Simulation of cold aisle thermal environment in a data room based on CFD and machine learning

In order to reduce the cost of data room simulation and quickly predict the thermal environment of the data room, this paper takes an actually operating data room in a large data center in Changzhou as the research object. Firstly, the porous media method is used to simulate the flow and heat transfer process inside the server, and a simplified CFD model is established. Then, the measured data is compared with the CFD simulation results to verify its effectiveness. On this basis, by changing the working condition parameters of the data room, three machine learning models, namely back propagation (BP) neural network, random forest, and extreme gradient boosting tree (XGBoost), are established and their prediction performances under different situations are compared. The results show that the BP neural network model has good generalization ability. When predicting temperature, the absolute error is within 0.56 ℃, and the coefficient of determination R2is above 0.966. When predicting speed, the absolute error is within 0.01 m/s, and R2is 0.999.