Fast and Accurate Performance Prediction and Optimization of Thermoelectric Generators with Deep Neural Networks. Issue 7 (18th May 2021)

• Record Type: Journal Article
• Authors: Wang, Pan; Wang, Kaifa; Xi, Li; Gao, Ruxin; Wang, Baolin
• Is Part Of: Advanced materials technologies. Volume 6:Issue 7(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-05-18
• Subjects: deep neural network -- machine learning -- performance optimization -- thermoelectric generator -- thermoelectric performance; Materials science -- Periodicals; Technological innovations -- Periodicals; Materials science; Technological innovations; Periodicals; 620.1105
• DOI: 10.1002/admt.202100011
• Languages: English
• ISSNs: 2365-709X

Abstract

Abstract: Predicting the performance of thermoelectric generators (TEGs) is an essential part of designing high‐performance TEGs. However, due to the complexity of the TEG system, the existing methods are either time‐consuming or not precise enough, inconvenient for device optimization. In this paper, the deep learning (DL) method to fast and accurately get the performance of TEG devices is presented. First, the key features of a typical TEG device are captured and the training dataset is prepared based on the extracted features and finite element simulations. Next, a proper deep neural network architecture is acquired and the model is trained to converge at a low loss. Finally, the experimental data is used to validate the generalization ability of the presented model. Besides, the device optimization based on the DL solution is performed and an output power enhancement of up to 182% is achieved for the authors' sample module. The presented DL solution thus can be well applied in designing or optimizing high‐performance TEGs. Furthermore, the established framework also sheds considerable light on applying the DL approach to solve general engineering problems. Abstract : The deep learning (DL) method to fast and accurately get the performance of thermoelectric generator (TEG) devices is presented. The trained network shows good generalization ability and thus can be well applied in designing or optimizing high‐performance TEGs. Furthermore, the established framework also sheds considerable light on applying the DL approach to solve general engineering problems.