Optimized Structural Damage Identification through Multi-Scale Embedded Sensing

卡拉·彼得斯(PI)，北卡罗来纳州立大学Mohammed Zikry(Co-PI)，北卡罗来纳州立大学提案编号0219690通过多尺度嵌入式传感器优化结构损伤识别拟议研究的主要目标是开发一种统一的传感和断裂方法，将用于识别金属结构系统中不同物理尺度的损伤。新的制造和加工技术被用于在金属系统中嵌入多路传感器，使得传感器和主体结构中的材料失配阻抗和残余应力都最小化。然后，这些传感器的位移、位移梯度和应变场被过滤，并与最近开发的三维本构和断裂方法相融合，以预测晶体固体中的失效起始和扩展。根据沿晶断裂和穿晶断裂对破坏模式和损伤进程进行了分类。需要一个基于物理的三维热力学预测框架来理解相互关联的影响，例如嵌入在主体金属材料中的传感器、界面的热、应力和应变梯度以及晶界分布和取向对全球水平上的结构响应的影响。这导致了基于物理材料机制的有效生命周期预测，而不是基于不充分的局部应变场分布的当前估计。传感器数据融合与三维预测框架的结合提供了对难以(如果不是不可能)进行实验研究的事件的洞察和理解，例如结构系统中的亚表面损伤和裂纹成核。实现这些相互关联的研究目标可以大大提高开发新一代现场损害识别和减轻系统的工程和科学能力。

Kara Peters (PI), North Carolina State UniversityMohammed Zikry (Co-PI), North Carolina State UniversityProposal No. 0219690Optimized Structural Damage Identification through Multi-Scale Embedded SensingThe proposed research has as its major aim the development of a unified sensing and fracture methodology that will be used to identify damage at different physical scales in metallic structural systems. New fabrication and processing techniques are used to embed multiplexed sensors in metallic systems, such that material mismatch impedances and residual stresses are minimized in both the sensors and the host structure. Displacement, displacement gradient, and strain fields from these sensors are then filtered and fused with recently developed three-dimensional constitutive and fracture methodologies to predict failure initiation and growth in crystalline solids. Failure modes and damage progression are categorized in terms of intergranular and transgranular fracture. A three dimensional thermo-mechanistic physically-based predictive framework is needed to understand interrelated effects, such as embedded sensors in host metallic materials, interfacial thermal, stress and strain gradients, and grain-boundary distributions and orientations on the structural response at the global level. This results in effective life cycle predictions that are based on physical material mechanisms, as opposed to current estimates based on inadequate local strain-field distributions. The coupling of sensor data fusion with the three dimensional predictive framework provides insight and understanding of events that are difficult, if not impossible, to experimentally study, such as subsurface damage and crack nucleation in structural systems. The realization of these interrelated research objectives could significantly improve engineering and scientific capabilities for developing a new generation of in-situ damage identification and mitigation systems.