4.6 Article

Neural network-assisted optimization of segmented thermoelectric power generators using active learning based on a genetic optimization algorithm

Journal

ENERGY REPORTS
Volume 8, Issue -, Pages 6633-6644

Publisher

ELSEVIER
DOI: 10.1016/j.egyr.2022.04.065

Keywords

Thermoelectric; Active learning; Bayesian regularization; Neural network; COMSOL-multiphysics

Categories

Funding

  1. National Research Foundation of Korea (Basic Science Research Program) [2022R1A2B5B02002365]
  2. Korea Advanced Institute of Science and Technology (Global Singularity Research Program) [N11190118]
  3. National Research Council of Science and Technology of Korea (KERI primary research program) [22A01008]
  4. National Research Council of Science & Technology (NST), Republic of Korea [22A01008] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
  5. National Research Foundation of Korea [2022R1A2B5B02002365, N11190118] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)

Ask authors/readers for more resources

In this study, a systematic approach leveraging deep learning is proposed to efficiently explore and optimize the design of segmented thermoelectric legs. By combining finite element analysis and neural network modeling with a genetic optimization algorithm, high-performance design candidates are identified and the model is updated using validation results to achieve optimization of thermoelectric legs.
Because the properties of thermoelectric (TE) materials are strongly dependent on temperature and differ considerably, segmented TE legs composed of multiple stacked TE materials have been investigated to provide efficient operation of TE devices. However, owing to the inherent nonlinearity that limits the application of conventional optimization approaches, the optimal configuration of segmented TEGs (STEGs) must have been sought heuristically. In this study, we propose a systematic approach that enables the efficient exploration and exploitation of the vast design space of STEGs by leveraging the fast inference of deep learning. First, we train a neural network (NN) model using a dataset generated from finite element analysis (FEA) for a single TE leg with four stacked segments from among 18 TE materials with varying segment lengths and external loads. We then use a genetic optimization algorithm (GOA) with our trained NN model to search for high-performance design candidates. The performance of the new candidates is computed and validated based on FEA, and the results are used to update the NN through an active learning technique. After iteratively performing the procedure, we can fine-tune the TE legs for optimal efficiency, optimal power, and given combinations of both power and efficiency. Furthermore, we discuss the physical origin of optimally performing STEGs. (C) 2022 The Author(s). Published by Elsevier Ltd.

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