A framework of data-driven wind pressure predictions on bluff bodies using a hybrid deep learning approach

• Published in: Measurement and control (London) 2023-01, Vol.56 (1-2), p.237-256
• Main Authors: Chen, Zengshun, Zhang, Likai, Jianmin, Hua, Kim, Bubryur, Li, Ke, Xue, Xuanyi
• Format: Article
• Language: English
• Subjects: Artificial neural networks; Bluff bodies; Cliffs; Feature extraction; Missing data; Model accuracy; Performance prediction; Pressure sensors; Sensors; Wind pressure; Wind tunnel testing; Wind tunnels
• ISSN: 0020-2940; 2051-8730
• DOI: 10.1177/00202940221099064

The static synchronous multi-pressure sensing system (SMPSS) test technique is one of the most conventional techniques used in a wind tunnel. In SMPSS tests, wind pressure sensors are prone to take off leading to missing segment data. This study has predicted single, short-term, and long-term wind pressures by a one-dimensional convolutional neural network based on empirical mode decomposition (EMD-1DCNN). The effectiveness of the EMD-1DCNN model in predicting single, short-term, and long-term wind pressures on bluff bodies has been discussed. It was found that the EMD-1DCNN model had a better performance in predicting single wind pressures compared with the DNN and LSTM models. It was also found that both the DNN and LSTM models failed to predict short-term wind pressures, while the EMD-1DCNN model was effective in addressing this problem. The EMD-1DCNN model extracted the spatial feature between wind pressure sensors and its surrounding sensors to predict long-term wind pressures with high accuracy. The effects of data length used for training the EMD-1DCNN model on the accuracy of prediction were also discussed. It was concluded that 1% datasets (500 samples) were enough for predicting long-term wind pressures with high efficiency. This study has not only presented a way to predict missing data of wind pressures using the EMD-1DCNN model but provided recommendations for the EMD-1DCNN model used for different conditions.