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Experimental and Numerical Study of the Ice Storage Process and Material Properties of Ice Storage Coils

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  • Xiaoyu Xu

    (College of Energy and Power Engineering, Inner Mongolia University of Technology, Hohhot 010051, China
    Institute of Electrical Engineering, Chinese Academy of Sciences, Haidian District, Beijing 100190, China)

  • Chun Chang

    (College of Energy and Power Engineering, Inner Mongolia University of Technology, Hohhot 010051, China
    Institute of Electrical Engineering, Chinese Academy of Sciences, Haidian District, Beijing 100190, China
    University of Chinese Academy of Sciences, Beijing 100049, China
    Engineering Management Department, Inner Mongolia Honder College of Arts and Sciences, Hohhot 010051, China)

  • Xinxin Guo

    (Institute of Electrical Engineering, Chinese Academy of Sciences, Haidian District, Beijing 100190, China
    University of Chinese Academy of Sciences, Beijing 100049, China)

  • Mingzhi Zhao

    (College of Energy and Power Engineering, Inner Mongolia University of Technology, Hohhot 010051, China)

Abstract

The coiled ice-storage-based air conditioning system plays a significant role in enhancing grid peak regulation and improving cooling economy. This paper presents theoretical and experimental studies conducted on the ice storage process of coiled ice storage air conditioning technology. The cooling of water is divided into two stages:10.0 °C to 4.0 °C and 4.0 °C to below 0.0 °C. Initially, the ice storage process forms an ice layer with a thickness of 2.50 mm on the lower surface of the coil, but eventually, the ice layer on the upper surface becomes 3.85 mm thicker than the lower surface as a result of the natural convection of water and density reversal at 4.0 °C. Furthermore, the impact of three coils with different thermal conductivity on the ice storage process was evaluated. It was observed that the thermal conductivity of R-HDPE (reinforced high-density polyethylene) was only 2.6 W/(m·K) higher than HDPE (high-density polyethylene), yet it reduced the freezing time by 34.85%, while the thermal conductivity of steel was 37.4 W/(m·K) higher than R-HDPE, but only decreased the freezing time by 9.40%. The results demonstrated that the rate of ice accumulation increased with thermal conductivity. However, when the coil material’s thermal conductivity surpassed that of ice, the further increase of thermal conductivity gradually weakened its impact on the ice storage rate.

Suggested Citation

  • Xiaoyu Xu & Chun Chang & Xinxin Guo & Mingzhi Zhao, 2023. "Experimental and Numerical Study of the Ice Storage Process and Material Properties of Ice Storage Coils," Energies, MDPI, vol. 16(14), pages 1-18, July.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:14:p:5511-:d:1198597
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    References listed on IDEAS

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    1. Candanedo, J.A. & Dehkordi, V.R. & Stylianou, M., 2013. "Model-based predictive control of an ice storage device in a building cooling system," Applied Energy, Elsevier, vol. 111(C), pages 1032-1045.
    2. Lu, Yuehong & Wang, Shengwei & Sun, Yongjun & Yan, Chengchu, 2015. "Optimal scheduling of buildings with energy generation and thermal energy storage under dynamic electricity pricing using mixed-integer nonlinear programming," Applied Energy, Elsevier, vol. 147(C), pages 49-58.
    3. Zeng, Yuan & Zhang, Ruiwen & Wang, Dong & Mu, Yunfei & Jia, Hongjie, 2019. "A regional power grid operation and planning method considering renewable energy generation and load control," Applied Energy, Elsevier, vol. 237(C), pages 304-313.
    4. Tay, N.H.S. & Bruno, F. & Belusko, M., 2013. "Comparison of pinned and finned tubes in a phase change thermal energy storage system using CFD," Applied Energy, Elsevier, vol. 104(C), pages 79-86.
    5. Cui, Borui & Gao, Dian-ce & Xiao, Fu & Wang, Shengwei, 2017. "Model-based optimal design of active cool thermal energy storage for maximal life-cycle cost saving from demand management in commercial buildings," Applied Energy, Elsevier, vol. 201(C), pages 382-396.
    6. Jannesari, Hamid & Abdollahi, Naeim, 2017. "Experimental and numerical study of thin ring and annular fin effects on improving the ice formation in ice-on-coil thermal storage systems," Applied Energy, Elsevier, vol. 189(C), pages 369-384.
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    Cited by:

    1. Vladimir Lebedev & Andrey Deev & Konstantin Deev, 2024. "Method for Calculating Heat Transfer in a Heat Accumulator Using a Phase Change Material with Intensification Due to Longitudinal Fins," Energies, MDPI, vol. 17(21), pages 1-41, October.
    2. Chang, Chun & Xu, Xiaoyu & Guo, Xinxin & Yu, Rong & Rasakhodzhaev, Bakhramzhan & Bao, Daorina & Zhao, Mingzhi, 2024. "Experimental and numerical study during the solidification process of a vertical and horizontal coiled ice storage system," Energy, Elsevier, vol. 298(C).

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