Master Thesis GPU-Accelerated Particle Simulation for OpenFOAM

In many industrial and research applications, tracking millions of particles within a fluid flow is essential, but current CPU-based methods are too slow. This project offers an exciting opportunity to break that bottleneck by building a state-of-the-art solver that leverages the massive power of modern GPUs to accelerate simulations by orders of magnitude. Join us in developing a cutting-edge tool that integrates directly with the industry-standard Computational Fluid Dynamics (CFD) software OpenFOAM, and make a significant impact on what's possible in computational science.

• As a part of your Master thesis, you will conduct a literature review on state-of-the-art methods for Lagrangian Particle Tracking, GPU computing in CFD and particle-wall interaction.
• You will develop a high-performance particle tracking solver using Python and the NVIDIA Warp framework to run on the GPU.
• In addition, you will implement a method for collision detection with complex 3D geometries and create a pre-processing pipeline to prepare simulation data (e.g., flow fields and meshes) from OpenFOAM for the GPU.
• Furthermore, you will integrate the new solver into a standard OpenFOAM workflow, establishing a seamless data exchange.
• Last but not least, you will validate and benchmark your solver's performance against OpenFOAM's native tools, quantifying the massive speedup.