2020级硕士生黄志文撰写的有关动态模态和深度学习方法相结合对流场、压力场、温度场进行时间上的预测文章被国际权威期刊Energy（中科院分区：1区）录用。

论文题目：Predictions of flow and temperature field in a T-junction based on dynamic mode decomposition and deep learning

作者：Zhiwen Huang, Tong Li, Kexin Huang, Hanbing Ke, Mei Lin, Qiuwang Wang

摘要：

Accurate flow field prediction methods are needed for the analysis of complex flows in energy and power field. Flow field and temperature field prediction methods combining Dynamic Mode Decomposition (DMD) and deep learning are proposed. A Convolutional Long Short-Term Memory (ConvLSTM) neural network model is built by adjusting the network structure reasonably. The DMD method, the ConvLSTM method and the method combining DMD and ConvLSTM are compared by the flow field and temperature field prediction results in a T-junction, which is widely used in energy industry. The time series dataset of the velocity, pressure and temperature field of a wall jet in a T-junction are obtained through large eddy simulation (LES). The overall relative errors in the predictions of velocity, pressure and temperature fields remained about 4%, 60% and 0.13% for the DMD method, 3%, 10% and 0.08% for the ConvLSTM method, and 2%, 10% and 0.06% for the method combining DMD and ConvLSTM, respectively. The combining method is the most accurate and stable prediction method. Its information loss rates of the velocity, pressure and temperature fields are the smallest and 2.21%, 13.38% and 0.11%, respectively, and will not increase significantly with the increase of the prediction duration.

Keywords: T-junction; Flow field prediction; Deep learning; Dynamic mode decomposition; Convolutional long short-term memory.

论文链接：https://authors.elsevier.com/sd/article/S0360-5442(22)02117-X