Predicting the Optimal Performance of a Concentrated Solar Segmented Variable Leg Thermoelectric Generator Using Neural Networks

Chika Maduabuchi (), Hassan Fagehi, Ibrahim Alatawi () and Mohammad Alkhedher
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Chika Maduabuchi: Department of Nuclear Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
Hassan Fagehi: Department of Mechanical Engineering, College of Engineering, Jazan University, Jazan 45142, Saudi Arabia
Ibrahim Alatawi: Mechanical Engineering Department, Engineering College, University of Ha’il, Ha’il 81451, Saudi Arabia
Mohammad Alkhedher: Department of Mechanical and Industrial Engineering, Abu Dhabi University, Abu Dhabi 59911, United Arab Emirates

Energies, 2022, vol. 15, issue 16, 1-25

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.

Keywords: thermoelectric optimization; segmented variable area leg thermoelectrics; finite element method; artificial neural networks (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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