IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v12y2019i8p1473-d224066.html
   My bibliography  Save this article

Comparing Performance Metrics of Partial Aisle Containments in Hard Floor and Raised Floor Data Centers Using CFD

Author

Listed:
  • Emelie Wibron

    (Division of Fluid and Experimental Mechanics, Luleå University of Technology, SE-971 87 Luleå, Sweden)

  • Anna-Lena Ljung

    (Division of Fluid and Experimental Mechanics, Luleå University of Technology, SE-971 87 Luleå, Sweden)

  • T. Staffan Lundström

    (Division of Fluid and Experimental Mechanics, Luleå University of Technology, SE-971 87 Luleå, Sweden)

Abstract

In data centers, efficient cooling systems are required to both keep the energy consumption as low as possible and to fulfill the temperature requirements. The aim of this work is to numerically investigate the effects of using partial aisle containment between the server racks for hard and raised floor configurations. The computational fluid dynamics (CFD) software ANSYS CFX was used together with the Reynolds stress turbulence model to perform the simulations. Velocity measurements in a server room were used for validation. Boundary conditions and the load of each rack were also retrieved from the experimental facility, implying an uneven load between the racks. A combination of the performance metrics Rack Cooling Index (RCI), Return Temperature Index (RTI) and Capture Index (CI) were used to evaluate the performance of the cooling systems for two supply flow rates at a 100% and 50% of operating condition. Based on the combination of performance metrics, the airflow management was improved in the raised floor configurations. With the supply flow rate set to operating conditions, the RCI was 100% for both raised floor and hard floor setups. The top- or side-cover fully prevented recirculation for the raised floor configuration, while it reduced the recirculation for the hard floor configuration. However, the RTI was low, close to 40% in the hard floor case, indicating poor energy efficiency. With the supply flow rate decreasing with 50%, the RTI increased to above 80%. Recirculation of hot air was indicated for all the containments when the supply rate was 50%, but the values of RCI still indicated an acceptable performance of the cooling system.

Suggested Citation

  • 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.
  • Handle: RePEc:gam:jeners:v:12:y:2019:i:8:p:1473-:d:224066
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/12/8/1473/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/12/8/1473/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Tatchell-Evans, Morgan & Kapur, Nik & Summers, Jonathan & Thompson, Harvey & Oldham, Dan, 2017. "An experimental and theoretical investigation of the extent of bypass air within data centres employing aisle containment, and its impact on power consumption," Applied Energy, Elsevier, vol. 186(P3), pages 457-469.
    2. Chu, Wen-Xiao & Wang, Chi-Chuan, 2019. "A review on airflow management in data centers," Applied Energy, Elsevier, vol. 240(C), pages 84-119.
    3. Ni, Jiacheng & Bai, Xuelian, 2017. "A review of air conditioning energy performance in data centers," Renewable and Sustainable Energy Reviews, Elsevier, vol. 67(C), pages 625-640.
    4. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Isazadeh, Amin & Ziviani, Davide & Claridge, David E., 2023. "Global trends, performance metrics, and energy reduction measures in datacom facilities," Renewable and Sustainable Energy Reviews, Elsevier, vol. 174(C).
    2. Kosuke Sasakura & Takeshi Aoki & Masayoshi Komatsu & Takeshi Watanabe, 2020. "A Temperature-Risk and Energy-Saving Evaluation Model for Supporting Energy-Saving Measures for Data Center Server Rooms," Energies, MDPI, vol. 13(19), pages 1-22, October.
    3. Naoki Futawatari & Yosuke Udagawa & Taro Mori & Hirofumi Hayama, 2020. "Improving Prediction Accuracy Concerning the Thermal Environment of a Data Center by Using Design of Experiments," Energies, MDPI, vol. 13(18), pages 1-21, September.
    4. Kosuke Sasakura & Takeshi Aoki & Masayoshi Komatsu & Takeshi Watanabe, 2020. "Rack Temperature Prediction Model Using Machine Learning after Stopping Computer Room Air Conditioner in Server Room," Energies, MDPI, vol. 13(17), pages 1-17, August.
    5. Isazadeh, Amin & Ziviani, Davide & Claridge, David E., 2023. "Thermal management in legacy air-cooled data centers: An overview and perspectives," Renewable and Sustainable Energy Reviews, Elsevier, vol. 187(C).
    6. 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).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Chu, Wen-Xiao & Wang, Chi-Chuan, 2019. "A review on airflow management in data centers," Applied Energy, Elsevier, vol. 240(C), pages 84-119.
    2. Manaserh, Yaman M. & Tradat, Mohammad I. & Bani-Hani, Dana & Alfallah, Aseel & Sammakia, Bahgat G. & Nemati, Kourosh & Seymour, Mark J., 2022. "Machine learning assisted development of IT equipment compact models for data centers energy planning," Applied Energy, Elsevier, vol. 305(C).
    3. Leehter Yao & Jin-Hao Huang, 2019. "Multi-Objective Optimization of Energy Saving Control for Air Conditioning System in Data Center," Energies, MDPI, vol. 12(8), pages 1-16, April.
    4. Jin, Chaoqiang & Bai, Xuelian & Yang, Chao & Mao, Wangxin & Xu, Xin, 2020. "A review of power consumption models of servers in data centers," Applied Energy, Elsevier, vol. 265(C).
    5. 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).
    6. 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).
    7. Hu, Zhi-Hua & Zheng, Yu-Xin & Wang, You-Gan, 2022. "Packing computing servers into the vessel of an underwater data center considering cooling efficiency," Applied Energy, Elsevier, vol. 314(C).
    8. Cho, Jinkyun & Kim, Youngmo, 2021. "Development of modular air containment system: Thermal performance optimization of row-based cooling for high-density data centers," Energy, Elsevier, vol. 231(C).
    9. Jinkyun Cho & Jesang Woo & Beungyong Park & Taesub Lim, 2020. "A Comparative CFD Study of Two Air Distribution Systems with Hot Aisle Containment in High-Density Data Centers," Energies, MDPI, vol. 13(22), pages 1-19, November.
    10. Xia, Guanghui & Zhuang, Dawei & Ding, Guoliang & Lu, Jingchao, 2020. "A quasi-three-dimensional distributed parameter model of micro-channel separated heat pipe applied for cooling telecommunication cabinets," Applied Energy, Elsevier, vol. 276(C).
    11. Wang, Fengjuan & Lv, Chengwei & Xu, Jiuping, 2023. "Carbon awareness oriented data center location and configuration: An integrated optimization method," Energy, Elsevier, vol. 278(C).
    12. Shunling Ruan & Haiyan Xie & Song Jiang, 2017. "Integrated Proactive Control Model for Energy Efficiency Processes in Facilities Management: Applying Dynamic Exponential Smoothing Optimization," Sustainability, MDPI, vol. 9(9), pages 1-22, September.
    13. Cheng Liu & Hang Yu, 2021. "Evaluation and Optimization of a Two-Phase Liquid-Immersion Cooling System for Data Centers," Energies, MDPI, vol. 14(5), pages 1-21, March.
    14. Silva-Llanca, Luis & Ortega, Alfonso & Fouladi, Kamran & del Valle, Marcelo & Sundaralingam, Vikneshan, 2018. "Determining wasted energy in the airside of a perimeter-cooled data center via direct computation of the Exergy Destruction," Applied Energy, Elsevier, vol. 213(C), pages 235-246.
    15. Borkowski, Mateusz & Piłat, Adam Krzysztof, 2022. "Customized data center cooling system operating at significant outdoor temperature fluctuations," Applied Energy, Elsevier, vol. 306(PB).
    16. Boza, Pal & Evgeniou, Theodoros, 2021. "Artificial intelligence to support the integration of variable renewable energy sources to the power system," Applied Energy, Elsevier, vol. 290(C).
    17. Han, Zongwei & Wei, Haotian & Sun, Xiaoqing & Bai, Chenguang & Xue, Da & Li, Xiuming, 2020. "Study on influence of operating parameters of data center air conditioning system based on the concept of on-demand cooling," Renewable Energy, Elsevier, vol. 160(C), pages 99-111.
    18. Jing Ni & Bowen Jin & Bo Zhang & Xiaowei Wang, 2017. "Simulation of Thermal Distribution and Airflow for Efficient Energy Consumption in a Small Data Centers," Sustainability, MDPI, vol. 9(4), pages 1-16, April.
    19. Jerez Monsalves, Juan & Bergaentzlé, Claire & Keles, Dogan, 2023. "Impacts of flexible-cooling and waste-heat recovery from data centres on energy systems: A Danish case study," Energy, Elsevier, vol. 281(C).
    20. Yeliang Qiu & Congfeng Jiang & Yumei Wang & Dongyang Ou & Youhuizi Li & Jian Wan, 2019. "Energy Aware Virtual Machine Scheduling in Data Centers," Energies, MDPI, vol. 12(4), pages 1-21, February.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:12:y:2019:i:8:p:1473-:d:224066. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.