Marc Henry de Frahan is helping to improve next-generation wind and combustion processes. As part of the Exascale Computing Project, Marc develops high-fidelity turbulence models to enhance simulation accuracy and efficient numerical algorithms for future high-performance computing hardware architectures. In addition to traditional physics-based modeling, he is integrating deep neural networks into modeling and reinforcement learning into advanced control strategies. Beyond his research, Marc is passionate about making science accessible to a broad audience and wrote a children's book about cavitation science. He delights in seeing people's eyes light up when they understand a concept, sparking the desire to learn more.

Research Interests

High-performance computing, GPU computing

High order numerical methods for computational fluid dynamics

Fluid mechanics (turbulence, multiphase flows, combustion)

Deep learning for computational fluid mechanics

Computational combustion

Education

Deep Learning Specialization certificate, Coursera

Ph.D., Mechanical Engineering, University of Michigan 

M.S., Applied Mathematics, Universite Catholique de Louvain

B.S., Applied Mathematics, Universite Catholique de Louvain

Featured Work

Release of Pele port for 90% of Summit hybrid architecture (2020)

“Deep learning for presumed probability density function models,” Combustion and Flame (2019)

Blade Resolved Wind Turbine Simulation with the Hybrid Time-Averaged Model Split Turbulence Model, American Physical Society Division of Fluid Dynamics, 2019

Numerical Simulations of the Supercritical Carbon Dioxide Round Turbulent Jet, Rocky Mountain Fluid Mechanics Research Symposium (2019)