3. DAKOTA-OpenFOAM optimization loop | Gradient-based optimization

Description:

The blunt body shape optimization case - Part 1. 😁 Gradient-based optimization. 👍 CFD optimization and automation. 👏 Engineering design optimization loop. 🤖 DAKOTA optimization toolbox In this demo case, we use the following tools: - Geometry generation: Salome. - Mesh generation: Salome. - Balck box solver: OpenFOAM 11. - Automatic post-processing: bash scripting and Python. - Plotting: Python. - Optimization and orchestrating tool: DAKOTA 6.19.0 Recall that gradient-based optimization relies on a well-formulated problem: initial point, step size, tolerances, goal, linear and nonlinear constraints, how to compute the gradients (forward, differences, central differences, complex step), hessian information (if needed by the method), type of design variables (integers, floats, cardinal) and so on. 00:00 The blunt body shape optimization case - Preliminaries 00:45 The case base 03:20 A reminder of what are we doing 06:24 DAKOTA's output format - How to feed information to DAKOTA 08:40 Some remarks on DAKOTA-s documentation 09:40 DAKOTA's input file 18:20 On the simulator script 25:20 LEt's launch the optimization case - Real-time simulation 30:00 The final solution 33:20 Let's do some post-processing 38:24 On the quantitative data manipulation and post-processing | Bash scripting 40:20 The params.in and resluts.out files | DAKOTA's input and output files 40:44 Final remarks - Main takeaways 42:42 Cleaning up the case directory 43:25 Simulator script execution permission Compressed tutorial: - Blunt body shape optimization case Deck of slides: - Blunt body shape optimization Related videos: Blunt body shape optimization | Case presentation Related playlists: - Transitioning to OpenFOAM 11 from OpenFOAM 10/9, - DAKOTA optimization toolbox | Explore and predict with confidence | Installation and getting started, Software links: - - - - - - - Additional links: - Excellent reference for optimization methods, 👍 CFD optimization and automation. 👏 Engineering design optimization loop. ********************************************************************************** Wolʇ Dynamics, your reliable partner for CAE solutions, CAD and solid modeling → Meshing → Simulations → Automation and optimization → Post-processing → Data analytics and ML → Reporting Why Wolʇ Dynamics? Simply look at our banner - notice how the F appears backwards. Wolʇ Dynamics ↔ Flow Dynamics 😯+💣=🤯 **************************************************************************** 👉 Subscribe and hit the bell to see new videos or we will go back to OpenFOAM 3: 🖖 Join our channel to help us create more content. By joining our channel, you can also get access to perks: Follow us: Twitter → twitter.com/WolfDynamics LinkedIn → linkedin.com/company/wolf-dynamics **************************************************************************** Wolf Dynamics makes no warranty, express or implied, about the completeness, accuracy, reliability, suitability, or usefulness of the information disclosed in this training material. This training material is intended to provide general information only. Any reliance the final user place on this training material is therefore strictly at his/her own risk. Under no circumstances and under no legal theory shall Wolf Dynamics be liable for any loss, damage or injury, arising directly or indirectly from the use or misuse of the information contained in this training material. **************************************************************************** #optimization #CFD #CAE #aerospace #WSL #linux #dakotaopt #snappyhexmesh #openfoam #computationalfluiddynamics