Brake squeal remains a major challenge for the automotive industry due to its transient nature and its multiphysics, multiscale characteristics. This study advances the understanding and prediction of these dynamic instabilities through experimental analysis using an instrumented pin-on-disk system. The proposed multiphysics and multiscale framework reveals a correlation between the onset of dynamic instability and contact localization during a friction test. The results indicate that contact localization shaped by wear history and thermomechanical evolution plays a key role in triggering the instability.

Riveted assemblies play a crucial role in the strength and failure of aeronautical structures subjected to shock and impact. The finite element evaluation of stress concentrations in these zones necessitate accurate modeling of both the full-scale structure and the local assembly zone to evaluate the structure survivability. To address this challenge, an approach based on the modeling of the assembly zones using Hybrid-Trefftz Displacement super-elements was considered. However, the main drawback of this approach was its restriction to linear problems caused by the use of Kolosov-Muskhelishvili (perforated membrane) analytical solution in the formulation process.To overcome this limitation, we propose to replace the analytical basis functions with numerical ones and to explore the integration of a Proper Orthogonal Decomposition basis into a finite element formulation. The results obtained in single and multi-hole problems show the cost-effectiveness and versatility of the Hybrid-Displacement POD-based finite element formulation. Guidelines are provided to build an efficient and versatile Hybrid-Displacement POD-based finite element formulation. Future work will focus on extending the proposed finite element formulation to non-linear problems.

Understanding the mechanisms that trigger braking instabilities is essential for more reliable brake-noise simulations, but remains challenging despite previous experimental and numerical studies. This study investigates brake squeal through the development of an experimental–numerical model that reproduces several experimentally observed instabilities and incorporates an enhanced description of the contact interface with uncertain parameters. Using complex eigenvalue analysis and surrogate models, the key parameters triggering each instability mode are identified, enabling efficient exploration of the parameter space. The methodology also makes it possible to assess contact localization as a driver of instability via virtual braking tests. The results show that contact conditions strongly influence the onset of dynamic instabilities. By connecting experimental observations with numerical modeling through surrogate modeling, this approach provides deeper insight into the mechanisms governing brake squeal and emphasizes the importance of an enriched contact-interface description for predictive modeling.

Functional covariates arise in many scientific and engineering applications when model inputs take the form of time-dependent or spatially distributed profiles, such as varying boundary conditions or changing material behaviours. In addition, new practices in digital simulation require predictions accompanied by confidence intervals. Models based on Gaussian processes (GPs) provide principled uncertainty quantification. However, GPs capable of jointly handling functional covariates and multiple correlated functional tasks remain largely under-explored. In this work, we extend the framework of GPs with functional covariates to multitask problems by introducing a fully separable kernel structure that captures dependencies across tasks and functional inputs. By taking advantage of the Kronecker structure of the covariance matrix, the model is made scalable. The proposed model is validated on a synthetic benchmark and applied to a realistic structure, a riveted assembly with functional descriptions of the material behaviour and response forces. The proposed functional multitask GP significantly improves over single task GPs. For the riveted assembly, it requires less than 100 samples to produce an accurate mean and confidence interval prediction. Despite its larger number of parameters, the multitask GP is computationally easier to learn than its single task pendant.

This article focuses on optimizing computational efficiency in the analysis of magneto-vibroacoustic models, particularly when addressing parametric variations introduced by manufacturing imperfections. The computational cost of using the high-fidelity Finite Element Method in such detailed analyses can be significant, especially when multiple scenarios need to be explored. Moreover, a certain degree of accuracy is required in electromagnetic quantities of interest before any accurate vibroacoustic qualitative analysis can be performed. To address this, advanced Reduced-Order Model techniques, such as an enhanced Greedy Proper Orthogonal Decomposition and double Component Mode Synthesis, are developed. These techniques not only reduce computational time but also retain high accuracy in capturing the vibroacoustic response of the system. The proposed approach offers an efficient numerical framework to account for a wide range of manufacturing-induced variations (eccentricities, supply harmonics and mechanical tolerances), making it highly suitable for early-stage design assessment.

The work presented here proposes a contribution on the analysis of brake squeal phenomenon using a transient coupled finite element-discrete element method (FEM-DEM) simulation with pad surface topography evolution. To build the coupled FEM-DEM model, a non-overlapping strong coupling is first employed between the FEM and DEM subdomains. Second, a new calibration methodology of the DEM microscopic properties is proposed based on the eigenvalue analysis of the full model. The results of the coupled FEM-DEM model show a good agreement in terms of unstable frequencies and the evolution of the pad contact state history when compared to full FEM models, both for new and worn pad topographies. The evolution of the pad surface topography during the transient analysis results in a complex frequency behaviour, with abrupt shifts of instabilities and new operating deflection shapes, in agreement with reported experimental results. The proposed coupled FEM-DEM model thus seems to be a valuable tool for a better understanding of the squeal triggering due to the evolution of the pad surface topography. This contribution paves the way to advanced numerical analyses of brake squeal phenomenon, which triggering conditions are still under investigation.

Short-fiber-reinforced-materials are widely used in industry today. In this paper, microstructure and modal analyses are performed on short-glass-fiber-reinforced polypropylene (PPGF) and short-natural-fiber-reinforced polypropylene (PPNF), to study possible links between the first natural frequency and fibers' orientation and quantify the associated variability. In view of this, different specimens were tested with clamped-free boundary conditions. The microstructural analysis, performed with micro-computed tomography studies fibers' properties in injection-molded plates. This study shows the importance of considering real fibers' orientation for modal predictions.

Despite numerous works over the past two decades, friction-induced vibrations, especially braking noises, are a major issue for transportation manufacturers as well as for the scientific community. To study these fugitive phenomena, the engineers need numerical methods to efficiently predict the mode coupling instabilities in a multiparametric context. The objective of this paper is to approximate the unstable frequencies and the associated damping rates extracted from a complex eigenvalue analysis under variability. To achieve this, a deep Gaussian process is considered to fit the non-linear and non-stationary evolutions of the real and imaginary parts of complex eigenvalues. The current challenge is to build an efficient surrogate modelling, considering a small training set. A discussion about the sample distribution density effect, the training set size and the kernel function choice is proposed. The results are compared to those of a Gaussian process and a deep neural network. A focus is made on several deceptive predictions of surrogate models, although the better settings were well chosen in theory. Finally, the deep Gaussian process is investigated in a multiparametric analysis to identify the best number of hidden layers and neurons, allowing a precise approximation of the behaviours of complex eigensolutions.

This paper presents a new method to efficiently approximate both linear buckling loads and associated mode shapes of finite element structures subject to perturbations. To achieve this, a coupling between a Reduced Order Model (ROM) based on the Homotopy Perturbation Method (HPM) and a Kriging model is presented here. The ROM maintains the link between eigenvalues, related eigenvectors and the dependencies between each eigenvector components, leading to a high precision level. The computational time is greatly reduced by the surrogate model which avoids the computation of modified finite element matrices for each prediction. Next, the capabilities of the method allow to efficiently handle the prediction, sensitivity and optimization steps of an uncertain propagation problem using fuzzy formalism. Additive Manufacturing is a powerful and impressive process but many factors can be responsible for relatively large discrepancies in the mechanical and geometrical characteristics of the manufactured structure. Lastly, a study shows how the proposed fuzzy strategy allows the prediction of the buckling variability of a set of lattice structures. (c) 2021 Elsevier Inc. All rights reserved.

ABSTRACT The evolution of mechanical properties of NR with carbon black fillers was examined after a thermal aging step through both experimentation and non-deterministic numerical simulations. A quantification of mechanical properties and associated variability is first proposed for a set of specimens exposed at different temperatures and exposure times. Second, a family of stretch–stress laws is numerically built with a James' hyperelastic model. Next, the whole of the behavior evolution is modeled with a Kriging model to quantify the effects of properties on a macroscopic stiffness, useful in dynamic simulations, and the least-favorable scenario is so determined. Finally, Arrhenius method is performed to numerically draw the evolution bounds of macroscopic stiffness as a function of aging exposure, followed by a comparison with a naturally aged suspension component. To our knowledge, the methodology developed has not already been proposed in this area.

This paper puts forward a projection technique for accurately calculating solutions of large Quadratic Eigenvalue Problem. The aim here is to stabilize the complex eigensolutions whilst reducing residual errors, especially when considering significant damping contribution or asymmetric stiffness matrices. Hence, more confident results can be obtained in the frequency band of interest. To achieve this, high order modes, calculated using the homotopy perturbation technique, are introduced in the projection step of the classical method. This numerical proposal is a generalization of the classical projection, based only on normal modes of the associated undamped problem. To evaluate the efficiency of the suggested method, a finite element application dedicated to a friction-induced vibration problem is investigated. (C) 2020 Elsevier Ltd. All rights reserved.

Injection-molded short-fiber-reinforced thermoplastics are widely used in today's industry. Nevertheless, their mechanical behavior is difficult to model, especially because of strong anisotropy induced by complex fiber distributions of orientation. Moreover, the intrinsic variability of plant fibers' properties leads to an even more complex behavior than with mineral fibers and therefore increase the uncertainty for behavior prediction of these materials. The aim of this review is to provide basic and more specific knowledge about dealing with the uncertainty related to injection molded short-plant-fiber-reinforced thermoplastics behavior, focusing on variability induced by both injection-molded process and natural variability of plant fibers properties. To achieve this goal, it is important to understand the behavior of SFRT before considering the uncertainty induced by the use of natural fibers. Thus, in the first place, the authors have chosen to limit the sources of uncertainty related to fibers by studying the case of a short-glass-fibers-reinforced thermoplastic. Then, after discussing the sources of uncertainty related to the use of natural fibers, the methods for the quantification, the propagation and the management of uncertainties are analyzed.

Transport industry and, more specifically, railway industry, is confronted with a permanent need of improvement of its products. The competitiveness of rolling stock does not come only from low-cost production, but also from wise-calculated lifecycle costs. Nowadays, many contracts for railway operators include not only rolling stock, but also its maintenance services throughout its lifetime, which may reach up to 30% of global costs. Hence, deep knowledge about the system’s ageing is a strong asset to ensure a good performance, both on quality of service and financial costs. Rubber parts are widely used in railway technology because of their mechanical properties, providing both stiffness and, to a certain extent, additional damping and vibration filtering. Unlike metallic parts, whose mechanical properties remain relatively stable, rubber’s behaviour can change throughout a lifecycle, due to service loads and environmental influence. Such changes might have an impact on the system’s overall behaviour and lead to undesirable scenarii. For a given bogie model, we seek to estimate the stiffness variation of some rubber parts, which are deemed critical for safe operation.

The paper focuses on the definition of a reduced order model for linear modal analysis. The aim is to supply a suitable mathematical alternative tool compatible for multiparametric analysis of large finite element model considering numerous variable parameters, numerous mode shapes and significant levels of variation. The initial full eigenvalue problem is so replaced by a reduced one considering an efficient projection basis. To build it, we propose to combine homotopy transformation and perturbation technique for each parameter direction to define a reduced order model compatible with the design space. Finally, a complete finite element application highlights the capabilities of the proposal in terms of precision and computational time. (C) 2017 Elsevier Ltd. All rights reserved.