arXiv subjects

Zhenghua Qian

Publications and source records attributed to Zhenghua Qian.

4 recordsLinked to original sources

arXiv2025

Topological resonance behaviors of surface acoustic waves under a surface liquid-layer loading and sensing applications

In this work, topological resonance behaviors of surface acoustic waves (SAW) under a surface liquid-layer loading are investigated. By revealing influences of the liquid-layer loading on wave velocity of SAW and topological indices (Berry curvature and Chern number) of topological interface-modes, a topological resonance peak with a high Q-factor is obtained based on couplings of a topological interface-mode waveguide and a resonant cavity under a surface liquid-layer loading. The results show that the degree of spatial-inversion-symmetry breaking resulting from structure parameters has an obvious influences on the topological resonance Q-factor, while the influences of the thickness of the liquid-layer loading on that is weak. It is worth noting that the topological resonance frequency is significantly sensitive to the liquid parameters. Based on that, a novel topological-resonance SAW liquid-phase sensor is proposed. Furthermore, sensing performances of this kind of sensor are simulated, which are used to sensing the concentration of hemoglobin, albumin, NaCl and NaI in aqueous solutions, and high sensitivities and Q-factors are obtained. The results presented in this paper can provide an important basis for the realization of highly sensitive and stable SAW micro-liquid-sample biomedical sensors in the future.

physics.app-ph↗

arXiv2024

Two-dimensional DtN-FEM scattering analysis of SH guided waves by an interface debonding in a double-layered plate

In this paper, a two-dimensional Dirichlet-to-Neumann (DtN) finite element method (FEM) is developed to analyze the scattering of SH guided waves due to an interface delamination in a bi-material plate. During the finite element analysis, it is necessary to determine the far-field DtN conditions at virtual boundaries where both displacements and tractions are unknown. In this study, firstly, the scattered waves at the virtual boundaries are represented by a superposition of guided waves with unknown scattered coefficients. Secondly, utilizing the mode orthogonality, the unknown tractions at virtual boundaries are expressed in terms of the unknown scattered displacements at virtual boundaries via scattered coefficients. Thirdly, this relationship at virtual boundaries can be finally assembled into the global DtN-FEM matrix to solve the problem. This method is simple and elegant, which has advantages on dimension reduction and needs no absorption medium or perfectly matched layer to suppress the reflected waves compared to traditional FEM. Furthermore, the reflection and transmission coefficients of each guided mode can be directly obtained without post-processing. This proposed DtN-FEM will be compared with boundary element method (BEM), and finally validated for several benchmark problems.

math.NA↗

arXiv2020

Manifold learning-based feature extraction for structural defect reconstruction

Data-driven quantitative defect reconstructions using ultrasonic guided waves has recently demonstrated great potential in the area of non-destructive testing. In this paper, we develop an efficient deep learning-based defect reconstruction framework, called NetInv, which recasts the inverse guided wave scattering problem as a data-driven supervised learning progress that realizes a mapping between reflection coefficients in wavenumber domain and defect profiles in the spatial domain. The superiorities of the proposed NetInv over conventional reconstruction methods for defect reconstruction have been demonstrated by several examples. Results show that NetInv has the ability to achieve the higher quality of defect profiles with remarkable efficiency and provides valuable insight into the development of effective data driven structural health monitoring and defect reconstruction using machine learning.

cs.CE↗

arXiv2020

A novel combination of theoretical analysis and data-driven method for reconstruction of structural defects

Ultrasonic guided wave technology has played a significant role in the field of non-destructive testing as it employs acoustic waves that have advantages of high propagation efficiency and low energy consumption during the inspect process. However, theoretical solutions to guided wave scattering problems using assumptions such as Born approximation, have led to the poor quality of the reconstructed results. To address this issue, a novel approach to quantitative reconstruction of defects using the integration of data-driven method with the guided wave scattering analysis has been proposed in this paper. Based on the geometrical information of defects and initial results by the theoretical analysis of defect reconstructions, a deep learning neural network model is built to reveal the physical relationship between defects and the received signals. This data-driven model is then applied to quantitatively assess and characterize defect profiles in structures, reduce the inaccuracy of the theoretical modelling and eliminate the impact of noise pollution in the process of inspection. To demonstrate advantages of the developed approach to reconstructions of defects with complex profiles, numerical examples including basic defect profiles and a defect with the noisy fringe have been examined. Results show that this approach has greater accuracy for reconstruction of defects in structures as compared with the analytical method and provides a valuable insight into the development of artificial intelligence-assisted inspection systems with high accuracy and efficiency in the field of non-destructive testing.

cs.CE↗