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Optimization of Nano-Additive Characteristics to Improve the Efficiency of a Shell and Tube Thermal Energy Storage System Using a Hybrid Procedure: DOE, ANN, MCDM, MOO, and CFD Modeling

Author

Listed:
  • Mohammed Algarni

    (Mechanical Engineering Department, Faculty of Engineering in Rabigh, King Abdulaziz University, Rabigh 21911, Saudi Arabia)

  • Mashhour A. Alazwari

    (Mechanical Engineering Department, Faculty of Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia)

  • Mohammad Reza Safaei

    (Department of Mechanical Engineering, Florida International University, Miami, FL 33174, USA
    Department of Medical Research, China Medical University Hospital, China Medical University, Taichung 40402, Taiwan)

Abstract

Using nano-enhanced phase change material (NePCM) rather than pure PCM significantly affects the melting/solidification duration and the stored energy, which are two critical design parameters for latent heat thermal energy storage (LHTES) systems. The present article employs a hybrid procedure based on the design of experiments (DOE), computational fluid dynamics (CFD), artificial neural networks (ANNs), multi-objective optimization (MOO), and multi-criteria decision making (MCDM) to optimize the properties of nano-additives dispersed in a shell and tube LHTES system containing paraffin wax as a phase change material (PCM). Four important properties of nano-additives were considered as optimization variables: volume fraction and thermophysical properties, precisely, specific heat, density, and thermal conductivity. The primary objective was to simultaneously reduce the melting duration and increase the total stored energy. To this end, a five-step hybrid optimization process is presented in this paper. In the first step, the DOE technique is used to design the required simulations for the optimal search of the design space. The second step simulates the melting process through a CFD approach. The third step, which utilizes ANNs, presents polynomial models for objective functions in terms of optimization variables. MOO is used in the fourth step to generate a set of optimal Pareto points. Finally, in the fifth step, selected optimal points with various features are provided using various MCDM methods. The results indicate that nearly 97% of the Pareto points in the considered shell and tube LHTES system had a nano-additive thermal conductivity greater than 180 Wm −1 K −1 . Furthermore, the density of nano-additives was observed to be greater than 9950 kgm −3 for approximately 86% of the optimal solutions. Additionally, approximately 95% of optimal points had a nano-additive specific heat of greater than 795 Jkg −1 K −1 .

Suggested Citation

  • Mohammed Algarni & Mashhour A. Alazwari & Mohammad Reza Safaei, 2021. "Optimization of Nano-Additive Characteristics to Improve the Efficiency of a Shell and Tube Thermal Energy Storage System Using a Hybrid Procedure: DOE, ANN, MCDM, MOO, and CFD Modeling," Mathematics, MDPI, vol. 9(24), pages 1-30, December.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:24:p:3235-:d:702113
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    References listed on IDEAS

    as
    1. Parsazadeh, Mohammad & Duan, Xili, 2018. "Numerical study on the effects of fins and nanoparticles in a shell and tube phase change thermal energy storage unit," Applied Energy, Elsevier, vol. 216(C), pages 142-156.
    2. Peng, Yeping & Parsian, Amir & Khodadadi, Hossein & Akbari, Mohammad & Ghani, Kamal & Goodarzi, Marjan & Bach, Quang-Vu, 2020. "Develop optimal network topology of artificial neural network (AONN) to predict the hybrid nanofluids thermal conductivity according to the empirical data of Al2O3 – Cu nanoparticles dispersed in ethy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 549(C).
    3. Wang, Kangning & Li, Shaomin, 2021. "Robust distributed modal regression for massive data," Computational Statistics & Data Analysis, Elsevier, vol. 160(C).
    4. Savvakis, Nikolaos & Tsoutsos, Theocharis, 2021. "Theoretical design and experimental evaluation of a PV+PCM system in the mediterranean climate," Energy, Elsevier, vol. 220(C).
    5. Peng, Yeping & Khaled, Usama & Al-Rashed, Abdullah A.A.A. & Meer, Rashid & Goodarzi, Marjan & Sarafraz, M.M., 2020. "Potential application of Response Surface Methodology (RSM) for the prediction and optimization of thermal conductivity of aqueous CuO (II) nanofluid: A statistical approach and experimental validatio," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 554(C).
    6. Ghasemi, Ali & Hassani, Mohsen & Goodarzi, Marjan & Afrand, Masoud & Manafi, Sahebali, 2019. "Appraising influence of COOH-MWCNTs on thermal conductivity of antifreeze using curve fitting and neural network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 514(C), pages 36-45.
    7. Mahdi, Jasim M. & Nsofor, Emmanuel C., 2017. "Melting enhancement in triplex-tube latent heat energy storage system using nanoparticles-metal foam combination," Applied Energy, Elsevier, vol. 191(C), pages 22-34.
    8. Kenzhekhanov, Sultan & Memon, Shazim Ali & Adilkhanova, Indira, 2020. "Quantitative evaluation of thermal performance and energy saving potential of the building integrated with PCM in a subarctic climate," Energy, Elsevier, vol. 192(C).
    9. Karimipour, Arash & Bagherzadeh, Seyed Amin & Taghipour, Abdolmajid & Abdollahi, Ali & Safaei, Mohammad Reza, 2019. "A novel nonlinear regression model of SVR as a substitute for ANN to predict conductivity of MWCNT-CuO/water hybrid nanofluid based on empirical data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 521(C), pages 89-97.
    10. Wang, Qingqing & Zhou, Dan & Chen, Yuming & Eames, Philip & Wu, Zhigen, 2020. "Characterization and effects of thermal cycling on the properties of paraffin/expanded graphite composites," Renewable Energy, Elsevier, vol. 147(P1), pages 1131-1138.
    11. Gil, Antoni & Oró, Eduard & Peiró, Gerard & Álvarez, Servando & Cabeza, Luisa F., 2013. "Material selection and testing for thermal energy storage in solar cooling," Renewable Energy, Elsevier, vol. 57(C), pages 366-371.
    12. Chen, Guijun & Su, Yunpeng & Jiang, Dongyue & Pan, Lujun & Li, Shuai, 2020. "An experimental and numerical investigation on a paraffin wax/graphene oxide/carbon nanotubes composite material for solar thermal storage applications," Applied Energy, Elsevier, vol. 264(C).
    13. Ioan Sarbu & Calin Sebarchievici, 2018. "A Comprehensive Review of Thermal Energy Storage," Sustainability, MDPI, vol. 10(1), pages 1-32, January.
    14. Shaikh, Mahad & Uzair, Muhammad & Allauddin, Usman, 2021. "Effect of geometric configurations on charging time of latent-heat storage for solar applications," Renewable Energy, Elsevier, vol. 179(C), pages 262-271.
    15. Bahrami, Mehrdad & Akbari, Mohammad & Bagherzadeh, Seyed Amin & Karimipour, Arash & Afrand, Masoud & Goodarzi, Marjan, 2019. "Develop 24 dissimilar ANNs by suitable architectures & training algorithms via sensitivity analysis to better statistical presentation: Measure MSEs between targets & ANN for Fe–CuO/Eg–Water nanofluid," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 519(C), pages 159-168.
    16. Bagherzadeh, Seyed Amin & D’Orazio, Annunziata & Karimipour, Arash & Goodarzi, Marjan & Bach, Quang-Vu, 2019. "A novel sensitivity analysis model of EANN for F-MWCNTs–Fe3O4/EG nanofluid thermal conductivity: Outputs predicted analytically instead of numerically to more accuracy and less costs," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 521(C), pages 406-415.
    17. Lai, Young-Jou & Liu, Ting-Yun & Hwang, Ching-Lai, 1994. "TOPSIS for MODM," European Journal of Operational Research, Elsevier, vol. 76(3), pages 486-500, August.
    18. Kutlu, Cagri & Zhang, Yanan & Elmer, Theo & Su, Yuehong & Riffat, Saffa, 2020. "A simulation study on performance improvement of solar assisted heat pump hot water system by novel controllable crystallization of supercooled PCMs," Renewable Energy, Elsevier, vol. 152(C), pages 601-612.
    19. Opricovic, Serafim & Tzeng, Gwo-Hshiung, 2004. "Compromise solution by MCDM methods: A comparative analysis of VIKOR and TOPSIS," European Journal of Operational Research, Elsevier, vol. 156(2), pages 445-455, July.
    20. Sodhi, Gurpreet Singh & Muthukumar, P., 2021. "Compound charging and discharging enhancement in multi-PCM system using non-uniform fin distribution," Renewable Energy, Elsevier, vol. 171(C), pages 299-314.
    21. M. Mofijur & Teuku Meurah Indra Mahlia & Arridina Susan Silitonga & Hwai Chyuan Ong & Mahyar Silakhori & Muhammad Heikal Hasan & Nandy Putra & S.M. Ashrafur Rahman, 2019. "Phase Change Materials (PCM) for Solar Energy Usages and Storage: An Overview," Energies, MDPI, vol. 12(16), pages 1-20, August.
    22. Mahdi, Jasim M. & Nsofor, Emmanuel C., 2017. "Solidification enhancement in a triplex-tube latent heat energy storage system using nanoparticles-metal foam combination," Energy, Elsevier, vol. 126(C), pages 501-512.
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