Machine learning model for transient exergy performance of a phase change material integrated-concentrated solar thermoelectric generator

Alghamdi, Hisham; Maduabuchi, Chika; Mbachu, Divine S.; Albaker, Abdullah; Alatawi, Ibrahim; Alsenani, Theyab R.; Alsafran, Ahmed S.; AlAqil, Mohammed

doi:10.1016/j.applthermaleng.2023.120540

Cited by 8 publications

(1 citation statement)

References 54 publications

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“…A few studies analyzed the energy and exergy efficiencies of a TEG system combined with a LHS system. Alghamdi et al (2023) proposed a machine learning model trained with numerically generated data to analyze the transient exergy performance of PCM integrated with a concentrated TEG system. Hua Hong et al (2023) explored the application of pulsed heat sources to a TEG-PCM hybrid system and evaluated the energy/exergy efficiency of the system under temperature limit and failure free-cycle times.…”

Section: Introductionmentioning

confidence: 99%

Energy and exergy analysis of a bidirectional solar thermoelectric generator combining thermal energy storage

Montero,

Lamba,

Singh

et al. 2023

Front. Energy Res.

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In this paper, energy and exergy analysis of a bidirectional solar thermoelectric generator (STEG) coupled to a latent heat storage and cooling system (LHSCS) has been carried out. The effect of various parameters of LHSCS on energy and exergy efficiencies of STEG have been analysed under climatic conditions of Chile’s Atacama Desert. It is found that the most relevant design parameter to improve the energy and exergy efficiencies of the thermoelectric generator (TEG) is the container insulation, followed by heat sink at the TEG hot side, fin thickness and the aspect ratio of the container. The results showed that an optimally designed insulation container can improve the energy and exergy efficiencies of LHSCS by 30% and 200%, respectively, and the TEG conversion efficiency by 30% during nighttime. Further, inclusion of heat sink at TEG hot side during reverse operation of TEG at night can improve the TEG efficiency by 20%. The optimal fin thickness can improve the TEG conversion efficiency by 20% during the night and LHSCS energy and exergy efficiencies by 30% and 23%, respectively. The container geometry should have higher aspect ratios. This study may help in optimal design of LHSCS for solar energy conversion applications in the desert locations.

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Section: Introductionmentioning

confidence: 99%

Energy and exergy analysis of a bidirectional solar thermoelectric generator combining thermal energy storage

Montero,

Lamba,

Singh

et al. 2023

Front. Energy Res.

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A synergistic approach to optimizing the performance of a concentrating solar segmented variable area leg thermoelectric generator using numerical methods and neural networks

Alghamdi,

Maduabuchi,

Albaker

et al. 2024

J Therm Anal Calorim

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This study presents an optimized design for segmented variable area leg thermoelectric modules using finite element methods and Bayesian regularized neural networks. We explored the impact of geometry and thermal parameters on module performance using ANSYS software, identifying optimal parameters for power output and efficiency. Key findings revealed the higher influence of geometric parameters and confirmed the advantages of segmented thermoelectric generators for high-temperature applications like concentrated solar systems. With this optimization, power output and efficiency of the module increased by 875% and 165%, respectively, under 25 Suns. To refine the optimization process, a Bayesian regularized neural network was utilized, proving effective in predicting module performance with a low mean squared error and high coefficient of determination. This research provides important insights into high-performance thermoelectric modules for sustainable energy applications, demonstrating the significant role of advanced computational methods in energy solutions.

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Harnessing solar power: Innovations in nanofluid-cooled segmented thermoelectric generators for exergy, economic, environmental, and thermo-mechanical excellence

Alghamdi,

Maduabuchi,

Okoli

et al. 2024

Alexandria Engineering Journal

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Machine learning model for transient exergy performance of a phase change material integrated-concentrated solar thermoelectric generator

Cited by 8 publications

References 54 publications

Energy and exergy analysis of a bidirectional solar thermoelectric generator combining thermal energy storage

Energy and exergy analysis of a bidirectional solar thermoelectric generator combining thermal energy storage

A synergistic approach to optimizing the performance of a concentrating solar segmented variable area leg thermoelectric generator using numerical methods and neural networks

Harnessing solar power: Innovations in nanofluid-cooled segmented thermoelectric generators for exergy, economic, environmental, and thermo-mechanical excellence

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