Prerequisites
- An introductory class in LS-DYNA® is recommended but not necessary.
Syllabus
This course overviews using the optimization code, LS-OPT, for design. It covers both theoretical concepts and practical aspects of design optimization. An emphasis is placed on interfacing LS-OPT with LS-DYNA. The course includes workshop sessions in which the covered theoretical topics are applied. The LS-OPT graphical user interface is used to teach input preparation and post-processing.
Over the duration of the class, you will work individually (sometimes in groups of 2) to solve the exercises. The exercises are simple, so that the run times are short, but contain enough complexity to give insight into the optimization process. Most of the problems are nonlinear dynamic and will be solved using LS-DYNA.
Content
- Introduction to design optimization using industrial examples
- LS-OPT features
- Optimization theory
- Optimization fundamentals
- Response surface methodology
- Experimental design
- Metamodeling
- Design model adequacy checking
- Optimization strategies
- Sensitivity analysis and variable screening
- Running LS-OPT & using the post-processor
- Studying the different LS-OPT components using the GUI setup of a simple optimization example & running the example
- Post-processing using the viewer, such as simulation & approximation results, optimization history, etc.
- Simple optimization with LS-DYNA stage
- Setting up a simple optimization with LS-DYNA stage from start
- Resource allocation
- Sampling, metamodeling and stage options
- LS-DYNA interface features, such as ASCII database, binary database, filtering, time history functions, injury criteria
- Composite functions
- Simple design optimization formulation
- Program execution
- Job monitoring
- Database and output
- Post-processing using the viewer
- Restarting the simple optimization with additional constraint
- Setting up & running a sequential optimization
- Discrete optimization
- Optimization with user defined stage/solver
- Importing analysis results table
- Direct optimization
- Theory
- Parameter identification using curve matching
- Multidisciplinary Optimization (MDO)
- Mode tracking
- Setting up, running, & post-processing material parameter identification examples
- Variable screening & MDO with reduced variables
- Shape optimization
- Job scheduling using queuing (optional)