Data-driven model reduction for fast temperature prediction in a multi-variable data center

Abstract

With the rapid development of digital economy, the number of data centers and their capacity have been increasing sharply, and data center energy consumption becomes a whole-society concern. Computational fluid dynamics (CFD) is currently widely used to obtain the thermal fields inside air-cooled data centers to enable design improvements and optimize the airflow organization. However, a CFD simulation needs a lot of time which can not be accepted for real-time operation. In the present study, a design tool called pairwise independent combinatorial testing (PICT) is applied to optimize the simulation conditions and to maximize the amount of useful information obtained with the minimum number of numerical tests. Based on the snapshots, the proper orthogonal decomposition(POD) method combined with the multivariate adaptive regression splines (MARS) method, is proposed and used in a real row-level data center of 199 independent variables. Under design conditions, POD-MARS predictions are in good agreement with CFD simulations with the average mean relative error for 20 tested cases being ∼0.01%. For another 20 randomized cases under off-design conditions, the average mean relative error is 6.45%, the corresponding mean absolute error is 1.89 °C and on average there is 92.36% area of the total three-dimensional temperature field where the relative error doesn't exceed 15%. The POD-MARS computation takes only 30s to obtain a 3D temperature field for the same test case which is ∼240 times faster than CFD simulation on the same desktop computer.