A Gaussian Process-Based emulator for modeling pedestrian-level wind field

Wind tunnel tests and computational fluid dynamics (CFD) simulations remain the main modeling techniques in wind engineering despite being expensive, time-consuming, and requiring special facilities and expert knowledge. There is a clear need for a fast, accurate, but, at the same time, computationally economical substitute. This study proposes a Gaussian Process-based (GP-based) emulator to predict the pedestrian-level wind environment near a lift-up building – an isolated, unconventionally configured building. The proposed GP-based emulator transcends the limitations of previous emulators as it can handle many inputs (8) and output parameters (384) and a large dataset (150 CFD simulations). To increase computational efficiency, the current study proposes a data reduction method based on Principal Component Analysis (PCA) and a technique to estimate hyper-parameters based on optimization. The latter can efficiently compute 250 hyper-parameters and requires no prior knowledge of their probability distributions. The emulator is faster, by a factor of 10⁷ than CFD simulations in predicting wind speeds, and its accuracy is substantiated using both qualitative and quantitative analyses, which reveal that the emulator's predictions of all-prominent flow features near a building have no systematic bias, are highly accurate, and have great reproductivity.