Sparsity and mixing effects in deep learning predictions of temperature and humidity

Abstract

Developing deep learning models for predicting environmental data is a powerful tool that can significantly enhance equipment design, optimize the implementation of engineering systems, and deepen our understanding of the limitations imposed by flow physics. This study unequivocally demonstrates the accuracy of forecasting models based on popular deep learning algorithms, such as the long-short-term memory model, in turbulent mixing regions associated with flow physics arising from ventilation. This accuracy is contingent on two essential conditions. First, the sparsity of the sampling data is consistent with the model's accuracy overall. Second, the data sparsity ensures reasonable accuracy in the turbulent mixing regions. The investigation combines high-resolution flow simulation data with deep learning predictions of velocity, temperature, and relative humidity in a ventilated confined space. The results of this study, with their high accuracy, not only help to understand the mixing arising from flow circulation but also pave the way for developing predictive capabilities for environmental data.