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A platform for research: civil engineering, architecture and urbanism
A Gaussian Process-Based emulator for modeling pedestrian-level wind field
https://orcid.org/0000-0003-3941-4596
https://orcid.org/0000-0002-8058-8873
Abstract 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.
Highlights A Gaussian Process (GP) based emulator is used to model pedestrian level wind environments (PLWEs). Principal Component Analysis is employed to achieve a 93.56% data reduction. A novel method based on optimization is proposed to calculate 250 hyper-parameters. The GP-based emulator uses 8 input parameters to predict mean wind speed at 388 locations. The GP-based emulator is faster by a factor of 107 than CFD in modeling PLWEs.
A Gaussian Process-Based emulator for modeling pedestrian-level wind field
https://orcid.org/0000-0003-3941-4596
https://orcid.org/0000-0002-8058-8873
Abstract 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.
Highlights A Gaussian Process (GP) based emulator is used to model pedestrian level wind environments (PLWEs). Principal Component Analysis is employed to achieve a 93.56% data reduction. A novel method based on optimization is proposed to calculate 250 hyper-parameters. The GP-based emulator uses 8 input parameters to predict mean wind speed at 388 locations. The GP-based emulator is faster by a factor of 107 than CFD in modeling PLWEs.
A Gaussian Process-Based emulator for modeling pedestrian-level wind field
https://orcid.org/0000-0003-3941-4596
https://orcid.org/0000-0002-8058-8873
Abstract 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.
Highlights A Gaussian Process (GP) based emulator is used to model pedestrian level wind environments (PLWEs). Principal Component Analysis is employed to achieve a 93.56% data reduction. A novel method based on optimization is proposed to calculate 250 hyper-parameters. The GP-based emulator uses 8 input parameters to predict mean wind speed at 388 locations. The GP-based emulator is faster by a factor of 107 than CFD in modeling PLWEs.
A Gaussian Process-Based emulator for modeling pedestrian-level wind field
Weerasuriya, A.U. (author) / Zhang, Xuelin (author) / Lu, Bin (author) / Tse, K.T. (author) / Liu, C.H. (author)
Building and Environment ; 188
2020-11-28
Article (Journal)
Electronic Resource
English
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