An analytical approach using artificial intelligence has been developed for assessing the cooling performance of data centers. This paper discusses the use of a Neural Network (NN) model in the real-time prediction of the cooling performance of a cluster of equipment in a data center environment. The NN model is used to predict the Capture Index (CI) [1] as a function of rack power, cooler airflow and physical/geometric arrangement for a cluster located in a simple room environment. The Neural Network is “trained” on thousands of hypothetical but realistic cluster variations for which CI values have been computed using either PDA [2] or full Computational Fluid Dynamics (CFD). The great value of the NN approach lies in its ability to capture the non-linear relationships between input parameters and corresponding capture indices. The accuracy of the NN approach is 3.8% (Root Mean Square Error) for a set of example scenarios discussed here. Because of the real-time nature of the calculations, the NN approach readily facilitates optimization studies. Example cases are discussed which show the integration of the NN approach and a genetic algorithm used for optimization.

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