimal product design quickly and easily, saving time and money in the process.

Typical applications of LS-OPT include:

  • Design optimization
  • System identification
  • Probabilistic analysis


  • Design Optimization

    LS-OPT combines multiple disciplines and/or cases to improve a unique design. Multiple criteria can be specified and analysis results can be combined arbitrarily using mathematical expressions.

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  • Response Surface Methodology

    In LS-OPT, RSM is used both in optimization and probabilistic analysis to reduce the number of simulations.

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  • Probabilistic Analysis

    With LS-OPT, you can investigate stochastic effects using Monte Carlo simulation involving either direct finite element analysis or analysis of surrogate models such as RSM or neural networks.

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  • Instability/Noise/Outlier Investigations (Version 2.2)

    LS-OPT computes various statistics of the displacement and history data for viewing in the LS-DYNA finite element model postprocessor (LS-PrePost).

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  • Trade-off

    A trade-off study lets you interactively study the effect of changes in the design constraints on the optimum design.

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  • Variable Screening

    For each response, the relative importance of all variables can be viewed on a bar chart together with their confidence intervals.

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