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Professor Bae's research focuses on the physical understanding and modeling of structures associated with near-wall turbulence. The main research goal is to develop high-fidelity models that reduce computational costs while simulating high-Reynolds-number turbulent flows. These models enable simulations that are utilized in the design cycles for wind farms and aircraft, aiding predictions of atmospheric flows and significantly reducing the overall time and effort associated with these processes. The studies investigate the physical mechanisms that generate and sustain turbulence, driving innovations in modeling approaches. Professor Bae has interests in applying machine learning, information theory, and developing novel methodologies for turbulence modeling.
California Institute of Technology • Pasadena, CA
Teaching and conducting research in Aerospace Engineering.
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