Fiber metal laminates (FML) are composite structures consisting of metals and fiber reinforced plastics (FRP) which have experienced an increasing interest as the choice of materials in aerospace and automobile industries. Due to a sophisticated built up of the material, not only the design and production of such structures is challenging but also its damage detection. This research work focuses on damage identification in FML with guided ultrasonic waves (GUW) through an inverse approach based on the Bayesian paradigm. As the Bayesian inference approach involves multiple queries of the underlying system, a parameterized reduced-order model (ROM) is used to closely approximate the solution with considerably less computational cost. The signals measured by the embedded sensors and the ROM forecasts are employed for the localization and characterization of damage in FML. In this paper, a Markov Chain Monte-Carlo (MCMC) based Metropolis-Hastings (MH) algorithm and an Ensemble Kalman filtering (EnKF) technique are deployed to identify the damage. Numerical tests illustrate the approaches and the results are compared in regard to accuracy and efficiency. It is found that both methods are successful in multivariate characterization of the damage with a high accuracy and were also able to quantify their associated uncertainties. The EnKF distinguishes itself with the MCMC-MH algorithm in the matter of computational efficiency. In this application of identifying the damage, the EnKF is approximately thrice faster than the MCMC-MH.

翻译：纤维金属层合板是由金属与纤维增强塑料组成的复合材料结构，在航空航天及汽车工业中作为优选材料受到日益广泛的关注。由于该材料构造复杂，不仅其设计与制造具有挑战性，损伤检测同样面临诸多难题。本研究聚焦于基于贝叶斯反演框架的纤维金属层合板导波损伤识别方法。鉴于贝叶斯推理需多次调用底层系统模型，本文采用参数化降阶模型以显著降低计算成本的方式逼近真实解。通过嵌入传感器实测信号与降阶模型预测值，实现了纤维金属层合板损伤的定位与表征。本文分别部署基于马尔可夫链蒙特卡洛的Metropolis-Hastings算法与集合卡尔曼滤波技术进行损伤识别。数值试验验证了两种方法的有效性，并从精度与效率角度对结果进行了比较。研究表明，两种方法均能高精度实现损伤多变量表征，并具备量化相关不确定性的能力。在计算效率方面，集合卡尔曼滤波显著优于MCMC-MH算法：在本损伤识别应用中，集合卡尔曼滤波的计算速度约为MCMC-MH的三倍。