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首页> 外文期刊>Journal of Heat Transfer >Proper Orthogonal Decomposition for Reduced Order Thermal Modeling of Air Cooled Data Centers
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Proper Orthogonal Decomposition for Reduced Order Thermal Modeling of Air Cooled Data Centers

机译：适当正交分解以降低风冷数据中心的阶次热模型

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摘要

Computational fluid dynamics/heat transfer (CFD/HT) methods are too time consuming and costly to examine the effect of multiple design variables on the system thermal performance, especially for systems with multiple components and interacting physical phenomena. In this paper, a proper orthogonal decomposition (POD) based reduced order thermal modeling approach is presented for complex convective systems. The basic POD technique is used with energy balance equations, and heat flux and/or surface temperature matching to generate a computationally efficient thermal model in terms of the system design variables. The effectiveness of the presented approach is studied through application to an air-cooled data center cell with a floor area of 23.2 m~2 and a total power dissipation of 240 kW, with multiple turbulent convective components and five design variables. The method results in average temperature rise prediction error of 1.24℃ (4.9%) for different sets of design variables, while it is ～150 times faster than CFD/HT simulation. Also, the effects of the number of available algebraic equations and retained POD modes on the accuracy of the obtained thermal field are studied.
机译：计算流体动力学/热传递（CFD / HT）方法过于耗时且成本高昂，无法检查多个设计变量对系统热性能的影响，尤其是对于具有多个组件和相互作用的物理现象的系统而言。本文针对复杂的对流系统，提出了一种基于正交分解的POD降阶热建模方法。基本的POD技术与能量平衡方程式一起使用，并且热通量和/或表面温度匹配可根据系统设计变量生成计算效率高的热模型。通过将其应用于面积为23.2 m〜2，总功耗为240 kW，具有多个湍流对流组件和五个设计变量的风冷数据中心单元，研究了该方法的有效性。该方法得出的不同设计变量集的平均温升预测误差为1.24℃（4.9％），比CFD / HT仿真快约150倍。另外，研究了可用代数方程和保留的POD模式对获得的热场精度的影响。

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