CFD-based response surface methodology for rapid thermal simulation and optimal design of data centers

In the design of large-scale data centres, CFD is used widely but very time-consuming with intensive computational resource requirement. When it comes to near real-time thermal control or optimizing multiple design parameters of data centres, this method becomes impractical. In this paper, response surface methodology (RSM) based on radial basis function (RBF) is used to significantly reduce the running time while maintaining a good accuracy. In the first application, by using 5%, 10%, and 20% of the original CFD data, the temperature profiles of the three corresponding cases are reconstructed based on RSM. Three reconstructed temperature profiles are then compared to the full temperature profile of a data centre model. The method shows good agreement with the CFD simulation result, especially for the case of 20% utilization of the original CFD data points. In the later application, RSM is used for generating a large set of generations during a two-objective optimization process which uses the genetic algorithm as its main engine. With three investigated design parameters including mass flow inlet, inlet temperature, and server heat load, the goal is to minimize both the temperature difference and the maximum temperature inside the data centre. The outcome shows a desirable range of design input parameters for a data centre.Highlights Response surface model is trained using high fidelity CFD simulation dataRadial basis function is found to show superior advantages in constructing response surfaceRapid thermal profile reconstruction for data centre using response surface method is illustratedCFD-based response surface method for data centre optimization process is investigated