University of Arizona, Tucson, AZ
Ph.D. Student in Aerospace Engineering | DRL-CFD Researcher | Active Flow Control
Computational Multiphase Transport Laboratory, Department of Aerospace & Mechanical Engineering, University of Arizona
I develop CFD-in-the-loop reinforcement learning workflows (TorchRL/Stable-Baselines3 + PyFR) for active flow control.
FOCUS AREAS
Policy learning for closed-loop control in unsteady aerodynamic environments.
TorchRL/Stable-Baselines3 + PyFR coupling for physically grounded control policy optimization.
DG/FR and spectral-element methods for accurate, control-relevant simulations.
MPI, OpenMP, CUDA, and Slurm workflows for scalable experimentation.
ParaView/Tecplot-driven diagnostics and publication-ready technical figures.
SELECTED RESEARCH
Each entry includes problem framing, computational setup, key findings, and technical next steps.
2025 · Current Focus
Under-review Computers & Fluids manuscript on PPO-based DRL control of a NACA0012 airfoil (, ) for quarter-chord pitching-moment trim using CFD-in-the-loop active flow control.
2024 · Framework
Research software framework for parallelized RL-CFD experiments with reproducible training, rollout, and evaluation workflows.
2026 · In Progress
In-progress study on sparse pressure-probe layouts for reconstructing lift and quarter-chord pitching moment in DRL-driven airfoil flow control.
PUBLICATIONS
Deep Reinforcement Learning for Airfoil Pitching Moment Control
Reinforcement-Learning-Driven Active Flow Control for Airfoil Pitching Moment Trim