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Machine learning assisted development of IT equipment compact models for data centers energy planning
Applied Energy ( IF 11.2 ) Pub Date : 2021-09-17 , DOI: 10.1016/j.apenergy.2021.117846
Yaman M. Manaserh 1 , Mohammad I. Tradat 1 , Dana Bani-Hani 2 , Aseel Alfallah 2 , Bahgat G. Sammakia 1 , Kourosh Nemati 3 , Mark J. Seymour 3
Affiliation  

In most data centers, performance reliability is often ensured by setting the amount of airflow provided by the cooling units to substantially exceed that which is needed by the IT equipment. This overly conservative strategy requires additional energy expenditure, which inevitably results in a huge amount of energy being wasted by the cooling system. To eliminate adopting such wasteful policies, conducting proper management of airflow, temperature, and energy is critical. To that end, this work proposes a novel approach to developing a compact IT equipment model at off-design conditions. This model is designed to support thermal and energy management functions in data centers. The benefit of this model is that it can accurately predict not only the IT equipment power consumption, but also the amount of flowrate required for the equipment and the air temperature leaving the equipment. While the compact model’s power consumption was derived as a function of CPU utilization, its flowrate demand and exhaust temperature were obtained from a dynamic detailed CFD model. Results from the compact model were validated with experiments where the maximum mismatch was found to be 5.7% in the outlet temperature field and 11.4% in flowrate. Compared to a state-of-the-art IT equipment compact model, the developed model was found to reduce the prediction error of the equipment’s flowrate and outlet air temperature by up to 5.2% and 9.3 % that of the state-of-the-art IT equipment compact model, respectively.



中文翻译:

机器学习辅助开发用于数据中心能源规划的 IT 设备紧凑模型

在大多数数据中心,性能可靠性通常是通过将冷却单元提供的气流量设置为大大超过 IT 设备所需的量来确保的。这种过于保守的策略需要额外的能源消耗,这不可避免地导致冷却系统浪费了大量的能源。为了避免采用这种浪费的政策,对气流、温度和能源进行适当的管理至关重要。为此,这项工作提出了一种在非设计条件下开发紧凑型 IT 设备模型的新方法。该模型旨在支持数据中心的热管理和能源管理功能。该模型的好处是不仅可以准确预测 IT 设备功耗,还有设备所需的流量和离开设备的空气温度。虽然紧凑模型的功耗是作为 CPU 利用率的函数推导出来的,但其流量需求和排气温度是从动态详细 CFD 模型中获得的。紧凑模型的结果通过实验进行了验证,发现出口温度场的最大失配为 5.7%,流量为 11.4%。与最先进的IT设备紧凑模型相比,发现开发的模型将设备流量和出口空气温度的预测误差降低了最先进的5.2%和9.3%。艺术 IT 设备紧凑模型,分别。它的流量需求和排气温度是从动态详细的 CFD 模型中获得的。紧凑模型的结果通过实验进行了验证,发现出口温度场的最大失配为 5.7%,流量为 11.4%。与最先进的IT设备紧凑模型相比,发现开发的模型将设备流量和出口空气温度的预测误差降低了最先进的5.2%和9.3%。艺术 IT 设备紧凑模型,分别。它的流量需求和排气温度是从动态详细的 CFD 模型中获得的。紧凑模型的结果通过实验进行了验证,发现出口温度场的最大失配为 5.7%,流量为 11.4%。与最先进的IT设备紧凑模型相比,发现开发的模型将设备流量和出口空气温度的预测误差降低了最先进的5.2%和9.3%。艺术 IT 设备紧凑模型,分别。

更新日期:2021-09-17
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