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.

The elastic constant tensor and its anisotropy are among the most critical mechanical properties, as they govern numerous mechanical phenomena and are prevalent in many natural materials. However, the efficient and accurate inverse design of metamaterials with desired elastic constants remains challenging, particularly for fully anisotropic elastic constants with low symmetries. Recent advances in artificial intelligence have opened new avenues to address this challenge. In this work, we propose a general framework that combines data-driven artificial neural networks with a gradient-based optimization algorithm to achieve high-precision inverse design of fully anisotropic elastic constants, exemplified using open cellular lattice Kelvin cells. First, an automatic parametric finite element method is introduced to calculate the elastic constants of any (distorted) Kelvin cells. Next, neural networks are developed to approximate the computationally costly finite element method, acting as the forward characterization function in the design process. Finally, an inverse design framework that integrates neural networks with a gradient-based optimization algorithm is proposed and validated. The successful design outcomes in practical examples, such as artificial bone implants and structures with unconventional Poisson's ratios, demonstrate the capability of our method to guide high-precision inverse design across various engineering applications.

The full model parameters estimation of heterogeneous multilayer composites (HMC), involving geometric parameters and static-dynamic viscoelastic properties, has attracted considerable attention for both damage diagnosis and the design of new materials. However, this remains a challenge in current research due to the complexity involved in identifying special layers. To this end, we developed a robust wave-based method to estimate the structural parameters of each layer in HMCs using full-field displacement data. The method follows a two-stage inversion process. In Stage I, it estimates geometric and elastic parameters, and in Stage II, it determines damping properties. These parameters can be static, dynamic, linear, nonlinear, or mixed. The objective is to optimize the identification process by combining the multi-scale wave and energy propagation modeling and characterization numerical methodology that automatically incorporates the limited knowledge on both the used predicted Finite Element model (whatever its complexity) and experimental data (inevitably noisy). The Condensed Wave Finite Element Method with Contour Integral solver (CWFEM-CI) is proposed to model wave and energy propagation in mesoscopic predicted models by solving a nonlinear eigenvalue problem. It enables complex wavenumber extraction in arbitrary directions while reducing computational cost through model order reduction approach, Component Mode Synthesis (CMS). At the macroscopic scale, Algebraic K-Space Identification 2D (AKSI 2D) is applied to retrieve complex wavenumbers from real materials, serving as reference data for inverse optimization. By embedding iterated integrals into the mathematical foundation of the method, signal noise is effectively suppressed, thereby ensuring accurate material identification. Finally, the identification problem is formulated and solved iteratively using the surrogate optimizer, which minimizes the difference between predicted and experimental wave propagation parameters. The accuracy and effectiveness of the proposed method are validated through numerical experiments on linear elastic, nonlinear viscoelastic, and heterogeneous multilayer models, using both synthetic and real full-field data.

In exterior acoustic simulations with the finite element method, accurately modeling an infinite domain using a finite computational space is challenging due to reflections at the truncated boundaries. This study introduces an m-th order operator for implementing absorbing boundary conditions that releases the geometrical constraints and minimizes reflections. Furthermore, the resulting finite element problem is naturally well-suited for a range of reduced-order models, such as the moment-matching, projection based well-conditioned asymptotic waveform evaluation (WCAWE), allowing efficient frequency sweep studies in large models. Our combined approach significantly enhances simulation efficiency, allowing for extensive frequency analysis with minimal domain size without compromising accuracy. However, the combination of higher order formulations of the considered absorbing boundary condition with the WCAWE approach also exhibits limitations in accuracy for a given size of reduced basis. This is aspect is the object of ongoing investigations.

The objective of this paper is to present a roadmap for the technology development toward a Planetary Sunshade System, a space-based solar geoengineering project aimed at reversible solar radiation modification to mitigate global warming. Earth's climate change is mostly due to the increasing concentration of greenhouse gases in the atmosphere, which leads to a general rise of the temperatures. A space-based geoengineering infrastructure has been previously proposed to reduce the oncoming solar irradiance, by placing a 'solar light umbrella', called Planetary Sunshade System, between the Sun and the Earth. To address the full development of a Planetary Sunshade System, a technology roadmap is needed which considers a step-by-step high-level plan of technology development, mission planning, launch preparation, international cooperation, highlighting the multi-phase development strategy from initial design to final deployment. First, the roadmap phases for production and deployment are outlined in chronological order. The analysis of technology development begins with the current technology readiness level, encompassing system design and factors such as mass, dimensions, area, and the total number of solar-sail satellites. Logistic aspects, including in-space assembly of the fully deployed system, are also examined. Finally, launch preparation is discussed encompassing heavy launcher design, facilities, production and launch sites. The proposed roadmap not only provides a starting point for the design and development of the Planetary Sunshade System but also a critical tool for evaluating the feasibility of direct climate action from space. Through this paper, we aim to establish the groundwork for a future Planetary Sunshade endeavour, and to contribute to the broader discussion on space-based climate action.

This paper presents a method to characterize six anisotropic thermal moduli for lattice structures, enabling the estimation of full anisotropic thermal elastic moduli. The study focuses on a group of distorted Kelvin cells, generated by twisting the four-node faces, to explore the relationship between distortion, anisotropic thermal expansions, and dynamic responses. Through parametric studies, the anisotropic thermal moduli are characterized as functions of the twisting angles, revealing that thermal moduli related to compression decrease with increasing twisting angles, while those related to shearing, which do not exist in isotropic materials, are identified. Dynamic responses reveal complex modal shapes and coupling between longitudinal and transverse directions, enhancing vibration mitigation. The proposed lattices and methods offer a promising structure for assembling and designing dual-functional metamaterials, featuring customizable thermal elastic moduli, ease of space assembly, lightweight structure, and effective vibration mitigation capabilities.

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.

This paper investigates the vibration characteristics of multi-span beams resting on an elastic foundation and subjected to axial forces. A comprehensive analytical expression of the dynamic response of multi-span beams on an elastic foundation that is developed to address various boundary conditions. The vibration equation is derived by employing Newton's second law. By Laplace transformations and the Green's function method, the solution of this governing equation can be obtained. Subsequently, a unified description is implemented for distinct types of boundary conditions using matrix representations. The correctness is verified through reference results and finite element methods (FEM). The effects of different parameters such as support stiffness, foundation elastic and shear layer stiffness, and axial force on the vibration characteristics are analyzed. This study demonstrates two findings: First, there are two thresholds for support stiffness, and the stiffness value is divided into three intervals. In the same interval, multi-span beams show the same properties. Second, for a rigidly supported multi-span beam, the critical axial force with a natural frequency of zero is just the corresponding Euler's buckling load; for elastically supported multi-span beams, the critical axial force falls between the Euler's buckling load corresponding to single-span and multi-span beams.