Ryan Johnson

Ryan Johnson is an associate professor in the Department of Mechanical Engineering at Carnegie Mellon University. His research focuses on predictive computational modeling of propulsion and reacting-flow systems, with interests spanning computational fluid dynamics, chemical kinetics, GPU-enabled high-performance computing, and embedded machine learning. He develops scalable prediction capabilities for complex, multiscale problems, with the goal of enabling accurate simulation of real propulsion and energy devices. Before joining Carnegie Mellon, he was a scientist and aerospace engineer at the U.S. Naval Research Laboratory in Washington, DC, where he led efforts in high-speed propulsion modeling. He also held a visiting scholar appointment at Stanford University, where he worked on problems at the intersection of high-performance computing, CFD, and chemical kinetics. Johnson received his Ph.D. from the University of Virginia in 2014 under the supervision of Professor Harsha Chelliah. He is a recipient of the Presidential Early Career Award for Scientists and Engineers (PECASE).

Ryan Frederick Johnson is a researcher specializing in combustion, reacting flow, fluid dynamics, and computational fluid dynamics. His work focuses on developing advanced numerical methods for simulating chemically reacting flows, including discontinuous Galerkin discretizations for the Navier-Stokes equations and entropy-bounded schemes for compressible Euler equations. He also investigates flame dynamics, supersonic jet noise, and rotating detonation engines. His research integrates computational modeling with experimental analysis to improve understanding of turbulent combustion and high-speed flows. Johnson's publications highlight the application of deep learning in chemical kinetics and the evaluation of aerodynamic breakup models in extreme flow conditions.