The transition from light internal combustion engine (ICE) vehicles, such as cars and vans, to light electric vehicles (EVs) presents an opportunity to reduce road traffic noise exposure in urban environments, which still needs to be quantified. Although the noise emissions of light ICE vehicles are generally well understood, including during acceleration and deceleration, the noise emitted by light EVs has so far not been studied as thoroughly, in particular during acceleration. This study thus proposes a correction model for the noise emissions of light EVs during acceleration, based on pass-by measurements under reference conditions. Data were collected for 6 vehicle models at both steady speed and full acceleration. The difference in noise levels between these two conditions was analysed to develop the correction model. This correction model accounts for both speed and acceleration at an octave-band level. The resulting model shows that acceleration has no impact on the noise emissions of light EVs in the 63 and 125 Hz octave bands, and that acceleration may increase the overall A-weighted emissions of a light EV by up to 5 dBA, at 20 km/h. Furthermore, the analysis suggests that deceleration does not increase noise emissions for light EVs. This contribution paves the way for the integration of EV-specific noise emissions into noise exposure assessment frameworks, enabling a more comprehensive understanding of the potential benefits associated with the transition towards EVs.

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 assessment of the exposure to road traffic noise pollution and of associated health conditions is usually based on energy-average noise levels. However, the number of noise events to which an individual is exposed has proven essential to the prediction of annoyance and sleep disturbance. Unfortunately, no standard method has been adopted for the counting of noise events. To address this shortcoming, Brown and De Coensel designed, in 2018, a generalised algorithm for the detection of road traffic noise events. The authors evaluated the performance of this algorithm for multiple sets of input parameters, but the setup employed for this testing was simplistic. The present study thus aims to benchmark the proposed parameter sets for the noise event detection algorithm in a controlled but realistic environment, consisting of a calibrated microscopic traffic simulation in the entire city of Tartu, Estonia, which includes interrupted traffic conditions and urban infrastructure. The performance assessment of a parameter set is shown to be highly dependent on context, i.e., location and time of day, making definitive, universally applicable conclusions unrealistic. Rather, this study enables comprehensive insights that guide the selection of adapted parameter sets for various traffic situations, including the number of parameter sets, suitable detection thresholds, and recommended time gaps to implement.

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.

This study introduces an agent-specific assessment method of traffic noise exposure in agent mobility simulations. The assessment is achieved through a combination of an energy-based noise exposure impact assessment using noise exposure cost, and the state-of-the-art traffic noise prediction tool NoiseModelling coupled with the activity-based agent mobility simulation software MATSim. The agent-specific noise exposure cost is a measure to evaluate how the noise emissions from the transport of agents relate to the noise-related impact on other agents performing stationary activities. By introducing an agent-specific level, each agent’s individual responsibility for the noise exposure may be estimated. The potential of the agent-specific noise exposure cost concept, combined with the MATSim-NoiseModelling framework, is illustrated through a case study, applying activity-based agent mobility simulations across Nantes, France. The results of the case study highlight, among other considerations, the insights that an agent-specific, activity-based noise exposure cost approach provides by visualizing the noise exposure ”footprint” resulting from an agent’s transportation activities.

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.

Simulating the exterior noise levels created by the train equipment requires a good representation of the equipment integration, the noise propagation, the ground reflection and, for some cases, the diffraction in the different train surfaces. In the framework of the European project Shift2Rail program S2R-CFM-CCA-01-2019 Energy and Noise and Vibration, the state-of-the-art of the exterior noise of rolling stock vehicle simulation is being compared between railway manufacturers and oper-ators and the advanced techniques proposed by the consortium Open Call project S2R-OC-CCA-01-2019 (TRANSIT https://transit-prj.eu). With respect to previous work done in the same direction (ACOUTRAIN https://cordis.europa.eu/project/id/284877/reporting/es, SILENCE http://www.silence-ip.org,FINE-1 https://projects.shift2rail.org/s2r_ipcc_n.aspx?p=FINE%201), the focus of the work is on the train integration effects of equipment in a rolling stock.