arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2310.03633physics.opticsphysics.app-phphysics.comp-ph

用于高调制对比度近场热辐射调制器的多层双曲超材料的数据驱动设计

Data-driven design of multilayer hyperbolic metamaterials for near-field thermal radiative modulator with high modulation contrast

Tuwei Liao, C. Y. Zhao, Hong Wang, Shenghong Ju

AI总结:

提出数据驱动的机器学习工作流程,结合多层感知机和贝叶斯优化设计由 $α$-MoO$_{\ m 3}$ 组成的多层双曲超材料,实现了 6.29 的最大热调制对比度,较单层结构提升 97%,为近场热辐射调制器设计提供了新途径。

摘要:

基于近场辐射传热的近场热调制器在热电二极管、热电晶体管和热存储中有着广泛的应用。然而,由于自由度极大,设计最优的近场热辐射结构是一个复杂且具有挑战性的问题。在本研究中,我们提出了一种数据驱动的机器学习工作流程,以高效设计由 $α$-MoO$_{\ m 3}$ 组成的多层双曲超材料,用于具有高调制对比度的近场热辐射调制器。通过结合多层感知机和贝叶斯优化,对多层超材料的旋转角度、层厚度和间隙距离进行了优化,实现了 6.29 的最大热调制对比度。与之前的单层结构相比,这提升了 97%。这种大的热调制对比度主要归因于由旋转控制的各层双曲等离激元极化激元和双曲表面声子极化激元的对齐与错位。研究结果为通过数据驱动方式加速各向异性双曲材料的设计与近场辐射传热操控提供了一种有前景的途径。

英文摘要:

The thermal modulator based on the near-field radiative heat transfer has wide applications in thermoelectric diodes, thermoelectric transistors, and thermal storage. However, the design of optimal near-field thermal radiation structure is a complex and challenging problem due to the tremendous number of degrees of freedom. In this work, we have proposed a data-driven machine learning workflow to efficiently design multilayer hyperbolic metamaterials composed of $α$-MoO$_{\rm 3}$ for near-field thermal radiative modulator with high modulation contrast. By combining the multilayer perceptron and Bayesian optimization, the rotation angle, layer thickness and gap distance of the multilayer metamaterials are optimized to achieve a maximum thermal modulation contrast ratio of 6.29. This represents a 97% improvement compared to previous single layer structure. The large thermal modulation contrast is mainly attributed to the alignment and misalignment of hyperbolic plasmon polaritons and hyperbolic surface phonon polaritons of each layer controlled by the rotation. The results provide a promising way for accelerating the designing and manipulating of near-field radiative heat transfer by anisotropic hyperbolic materials through the data-driven style.

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