Artificial neural network enabled accurate geometrical design and optimisation of thermoelectric generator
Artificial neural network enabled accurate geometrical design and optimisation of thermoelectric generator
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DOI：
10.1016/j.apenergy.2021.117800
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发表时间：
2022-01
期刊：
影响因子：
11.2
通讯作者：
Yuxiao Zhu;D. Newbrook;Peng Dai;C. H. Groot;R. Huang
中科院分区：
文献类型：
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作者：
Yuxiao Zhu;D. Newbrook;Peng Dai;C. H. Groot;R. Huang
对可再生能源和零二氧化碳排放的需求不断增长，这是开发具有更好发电性能的热电发电机的动力。除了努力发现具有更高品质因数的热电材料外，热电发电机的几何和结构优化对于最大化发电和效率也至关重要。这项工作首次展示了人工神经网络（一种深度学习技术）在对热电发电机的最大发电量和效率及其在发电机设计和优化中的应用进行正向建模中的应用。在使用包含5000个基于三维有限元方法的模拟的数据集进行训练后，具有5层和每层400个神经元的人工神经网络表现出极高的预测精度超过98%，并且能够在恒定温差和热流条件下运行，同时考虑接触电阻，表面传热和其他热电效应。与遗传算法相结合，训练的人工神经网络可以优化的腿高度，腿宽度，填充因子和互连高度的温差发电器不同的操作和接触电阻条件。在获得几乎相同的优化值的情况下，我们的神经网络可以在40 s内实现每个操作条件的几何优化，这比有限元法进行的优化平均快1,000倍以上。当需要超过2次优化时，可以恢复神经网络的前期计算时间。这种数据驱动方法在这项工作中的成功应用显然代表了一种新的、具有成本效益的途径，用于进行热电发电机和其他能量收集技术的系统级设计和优化。
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.