Zhang Guangdong, Kundu Tribikram, Deymier Pierre A, Runge Keith
New Frontiers of Sound Science and Technology Center, University of Arizona, Tucson, AZ 85721, USA.
Department of Aerospace and Mechanical Engineering, University of Arizona, Tucson, AZ 85721, USA.
Sensors (Basel). 2024 Oct 11;24(20):6552. doi: 10.3390/s24206552.
This work presents numerical modeling-based investigations for detecting and monitoring damage growth and material nonlinearity in plate structures using topological acoustic (TA) and sideband peak count (SPC)-based sensing techniques. The nonlinear ultrasonic SPC-based technique (SPC-index or SPC-I) has shown its effectiveness in monitoring damage growth affecting various engineering materials. However, the new acoustic parameter, "geometric phase change (GPC)" and GPC-index (or GPC-I), derived from the TA sensing technique adopted for monitoring damage growth or material nonlinearity has not been reported yet. The damage growth modeling is carried out by the peri-ultrasound technique to simulate nonlinear interactions between elastic waves and damages (cracks). For damage growth with a purely linear response and for the nonlinearity arising from only the nonlinear stress-strain relationship of the material, the numerical analysis is conducted by the finite element method (FEM) in the Abaqus/CAE 2021 software. In both numerical modeling scenarios, the SPC- and GPC-based techniques are adopted to capture and compare those responses. The computed results show that, from a purely linear scattering response in FEM modeling, the GPC-I can effectively detect the existence of damage but cannot monitor damage growth since the linear scattering differences are small when crack thickness increases. The SPC-I does not show any change when a nonlinear response is not generated. However, the nonlinear response from the damage growth can be efficiently modeled by the nonlocal peri-ultrasound technique. Both the GPC-I and SPC-I techniques can clearly show the damage evolution process if the frequencies are properly chosen. This investigation also shows that the GPC-I indicator has the capability to distinguish nonlinear materials from linear materials while the SPC-I is found to be more effective in distinguishing between different types of nonlinear materials. This work can reveal the mechanism of GPC-I for capturing linear and nonlinear responses, and thus can provide guidance in structural health monitoring (SHM).
本文通过基于拓扑声学（TA）和边带峰值计数（SPC）的传感技术，对板结构中的损伤生长和材料非线性进行检测与监测，开展了基于数值建模的研究。基于非线性超声SPC的技术（SPC指数或SPC-I）已证明其在监测影响各种工程材料的损伤生长方面的有效性。然而，尚未有关于用于监测损伤生长或材料非线性的TA传感技术所衍生的新声学参数“几何相位变化（GPC）”和GPC指数（或GPC-I）的报道。损伤生长建模通过近场超声技术进行，以模拟弹性波与损伤（裂纹）之间的非线性相互作用。对于具有纯线性响应的损伤生长以及仅由材料的非线性应力 - 应变关系引起的非线性，在Abaqus/CAE 2021软件中通过有限元方法（FEM）进行数值分析。在这两种数值建模场景中，均采用基于SPC和GPC的技术来捕捉和比较这些响应。计算结果表明，在FEM建模中的纯线性散射响应下，GPC-I能够有效检测损伤的存在，但由于裂纹厚度增加时线性散射差异较小，无法监测损伤生长。当未产生非线性响应时，SPC-I没有任何变化。然而，损伤生长的非线性响应可以通过非局部近场超声技术有效地建模。如果频率选择得当，GPC-I和SPC-I技术都能清晰地显示损伤演化过程。本研究还表明，GPC-I指标有能力区分非线性材料和线性材料，而SPC-I在区分不同类型的非线性材料方面更有效。这项工作可以揭示GPC-I捕捉线性和非线性响应的机制，从而为结构健康监测（SHM）提供指导。
