Artificial neural network enabled accurate geometrical design and optimisation of thermoelectric generator

• Published in: Applied energy 2022-01, Vol.305, p.117800, Article 117800
• Main Authors: Zhu, Yuxiao, Newbrook, Daniel W., Dai, Peng, de Groot, C.H. Kees, Huang, Ruomeng
• Format: Article
• Language: English
• Subjects: Artificial neural network; Genetic algorithm; Optimisation; Thermoelectric generator
• ISSN: 0306-2619; 1872-9118
• DOI: 10.1016/j.apenergy.2021.117800

[Display omitted] •Forward modelling of thermoelectric generator via artificial neural network.•Consideration of geometrical parameters, operating conditions and contact resistance.•Prediction of both power density and efficiency with high accuracy over 98%•Superior cost-effectiveness in geometrical design and optimisation. The ever-increasing demand for renewable energy and zero carbon dioxide emission have been the driving force for the development of thermoelectric generators with better power generation performance. Alongside with the effort to discover thermoelectric materials with higher figure-of-merit, the geometrical and structural optimisation of thermoelectric generators are also essential for maximized power generation and efficiency. This work demonstrates for the first time the application of artificial neural network, a deep learning technique, in forward modelling the maximum power generation and efficiency of a thermoelectric generator and its application in the generator design and optimisation. After training using a dataset containing 5000 3-D finite element method based simulations, the artificial neural networks with 5 layers and 400 neurons per layer demonstrate extremely high prediction accuracy over 98% and are able to operate under both constant temperature difference and heat flux conditions while taking into account of the contact electrical resistance, surface heat transfer and other thermoelectric effects. Coupling with genetic algorithm, the trained artificial neural networks can optimise the leg height, leg width, fill factor and interconnect height of the thermoelectric generator for different operating and contact resistance conditions. With almost identical optimised values obtained, our neural networks can realise geometrical optimisation within 40 s for each operating condition, which is averagely over 1,000 times faster than the optimisation performed by finite element method. The up-front computational time for the neural network can be recovered when more than 2 optimisations are needed. The successful application of this data-driven approach in this work clearly represents a new and cost-effective avenue for conducting system level design and optimisation of thermoelectric generators and other energy harvesting technologies.