TY - JOUR
T1 - Machine learning-based prediction of crosswind vibrations of rectangular cylinders
AU - Lin, Pengfei
AU - Hi, Gang
AU - Li, Chao
AU - Li, Lixiao
AU - Xiao, Yiqing
AU - Tse, K. T.
AU - Kwok, K. C. S.
PY - 2021
Y1 - 2021
N2 - Due to the complexity of crosswind vibrations of rectangular cylinders, current research on crosswind vibrations of rectangular cylinders mainly relies on expensive wind tunnel tests and time-consuming numerical simulation techniques. In this study, in order to evaluate crosswind vibrations of rectangular cylinders, machine learning method was used to build an efficient and effective prediction model for supplementing the above two research tools. 5 machine learning models based on decision tree regression, k-nearest neighbor regression, random forest, gradient boosting regression tree (GBRT) and histogram gradient boosting regression tree algorithms were trained based on the existing high-quality and reliable wind tunnel test datasets of crosswind responses of rectangular cylinders. The hyper-parameters were optimized by using particle swarm optimization method. 4 types of crosswind vibration phenomena, including over-coupled, coupled, semi-coupled and decoupled, were predicted. It was found that the GBRT model is capable of predicting crosswind responses of rectangular cylinders at side ratios from 0.75 to 3 and Scruton numbers from 0 to 150 under wind flow with turbulence intensities from 0 to 16%. Evidently, GBRT model can be an effective and economical method to study crosswind vibrations of rectangular cylinders and hence supplement traditional wind tunnel tests and numerical simulation techniques.
AB - Due to the complexity of crosswind vibrations of rectangular cylinders, current research on crosswind vibrations of rectangular cylinders mainly relies on expensive wind tunnel tests and time-consuming numerical simulation techniques. In this study, in order to evaluate crosswind vibrations of rectangular cylinders, machine learning method was used to build an efficient and effective prediction model for supplementing the above two research tools. 5 machine learning models based on decision tree regression, k-nearest neighbor regression, random forest, gradient boosting regression tree (GBRT) and histogram gradient boosting regression tree algorithms were trained based on the existing high-quality and reliable wind tunnel test datasets of crosswind responses of rectangular cylinders. The hyper-parameters were optimized by using particle swarm optimization method. 4 types of crosswind vibration phenomena, including over-coupled, coupled, semi-coupled and decoupled, were predicted. It was found that the GBRT model is capable of predicting crosswind responses of rectangular cylinders at side ratios from 0.75 to 3 and Scruton numbers from 0 to 150 under wind flow with turbulence intensities from 0 to 16%. Evidently, GBRT model can be an effective and economical method to study crosswind vibrations of rectangular cylinders and hence supplement traditional wind tunnel tests and numerical simulation techniques.
UR - https://hdl.handle.net/1959.7/uws:63905
U2 - 10.1016/j.jweia.2021.104549
DO - 10.1016/j.jweia.2021.104549
M3 - Article
SN - 0167-6105
VL - 211
JO - Journal of Wind Engineering and Industrial Aerodynamics
JF - Journal of Wind Engineering and Industrial Aerodynamics
M1 - 104549
ER -