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Thermoelectric generation in bifurcating channels and efficient modeling by using hybrid CFD and artificial neural networks

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  • Selimefendigil, Fatih
  • Öztop, Hakan F.

Abstract

Thermoelectric power generation within TEG mounted branching channels is considered with finite element method. In the heat transfer fluid of bifurcating channels, nanodiamond + Fe3O4 binary particles are used for further system performance improvement. It was observed that when compared to non-bifurcating channels, TEG power will be reduced with the use of branching channels while branching location also affects the interface temperature variations. At (Re1, Re2)=(1000, 200), TEG power is reduced 34.7% when both channels are branching while it is 9.9% for only upper channel branching case as compared to non-branching channel case. Up to 18% variation of power is obtained when location of the upper branching channel varies. Highest powers are achieved when both channels are filled with hybrid nanofluid while at (Re1,Re2)=(1000,200) TEG power rises by about 33% and 15.5% with nanofluid in both channels and with nanofluid in only one channel cases when compared to fluid in both channel configuration. The computational cost of electric potential and power generation in TEG device is drastically reduced from 6 hours with fully coupled high fidelity CFD to 3 minutes by using hybrid CFD and artificial neural networks. The proposed approach will very helpful in the efficient design and optimization of TEG installed renewable energy systems.

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  • Selimefendigil, Fatih & Öztop, Hakan F., 2021. "Thermoelectric generation in bifurcating channels and efficient modeling by using hybrid CFD and artificial neural networks," Renewable Energy, Elsevier, vol. 172(C), pages 582-598.
    • Handle: RePEc:eee:renene:v:172:y:2021:i:c:p:582-598
    DOI: 10.1016/j.renene.2021.03.046
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    1. Amirkhani, S. & Nasirivatan, Sh. & Kasaeian, A.B. & Hajinezhad, A., 2015. "ANN and ANFIS models to predict the performance of solar chimney power plants," Renewable Energy, Elsevier, vol. 83(C), pages 597-607.
    2. Mahbubul, I.M. & Khan, Mohammed Mumtaz A. & Ibrahim, Nasiru I. & Ali, Hafiz Muhammad & Al-Sulaiman, Fahad A. & Saidur, R., 2018. "Carbon nanotube nanofluid in enhancing the efficiency of evacuated tube solar collector," Renewable Energy, Elsevier, vol. 121(C), pages 36-44.
    3. Selimefendigil, Fatih & Öztop, Hakan F., 2020. "Identification of pulsating flow effects with CNT nanoparticles on the performance enhancements of thermoelectric generator (TEG) module in renewable energy applications," Renewable Energy, Elsevier, vol. 162(C), pages 1076-1086.
    4. Sajid, Muhammad Usman & Ali, Hafiz Muhammad, 2019. "Recent advances in application of nanofluids in heat transfer devices: A critical review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 103(C), pages 556-592.
    5. Mellit, Adel & Kalogirou, Soteris A., 2011. "ANFIS-based modelling for photovoltaic power supply system: A case study," Renewable Energy, Elsevier, vol. 36(1), pages 250-258.
    6. Khanafer, Khalil & Vafai, Kambiz, 2018. "A review on the applications of nanofluids in solar energy field," Renewable Energy, Elsevier, vol. 123(C), pages 398-406.
    7. Kalogirou, Soteris A., 2001. "Artificial neural networks in renewable energy systems applications: a review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 5(4), pages 373-401, December.
    8. Fatih Selimefendigil & Hakan F. Oztop & Mikhail A. Sheremet & Nidal Abu-Hamdeh, 2019. "Forced Convection of Fe 3 O 4 -Water Nanofluid in a Bifurcating Channel under the Effect of Variable Magnetic Field," Energies, MDPI, vol. 12(4), pages 1-16, February.
    9. Kalogirou, Soteris A. & Bojic, Milorad, 2000. "Artificial neural networks for the prediction of the energy consumption of a passive solar building," Energy, Elsevier, vol. 25(5), pages 479-491.
    10. Soteris A. Kalogirou, 2006. "Artificial neural networks in energy applications in buildings," International Journal of Low-Carbon Technologies, Oxford University Press, vol. 1(3), pages 201-216, July.
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    Cited by:

    1. Fatih Selimefendigil & Damla Okulu & Hakan F. Öztop, 2023. "Photovoltaic Thermal Management by Combined Utilization of Thermoelectric Generator and Power-Law-Nanofluid-Assisted Cooling Channel," Sustainability, MDPI, vol. 15(6), pages 1-29, March.
    2. Nidhal Ben Khedher & Fatih Selimefendigil & Lioua Kolsi & Walid Aich & Lotfi Ben Said & Ismail Boukholda, 2022. "Performance Optimization of a Thermoelectric Device by Using a Shear Thinning Nanofluid and Rotating Cylinder in a Cavity with Ventilation Ports," Mathematics, MDPI, vol. 10(7), pages 1-20, March.
    3. Cai, Yeyun & Ding, Ning & Rezania, A. & Deng, Fang & Rosendahl, L. & Chen, Jie, 2023. "A multi-objective optimization in system level for thermoelectric generation system," Energy, Elsevier, vol. 281(C).
    4. Wang, Qingyuan & Zhang, Guomin & Wu, Qihong & Li, Wenyuan & Shi, Long, 2022. "A combined wall and roof solar chimney in one building," Energy, Elsevier, vol. 240(C).
    5. Wang, Z.H. & Ma, Y.J. & Tang, G.H. & Zhang, Hu & Ji, F. & Sheng, Q., 2023. "Integration of thermal insulation and thermoelectric conversion embedded with phase change materials," Energy, Elsevier, vol. 278(C).
    6. Tae Young Kim, 2021. "Prediction of System-Level Energy Harvesting Characteristics of a Thermoelectric Generator Operating in a Diesel Engine Using Artificial Neural Networks," Energies, MDPI, vol. 14(9), pages 1-14, April.

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