Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/82174
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Title: A novel methodology to improve cooling efficiency at data centers
Authors: Cheong, KH
Tang, KJW
Koh, JM
Yu, SCM 
Acharya, UR
Xie, NG
Issue Date: 2019
Source: IEEE access, 9 Oct. 2019, v. 7, p. 153799-153809
Abstract: Data centers are mission-critical infrastructures. There are strict service level requirements and standards imposed on operators and maintainers to ensure reliable run-the-clock operation. In the context of thermal management and data hall environmental control, the formation of hot and cold spots around server cabinets are especially undesirable, and can result in overheating, lifespan reductions, and performance throttling in the former and condensation damage in the latter. In this paper, we present a comprehensive multi-pronged methodology in data center environmental control, comprising computational fluid dynamics (CND) simulation-aided predictive design first-stage approach, and a complementing Internet of Things (IoT) reactive management system that autonomously monitors and regulates fluctuations in thermal parameters. The novel hybrid methodology is demonstrated on various test scenarios derived from real-world context, and prototypes of the IoT system have been experimentally validated. The approach is shown to be efficient in eliminating unfavourable environmental variations and provides an enhanced understanding of common design problems and respective mitigation measures.
Keywords: Optimization
CI-D modelling
Simulation
Cooling
Publisher: Institute of Electrical and Electronics Engineers
Journal: IEEE access 
EISSN: 2169-3536
DOI: 10.1109/ACCESS.2019.2946342
Rights: This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see http://creativecommons.org/licenses/by/4.0/
The following publication K. H. Cheong, K. J. W. Tang, J. M. Koh, S. C. M. Yu, U. R. Acharya and N. Xie, "A Novel Methodology to Improve Cooling Efficiency at Data Centers," in IEEE Access, vol. 7, pp. 153799-153809, 2019 is available at https://dx.doi.org/10.1109/ACCESS.2019.2946342
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