LS-OPT
LS-OPT is a standalone design optimization and probabilistic analysis tool developped by LST LLC. The graphical optimization tool tightly interfaces with LS-DYNA.
Capabilities
Design Optimization
LS-OPT is designed to meet all requirements to solve arbitrary non-linear optimization tasks.
- Sizing Optimization
- Shape Optimization
- Interfaces to LS-PrePost, ANSA, PRIMER, HyperMorph
- Multi-disciplinary Optimization
- Multi-objective Optimization
- Pareto Optimal Solutions- Sizing Optimization
- Shape Optimization
- Interfaces to LS-PrePost, ANSA, PRIMER, HyperMorph
- Multi-disciplinary Optimization
- Multi-objective Optimization
- Pareto Optimal Solutions
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.
LS-OPT is excellently suited for the identification of these parameters.
- Curve matching metrics
- Hysteresis
- Noise
- Full-field calibration
- Interface to gom/ARAMIS- Curve matching metric
- Hysteresis
- Noise
- Full-field calibration
- Interface to gom/ARAMIS
Design Exploration and Sensitivity Studies
LS-OPT allows global approximations of the design space using meta models. These meta models may be used for
design exploration. Methods for the determination of significant variables are implemented as well.
- Metamodel types
- Feedforward Neural Networks
- Radial Basis Functions
- Sensitivity measures
- linear ANOVA
- Global Sensitivity Analysis
- Prinicipal Component Analysis
Probabilistic Analysis
Stochastic methods and features for robustness analysis are implemented in LS-OPT.
- Monte Carlo Analysis
- Robustness analysis
- Reliability studies
- Outlier analysis
- Bifurcation
- DYNAStats
- Visualization of statistical values on Finite Element model
- Reliabiliy Based Design Optimization/ Robust Parameter Design
- Tolerance Optimization
Methodologies
- Sequential Response Surface Method
- Genetic Algorithm
- Classifiers