数据中心中机柜出风温度的快速模拟.

In order to study the airflow organization in the data center and quickly predict the outlet air temperature of the rack, a rapid prediction method that combines the proper orthogonal decomposition (POD) and the surrogate model was proposed. Taking a data center as an example, the temperature field data under a large number of operating conditions were calculated via using computational fluid dynamics (CFD) software. The effects of air conditioning on the temperature field of the micro-module was analyzed. Then, the reduced order model of the cabinet outlet air temperature was established via using the POD method. The calculated temperature field data was divided into training set and test set, and the prediction accuracy of regression tree, boosted tree, support vector regression, neural network, Gaussian process regression, cubic spline interpolation, and piecewise cubic Hermite interpolation as surrogate model was compared. The consuming time of calculating the temperature field via using surrogate model and CFD software was compared. It is found that the computational speed of surrogate model composed of POD, machine learning method, and interpolation method was more than 100 times that of CFD software. [ABSTRACT FROM AUTHOR]