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

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

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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 metabo 15-gauge finish nailer cordless 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 hydrangea red sensation 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.