Error estimation in POD-based dynamic reduced-order thermal modeling of data centers

2013 ◽  
Vol 57 (2) ◽  
pp. 698-707 ◽  
Author(s):  
Rajat Ghosh ◽  
Yogendra Joshi
Author(s):  
K. Fouladi ◽  
A. P. Wemhoff ◽  
L. Silva-Llanca ◽  
A. Ortega

Much of the energy use by data centers is attributed to the energy needed to cool the data centers. Thus, improving the cooling efficiency and thermal management of data centers can translate to direct and significant economic benefits. However, data centers are complex systems containing a significant number of components or sub-systems (e.g., servers, fans, pumps, and heat exchangers) that must be considered in any synergistic data center thermal efficiency optimization effort. The Villanova Thermodynamic Analysis of Systems (VTAS) is a flow network tool for performance prediction and design optimization of data centers. VTAS models the thermodynamics, fluid mechanics, and heat transfer inherent to an entire data center system, including contributions by individual servers, the data center airspace, and the HVAC components. VTAS can be employed to identify the optimal cooling strategy among various alternatives by computing the exergy destruction of the overall data center system and the various components in the system for each alternative. Exergy or “available energy” has been used to identify components and wasteful practices that contribute significantly in cooling inefficiency of data centers including room air recirculation — premature mixing of hot and cold air streams in a data center. Flow network models are inadequate in accurately predicting the magnitude of airflow exergy destruction due to simplifying assumptions and the three-dimensional nature of the flow pattern in the room. On the other hand, CFD simulations are time consuming, making them impractical for iterative-based design optimization approaches. In this paper we demonstrate a hybrid strategy, in which a proper orthogonal decomposition (POD) based airflow modeling approach developed from CFD simulation data is implemented in VTAS for predicting the room airflow exergy destruction. The reduced order POD tool in VTAS provides higher accuracy than 1-D flow network models and is computationally more efficient than 3-D CFD simulations. The present VTAS – POD tool has been applied to a data center cell to illustrate the use of exergy destruction minimization as an objective function for data center thermal efficiency optimization.


2014 ◽  
pp. 97-154
Author(s):  
Bahgat Sammakia ◽  
Yogendra Joshi ◽  
Dereje Agonafer ◽  
Emad Samadiani ◽  
Avram Bar-Cohen

2012 ◽  
Vol 134 (4) ◽  
Author(s):  
Emad Samadiani ◽  
Yogendra Joshi ◽  
Hendrik Hamann ◽  
Madhusudan K. Iyengar ◽  
Steven Kamalsy ◽  
...  

In this paper, an effective and computationally efficient proper orthogonal decomposition (POD) based reduced order modeling approach is presented, which utilizes selected sets of observed thermal sensor data inside the data centers to help predict the data center temperature field as a function of the air flow rates of computer room air conditioning (CRAC) units. The approach is demonstrated through application to an operational data center of 102.2 m2 (1100 square feet) with a hot and cold aisle arrangement of racks cooled by one CRAC unit. While the thermal data throughout the facility can be collected in about 30 min using a 3D temperature mapping tool, the POD method is able to generate temperature field throughout the data center in less than 2 s on a high end desktop personal computer (PC). Comparing the obtained POD temperature fields with the experimentally measured data for two different values of CRAC flow rates shows that the method can predict the temperature field with the average error of 0.68 °C or 3.2%. The maximum local error is around 8 °C, but the total number of points where the local error is larger than 1 °C, is only ∼6% of the total domain points.


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