利用人工神经网络进行一维声子晶体纳米梁的慢声模预测和带状结构计算

IF 2.6 4区 综合性期刊 Q2 MULTIDISCIPLINARY SCIENCES Science Progress Pub Date : 2024-07-01 DOI:10.1177/00368504241272461
Fu-Li Hsiao, Yen-Tung Yang, Wen-Kai Lin, Ying-Pin Tsai
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引用次数: 0

摘要

声子晶体是由具有不同弹性系数的材料在空间周期性排列形成的人工晶体,可以显示在其内部传播的调制声波。与半导体研究中使用的具有电子带隙的天然晶体类似,声子晶体也具有声子带隙的特性。可利用带隙设计创建各种共振腔,将特定共振模式限制在结构缺陷内。在声子晶体研究中,声子带结构图通常用于研究声子带隙和弹性共振模式的变化。由于声带频率与结构参数呈非线性变化,因此需要进行大量计算来分析声带结构图中的间隙或模式频率偏移。然而,传统的计算方法非常耗时。因此,本研究提出使用神经网络来替代传统方法的耗时计算过程。首先通过有限元法获得大量带状结构图作为原始数据集,然后从数据集中随机抽取一定比例的数据进行神经网络训练。通过将带状结构图中的每个模态点视为一个独立的数据点,神经网络的训练数据集可以从少量的带状结构图扩展到大量的带状结构图。本研究还引入了另一种网络,通过训练神经网络关注特定模式,有效提高了模式预测精度。所提出的方法有效降低了重复计算的成本。
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Slow sound mode prediction and band structure calculation in 1D phononic crystal nanobeams using an artificial neural network.

Phononic crystals, which are artificial crystals formed by the periodic arrangement of materials with different elastic coefficients in space, can display modulated sound waves propagating within them. Similar to the natural crystals used in semiconductor research with electronic bandgaps, phononic crystals exhibit the characteristics of phononic bandgaps. A gap design can be utilized to create various resonant cavities, confining specific resonance modes within the defects of the structure. In studies on phononic crystals, phononic band structure diagrams are often used to investigate the variations in phononic bandgaps and elastic resonance modes. As the phononic band frequencies vary nonlinearly with the structural parameters, numerous calculations are required to analyze the gap or mode frequency shifts in phononic band structure diagrams. However, traditional calculation methods are time-consuming. Therefore, this study proposes the use of neural networks to replace the time-consuming calculation processes of traditional methods. Numerous band structure diagrams are initially obtained through the finite-element method and serve as the raw dataset, and a certain proportion of the data is randomly extracted from the dataset for neural network training. By treating each mode point in the band structure diagram as an independent data point, the training dataset for neural networks can be expanded from a small number to a large number of band structure diagrams. This study also introduces another network that effectively improves mode prediction accuracy by training neural networks to focus on specific modes. The proposed method effectively reduces the cost of repetitive calculations.

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来源期刊
Science Progress
Science Progress Multidisciplinary-Multidisciplinary
CiteScore
3.80
自引率
0.00%
发文量
119
期刊介绍: Science Progress has for over 100 years been a highly regarded review publication in science, technology and medicine. Its objective is to excite the readers'' interest in areas with which they may not be fully familiar but which could facilitate their interest, or even activity, in a cognate field.
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