BEGIN:VCALENDAR VERSION:2.0 PRODID:-//DIGITAL FACTORY//EVENTS MAKER V1.6.14//EN CALSCALE:GREGORIAN BEGIN:VEVENT DTEND:20220203 UID:669dac3cd088d DTSTAMP:20240722T004756Z CATEGORIES:OF_Optimization LOCATION:Rostock Joachim-Jungius-Str. 9, Rostock, Mecklenburg-Vorpommern, 18059, DE ORGANIZER;CN=Dr.-Ing. Johann Turnow:MAILTO: DESCRIPTION:[wbcr_text_snippet id="1763"] Basic idea The course focuses are: Quick setup of simulation in OpenFOAM Acceleration of simulation processes (script-based) with the program OpenFOAM and Python Application of optimization strategies DOE / DAKOTA In addition to teaching optimization strategies using DOE / DAKOTA\, the basics for script-based initialization of simulations to increase the workflow efficiency in OpenFOAM are taught. Content 1. Basics Introduction to script-based control of OpenFOAM simulations Configuration of automated OpenFOAM environment Step-by-Step Parametric Simulation Setup Control via Python script 2. Basic building blocks Existing tools for automatic setup of OpenFOAM simulations Overview of Features in OpenFOAM / Opensource Features in InsightCAE Introduction to Parametric Geometry Creation for Optimization Tasks Conversion of patametric geometry for meshing in OpenFOAM Fundamentals of optimization strategies 3. Setting up simulations Step-by-step setup of simulations with OpenFOAM using OpenFOAM Touls and the Open Source Framework InsightCAE Import and processing of parametric CAD data Controlled modification of CAD files Implementation of the simulation environment with automatic evaluation of the simulation results 4. Acceleration simulation setup Concept: Script-based control using Python Numerical setup for robust simulations and fast convergence Simple handling and control on computer clusters Tutorial: Automatic setting up &\; simulation &\; PostProcessing of a wing flow with variation of wing geometry and angle of incidence 5. Optimization strategies Application Design of Experiments (DoE) in OpenFOAM Definition of the parameter space Parametric geometry creation Implementation of automated simulations Evaluation of differently weighted target functions Coupling of OpenFOAM and Dakota Introduction to DAKOTA Application of DAKOTA to control simulation processes for optimization calculations Application of different optimization strategies (gradient methods\, genetic algorithms)  \;  \; Upcoming Events of the course [em-events number_of_events=5 categories=146 disable_pagination=1 style=widget] [wbcr_text_snippet id="1766"] URL;VALUE=URI: SUMMARY:OpenFOAM Optimization DTSTART:20220203 RDATE:20220204T000000Z RDATE:20220325T000000Z END:VEVENT END:VCALENDAR