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

Abstract

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.

A Deep Learning-Based Fault Warning Model for Exhaust Temperature Prediction and Fault Warning of Marine Diesel Engine

“…However, its dimension is different from that of time series, so it is not suitable for temporal sequence forecasts. To solve this problem, one-dimensional convolutional neural networks (Convolution1d, 1DCNN) are used to perform data mining on time-series data to extract local features of time-series data and improve the accuracy of prediction models [32].…”