Predicting wind pressures around circular cylinders using machine learning techniques
This provides an efficient and economical alternative to traditional wind tunnel tests and computational fluid dynamics for engineers in structural and aerodynamic design, though it is incremental as it applies existing ML methods to a specific domain.
This study tackled the problem of predicting wind pressures around circular cylinders by training machine learning models using Reynolds number, turbulence intensity, and circumferential angle as inputs, with gradient boosting regression trees achieving accurate predictions for Reynolds numbers from 10^4 to 10^6 and turbulence intensities from 0% to 15%.
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 the 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 10^4 to 10^6 and Ti ranging from 0% to 15%. It is believed that the GBRT models provide very efficient and economical alternative to traditional wind tunnel tests and computational fluid dynamic simulations for determining wind pressures around smooth circular cylinders within the studied Re and Ti range.