Welcome to LS-OPT Support Site... (original) (raw)

Documents Collection of documents related to LS-OPT, Optimization and Stochastics https://www.lsoptsupport.com/documents https://www.lsoptsupport.com/@@site-logo/LS-Opt-Support-Logo480x80.png

Documents

Collection of documents related to LS-OPT, Optimization and Stochastics

Examples This section demonstrates LS-OPT capabilities by means of a series of examples. https://www.lsoptsupport.com/examples https://www.lsoptsupport.com/@@site-logo/LS-Opt-Support-Logo480x80.png

Examples

This section demonstrates LS-OPT capabilities by means of a series of examples.

HowTos Collection of information and examples for several tasks with LS-OPT https://www.lsoptsupport.com/howtos https://www.lsoptsupport.com/@@site-logo/LS-Opt-Support-Logo480x80.png

HowTos

Collection of information and examples for several tasks with LS-OPT

FAQs FAQ's related to Optimization, Robustness and Reliability Analysis https://www.lsoptsupport.com/faqs https://www.lsoptsupport.com/@@site-logo/LS-Opt-Support-Logo480x80.png

FAQs

FAQ's related to Optimization, Robustness and Reliability Analysis

Videos

Tutorial videos

News

Site News

Optimization LS-OPT is designed to meet all requirements to solve arbitrary non-linear optimization tasks. https://www.lsoptsupport.com/optimization https://www.lsoptsupport.com/@@site-logo/LS-Opt-Support-Logo480x80.png

Optimization

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

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-DisciplineParallel 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 partInterface to parametric pre-processors: ANSA, HyperMorph, TrueGrid, User-Defined

back to Home

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. https://www.lsoptsupport.com/system-parameter-identification https://www.lsoptsupport.com/@@site-logo/LS-Opt-Support-Logo480x80.png

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 Successive Response Surface Method (SRSM) Calibration of Scalar values Global curves Full-field calibration Curve Extraction Interface to LS-DYNA output Target curve from file Interface to gom/ARAMIS Crossplots Curve matching metrics Mean Squarred Error Curve Mapping (e.g. for hysteretic curves) Dynamic Time Warping Visualization History Plot Visualization of simulated and target curve LSPP fringe plots
Examples: Parameter Identification Parameterization of input curves

Optimization Algorithm Successive Response Surface Method (SRSM) Calibration of Scalar values Global curves Full-field calibration Curve Extraction Interface to LS-DYNA output Target curve from file Interface to gom/ARAMIS Crossplots Curve matching metrics Mean Squarred Error Curve Mapping (e.g. for hysteretic curves) Dynamic Time Warping Visualization History Plot Visualization of simulated and target curve LSPP fringe plots

Examples: Parameter Identification Parameterization of input curves

back to Home

Design Exploration LS-OPT allows global approximations of the design space using meta models. These meta models may be used for design exploration. https://www.lsoptsupport.com/design-exploration https://www.lsoptsupport.com/@@site-logo/LS-Opt-Support-Logo480x80.png

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

back to Home

Sensitivity Studies Methods for the determination of significant variables are implemented in LS-OPT. https://www.lsoptsupport.com/sensitivity-studies https://www.lsoptsupport.com/@@site-logo/LS-Opt-Support-Logo480x80.png

Sensitivity Studies

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

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) 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

Examples:

back to Home

Robustness Analysis Stochastic methods and features for robustness analysis are implemented in LS-OPT. https://www.lsoptsupport.com/robustness-analysis https://www.lsoptsupport.com/@@site-logo/LS-Opt-Support-Logo480x80.png

Robustness Analysis

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

	Monte Carlo Investigations Direct and metamodel based Estimation of Mean, Std. Deviation Correlation Analysis Confidence Intervals Outlier Analysis Stochastic contribution analysis Reliability studies Determination of failure probability Methods: FOSM, FORM Reliability Based Design Optimization Optimization that directly accounts for the variability and the probability of failure Robust Design Optimization Optimizing design and robustness simultanously 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
Examples: RBDO Robust Parameter Design Reliability Analysis

Monte Carlo Investigations Direct and metamodel based Estimation of Mean, Std. Deviation Correlation Analysis Confidence Intervals Outlier Analysis Stochastic contribution analysis Reliability studies Determination of failure probability Methods: FOSM, FORM Reliability Based Design Optimization Optimization that directly accounts for the variability and the probability of failure Robust Design Optimization Optimizing design and robustness simultanously 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

Examples: RBDO Robust Parameter Design Reliability Analysis

back to Home

Topology Optimization LS-TaSC™ is a Topology and Shape Computation tool. Developed for engineering analysts who need to optimize structures, LS-TaSC works with both the implicit and explicit solvers of LS-DYNA. LS-TaSC handles topology optimization of large non-linear problems, involving dynamic loads and contact conditions. https://www.lsoptsupport.com/topology-optimization https://www.lsoptsupport.com/@@site-logo/LS-Opt-Support-Logo480x80.png

Topology Optimization

LS-TaSC™ is a Topology and Shape Computation tool. Developed for engineering analysts who need to optimize structures, LS-TaSC works with both the implicit and explicit solvers of LS-DYNA. LS-TaSC handles topology optimization of large non-linear problems, involving dynamic loads and contact conditions.

General capabilities Solid design using first-order hexahedrons and tetrahedral elements Shell design using first-order quadrilateral and triangular elements Global constraints Multiple load cases, e.g. static, impact and NVH Tight integration with LS-DYNA Large models with millions of elements Geometry definitions Multiple parts Symmetry Extrusions Casting, one sided Casting, two sided Forging Postprocessing Design histories LS-PrePost plots of the geometry evolution and the final design Iso-surfaces

back to Home

Imprint https://www.lsoptsupport.com/imprint https://www.lsoptsupport.com/@@site-logo/LS-Opt-Support-Logo480x80.png

Imprint

The following company contributes to this web site:

Anschrift DYNAmore GmbH, an Ansys Company
Zentrale
Industriestraße 2
D-70565 Stuttgart

Telefon +49 (0)711-459600-0

Telefax +49 (0)711-459600-29

Geschäftsführer Richard Belcher

Umsatzsteuer-Identifikationsnummer (gemäß § 27 a Umsatzsteuergesetz)
DE320033770

Welcome to LS-OPT Support Site LS-OPT, the graphical optimization tool that interfaces perfectly with LS-DYNA allows the user to structure the design process, explore the design space and compute optimal designs according to specified constraints and objectives. The program is also highly suited to the solution of system identification problems and stochastic analysis. https://www.lsoptsupport.com/welcome-to-ls-opt-support-site https://www.lsoptsupport.com/@@site-logo/LS-Opt-Support-Logo480x80.png