Multi-objective optimization investigation on the loop thermosyphon system by Taguchi-grey method under fan failure conditions

IF 6.1 2区工程技术 Q2 ENERGY & FUELS

Applied Thermal Engineering Pub Date : 2025-06-25 DOI:10.1016/j.applthermaleng.2025.127294

Sikai Zou, Ting Xiao, Yanjin Wang, Jiahao Zhang, Jianliang Huang

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引用次数: 0

Abstract

Fan failure in loop thermosyphon systems (LTS) is a major factor that increases the thermal risk in data centers. To reduce this risk, an optimization framework for a LTS based on the Taguchi-grey relational analysis method is proposed to enhance its performance under fan failure conditions. To accurately predict the heat transfer performance, a one-dimensional steady-state model is established. Leveraging the Taguchi method, the effects of flat tube height (H_to), fin thickness (δ_f), fin spacing (P_f), fin height (H_f), number of micro-channels (N_m), and collector tube diameter (D_j) on the energy efficiency ratio (EER), anti-failure performance index (API), and economic efficiency index (EEI) are comprehensively analyzed. The results indicate that for EER, the H_f and H_to are key design factors, accounting for 26.1% and 25.5%, respectively. In terms of API, the H_f and P_f dominate, comprising 39.2% and 37.6%. For EEI, the H_f and H_to are the main contributors, with contributions of 29.8% and 20.6%, respectively. Comprehensive analysis reveals that H_f is the most influential factor on LTS performance, and reducing H_f helps simultaneously improve EER, API, and EEI. In addition, a multi-objective optimization based on grey relational analysis is used to optimize the anti-failure performance and the economic efficiency of the loop thermosyphon system. After multi-objective optimization, the EER, API, and EEI are increased by 28.6%, 96.6%, and 25.4%, respectively.

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风机失效条件下循环热虹吸系统的多目标优化研究

循环热虹吸系统（LTS）中的风扇故障是增加数据中心热风险的主要因素。为了降低这种风险，提出了一种基于田口灰关联分析方法的LTS优化框架，以提高其在风机失效条件下的性能。为了准确地预测传热性能，建立了一维稳态模型。利用Taguchi方法，综合分析了平板管高度（Hto）、翅片厚度（δf）、翅片间距（Pf）、翅片高度（Hf）、微通道数（Nm）和集热器管直径（Dj）对能效比（EER）、抗失效性能指数（API）和经济效率指数（EEI）的影响。结果表明，对于EER， Hf和Hto是关键设计因素，分别占26.1%和25.5%。在API方面，Hf和Pf占主导地位，分别占39.2%和37.6%。对于EEI， Hf和Hto是主要贡献者，分别贡献了29.8%和20.6%。综合分析发现，Hf是影响LTS性能的最大因素，降低Hf有助于同时提高EER、API和EEI。此外，采用基于灰色关联分析的多目标优化方法对循环热虹吸系统的抗故障性能和经济效益进行了优化。多目标优化后，EER、API和EEI分别提高28.6%、96.6%和25.4%。

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来源期刊

Applied Thermal Engineering 工程技术-工程：机械

CiteScore

11.30

自引率

15.60%

发文量

1474

审稿时长

57 days

期刊介绍： Applied Thermal Engineering disseminates novel research related to the design, development and demonstration of components, devices, equipment, technologies and systems involving thermal processes for the production, storage, utilization and conservation of energy, with a focus on engineering application. The journal publishes high-quality and high-impact Original Research Articles, Review Articles, Short Communications and Letters to the Editor on cutting-edge innovations in research, and recent advances or issues of interest to the thermal engineering community.