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Ansys optiSLang 기본교육

소개

본 교육에서는 설계공간을 이해하기 위해 optiSLang에 대한 사용방법을 제공합니다. 설계 변동 또는 측정 기반으로 최소한의 사용자 입력과 적은 수의 솔버 호출로 효율적인 변동 분석을 수행할 수 있는 방법을 연습하고 파라메트릭 설계 및 시스템 평가에 필요한 모든 구성 요소가 포함된 표준화된 워크 플로우를 생성하고 하나의 플랫폼 내에서 민감도 분석, 최적화 및 견고성 평가를 위한 모듈과 파라 메트릭 모델을 사용하여 CAE 기반 시뮬레이션의 워크 플로우를 효율적으로 생성하는 방법을 연습합니다.

대상

파라메트릭 최적설계에 관심이 있는 연관분야의 대학(원)생 및 엔지니어   

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Learning Outcome

Module 1: Graphical user interface and process integration

  • Introduction to Ansys optiSLang and the graphical user interface
  • Automate manual simulation steps to conduct parametric variation analyses

Module 2: Sensitivity analysis

  • Sensitivity analysis that helps you to understand your numerical task
  • Investigate parameter sensitivities, reduce complexity, and generate best possible meta models
  • Analysis of experimental data

Module 3: Single- and multi-objective optimization

  • Introduction to optimization goals and constraints
  • Response surfaces based and direct optimization
  • Single- and multi-objective optimization

Available Dates

Learning Options

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

Agenda 

This is a 1-day classroom course covering both lectures and workshops. For virtual training, this course is covered over 3 x 2-hour sessions lectures only.

Virtual Classroom Session 1

Process integration and Graphical User Interface as the first steps towards a parameter study

  • Lecture: Parametrization
  • Demonstration: optiSLang Graphical User Interface (GUI)
  • Lecture and Demonstration: Interfaces to common solvers (Text based, Python) 
  • Lecture and Demonstration: Interfaces to ANSYS (Workbench plugin and Workbench node) 
  • Demonstration: Analytical nonlinear function (Text based + Python + Workbench)
  • Workshop: Kursawe function (Python)

Virtual Classroom Session 2

Sensitivity Analysis

  • Lecture: Design of experiments
  • Lecture: One-dimensional correlations
  • Lecture: Response Surface Method 
  • Lecture: Meta-model of Optimal Prognosis (MOP) and Best Practices
  • Lecture: Adaptive MOP
  • Lecture: Metamodel of optimal Prognosis
  • Demonstration: Usage of the Sensitivity Wizard
  • Lecture/ Demonstration: interpretation of a sensitivity analysis of analytical nonlinear function (Text based + Workbench)
  • Workshop: Sensitivity of Kursawe function (Python)

Virtual Classroom Session 3

Optimization studies

  • Lecture: Single objective, constraint optimization
  • Gradient-Based Methods (e.g. NLPQL)
  • Adaptive Response Surface Methods (e.g. ARSM)
  • Nature-Inspired Optimization (e.g. Evolutionary Algorithm)
  • Multi objective & Pareto optimization (e.g. Evolutionary Algorithm)
  • Demonstration of all steps: Process integration, Sensitivity study and optimization Kursawe function (Python) with different Algorithms
  • Workshop: Single Objective Optimization of Analytical nonlinear function (Text based)