
Phoenix D. 2025 | BASIS Independent McLean
- Project Title: Enhancing Computational Fluid Dynamics Through Machine Learning Algorithms
- BASIS Independent Advisor: Anmol Bhardwaj
- Internship Location: ASDRP (Ca.)
- Onsite Mentor: Chris DeGrendele
Computational fluid dynamics (CFD) is a branch of computer science simulation that uses flow equations and partial differential equations to model fluid motion based on the
governing physical environments. Since computers revolutionized the design and innovation process, scientists have relied on CFD to model environments to predict turbulent or laminar
flow. Everything from the cars we drive to the HVAC systems that cool our homes rely on CFD to simplify costs and save time. At ASDRP, we hope to expand upon the field of CFD through
simulation of magnetohydrodynamics (MHD). MHD is a model that accounts for an additional flux being electric and magnetic fields. One such use for MHD are hall-effect thrusters which rely on accelerated electric fields to drive an ion thruster to produce propulsion. Currently, efforts to accurately simulate MHD have been limited due to high computational expenses. We hope to account for the many fluxes that drive MHD using high-performance computing (HPC) clusters. Previously, our group was capable of producing accurate simulations for very simple one and two dimensional fluid flows through coding advection, burgers, and Euler equations which provide a foundation for more complex simulations.