2. DAKOTA-OpenFOAM code coupling and optimization example | The blunt body shape optimization case

In this demo case, we use geometry generation with Salome, mesh generation with Salome, a black box solver OpenFOAM 11, automatic post‑processing with bash scripting and Python, plotting with Python, and the optimization and orchestrating tool DAKOTA 6.19.0. The goal is to optimize the shape of a blunt body to minimize the drag coefficient at a Reynolds number of 1000, using a fully parametrized Bezier curve with four control points, four linear, and one nonlinear constraint. We employ monolithic gradient‑based, design space exploration, surrogate‑based optimization (AI/ML), and adjoint optimization methods. The video covers the introduction, optimization loop, gradient‑based optimization, design space exploration, surrogate‑based optimization, warning about biased data, adjoint optimization, and final remarks. 00:00 The blunt body shape optimization case - Introduction 02:38 Optimization loop 03:16 Gradient-based optimization 05:00 Design space exploration 09:20 Surrogate-based optimization | AI/ML 11:42 Warning: do not use biased data to construct models 12:15 Adjoint optimization 16:05 Final remarks - Main takeaways