Dynamic Reduced Order Thermal Modeling of Data Center Air Temperatures

We developed a Proper Orthogonal Decomposition (POD) based dynamic reduced order model that can predict transient temperature field in an air-cooled data center. A typical data center is modeled as a turbulent convective thermal system with multiple length scales. A representative case study is presented to validate the developed methodology. The model is observed to be capable of predicting the transient air temperature field accurately and rapidly. Comparing with the computational fluid mechanics/heat transfer (CFD/HT) based model, it is revealed that our model is 100x faster without compromising solution accuracy. The developed modeling framework is potentially useful for designing a control system that can regulate flow parameters in a transient data center.