1 / 10

Outline

Sensitivity Analysis, Multidisciplinary Optimization, Robustness Evaluation, and Robust Design Optimization with optiSLang 3.2. Outline. Introduction Process Integration Parametrize editor Interfaces to common solvers Post processing Sensitivity analysis Design of experiments

imelda
Télécharger la présentation

Outline

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Sensitivity Analysis, Multidisciplinary Optimization, Robustness Evaluation, and Robust Design Optimization with optiSLang 3.2

  2. Outline • Introduction • Process Integration • Parametrize editor • Interfaces to common solvers • Post processing • Sensitivity analysis • Design of experiments • Coefficient of correlation • Simple regression, quadratic & rank order correlation • Multiple regression, Coefficient of Determination (CoD) • Coefficient of Importance (CoI) • Significance filter • Moving Least Squares approximation • Coefficient of Prognosis (CoP) • Meta-model of Optimal Prognosis (MOP) • Applications • Accompanying example: Sensitivity analysis of an analytical function (Tutorial 1) Outline & Flowcharts

  3. Outline • Multidisciplinary Optimization • Single objective, constraint optimization • Gradient based optimization • Global and adaptive response surface methods • Evolutionary algorithm (EA) • Particle swarm optimization (PSO) • Multi objective optimization • Pareto optimization with evolutionary algorithm • Applications • Accompanying example: Optimization of a damped oscillator (Tutorial 2, Part 1) • Model calibration/identification • Parametrization of characteristic curves as signals • Sensitivity analysis • Definition of objective functions • Dependent parameters • Accompanying example: Calibration of a damped oscillator (Tutorial 2, Part 2) Outline & Flowcharts

  4. Outline • Robustness analysis • Definition of robustness • Random variables • Definition of uncertainties • Variance-based robustness analysis • Statistical measures • Applications • Reliability analysis • Accompanying example: Robust design optimization of a damped oscillator (Tutorial 2, Part 3) • Robust design optimization • Definition of robust design optimization (RDO) • Design for Six-Sigma • Iterative RDO procedure • Applications • Simultaneous RDO procedure • Accompanying example: Robust design optimization of a damped oscillator (Tutorial 2, Part 3) Outline & Flowcharts

  5. Standard optimization • Full design variable space X for sensitivity analysis • Scanning the design space with DOE by direct solver calls • Generating MOP on DOE samples • Sensitivity analysis gives reduced design variable space Xred • Optimization requires start value x0, objective function f(x) and constraint conditions gj(x) • Optimizer determines optimal design xopt by direct solver calls Optimization • Optimizer • Gradient • ARSM • EA/GA Sensitivity analysis MOP DOE Solver Solver Outline & Flowcharts

  6. Optimization with MOP pre-search • Full optimization is performed on MOP by approximating the solver response • Optimal design on MOP can be used as • final design (verification with solver is required!) • as start value for second optimization step with direct solver • Good approximation quality of MOP is necessary for objective and constraints (CoP ≥ 90%) Optimization • Optimizer • Gradient • ARSM • EA/GA • Optimizer • Gradient • ARSM • EA/GA Sensitivity analysis MOP DOE MOP Solver Solver Outline & Flowcharts

  7. Optimization with MOP using external DOE • External DOE exists from experiments or other sources • Excel plugin is used to generate optiSLang binary file • MOP uses external DOE scheme to generate approximation and to perform sensitivity analysis • Optimization is performed on MOP to obtain approximate optimum Optimization • Optimizer • Gradient • ARSM • EA/GA Sensitivity analysis External DOE Excel plugin MOP MOP Outline & Flowcharts

  8. Optimization + Robustness evaluation • Full optimization variable space X for sensitivity analysis • Sensitivity analysis gives reduced optimization variable space Xred • Optimizer determines optimal design xopt by direct solver calls • Robustness evaluation (varianced-based or reliability-based) in the random variable space Xrob at optimal design xopt Optimization Robustness • Optimizer • Gradient • ARSM • EA/GA • Robustness • Variance • Sigma-level • Reliability Sensitivity analysis MOP DOE Solver Solver Solver Outline & Flowcharts

  9. Iterative Robust Design Optimization • Sensitivity analysis gives reduced optimization variable space Xred • Optimizer determines optimal design xopt by direct solver calls • Robustness evaluation • Robust optimum – end of iteration • Non-robust optimum - update constraints and repeat optimization + robustness evaluation Optimization Robustness • Optimizer • Gradient • ARSM • EA/GA • Robustness • Variance • Sigma-level • Reliability Sensitivity analysis MOP DOE Solver Solver Solver No Yes Update constraints End Outline & Flowcharts

  10. Simultaneous Robust Design Optimization • Sensitivity analysis gives reduced optimization variable space Xred • Optimizer determines optimal design xopt by direct solver calls with simultaneous robustness evaluation for every design • Each robustness evaluation determines robustness values by direct solver calls Robust Design Optimization Sensitivity analysis Optimizer MOP DOE Solver Solver Robustness Solver Outline & Flowcharts

More Related