Fiber metal laminates (FMLs) are of high interest for lightweight structures as they combine the advantageous material properties of metals and fiber-reinforced polymers (FRPs). However, low-velocity impacts can lead to complex internal damage. Therefore, structural health monitoring with guided ultrasonic waves (GUWs) is a methodology to identify such damage. Numerical simulations form the basis for corresponding investigations, but experimental validation of dispersion diagrams over a wide frequency range is hardly found in the literature. In this work the dispersive relation of GUWs is experimentally determined for an FML made of carbon FRP and steel. For this purpose, multi-frequency excitation signals are used to generate GUWs and the resulting wave field is measured via laser scanning vibrometry. The data are processed by means of a non-uniform discrete 2d Fourier transform and analyzed in the frequency-wavenumber domain. The experimental data are in excellent agreement with data from a numerical solution of the analytical framework. In conclusion, this work presents a highly automatable method to experimentally determine dispersion diagrams of GUWs in FML over large frequency ranges with high accuracy.

Methodologies are developed for analyzing failure initiation and crack propagation in highly heterogeneous concrete mesostructures. Efficient algorithms are proposed in Python to generate and pack geometric features into a continuous phase. The continuous phase represents the mortar matrix, while the aggregates and voids of different sizes represent the geometric features randomly distributed within the matrix. The cohesive zone model (CZM) is utilized to investigate failure initiation and crack propagation in mesoscale concrete specimens. Two-dimensional zero-thickness cohesive interface elements (CIEs) are generated at different phases of the concrete mesostructure: within the mortar matrix, aggregates, and at the interfacial transition zone (ITZ). Different traction–separation laws (TSL) are assigned to different phases to simulate potential crack paths in different regions of the mesoscale concrete specimen. The mesoscale finite element simulations are verified using experimental results from the literature, with a focus on implementing mixed-mode fracture and calibrating its corresponding parameters with respect to the experimental data. In addition, the current study addresses the limited exploration of void effects in mesoscale concrete simulations. By investigating voids of diverse sizes and volume fractions, this research sheds light on their influence on the mechanical behavior of concrete materials. The algorithms for generating cohesive interface elements and concrete microstructures are described in detail and can be easily extended to more complex states. This methodology provides an effective tool for the mesostructural optimization of concrete materials, considering specific strength and toughness requirements.

This paper focuses on parametric model order reduction (PMOR) of guided ultrasonic wave propagation and its interaction with damage in a fiber metal laminate (FML). Structural health monitoring in FML seeks to detect, localize and characterize the damage with high accuracy and minimal use of sensors. This can be achieved by the inverse problem analysis approach, which employs the signal measurement data recorded by the embedded sensors in the structure. The inverse analysis requires us to solve the forward simulation of the underlying system several thousand times. These simulations are often exorbitantly expensive and trigger the need for improving their computational efficiency. A PMOR approach hinged on the proper orthogonal decomposition method is presented in this paper. An adaptive parameter sampling technique is established with the aid of a surrogate model to efficiently update the reduced-order basis in a greedy fashion. A numerical experiment is conducted to illustrate the parametric training of the reduced-order model. The results show that the reduced-order solution based on the PMOR approach is accurately complying with that of the high fidelity solution.