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Introduction to
Ansys LS-OPT

Course Overview

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 work¬hop 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 (some¬times 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 non¬linear dynamic and will be solved using LS-DYNA.

Prerequisites

  • An in­tro­duc­to­ry class in LS-DY­NA is rec­om­mend­ed but not nec­es­sary.

Teaching Method

Lectures and workshop sessions to apply theoretical knowledge to practical examples. A major emphasis is placed on teaching by software demonstration and on the development of a solution to a design challenge.

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Learning Outcome

Following completion of this course, you will be able to:

  • Apply fundamentals and theoretical concepts of design optimization and metamodeling for industrial applications.
  • Setup and solve nonlinear design optimization problems using LS-DYNA.
  • Apply sensitivity analysis to screen out insignificant design parameters.
  • Apply design optimization techniques for multi-disciplinary and multi-objective problems.
  • Calibrate unknown material model parameters by matching computed data to target experimental data.

Available Dates

Currently, no training dates available

Learning Options

Training materials for this course are available with a Ansys Learning Hub Subscription. If there is no active public schedule available, private training can be arranged. Please contact us.

Agenda

This is a 2-day classroom course covering both lectures and workshops. For virtual training, this course is covered over 4 x 2-hour sessions lectures only.

Virtual Classroom Sessions Topics Covered

Virtual Classroom Session 1

  • Module 1 – LS-OPT overview
    • LS-OPT capabilities
    • Design process setup and flexibility
    • Optimization fundamentals
    • Gradient-free optimization using genetic algorithm
    • User interface, setup, and results
    • Workshop 1.1 – Direct optimization

Virtual Classroom Session 2 

  • Module 2 – Metamodeling
    • Polynomial response surface methodology
    • Advanced metamodels
    • Metamodel accuracy and error analysis
    • Workshop 2.1 – Simple metamodel-based optimization and results
    • Workshop 2.2 – Run from scratch
    • Workshop 2.3 – Repair optimization
    • Workshop 2.4 – Discrete optimization
    • Workshop 2.5 – Import user-results

Virtual Classroom Session 3 

  • Module 3 – Sensitivity analysis
    • Linear ANOVA
    • Global sensitivity analysis
  • Module 4 – Metamodel-based optimization strategies
    • Sequential optimization
    • Sequential optimization with domain reduction
    • Workshop 4.1 – Sequential optimization with domain reduction
    • Workshop 4.2 – Working with non-LS-DYNA solvers
    • Workshop 4.3 – Using dependent variables
  • Module 5 – Model analysis and multidisciplinary optimization
    • Mode tracking
    • Variable deactivation
    • Workshop 5.1 – Modal analysis and tracking- DOE (optional)
    • Workshop 5.2 – Modal analysis and tracking- optimization (optional)
    • Workshop 5.3 – Multidisciplinary design optimization

Virtual Classroom Session 4 

  • Module 6 – Shape optimization examples
  • Module 7 – Material parameter estimation
    • Mean square error, partial curve mapping
    • Dynamic time warping (DTW) for handling noise
    • Workshop 7.1 – Ordinate-based mean square error (MSE)
    • Workshop 7.2 – Ordinate-based (MSE) for multiple cases
    • Workshop 7.2 – Point-based MSE (optional)
    • Workshop 7.2 – Hysteretic response- Multiple cases
    • Workshop 7.2 – GISSMO failure model (shear load case)
  • LS-OPT overview
  • Optimization fundamentals
  • Direct simulation-based optimization
  • Metamodeling the­o­ry
  • Polynomial response sur­face method­ol­o­gy
  • Ex­per­i­men­tal de­sign
  • Advanced metamodels
  • Metamodel accuracy and error analysis
  • Sim­ple op­ti­miza­tion with LS-DY­NA stage
  • Set­ting up a sim­ple op­ti­miza­tion with LS-DY­NA stage from start
  • 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
  • Data­base and out­put
  • Post-pro­cess­ing us­ing the view­er, such as sim­u­la­tion & ap­prox­i­ma­tion re­sults, op­ti­miza­tion his­to­ry, etc.
  • Repair options, dis­crete op­ti­miza­tion, im­port­ing user-re­sults
  • Sensitivity analysis
  • Metamodel-based optimization strategies
  • Op­ti­miza­tion with user-de­fined stage/­solver
  • Modal analysis and Multidisciplinary design optimization (MDO)
  • Shape optimization
  • Material parameter estimation
  • Theory- Curve similarity measures
  • Set­ting up, run­ning, and post-pro­cess­ing ma­te­r­i­al pa­ra­me­ter estimation ex­am­ples