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ISSN Online: 2377-424X

ISBN Print: 978-1-56700-474-8

ISBN Online: 978-1-56700-473-1

International Heat Transfer Conference 16
August, 10-15, 2018, Beijing, China

CONTROL-ORIENTED MODELLING AND EVALUATION FOR THE TEMPERATURE DISTRIBUTION IN DATA-CENTERS

Get access (open in a dialog) DOI: 10.1615/IHTC16.nee.022956
pages 7451-7458

Аннотация

Energy efficiency optimization for data centers received wide attention in recent years. In order to optimize the energy consumption of data center facilities via real-time control strategies, it is essential to establish fast and accurate thermal predicting/evaluating models. Existing researches have proposed fast temperature evaluation models (FTEMs) for data centers for steady-state flow pattern. The model parameters therein were identified through CFD simulations. The main drawback of these models is that its accuracy will decrease when the flow field is deviated from its designed state. The parameters corresponding to the new flow field have to be re-identified through a set of CFD simulations. In practice, the flow rates of racks are usually being tuned, with the goal of improving cooling efficiency and saving power. Hence, it is necessary to take different flow patterns into consideration in the control-oriented data center thermal modeling. This paper proposed a machine learning method to improve the FTEM. An artificial neural network (ANN) is constructed on top of the FTEM. It learns the relationship between flow patterns and model parameters, and then it replaces the time-consuming CFD-based parameter identifying process. Then, the temperature evaluation under different flow patterns can be implemented by coordinating the ANN model and the FTEM. The accuracy of the ANN based FTEM is validated by comparing with the pure CFD results. The proposed model can be used to design real-time controllers for data centers with changing flow field.