Acoustic bandgap metamaterials-based directional propagation regulation and detection of ultrasonic guided waves in special-shaped structure
Source abstract
Abstract
To address strong signal interference and poor damage assessment induced by irregular guided wave diffusion in special-shaped structure detection, an acoustic bandgap metamaterial-based directional regulation method for ultrasonic guided waves is proposed to enhance detection performance. First, the regulation rules of the structural parameters of the aluminium-copper square columnar metamaterial on the bandgap characteristics are explored, and the particle swarm optimisation algorithm is used to design and optimise three metamaterials for suppressing 100 kHz, 150 kHz, and 200 kHz guided waves, respectively. Simulation and experiments verify that the metamaterial combination applied to the cross-shaped aluminium plate can achieve directional propagation of corresponding-frequency guided waves to specific branches. In the damage detection of the cross-shaped aluminium plate, the excitation frequency is adjusted so that the guided wave is directionally transmitted to the target branch, and only the damaged branch is located has a significant differential signal, enabling efficient damage identification. The detection results are consistent with the actual damage location, confirming the effectiveness of the proposed method. This research provides a new technical solution for efficient damage detection of special-shaped components, with important engineering value in the field of structural health monitoring.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Ultrasonics and Acoustic Wave Propagation · Acoustic Wave Phenomena Research · Metamaterials and Metasurfaces Applications
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
South China University of Technology
Funding
National Key Research and Development Program of China under Grant
References (34)
Show source-provided references
1. 10.3390/jmse1107147810.3390/jmse11071478 ↗
2. 10.1080/17445302.2014.88936710.1080/17445302.2014.889367 ↗
3. 10.1038/s41598-024-80762-y10.1038/s41598-024-80762-y ↗
4. 10.4028/www.scientific.net/KEM.270-273.1410.4028/www.scientific.net/KEM.270-273.14 ↗
5. 10.3390/coatings1403035810.3390/coatings14030358 ↗
6. 10.3390/s2504105010.3390/s25041050 ↗
7. 10.1016/j.ndteint.2025.10338410.1016/j.ndteint.2025.103384 ↗
8. 10.1016/j.sna.2022.11371010.1016/j.sna.2022.113710 ↗
9. 10.3390/s2203078010.3390/s22030780 ↗
10. 10.1109/TUFFC.2023.330503210.1109/TUFFC.2023.3305032 ↗
11. 10.1016/j.ymssp.2023.11026810.1016/j.ymssp.2023.110268 ↗
12. 10.1115/1.406606210.1115/1.4066062 ↗
13. 10.1121/1.42862810.1121/1.428628 ↗
14. 10.1109/TUFFC.2016.262810010.1109/TUFFC.2016.2628100 ↗
15. 10.1109/TUFFC.2003.119796510.1109/TUFFC.2003.1197965 ↗
16. 10.1016/j.ymssp.2023.11043210.1016/j.ymssp.2023.110432 ↗
17. Atashipour SA, Mirdamadi HR, Amirfattahi R, et al. Application of wavelet transform in damage identification in a thick steel beam based on ultrasonic guided wave propagation. Modares Mech Eng. 2013;12(5):154–164.
18. 10.1016/j.ultras.2019.10594810.1016/j.ultras.2019.105948 ↗
19. 10.1109/TUFFC.2006.161056810.1109/TUFFC.2006.1610568 ↗
20. 10.1016/j.ultras.2020.10608710.1016/j.ultras.2020.106087 ↗
21. 10.1038/natrevmats.2016.110.1038/natrevmats.2016.1 ↗
22. 10.3389/fphy.2022.106945410.3389/fphy.2022.1069454 ↗
23. 10.1088/1361-665X/ad039310.1088/1361-665X/ad0393 ↗
24. 10.1016/j.measurement.2026.12140710.1016/j.measurement.2026.121407 ↗
25. 10.1109/TIM.2025.358893410.1109/TIM.2025.3588934 ↗
26. 10.1016/j.physb.2022.41454510.1016/j.physb.2022.414545 ↗
27. 10.1016/j.jsv.2022.11726010.1016/j.jsv.2022.117260 ↗
28. 10.1063/5.011763410.1063/5.0117634 ↗
29. 10.1063/1.504248810.1063/1.5042488 ↗
30. 10.1080/15376494.2024.242035110.1080/15376494.2024.2420351 ↗
31. 10.1103/PhysRevLett.117.03430210.1103/PhysRevLett.117.034302 ↗
32. 10.1016/j.physleta.2023.12911410.1016/j.physleta.2023.129114 ↗
33. 10.1016/j.apacoust.2026.11124810.1016/j.apacoust.2026.111248 ↗
34. 10.1080/10589759.2025.254833910.1080/10589759.2025.2548339 ↗