Leveraging Bayesian Optimisation for Expensive Experiments and Simulations in Fluid Dynamics with Uncontrollable Dynamic Variables

The optimisation of physical experiments and computer simulations is a challenge frequently encountered in fluid dynamics. It is a difficult task as the underlying physical processes or mathematical models are often too complex to find an optimum analytically, limiting us to controlling some parameters and observing a response - typically an expensive task.

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.