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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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    Cited by:

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    2. 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.
    3. 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.
    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. 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).
    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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