Yu Chengguo, Gao Xinyu, Liao Wenlin, Zhang Zhili, Wang Guishan
Xi'an Research Institute of High Technology, Xi'an 710025, China.
Facility Design and Instrumentation Institute, China Aerodynamics Research and Development Center, Mianyang 621000, China.
Micromachines (Basel). 2022 Jun 8;13(6):910. doi: 10.3390/mi13060910.
Smart deformable structures that integrate designing, sensing, and controlling technology have been widely applied in the fields of aerospace, robotics, and biomedical engineering due to their multi-functional requirements. The deformation reconstruction method essential for security monitoring and shape controlling, especially for the large deflection deformation, remains a challenge on accuracy and efficiency. This paper takes a wind tunnel's fixed-flexible nozzle (FFN) plate as the research object to develop a highly accurate deformation reconstruction method based on sensing information from flexible strain sensors. The mechanical behaviors of the FFN plate with large deflection deformation, which is modeled as a cantilever beam, are studied to analyze the relationship of the strain and moment. Furthermore, the large deflection factor and shell bending theory are creatively utilized to derive and modify the strain-moment based reconstruction method (SMRM), where the contour of the FFN plate is solved by particular elliptic integrals. As a result, structural simulation based on ABAQUS further demonstrates that the reconstruction error of SMRM is 21.13% less than that of the classic Ko-based reconstruction method (KORM). An FFN prototype accompanied by customized flexible sensors is developed to evaluate the accuracy and efficiency of the SMRM, resulting in a maximum relative error of 3.97% that is acceptable for practical applications in smart deformable structures, not limited to the FFN plate.
集成设计、传感和控制技术的智能可变形结构，因其多功能需求已在航空航天、机器人技术和生物医学工程等领域得到广泛应用。对于安全监测和形状控制至关重要的变形重建方法，尤其是对于大挠度变形，在精度和效率方面仍然是一个挑战。本文以风洞的固定-柔性喷管（FFN）板为研究对象，基于柔性应变传感器的传感信息开发一种高精度变形重建方法。将具有大挠度变形的FFN板建模为悬臂梁，研究其力学行为以分析应变与弯矩的关系。此外，创造性地利用大挠度因子和壳体弯曲理论推导并修正基于应变-弯矩的重建方法（SMRM），其中FFN板的轮廓通过特殊椭圆积分求解。结果，基于ABAQUS的结构模拟进一步表明，SMRM的重建误差比经典的基于Ko的重建方法（KORM）小21.13%。开发了一个配有定制柔性传感器的FFN原型，以评估SMRM的精度和效率，结果最大相对误差为3.97%，这对于智能可变形结构（不限于FFN板）的实际应用是可以接受的。
