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Journal of Enhanced Heat Transfer

Published 8 issues per year

ISSN Print: 1065-5131

ISSN Online: 1563-5074

The Impact Factor measures the average number of citations received in a particular year by papers published in the journal during the two preceding years. 2017 Journal Citation Reports (Clarivate Analytics, 2018) IF: 2.3 To calculate the five year Impact Factor, citations are counted in 2017 to the previous five years and divided by the source items published in the previous five years. 2017 Journal Citation Reports (Clarivate Analytics, 2018) 5-Year IF: 1.8 The Immediacy Index is the average number of times an article is cited in the year it is published. The journal Immediacy Index indicates how quickly articles in a journal are cited. Immediacy Index: 0.2 The Eigenfactor score, developed by Jevin West and Carl Bergstrom at the University of Washington, is a rating of the total importance of a scientific journal. Journals are rated according to the number of incoming citations, with citations from highly ranked journals weighted to make a larger contribution to the eigenfactor than those from poorly ranked journals. Eigenfactor: 0.00037 The Journal Citation Indicator (JCI) is a single measurement of the field-normalized citation impact of journals in the Web of Science Core Collection across disciplines. The key words here are that the metric is normalized and cross-disciplinary. JCI: 0.6 SJR: 0.433 SNIP: 0.593 CiteScore™:: 4.3 H-Index: 35

Indexed in

ENERGY EFFICIENT THERMAL MANAGEMENT OF DATA CENTERS VIA OPEN MULTI-SCALE DESIGN: A REVIEW OF RESEARCH QUESTIONS AND APPROACHES

Volume 18, Issue 1, 2011, pp. 15-30
DOI: 10.1615/JEnhHeatTransf.v18.i1.20
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ABSTRACT

Currently, the airflow and heat transfer characteristics of data centers are simulated through computational fluid dynamics and heat transfer (CFD/HT) modeling. The degrees of freedom of these models are too large to be efficiently used with methodologies for effective design around the significant parameters of interest. Effective modeling and design of a data center is complicated due to its multi-scale nature, where length scales span four orders of magnitude from the room and plenum to the rack, to the server, and to the chip levels. Multi-scale resolution in simulation and design methodologies is important because the system design objectives (e.g., keeping the chip temperature in a desirable range) and the heat generation sources are located at the chip scale, yet the design solution (cold air from cooling units) spans the plenum and room scales. Using a complete CFD/HT model involving all of these length scales is infeasible for design. This paper begins with a review of the recent studies on energy usage trends and the need for energy efficient thermal management of data centers. Reduced order modeling of the thermal/fluid phenomena, by coupling the transport at different scales of the data center, is reviewed as a method that can be easily incorporated in design methodologies to find the optimum design variables involved at all scales. The energy-efficient design of data centers is a multi-objective problem. This design problem and possible multi-objective methodologies are reviewed. Incorporation of the coupled multi-scale model within established multi-objective design frameworks potentially allows for energy efficient open or scalable design of new facilities as well as retro-fits of current ones. These concepts are illustrated with selected examples.

CITED BY
  1. Ebrahimi Khosrow, Jones Gerard F., Fleischer Amy S., A review of data center cooling technology, operating conditions and the corresponding low-grade waste heat recovery opportunities, Renewable and Sustainable Energy Reviews, 31, 2014. Crossref

  2. Bhalerao Anish, Fouladi K., Silva-Llanca Luis, Wemhoff Aaron P., Rapid prediction of exergy destruction in data centers due to airflow mixing, Numerical Heat Transfer, Part A: Applications, 70, 1, 2016. Crossref

  3. Chu Wen-Xiao, Hsu Chang-Sheng, Tsui Yeng-Yung, Wang Chi-Chuan, Experimental investigation on thermal management for small container data center, Journal of Building Engineering, 21, 2019. Crossref

  4. Huang Qionghai, Shao Shuangquan, Zhang Hainan, Tian Changqing, Development and composition of a data center heat recovery system and evaluation of annual operation performance, Energy, 189, 2019. Crossref

  5. Chu Wen-Xiao, Wang Chi-Chuan, A review on airflow management in data centers, Applied Energy, 240, 2019. Crossref

  6. Gong Xiaoming, Zhang Zhongbin, Gan Sixuan, Niu Baolian, Yang Liu, Xu Haijin, Gao Manfang, A review on evaluation metrics of thermal performance in data centers, Building and Environment, 177, 2020. Crossref

  7. Amiri Leyla, Madadian Edris, Bahrani Navid, Ghoreishi-Madiseh Seyed Ali, Techno-Economic Analysis of Waste Heat Utilization in Data Centers: Application of Absorption Chiller Systems, Energies, 14, 9, 2021. Crossref

  8. Ahmed Kazi Main Uddin, Bollen Math H. J., Alvarez Manuel, A Review of Data Centers Energy Consumption and Reliability Modeling, IEEE Access, 9, 2021. Crossref

  9. Chu Wen-Xiao, Lee Po-Heng, Tsui Yeng-Yung, Wang Chi-Chuan, Performance analysis of a simulated container data center subject to airflow resistance, Energy Efficiency, 14, 2, 2021. Crossref

  10. Koposova E. N., The Correlation Between External and Internal Factors of the Libyan Crisis, MGIMO Review of International Relations, 5(44), 2015. Crossref

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