TY - JOUR
T1 - Predicting wind pressures around circular cylinders using machine learning techniques
AU - Hu, Gang
AU - Kwok, K. C. S.
PY - 2020
Y1 - 2020
N2 - Numerous studies have been carried out to measure wind pressures around circular cylinders since the early 20th century due to its engineering significance. Consequently, a large amount of wind pressure data sets have accumulated, which presents an excellent opportunity for using machine learning (ML) techniques to train models to predict wind pressures around circular cylinders. Wind pressures around smooth circular cylinders are a function of mainly Reynolds number (Re), turbulence intensity (Ti) of the incident wind, and circumferential angle of the cylinder. Considering these three parameters as the inputs, this study trained two ML models to predict mean and fluctuating pressures respectively. Three machine learning algorithms including decision tree regressor, random forest, and gradient boosting regression trees (GBRT) were tested. The GBRT models exhibited the best performance for predicting both mean and fluctuating pressures, and they are capable of making accurate predictions for Re ranging from 104 to 106 and Ti ranging from 0% to 15%. It is believed that the GBRT models provide an efficient and economical alternative to traditional wind tunnel tests and computational fluid dynamic simulations for determining wind pressures around two-dimensional smooth circular cylinders within the studied Re and Ti range.
AB - Numerous studies have been carried out to measure wind pressures around circular cylinders since the early 20th century due to its engineering significance. Consequently, a large amount of wind pressure data sets have accumulated, which presents an excellent opportunity for using machine learning (ML) techniques to train models to predict wind pressures around circular cylinders. Wind pressures around smooth circular cylinders are a function of mainly Reynolds number (Re), turbulence intensity (Ti) of the incident wind, and circumferential angle of the cylinder. Considering these three parameters as the inputs, this study trained two ML models to predict mean and fluctuating pressures respectively. Three machine learning algorithms including decision tree regressor, random forest, and gradient boosting regression trees (GBRT) were tested. The GBRT models exhibited the best performance for predicting both mean and fluctuating pressures, and they are capable of making accurate predictions for Re ranging from 104 to 106 and Ti ranging from 0% to 15%. It is believed that the GBRT models provide an efficient and economical alternative to traditional wind tunnel tests and computational fluid dynamic simulations for determining wind pressures around two-dimensional smooth circular cylinders within the studied Re and Ti range.
UR - https://hdl.handle.net/1959.7/uws:63663
U2 - 10.1016/j.jweia.2020.104099
DO - 10.1016/j.jweia.2020.104099
M3 - Article
SN - 0167-6105
VL - 198
JO - Journal of Wind Engineering and Industrial Aerodynamics
JF - Journal of Wind Engineering and Industrial Aerodynamics
M1 - 104099
ER -