LS-OPT

A standalone Design Optimization and Probabilistic Analysis package with an interface to LS-DYNA

LS-OPT

Optimization

System/Parameter Identification

Design Exploration

Sensitivity Study

Robustness Analysis
logo lstc
logo ls-opt

Ls-Opt

LS-OPT is an optimization tool which interfaces perfectly with LS-DYNA, allowing the user to structure the design process, explore the design space and compute optimal designs according to specified constrains and objectives. It is highly suitable for solving system identification problems and stochastic analysis.

ls-opt hot stamping

Optimization

LS-OPT is designed to meet all requirements to solve arbitrary non-linear optimization tasks.

Successive Response Surface Method (SRSM)

Successive Response Surface Method (SRSM)

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

Genetic Optimization Algorithm (GA)

Genetic Optimization Algorithm (GA)

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

Multidisciplinary Optimization (MDO)

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

Multi-Objective Optimization

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

Reliability Based Design Optimization (RBDO)

Reliability Based Design Optimization (RBDO)

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

Robust Design Optimization (RDO)

Robust Design Optimization (RDO)

Optimizing design and robustness simultaneously

Optimization Variables

Optimization Variables

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

Identification of System/Material Parameters

Identification of System/Material Parameters

Calibration of models to experimental data

Shape optimization

Shape Optimization

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

System/Parameter Identification

The utilization of new materials such as plastics, composites, foams, textile or high-strength steels require the application of highly complex material models. These material models generally bring along numerous material parameters, which are difficult to define.

The optimization program LS-OPT is excellently suited for the identification of these parameters. By the parameterized simulation of the physical tests with LS-DYNA an automated calibration to the test results is performed. The objective is to minimize the error between the test results and the simulation results.

Optimization Algorithm

Optimization Algorithm

Successive Response Surface Method (SRSM)

Curve Extraction

Curve Extraction

  • Interface to LS-DYNA output
  • Target curve from file
  • Crossplots

Curve Matching Metrics

Curve Matching Metrics

  • Mean Squared Error
  • Curve Mapping (e.g. for hysteretic curves)

Visualization

Visualization

  • History Plot
  • Visualization of simulated and target curve

Design Exploration

LS-OPT allows global approximations of the design space using meta models. These meta models may be used for design exploration.

Response Surfaces (Meta Models)

Response Surfaces (Meta Models)

  • Global approximation of Responses and Histories
  • Metamodel types: Polynomials, Radial Basis Functions, Feedforward Neural networks

Visualization Surfaces

Visualization

  • 2D/3D sections of the surfaces
  • 1/2 selected variables vs. any response
  • Constraints on the meta models
  • Influence of single parameter on a history curve
  • Interactive prediction of response values

Sensitivity Studies

Methods for the determination of significant variables are implemented in LS-OPT.

Linear ANOVA (Analysis of Variance)

Linear ANOVA (Analysis of Variance)

  • Regression based method
  • Evaluated on metamodel
  • 90% confidence interval
  • Normalized with respect to design space
  • Influence of variables on single response

Global Sensitivity Analysis (Sobol)

Global Sensitivity Analysis (Sobol)

  • Variance based method
  • Evaluated on metamodel
  • Nonlinear for nonlinear metamodel
  • Normalized
  • Absolute value
  • Determination of influence of variables an multiple responses or on the whole problem possible

Robustness Analysis

Stochastic methods and features for robustness analysis are implemented in LS-OPT.

Monte Carlo Investigations

Monte Carlo Investigations

  • Direct and metamodel based
  • Estimation of Mean, Std. Deviation
  • Correlation Analysis
  • Confidence Intervals
  • Outlier Analysis
  • Stochastic contribution analysis

Reliability Studies

Reliability Studies

  • Determination of failure probability
  • Methods: FOSM, FORM

Reliability Based Design Optimization

Reliability Based Design Optimization

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

Robust Design Optimization

Robust Design Optimization

  • Optimizing design and robustness simultaneously

Visualization of statistical results on the FE-Model (DYNAstats)

Visualization of statistical results on the FE-Model (DYNAstats)

  • Fringe of mean and standard deviation on the FE-model utilizing LS-PrePost
  • Display of variation of element results such as stress, thinning, plastic strain
  • Correlation of node displacements with respect to any response
  • Statistics of time history curves

Turn your idea into reality

Engineered Solution. Our Passion.

If you want to learn more about DFETECH, please send us a message or give us a call. We would love to hear from you

Contact Us