Wave-based structural identification for real honeycomb sandwich structures has become an important research focus. However, most existing wave-based identification methods suffers from experimental uncertainties and a limited frequency range of applicability. To this end, we present a new wave-based structural identification framework, which includes two promising material identification methods – linear and nonlinear – suitable for honeycomb sandwich structures. The advantages of the identification process are reflected on two aspects: Firstly, the Algebraic Wavenumber Identification (AWI) technique reliably extracts complex wavenumbers over a wide frequency range under stochastic conditions, serving as input for the identification process. Secondly, a novel frequency-dependent, stepwise estimation strategy is proposed for honeycomb sandwich structures, greatly enhancing the precision of material parameter determination. Noteworthy, the proposed structural identifications enable the recovery of both equivalent dynamic and static mechanical properties. The experimental applications on a real beam, plate, and shell are presented. Key results show that (1) The proposed stepwise strategy reduces the relative error of wavenumbers of the tested beam to below 3.5%, improving parameter accuracy and ensuring estimation success; (2) For the tested plate, the estimated Young's modulus of skins, shear modulus of the core, and dynamic Hooke's matrix demonstrate satisfied precision; (3) It is the first to extract mechanical parameters of real curved structures using wave-based propagation parameters.

Accurate real material modeling is essential for structural dynamic analysis and design. Reliable structural parameters estimation, involving geometric and material parameters, is a key prerequisite, yet many existing methods primarily address homogenized material properties, which is inadequate for multilayer composites with complex geometrical core. To this end, this paper introduces a robust wave-based approach to structural parameter identification of individual layers, using only full-field displacement data. Specifically, the Algebraic K-Space Identification 2D technique (AKSI 2D) initially extracts wavenumber space (k-space) from measured structural responses, while surrogate optimization subsequently aligns this experimental k-space with the Wave Finite Element Method (WFEM)-derived numerical k-space to estimate structural parameters. The superiority of the proposed identification method stems from: (1) the ability of the AKSI 2D to automatically and accurately identify wavenumbers in any wave propagation direction from displacement fields on 2D grids, even in noisy environments, eliminating the need for complex filtering and specific point layouts; (2) the capacity of the WFEM in modeling wave propagation within multilayer structures with complex geometries, using unit cell-based operations within finite element software; and (3) the efficiency of the surrogate optimization in solving high-dimensional problems by finding the global minimum with high computational efficiency. To validate the accuracy of the proposed method, the structural parameters of each layer in two numerical cases, a four-layer laminated carbon fiber panel and a kelvin cell-based sandwich composite panel, are estimated. The inverted structural parameters show good agreement with the reference values, with an averaged relative error of less than 3.5%, even when a high level of white noise is added to the simulated displacement field. In addition, the structural parameters of a real parallelogram core sandwich panel is updated experimentally. These studies confirm that the proposed approach aligns with the intuitive decision-making of structural engineers for material characterization and modeling, offering adaptability for diverse structural design tasks.

This paper proposes an inverse method to characterize wave and energy propagation in curved structures, addressing the challenges of accurately obtaining dispersion curves, wavenumber space, and damping loss factors caused by their complex dynamics. The proposed method, Generalized Algebraic K-Space Identification (GAKSI) technique, is developed within the algebraic identification framework, enables the extraction of complex wavenumbers of multidimensional signals from full-field measured maps for the first time. By introducing iterated integrals and multivariate Laplace transform, the method can effectively filter signal noise, enhancing the accuracy of extracted wave propagation parameters. In this paper, the proposed method is applied to isotropic open shells with different geometric parameters and a real honeycomb cylindrical shell. Extracted results are compared with those from the reference methods. An in-depth analysis compares the characterization of shells and plates under varying signal noise levels. The findings demonstrate that the proposed method achieves high precision even under noisy conditions: the relative error for the extracted wavenumber converges to around 2.5% when the signal-to-noise ratio (SNR) exceeds 5, while the relative error for the extracted damping loss factor converges to approximately 5.5% when the SNR exceeds 10. Furthermore, the observations reveal that curvature-induced bending-membrane coupling enhances the damping properties, with this effect becoming more pronounced as the wave propagation direction transitions from the axial to the circumferential direction. These findings validate the capability of proposed method to characterize dispersion and damping properties in curved structures, offering promising potential for further applications in structural analysis, such as structural optimization and design.

A static-dynamic topology-sizing optimisation method is presented. The solution is based on a sequential Mixed-Integer Linear Programming solution and aims to minimise the mass of a structure subjected to concurrent constraints on static and dynamic response. It is shown that the classical problem of the dynamics of lightweight sandwich structures may be mitigated through core topology and face sheet thickness combinations, retaining the static load carrying capacity while presenting stringent dynamic properties at a low mass penalty. A numerical example, in the form of a load carrying sandwich beam which is excited at different frequencies, is used to demonstrate the method.

Despite the increasing availability of tools and methods for sustainable and circular product design (DfS), their uptake in practice is slow. This is also true in the automotive industry, where DfS is an important measure for addressing the industry's negative environmental and social impacts. To facilitate DfS implementation, this paper uses an analytic hierarchy process (AHP) and offers, for the first time, a classification and prioritisation of the barriers that need to be overcome when implementing DfS into vehicle development processes. Based on a systematic literature review and on an expert workshop, the top 15 DfS barrier factors were derived and divided equally into five groups, following a multi-level structure. These factors and groups formed the input for a survey-based analytic hierarchy process with 38 European industry experts. The results show that strategic issues are the most important barriers, followed by the group of operational, personal, external, and tool-related barriers. Among the 15 barrier factors identified, the top five were (1) an unclear link to profitability, (2) lack of top management support, (3) difficulties in handling trade-offs, (4) high operational costs, and (5) a lack of integration of DfS into corporate strategy. The results indicate that while external constraints already exert pressure on automotive companies, they still face particular challenges when attempting to integrate sustainability into corporate strategies and in transferring such strategies to DfS activities at the operational level. The study results may be used to inform managerial policy and further research.

The extraction of experimental wavenumbers through wavenumber identification methods holds a pivotal role in addressing realistic material identification. The robustness of wavenumber identification methods under practical conditions significantly influences the accuracy of estimated material properties. Thus, this study aims to propose two identification procedures, integrating two wave-based structural identification methods with the Algebraic Wavenumber Identification (AWI) technique, to precisely estimate the equivalent static and dynamic properties of honeycomb sandwich structures, respectively. The AWI method can identify reliable wavenumbers using structural response, serving as input for wave-based linear-and nonlinear-structural identification methods. Moreover, a novel frequency-dependent stepwise estimation strategy is proposed to significantly improve the estimation accuracy. This paper presents an application of the proposed identification procedures on material properties estimation of a real honeycomb sandwich beam.

A potential framework to estimate the volume of water stored in a porous storage reservoir from seismic data is neural networks. In this study, the man-made groundwater reservoir is modeled as a coupled poroviscoelastic-viscoelastic medium, and the underlying wave propagation problem is solved using a three-dimensional discontinuous Galerkin method coupled with an Adams-Bashforth time stepping scheme. The wave problem solver is used to generate databases for the neural network-based machine learning model to estimate the water volume. In the numerical examples, we investigate a deconvolution-based approach to normalize the effect from the source wavelet in addition to the network's tolerance for noise levels. We also apply the SHapley Additive exPlanations method to obtain greater insight into which part of the input data contributes the most to the water volume estimation. The numerical results demonstrate the capacity of the fully connected neural network to estimate the amount of water stored in the porous storage reservoir.

This contribution addresses elastic waveguiding properties of 1D-periodic lattice microstructures derived from the Kelvin cell. The Kelvin cell serves as a lattice template to subsequently introduce microstructural changes by imposing twists on the cell's square faces. Such modifications break the cell mirror symmetries and offer the possibility to adjust the wave filtering characteristics based on the twist angle and choice of tesselation. Band structure analyses reveal that altering the template geometry enforces frequency gaps stemming from coupled longitudinal-torsional modes and Bragg scattering. To validate the applicability of Kelvin cell lattice structures for vibration control, finite-size samples are manufactured from SLA-printing and tested in terms of transmission spectra. The experimentally observed frequency regions of reduced transmission correspond well with the band gap layout. Simulations of finite-size samples show that visco-elastic and frequency-dependent material behavior must be accounted for to numerically predict the measured transmission characteristics.

This paper presents a novel 3D lattice metastructure featuring customizable elastic moduli in all three dimensions, achieved through distorted Kelvin cells. These structures are fabricated using thermoplastic polyurethane (TPU) materials through selective laser sintering (SLS) additive manufacturing techniques. Static compression tests reveal significant recoverable deformations and near-zero Poisson effects. Numerical simulations indicate that distorted Kelvin cells (DKCs) exhibit lower transmission in the longitudinal direction compared to standard Kelvin cells within the frequency range of interest. Additionally, DKCs demonstrate increased coupling between longitudinal and transverse directions at resonant frequencies. Parametric studies explore various lattice sizes, face configurations (closed or open), twisting angles, and matrix materials. Optimization studies, focusing on different twisting angles on each pair's faces, aim to minimize the response under 400 Hz, showcasing the potential for tuning these structures for specific applications.

The tuning of the low frequency sound absorption of open-cell anisotropic porous materials is studied in the form of an optimisation problem. Using modelling based on micro-structural representations of the anisotropic elasticity, the dynamic viscous drag forces and cell porosity, a physically meaningful dependence between these quantities is ensured. The micro-geometry used here is Kelvin Cell based, which may be distorted in a controlled way, creating a degree of anisotropy in the dynamic viscous drag forces and the elastic properties. Here, the distortions are implemented through twisting the square faces of the cells, resulting in a controllable interaction between shear and compression in the elastic deformation. Low frequency sound absorption is maximised using a gradient-based optimisation approach. Importantly, the design parameters in this approach are purely geometrical: the twist angles, strut radii and the respective layer thicknesses. A multi-layer arrangement undergoing plane wave acoustic excitation is considered as an application example.