Bulletin of the American Physical Society
76th Annual Meeting of the Division of Fluid Dynamics
Sunday–Tuesday, November 19–21, 2023; Washington, DC
Session G29: Data-Driven Modeling, Control and Analysis for Fluid Dynamics
3:00 PM–4:05 PM,
Sunday, November 19, 2023
Room: 152B
Chair: Scott Dawson, Illinois Institute of Technology
Abstract: G29.00002 : Leveraging Bayesian Optimisation for Expensive Experiments and Simulations in Fluid Dynamics with Uncontrollable Dynamic Variables*
3:13 PM–3:26 PM
Presenter:
Mike Diessner
(Newcastle University)
Authors:
Mike Diessner
(Newcastle University)
Joseph O'Connor
(Imperial College London)
Andrew Wynn
(Imperial College London)
Sylvain Laizet
(Imperial College London)
Xiaonan Chen
(Newcastle University)
Kevin Wilson
(Newcastle University)
Richard D Whalley
(Newcastle University)
Collaborations:
Fluid Dynamics Lab, Newcastle University, Turbulence Simulation Group, Imperial College London
We introduce NUBO (Newcastle University Bayesian Optimisation), an open-source machine learning framework that takes a data-driven approach to find close-to-optimal parameter values for such problems. It uses a surrogate model to approximate the objective function and heuristics (acquisition functions) to guide the search over the parameter space. This model-based strategy makes NUBO cost-effective and well-suited for expensive simulations and experiments in fluid dynamics.
We apply NUBO to high-fidelity simulations and wind tunnel experiments, where we aim to reduce the skin-friction drag of turbulent boundary-layer flows by optimising multiple actuation parameters (e.g., amplitude, frequency, wavelengths of low-amplitude wall-blowing). We further consider the presence of uncontrollable variables affecting drag, particularly finding optimal control strategies when oncoming wind speeds are changing continually. We will show that NUBO finds strategies yielding significant skin-friction drag reduction that may eventually generate net-energy savings for the system.
*EPSRC Grants: EP/T020946/1, EP/T021144/1, EP/L015358/1
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