Abstract

Computational fluid dynamics (CFD) has become a popular tool compared to experimental measurement for thermal management in data centers. However, it is very time-consuming and resource-intensive when used to model large-scale data centers, and often unrealistic for real-time thermal analyses. In addition, it is prohibitive to use CFD for the optimization process where thousands of designs need to be generated. Floor tile airflow distribution control is a key technique for maintaining a sufficient cold air delivery to variable thermal loadings of server cabinets. Regular practice of deploying a set of identical floor tiles may not result in the best solution for airflow uniformity through tiles. In this paper, an optimization procedure based on response surface methodology (RSM) is proposed to find the best arrangement of mixed-porosity floor tiles for different targeted tile airflow distributions. Fast-approximation RSM based on radial basis function (RBF) allows thousands of designs to be generated for the optimization process which uses a genetic algorithm as its main solver. The method shows proven success in maximizing floor tile airflow uniformity, and in the inverse design optimization where various tile airflow distribution topologies, i.e., linear, parabolic, and sinusoidal shapes are targeted. For the considered data center and aisle configuration, the improvement over the all-uniform-tile design is 55% in terms of standard deviation to the average tile airflow rate, whereas 90%, 91%, and 94% in root-mean-square error (RMSE) for the linear, parabolic, and sinusoidal floor tile airflow distribution objectives, respectively.

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