Long-term transient thermal analysis using compact models for data center applications

Reduced-order thermal models are necessary to enable real-time assessment of the optimum operating and control conditions to improve data center energy efficiency. A 3D computational fluid dynamics (CFD) and heat transfer model of a basic raised floor, air cooled, hot aisle/cold aisle data center configuration was developed and simulation results were used to generate snapshots for initializing compact models based on the Proper Orthogonal Decomposition (POD) and the Nonlinear Principle Component Analysis (NLPCA) methods. The specific focus of the study was to numerically investigate how the thermal trends can be affected by the long-term transient flow patterns associated with leakage and how the compact models (e.g. POD and NLPCA) can be utilized for much faster implementations and characterizations of the transient flows. Using both the POD and the NLPCA method, good agreement was achieved between the full simulation results and the compact models for predicting the dynamically developed local flow structure over a range of transient cooling air supply operating conditions. In addition, the NLPCA method was implemented to better characterize the nonlinear aspects of the CFD results. The benefits of using both the POD and NLPCA methods are discussed in relation to constructing compact models as real-time predictive tools. A systematic use of the compact models has also been proposed, to enable more robust thermal management and control of data centers.