A neural-network enhanced modeling method for real-time evaluation of the temperature distribution in a data center

The thermal predicting/evaluating model of data centers is pivotal in designing their thermal control systems. The existing modeling methods are based on the computational fluid dynamics (CFD) simulations, which is accurate in modeling for a steady-state flow pattern but considerably time-consuming. Besides, the corresponding parameters of CFD have to be re-identified with the deviation of the flow field, which makes it extremely inefficient in real-time thermal control system design of data centers. This paper proposed a machine learning method to derive the fast-temperature evaluation model with a constructed artificial neural network. It learns the relationship between the flow patterns and model parameters based on the system thermal-physical analysis, which replaces the time-consuming CFD-based parameter identifying process. Then, the temperature evaluation is implemented under different flow patterns with the proposed neural-network enhanced modeling method. In the learning process, multi-type of neural networks, i.e., backpropagation network, radial basis function network and extreme learning machine, are considered and compared. The accuracy of the proposed model is validated by comparing with the pure CFD results as the satisfactory standard. With the efficiency and accuracy, the proposed modeling method is more suitable to design real-time controllers for data centers with changing flow fields.