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首页> 外文期刊>Nano Energy >Machine learning-optimized Tamm emitter for high-performance thermophotovoltaic system with detailed balance analysis
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Machine learning-optimized Tamm emitter for high-performance thermophotovoltaic system with detailed balance analysis

机译：机器学习优化的TAMM发射器，具有详细平衡分析的高性能蒸发器系统

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摘要

Light-matter interaction upon nanophotonic structures in the infrared wavelength has drew increasing attentions due to the extensive potential applications. Among them, thermophotovoltaic (TPV) systems can exhibit higher efficiency over the Shockley-Queisser limit due to the nanophotonic structure-enabled tunable narrowband thermal emission rather than the broadband incident spectrum. However, two long-standing issues remain formidable as bottlenecks for achieving better performances of TPV system. One is the competing role of the power density and the system efficiency of TPV system, and the other is the magnanimity possibilities of structures, configurations, dimensions, and materials of thermal emitters that disables the manual optimization of TPV system. Here, we attempt to achieve high-performance TPV system by employing the machine learning algorithm under the framework of material informatics. The power density and system efficiency are well modelled through the detailed balance analysis with full considering the photocurrent generation in the PV cells. Through optimization, the non-trial aperiodic Tamm emitters are obtained and the metal-side one is preferable in terms of the TPV performance. The present work is demonstrated to be feasible and efficient in optimizing the TPV performance, and opens a new door for the optimization problems in other fields.
机译：由于广泛的潜在应用，在红外波长中的纳米光影结构对纳米光电结构的浅孔相互作用。其中，由于纳米光电结构的可调谐窄带热发射而不是宽带事件光谱，蒸发酚（TPV）系统可以在震撼销售器极限上表现出更高的效率。然而，两个长期问题仍然是实现TPV系统更好表现的瓶颈。一个是TPV系统的功率密度和系统效率的竞争作用，另一个是禁用TPV系统手动优化的结构，配置，尺寸和热发射器的材料的宽度可能性。在这里，我们试图通过在材料信息框架下采用机器学习算法来实现高性能TPV系统。通过对PV电池中的光电流产生充分的详细平衡分析，功率密度和系统效率充分建模。通过优化，获得非试验性非周期性Tamm发射器，并且在TPV性能方面是优选的金属侧。目前的工作被证明是可行和有效的优化TPV性能，并在其他领域的优化问题打开新门。

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