Energy consumption and thermal management have become key challenges in the design of large-scale data centers, where perforated tiles are used together with cold and hot aisles configuration to improve thermal management. 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, response surface methodology based on radial basis function is used to significantly reduce the running time for generating a large set of generations during a two-objective minimization process which uses the genetic algorithm as its main engine. Three design parameters including mass flow inlet, inlet temperature, and server heat load are investigated for a two-objective optimization. The goal is to minimize both the temperature difference and the maximum temperature inside the data center and search for a range of design parameters that satisfy both of these objectives. Numerous radial basis function models are studied and compared. Discussion on a more preferred scheme for the response surface construction is provided. Finally, a graph of Pareto font is generated showing the set of optimal designs in the objective space, and Pareto design validation is also performed.

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