Reduced order modeling of a data center model with multi-Parameters

Although full-field simulations using computational fluid dynamics and heat transfer (CFD/HT) tools can be applied to predict the flow and temperature fields inside data centers, their running time remain the biggest challenge to most modelers. In this paper, a reduced order modeling method is used to drastically reduce the running time (up to 600 times faster) while still acceptably preserving the accuracy of the model. The results obtained from Proper Orthogonal Decomposition (POD) method are in good agreement with the results obtained from CFD/HT model simulation. The sensitivity analysis of some of the design parameters in the POD model is evaluated. In addition, a 3-D temperature profile of the data center model constructed from 2D slices are generated with a linear interpolation technique. Tradeoff between accuracy and running time is observed and discussed.