Predicting the Optimal Performance of a Concentrated Solar Segmented Variable Leg Thermoelectric Generator Using Neural Networks
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energies-15-06024-v2.pdf
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9.79 MB
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Checksum (MD5)
57ca66ad5c412fc5a6d8989969f48a48
Author(s)
Maduabuchi, Chika
Fagehi, Hassan
Alatawi, Ibrahim
Alkhedher, Mohammad
Date Issued
August 19, 2022
Publisher
Multidisciplinary Digital Publishing Institute
Citation
Energies 15 (16): 6024 (2022)
Version
Final published version
Abstract
The production of high-performing thermoelectrics is limited by the high computational energy and time required by the current finite element method solvers that are used to analyze these devices. This paper introduces a new concentrating solar thermoelectric generator made of segmented materials that have non-uniform leg geometry to provide high efficiency. After this, the optimum performance of the device is obtained using the finite element method conducted using ANSYS software. Finally, to solve the high energy and time requirements of the conventional finite element method, the data generated by finite elements are used to train a regressive artificial neural network with 10 neurons in the hidden layer. Results are that the power and efficiency obtained from the optimized device design are 3× and 2× higher than the original unoptimized device design. Furthermore, the developed neural network has a high accuracy of 99.95% in learning the finite element data. Finally, the neural network predicts the modified device performance about 800× faster than the conventional finite element method. Overall, the paper provides insights into how thermoelectric manufacturing companies can harness the power of artificial intelligence to design very high-performing devices while saving time and cost.
MIT Department
Massachusetts Institute of Technology. Department of Nuclear Science and Engineering
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Creative Commons Attribution
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DOI of Published Version
https://doi.org/10.3390/en15166024
