Details
| Original language | English |
|---|---|
| Article number | 112722 |
| Journal | Mechanical Systems and Signal Processing |
| Volume | 232 |
| Early online date | 15 Apr 2025 |
| Publication status | Published - 1 Jun 2025 |
Abstract
Pneumatic soft acoustic metamaterials have gradually attracted attention inspired by pneumatic soft robots. However, current researches ignore the ubiquitous uncertainty factor, which may cause the designed pneumatic soft acoustic metamaterials to fail to achieve the expected performance. In this paper, the influence of uncertainty on pneumatic soft acoustic metamaterial system is investigated. To quantify uncertainties for the system input based on available data, two different uncertainty characterization methods are utilized. By integrating the bootstrap method with kernel density estimation, the input distribution of bounded random model can be determined based on the limited experiment data. For cases with even less experiment data, an unbiased estimation method is introduced to construct interval model. Then, an uncertainty propagation method based on Kriging model and an improved active learning strategy is developed for the pneumatic soft acoustic metamaterial system with bounded hybrid uncertain parameters. Finally, we experimental demonstrated the effectiveness of the uncertainty analysis on the deformation and acoustic property of the pneumatic soft acoustic metamaterial system. The results show the necessity of regarding uncertainties in pneumatic soft acoustic metamaterial system. The study provides a feasible and practical method to model and propagate uncertainty for pneumatic soft acoustic metamaterials systems, which can promote their application in industrial sectors.
Keywords
- Active learning, Kriging model, Pneumatic soft acoustic metamaterial, Uncertainty characterization, Uncertainty propagation
ASJC Scopus subject areas
- Engineering(all)
- Control and Systems Engineering
- Computer Science(all)
- Signal Processing
- Engineering(all)
- Civil and Structural Engineering
- Engineering(all)
- Aerospace Engineering
- Engineering(all)
- Mechanical Engineering
- Computer Science(all)
- Computer Science Applications
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In: Mechanical Systems and Signal Processing, Vol. 232, 112722, 01.06.2025.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Uncertainty characterization and propagation analysis for pneumatic soft acoustic metamaterial system
AU - Zhang, Kun
AU - Chen, Ning
AU - Liu, Jian
AU - Beer, Michael
N1 - Publisher Copyright: © 2025 Elsevier Ltd
PY - 2025/6/1
Y1 - 2025/6/1
N2 - Pneumatic soft acoustic metamaterials have gradually attracted attention inspired by pneumatic soft robots. However, current researches ignore the ubiquitous uncertainty factor, which may cause the designed pneumatic soft acoustic metamaterials to fail to achieve the expected performance. In this paper, the influence of uncertainty on pneumatic soft acoustic metamaterial system is investigated. To quantify uncertainties for the system input based on available data, two different uncertainty characterization methods are utilized. By integrating the bootstrap method with kernel density estimation, the input distribution of bounded random model can be determined based on the limited experiment data. For cases with even less experiment data, an unbiased estimation method is introduced to construct interval model. Then, an uncertainty propagation method based on Kriging model and an improved active learning strategy is developed for the pneumatic soft acoustic metamaterial system with bounded hybrid uncertain parameters. Finally, we experimental demonstrated the effectiveness of the uncertainty analysis on the deformation and acoustic property of the pneumatic soft acoustic metamaterial system. The results show the necessity of regarding uncertainties in pneumatic soft acoustic metamaterial system. The study provides a feasible and practical method to model and propagate uncertainty for pneumatic soft acoustic metamaterials systems, which can promote their application in industrial sectors.
AB - Pneumatic soft acoustic metamaterials have gradually attracted attention inspired by pneumatic soft robots. However, current researches ignore the ubiquitous uncertainty factor, which may cause the designed pneumatic soft acoustic metamaterials to fail to achieve the expected performance. In this paper, the influence of uncertainty on pneumatic soft acoustic metamaterial system is investigated. To quantify uncertainties for the system input based on available data, two different uncertainty characterization methods are utilized. By integrating the bootstrap method with kernel density estimation, the input distribution of bounded random model can be determined based on the limited experiment data. For cases with even less experiment data, an unbiased estimation method is introduced to construct interval model. Then, an uncertainty propagation method based on Kriging model and an improved active learning strategy is developed for the pneumatic soft acoustic metamaterial system with bounded hybrid uncertain parameters. Finally, we experimental demonstrated the effectiveness of the uncertainty analysis on the deformation and acoustic property of the pneumatic soft acoustic metamaterial system. The results show the necessity of regarding uncertainties in pneumatic soft acoustic metamaterial system. The study provides a feasible and practical method to model and propagate uncertainty for pneumatic soft acoustic metamaterials systems, which can promote their application in industrial sectors.
KW - Active learning
KW - Kriging model
KW - Pneumatic soft acoustic metamaterial
KW - Uncertainty characterization
KW - Uncertainty propagation
UR - http://www.scopus.com/inward/record.url?scp=105002589206&partnerID=8YFLogxK
U2 - 10.1016/j.ymssp.2025.112722
DO - 10.1016/j.ymssp.2025.112722
M3 - Article
AN - SCOPUS:105002589206
VL - 232
JO - Mechanical Systems and Signal Processing
JF - Mechanical Systems and Signal Processing
SN - 0888-3270
M1 - 112722
ER -