Study on Band Gap Characteristics of Phononic Crystal Double-Layer Beam Structure Attached by Multilayer Cylinder

Zhang, Zexin; Qian, Denghui; Zou, Peng

doi:10.3390/cryst13040638

Cited by 5 publications

(1 citation statement)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The effects of vibrate absorption and noise reduction should be paid more attention. The Honeycomb has the advantage of damping vibration and absorbing energy [14][15][16][17][18][19]. Many periodic honeycomb sound insulation materials are widely used in engineer applications due to the development of technology, like the designing of sound insulation materials for armors of aircrafts and automobiles [20,21].…”

Section: Introductionmentioning

confidence: 99%

The Phononic Properties and Optimization of 2D Multi-Ligament Honeycombs

Yin,

Guan,

Kou

2024

Materials

View full text Add to dashboard Cite

Honeycomb structures have attracted much attention for their excellent characteristics of reducing vibration and noise in recent years. In this study, through band analysis of different ligament structures, we aim to optimize the design of a steel structure that can isolate most of the noise in the 1500–5000 Hz range. The present study examines several different chiral structures. We calculate the band gaps of chiral structures under different geometric configurations and identify the variations in band gaps with geometric layouts. It is found that compared to other chiral structures, the triligaments chiral structure exhibits excellent band gap characteristics. The calculation results demonstrate that enhancing axial symmetry while filling central nodes can effectively enhance the structure’s band gap properties. Frequency–response functions of different lattice structures are computed, and the results align with the calculations of band structures. This study then analyzes the influence of the number of periods on the magnitude of vibration attenuation, revealing that under the same number of periods, the wider the band gap of the structure, the greater the vibration attenuation. Both the triligaments chiral structure and the vertical triligaments structure possess ideal band gap widths, effectively suppressing wave propagation. Subsequently, harmonic response analyses and transient wave calculations further validate the accuracy of the band structure and frequency–response curve calculations. Our study results provide a new way to design a sound insulation structure that can isolate noise signals within the frequency range from 1500 to 5000 Hz in engineering.

show abstract

Section: Introductionmentioning

confidence: 99%

The Phononic Properties and Optimization of 2D Multi-Ligament Honeycombs

Yin,

Guan,

Kou

2024

Materials

View full text Add to dashboard Cite

show abstract

Slow sound mode prediction and band structure calculation in 1D phononic crystal nanobeams using an artificial neural network

Hsiao,

Yang,

Lin

et al. 2024

Science Progress

View full text Add to dashboard Cite

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.

show abstract