Multi-objective optimization of temperature distributions using Artificial Neural Networks

Modeling the thermal environment of data centers, including prediction of the air flow and temperature distributions can be computationally intensive using CFD. Reduced order models or data-driven meta-models are necessary to provide real-time assessment of optimum operating conditions for data centers to reduce energy usage. Here, a simulation-based Artificial Neural Network (ANN) approach is employed as a predictive tool. A model for a basic single cold aisle data center configuration is analyzed using the commercial CFD software FloTHERM. The simulation results are used to generate a database for training and cross validation of a primary ANN corresponding to a specific set of input and output operating conditions. Good agreement is achieved between the CFD and ANN based model predictions for maximum rack inlet temperatures over a range of operating conditions. In addition, by combining the ANN with a cost function based Multi-Objective Genetic Algorithm (MOGA), the operating conditions can be inversely predicted for desired outputs (e.g. rack inlet temperatures). The total simulation time for the ANN-MOGA approach is reduced significantly compared to a fully CFD-based optimization methodology.