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：金属金属层板(FML)是由金属和纤维强化塑料组成的复合结构(FML),在航空航天工业和汽车工业中,对材料的选择越来越感兴趣,由于材料的精密结构,不仅这种结构的设计和生产具有挑战性,而且其损坏探测也十分困难。这一研究工作的重点是在FML中通过以巴伊西亚模式为基础的反向方法,用引导超声波(GUW)识别损害。由于Bayesian 推断法涉及对基础系统进行多次查询,因此使用一个参数化的减序模型(ROM)来以大大降低计算成本来接近解决方案。由于嵌入式传感器和ROM预测所测量的信号被用于FMM的损坏定位和定性。在本论文中,基于Metopolis-Hastings(MH)的Markov连锁蒙特-Carlo(MC)算法和Ensemble Kalman过滤法(EnKFF)用于确定损害,在数值测试方法和结果中比较方法和结果与精确性和效率的比较,其精确度本身。发现,其精确度是成功的方法。