Physics‐Informed Bayesian Optimisation of Utility‐Scale Wind Farms in Synthetically Generated Complex Terrain Using LES

ABSTRACT Optimising turbine layouts to maximise power output is crucial for wind farm development, particularly in complex terrain where analytical wake models fail to capture key flow physics. We present the first application of Bayesian optimisation (BO) combined with large eddy simulations (LES) for utility‐scale wind farm layout optimisation in synthetically generated, realistic and reproducible terrain. LES were performed using WInc3D, a fast, high‐fidelity simulator that resolves atmospheric boundary layers, terrain effects and turbine wake interactions. Initial tests show that standard black‐box BO achieves only marginal improvements in complex terrain, with limited capacity to identify high‐performing layouts. To overcome this, we developed physics‐informed sampling strategies that use terrain‐induced flow patterns from base‐flow LES that result in high‐performing initial layouts. We tested four 16‐turbine optimisation cases with different spacing constraints and wind directionality (single and multiple directions), evaluating 512 layouts with LES for each optimisation. Results show that optimal layouts exploit terrain features through clustering on elevated ridges for single‐direction cases, whereas multi‐directional optimisation requires balanced distributions to maintain performance. LES reveal complex flow phenomena, including terrain‐dependent wake behaviour, bidirectional turbine‐terrain interactions and non‐linear wake effects that necessitate high‐fidelity resolution. Analysis of the optimisation evolution reveals that BO serves primarily as a refinement tool, effectively exploiting physics‐informed initial designs but struggling to discover new optimal regions independently. These findings demonstrate that incorporating physical insight is essential for WFLO in complex terrain and that high‐fidelity simulations are necessary for reliable wind farm layout optimisation under such conditions.