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Dynamic thermal environment management technologies for data center: A review

Author

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  • Du, Yahui
  • Zhou, Zhihua
  • Yang, Xiaochen
  • Yang, Xueqing
  • Wang, Cheng
  • Liu, Junwei
  • Yuan, Jianjuan

Abstract

The energy demand of the data center (DC) industry has accounted for 2% of the total global energy consumption, and its operating power consumption has reached 50 times that of the commercial buildings. Therefore, improving the thermal environment performance to reduce the energy cost is major trend to realize the energy savings of DC. It has become a positive measure to integrate smart technologies to improve the operating efficiency of HVAC systems, especially due to the development of informatization and intelligence. This study provides detailed overview of the dynamic thermal environment management technologies of the DCs. The requirements of thermal environment in DCs were reviewed by different levels, which are the basis of the design and operation. Accordingly, various dynamic prediction technologies were presented concerning the theoretical mechanics, the computation efficiency and the applicable circumstances. The performance of the PID, the model predictive control and the reinforcement learning in the dynamic thermal environment control of DC were analyzed. Moreover, the evaluation systems based on cooling capacity, exergy analysis and entransy analysis were introduced respectively to measure the performance of the thermal environment. By investigating the knowledge related to the “requirement, prediction, control, and evaluation” of the DC dynamic thermal environment, this study aims to provide reference and guidance for energy conservation, airflow organization and smart operation of DCs.

Suggested Citation

  • Du, Yahui & Zhou, Zhihua & Yang, Xiaochen & Yang, Xueqing & Wang, Cheng & Liu, Junwei & Yuan, Jianjuan, 2023. "Dynamic thermal environment management technologies for data center: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 187(C).
    • Handle: RePEc:eee:rensus:v:187:y:2023:i:c:s1364032123006184
    DOI: 10.1016/j.rser.2023.113761
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    References listed on IDEAS

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    1. Zhang, Ji & Yuan, Jianjuan & Liu, Junwei & Zhou, Zhihua & Sui, Jiyuan & Xing, Jincheng & Zuo, Jian, 2021. "Cover shields for sub-ambient radiative cooling: A literature review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 143(C).
    2. Zhang, Yunfei & Zhou, Zhihua & Du, Yahui & Shen, Jun & Li, Zhenxing & Yuan, Jianjuan, 2023. "A data transfer method based on one dimensional convolutional neural network for cross-building load prediction," Energy, Elsevier, vol. 277(C).
    3. Fulpagare, Yogesh & Bhargav, Atul, 2015. "Advances in data center thermal management," Renewable and Sustainable Energy Reviews, Elsevier, vol. 43(C), pages 981-996.
    4. Anastasiia Grishina & Marta Chinnici & Ah-Lian Kor & Eric Rondeau & Jean-Philippe Georges, 2020. "A Machine Learning Solution for Data Center Thermal Characteristics Analysis," Energies, MDPI, vol. 13(17), pages 1-13, August.
    5. Meng, Xiongzhuang & Zhou, Junli & Zhang, Xuejiao & Luo, Zhiwen & Gong, Hui & Gan, Ting, 2020. "Optimization of the thermal environment of a small-scale data center in China," Energy, Elsevier, vol. 196(C).
    6. Emelie Wibron & Anna-Lena Ljung & T. Staffan Lundström, 2019. "Comparing Performance Metrics of Partial Aisle Containments in Hard Floor and Raised Floor Data Centers Using CFD," Energies, MDPI, vol. 12(8), pages 1-17, April.
    7. Zhang, Yunfei & Zhou, Zhihua & Liu, Junwei & Yuan, Jianjuan, 2022. "Data augmentation for improving heating load prediction of heating substation based on TimeGAN," Energy, Elsevier, vol. 260(C).
    8. Gupta, Rohit & Asgari, Sahar & Moazamigoodarzi, Hosein & Pal, Souvik & Puri, Ishwar K., 2020. "Cooling architecture selection for air-cooled Data Centers by minimizing exergy destruction," Energy, Elsevier, vol. 201(C).
    9. Moazamigoodarzi, Hosein & Gupta, Rohit & Pal, Souvik & Tsai, Peiying Jennifer & Ghosh, Suvojit & Puri, Ishwar K., 2020. "Modeling temperature distribution and power consumption in IT server enclosures with row-based cooling architectures," Applied Energy, Elsevier, vol. 261(C).
    10. Chu, Wen-Xiao & Wang, Chi-Chuan, 2019. "A review on airflow management in data centers," Applied Energy, Elsevier, vol. 240(C), pages 84-119.
    11. Gupta, Rohit & Asgari, Sahar & Moazamigoodarzi, Hosein & Down, Douglas G. & Puri, Ishwar K., 2021. "Energy, exergy and computing efficiency based data center workload and cooling management," Applied Energy, Elsevier, vol. 299(C).
    12. Zhang, Yingbo & Shan, Kui & Li, Xiuming & Li, Hangxin & Wang, Shengwei, 2023. "Research and Technologies for next-generation high-temperature data centers – State-of-the-arts and future perspectives," Renewable and Sustainable Energy Reviews, Elsevier, vol. 171(C).
    13. Emelie Wibron & Anna-Lena Ljung & T. Staffan Lundström, 2018. "Computational Fluid Dynamics Modeling and Validating Experiments of Airflow in a Data Center," Energies, MDPI, vol. 11(3), pages 1-15, March.
    14. Habibi Khalaj, Ali & Halgamuge, Saman K., 2017. "A Review on efficient thermal management of air- and liquid-cooled data centers: From chip to the cooling system," Applied Energy, Elsevier, vol. 205(C), pages 1165-1188.
    15. Aras Dogan & Sibel Yilmaz & Mustafa Kuzay & Cagatay Yilmaz & Ender Demirel, 2022. "CFD Modeling of Pressure Drop through an OCP Server for Data Center Applications," Energies, MDPI, vol. 15(17), pages 1-17, September.
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