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A Novel AI-Based Thermal Conductivity Predictor in the Insulation Performance Analysis of Signal-Transmissive Wall

Author

Listed:
  • Xiaolei Wang

    (Department of Electrical Engineering and Energy Technology, University of Vaasa, P.O. Box 700, FIN-65101 Vaasa, Finland)

  • Xiaoshu Lü

    (Department of Electrical Engineering and Energy Technology, University of Vaasa, P.O. Box 700, FIN-65101 Vaasa, Finland
    Department of Civil Engineering, Aalto University, P.O. Box 11000, FIN-02150 Espoo, Finland)

  • Lauri Vähä-Savo

    (Department of Electronics and Nanoengineering, School of Electrical Engineering, Aalto University, P.O. Box 13500, FIN-00076 Espoo, Finland)

  • Katsuyuki Haneda

    (Department of Electronics and Nanoengineering, School of Electrical Engineering, Aalto University, P.O. Box 13500, FIN-00076 Espoo, Finland)

Abstract

It is well known that thermal conductivity measurement is a challenging task, due to the weaknesses of the traditional methods, such as the high cost, complex data analysis, and limitations of sample size. Nowadays, the requirement of quality of life and tightening energy efficiency regulations of buildings promote the demand for new construction materials. However, limited by the size and inhomogeneous structure, the thermal conductivity measurement of wall samples becomes a demanding topic. Additionally, we find the thermal parameter values of the samples measured in the laboratory are different from those obtained by theoretical computation. In this paper, a novel signal-transmissive wall is designed to provide the problem solving of signal connectivity in 5G. We further propose a new thermal conductivity predictor based on the Harmony Search (HS) algorithm to estimate the thermal properties of laboratory-made wall samples. The advantages of our approach over the conventional methods are simplicity and robustness, which can be generalized to a wide range of solid samples in the laboratory measurement.

Suggested Citation

  • Xiaolei Wang & Xiaoshu Lü & Lauri Vähä-Savo & Katsuyuki Haneda, 2023. "A Novel AI-Based Thermal Conductivity Predictor in the Insulation Performance Analysis of Signal-Transmissive Wall," Energies, MDPI, vol. 16(10), pages 1-16, May.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:10:p:4211-:d:1151479
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    References listed on IDEAS

    as
    1. Palacios, Anabel & Cong, Lin & Navarro, M.E. & Ding, Yulong & Barreneche, Camila, 2019. "Thermal conductivity measurement techniques for characterizing thermal energy storage materials – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 108(C), pages 32-52.
    2. Güven, Aykut Fatih & Yörükeren, Nuran & Samy, Mohamed Mahmoud, 2022. "Design optimization of a stand-alone green energy system of university campus based on Jaya-Harmony Search and Ant Colony Optimization algorithms approaches," Energy, Elsevier, vol. 253(C).
    3. Tao Lu & Lauri Vähä-Savo & Xiaoshu Lü & Katsuyuki Haneda, 2023. "Thermal Impact of 5G Antenna Systems in Sandwich Walls," Energies, MDPI, vol. 16(6), pages 1-17, March.
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