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Level Repulsion and Band Sorting in Phononic Crystals

机译:声子晶体中的水平排斥和带分选

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摘要

In this paper we consider the problem of avoided crossings (level repulsion)in phononic crystals and suggest a computationally efficient strategy todistinguish them from normal cross points. This process is essential for thecorrect sorting of the phononic bands and, subsequently, for the accuratedetermination of mode continuation, group velocities, and emergent propertieswhich depend on them such as thermal conductivity. Through explicit phononiccalculations using generalized Rayleigh quotient, we identify exact locationsof exceptional points in the complex wavenumber domain which results in levelrepulsion in the real domain. We show that in the vicinity of the exceptionalpoint the relevant phononic eigenvalue surfaces resemble the surfaces of a 2 by2 parameter-dependent matrix. Along a closed loop encircling the exceptionalpoint we show that the phononic eigenvalues are exchanged, just as they are forthe 2 by 2 matrix case. However, the behavior of the associated eigenvectors isshown to be more complex in the phononic case. Along a closed loop around anexceptional point, we show that the eigenvectors can flip signs multiple timesunlike a 2 by 2 matrix where the flip of sign occurs only once. Finally, weexploit these eigenvector sign flips around exceptional points to propose asimple and efficient method of distinguishing them from normal crosses and ofcorrectly sorting the band-structure. Our proposed method is roughly anorder-of magnitude faster than the zoom-in method and correctly identifies >97% of the cases considered. Both its speed and accuracy can be furtherimproved and we suggest some ways of achieving this. Our method is general and,as such, would be directly applicable to other eigenvalue problems where theeigenspectrum needs to be correctly sorted.
机译:在本文中,我们考虑了声子晶体中避免交叉(能级排斥)的问题,并提出了一种计算有效的策略来将它们与正常交叉点区分开。这个过程对于正确地分类声子带是必不可少的,随后对于准确确定模式连续性,基团速度和依赖于它们的诸如热导率的出射特性也是必不可少的。通过使用广义瑞利商的显式声子计算,我们确定了复波数域中例外点的确切位置,这导致了实域中的电平排斥。我们表明,在例外点附近,相关的声子特征值表面类似于2 x 2参数依赖矩阵的表面。沿着环绕例外点的闭环,我们显示了声子特征值是交换的,就像2 x 2矩阵的情况一样。但是,在声子情况下,关联的特征向量的行为显示更为复杂。沿着围绕异常点的闭环,我们表明本征向量可以多次翻转符号,这与2 x 2矩阵不同,在2 x 2矩阵中,符号翻转仅发生一次。最后,我们利用这些特征向量符号在例外点附近的翻转来提出一种简单有效的方法,将它们与正常叉进行区分,并正确地对能带结构进行分类。我们提出的方法比放大方法快大约一个数量级,并且可以正确地识别出> 97%的情况。它的速度和准确性都可以进一步提高,我们建议一些实现此目的的方法。我们的方法是通用的,因此可以直接应用于需要正确分类特征谱的其他特征值问题。

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    Lu, Yan; Srivastava, Ankit;

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  • 年度 2017
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