OpenFOAM Case Generator

1. The Challenge & Engineering Objectives

Traditional OpenFOAM setups demand painstaking manual tuning of countless dictionary files. To accelerate iterative aerodynamic testing for Formula Student vehicles, I collaborated with AI development tools to translate complex CFD domain knowledge into a robust, generative Python automation script.

2. Architecture & Execution

By establishing strict architectural boundaries, I directed AI models to scaffold the cfd_gen engine around a declarative JSON configuration.

• Automated Meshing Pipeline: Prompted AI to generate precise logic orchestrating blockMesh and snappyHexMesh, dynamically calculating scaling and refinement levels on the fly based on STL bounding boxes.
• Turbulence & Solver Initialization: Orchestrated the generation of scripts that inject k-ω SST turbulence parameters directly into simpleFoam dictionaries.
• HPC Integration: Guided the AI to output deployment-ready SLURM execution scripts and domain decomposition routines for high-performance computing clusters.

Key Engineering Takeaway

"By strategically prompting AI with complex scientific computing requirements, I successfully bridged the gap between raw aerodynamic theory and a fully functional Python CLI tool, reducing CFD setup times by a factor of five."

3. Results & Impact

This AI-assisted automation empowered the engineering team to focus on aerodynamic innovation rather than software debugging, achieving a zero-fault initialization rate across all test cases.