Damage Identification in Fiber Metal Laminates using Bayesian Analysis with Model Order Reduction

TL;DR

This paper presents a Bayesian inverse approach combined with model order reduction for damage detection in fiber metal laminates using guided ultrasonic waves, demonstrating high accuracy and improved computational efficiency.

Contribution

It introduces a novel combination of Bayesian inference, reduced-order modeling, and sensor data for damage localization in FML, with a comparative analysis of MCMC and EnKF methods.

Findings

Both methods accurately characterize damage with quantified uncertainties.

EnKF is approximately three times faster than MCMC-MH in this application.

The approach effectively balances accuracy and computational efficiency.

Abstract

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.