Archive of Applied Mechanics最新文献

This paper addresses the computational modeling of selective laser sintering (SLS) and 3D concrete printing (3DCP) processes in advanced manufacturing. We focus on the phenomena experienced by their feedstock materials at the level of their mesoscale (the sintering powder grain and concrete aggregate scales) during the manufacturing process. Our approach is based on the discrete element method (DEM) for representing the material’s mechanical behavior. In SLS, the DEM is then combined with a lumped heat transfer model for describing the powder particles’ thermal states when scanned by the laser beam. In 3DCP, in turn, the DEM is combined with the so-called discrete fresh concrete (DFC) model for representing the fresh concrete paste rheology. We then present a simple numerical solution scheme followed by numerical simulations on two model-problems, with which we illustrate the applicability of such modeling approach.

The article is devoted to the study of the influence of flexural deformation of the body of a freight wagon on the indicators of the interaction of rail fleet with rails. The considered indicators depend both on the design of the rail fleet, its condition and speed, and on the design and condition of the railroad track. A theoretical study was carried out using a model of spatial vibrations of a freight wagon as part of a homogeneous train. When carrying out calculations, the wagon body was considered as an absolutely rigid body and as a discrete multi-mass system with elastic connections between the masses. When choosing a design scheme, it was assumed that the wagon body is a deformable body and, when bending, has finite rigidity in the vertical and horizontal planes. As a result of the research, the dependences of the dynamic indicators of a freight wagon on the flexural deformation of the body and the speed of movement were obtained. Based on theoretical calculations, the influence of the deformability of the body on the interaction of rail fleet with the railroad track on a tangent level track and curved section with irregularities was assessed.

The increasing popularity of composites reinforced with fiber has spurred the development of sophisticated additive manufacturing technologies, allowing for precise tailoring of fiber orientation for optimization purposes. Despite significant advancements in fiber orientation optimization, the key challenge posed by stress yield criteria still needs to be solved. This work presents a novel optimization approach, aiming to minimize structural volume while incorporating local stress constraints based on the Tsai–Wu criterion. The proposed NDFO-adapt method optimizes material distribution, fiber angles, and the penalization field. This optimization process involves multiple design variables, and new schemes are introduced to determine these variables using an optimization algorithm and adaptive continuations based on the structural grayscale. Numerical examples show the effectiveness of the proposed method, providing valuable insights for optimizing fiber-reinforced materials considering stress constraints with potential applications in the design of lightweight, high-strength structures.

Wire arc additive manufacturing enables the production of components with high deposition rates and the incorporation of multiple materials. However, the manufactured components possess a wavy surface, which is a major difficulty when it comes to simulating the mechanical behavior of wire arc additively manufactured components and evaluation of experimental full-field measurements. In this work, the wavy surface of a thick-walled tube is measured with a portable 3D scanning technique first. Then, the surface contour is considered numerically using the finite cell method. There, hierarchic shape functions based on integrated Legendre polynomials are combined with a fictitious domain approach to simplify the discretization process. This enables a hierarchic p-refinement process to study the convergence of the reaction quantities and the surface strains under tension–torsion load. Throughout all considerations, uncertainties arising from multiple sources are assessed. This includes the material parameter identification, the geometry measurement, and the experimental analysis. When comparing experiment and numerical simulation, the in-plane surface strains are computed based on displacement data using radial basis functions as ansatz for global surface interpolation. It turns out that the finite cell method is a suitable numerical technique to consider the wavy surface encountered for additively manufactured components. The numerical results of the mechanical response of thick-walled tubes subjected to tension–torsion load demonstrate good agreement with real experimental data, particularly when employing higher-order polynomials. This agreement persists even under the consideration of the inherent uncertainties stemming from multiple sources, which are determined by Gaussian error propagation.

This paper studies the dynamics of an atomic force microscopy (AFM) cantilever that is considered to be operating under continuous viscoelastic surface contact with material profiles based on the modified nonlocal theory of elasticity. The contact between the cantilever’s tip and the sample surface is modeled using a linear stiffness–damper pair and a lumped mass at the beam’s free end. The higher-order partial differential equation (PDE) governing the AFM nanocantilever transverse motion and its associated higher-order boundary conditions (BCs) are derived employing extended Hamilton’s principle based on the nonlinear nonlocal higher-order constitutive relation in Euler–Bernoulli beam model. The Galerkin’s decomposition method is applied to discretize the higher-order PDE and BCs of motion into a set of ordinary differential equations (ODEs) via the mode summation technique using eigenfunctions (mode shapes) of a classic cantilever thin beam. Then, using state-space form of ODEs of motion the frequency analysis is performed based on the eigenvalues of vibration motion. The obtained results are validated with the literature works. The impact of various parameters including nonlocal nanoscale elasticity parameter, added point mass, contact stiffness and viscous damping factors and the specific position where the concentrated mass and the contact stiffness–damper pair are attached to the beam on the resonant frequencies of AFM cantilever is comprehensively investigated. Numerical simulations showed that the resonance frequencies of the AFM cantilever increase by increasing the value of nonlocal nanoscale parameter. Also, it was concluded that an increase in the nonlocal parameter and surface contact stiffness leads the AFM cantilever to be more stiffened. Moreover, it was seen that by increasing the position distance of lumped mass on the beam and contact spring–damper pair from the beam’s fixed end, the resonant frequency reduction in the larger values of the surface contact stiffness is more noticeable.

The present work presents the investigation of the dynamics and influence of chaotic behavior on energy capture for a U-shaped structure (portal frame) that contains shape memory alloy (SMA), piezoelectric material (PZT), a nonlinear energy sink (NES) and a non-ideal excitation source represented by an unbalanced electric motor coupled to the U-structure. The mathematical model presents nonlinearities arising from the nonlinear stiffness of the U-structure, the NES system, the SMA, and the PZT material. Chaotic behavior is assessed through time history, bifurcation diagrams, phase diagrams, and the 0–1 test. Energy capture is carried out through a piezoelectric material (PZT), represented by a non-linear electromechanical coupling model, and electromagnetic induction generated by the non-linear electromagnetic energy sink coupled to the structure (NES). Dynamic analysis is performed through parametric analysis of parameters related to piezoelectric coupling and NES parameters. Numerical simulations demonstrate that the system has chaotic behavior for specific parameters and that its energy capture is influenced by parametric variation. It is shown numerically that the parameters of the SMA material, the PZT material, and the NES significantly influence the chaotic behavior and energy capture of the investigated electromechanical system.

This work studies crack propagation in particulate nanocomposites using the phase-field method. The crack propagation has been simulated in a wide range of loadings and the critical load for the crack growth has been obtained. Surface tension, as an inelastic stress, is introduced in the model in a thermodynamically consistent way. The effect of surface tension on the crack tip velocity and the crack evolution has been discussed. The finite element method via COMSOL multiphysics software has been utilized to solve the coupled phase-field and elasticity equations. Modeling and prediction of crack propagation for nanocomposites including different nanoparticles and under different loadings are the main purposes of this work. It is found that the kinetics and morphology of the crack propagation depend on the elastic moduli and the surface energy of nanoparticles as well as their longitudinal and angular distances to each other.

Scissor-like isolation platform (SIP) with magnetorheological damper (MRD) has been commonly studied and applied successfully in vehicle vibration isolation. This paper concerns passive nonlinear magnetorheological (MR) characteristics of the SIP via geometric nonlinearity induced by MRD’s layout ways. A dynamic parametric model of the SIP with six assembly types is derived based on Lagrange equation. Then, the parameter analysis is performed to estimate MR damping function in SIP. The analytical steady-state response of the isolator is derived using harmonic balance method, and its effectiveness is validated with numerical results. Metrics are defined to access the performance of the isolator, followed by comparison on displacement transmissibility for six types. The effect of MR damping coefficient and input amplitude on the performance of the isolator is investigated. Finally, comparative study with existing isolators is conducted. Results indicate that, passive MR damping is dependent on vibration displacement, which is beneficial to suppressing peak transmissibility with a little effect at non-resonant frequencies. The results also reveal that the isolator by type 1 or 3 has broader isolation band over other types. And the SIP in type 1 has wider isolation band and lower peak transmissibility compared with existing isolators in allowable workspace.

Solving problems in fluid mechanics plays an essential role in science and engineering, especially when it comes to optimal design, reconstruction of biomedical and geophysical flows, parameter estimation, and more. In some of these problems, only part of the data (or parameters) are known, and they fall within the broad categories of inverse and mixed problems. Thus, solving them using traditional methods is challenging or sometimes even impossible. Moreover, generating simulated data for such problems can become very costly since simulations need to be performed several times to either discover missing physics or calibrate the free parameters in the model. One possible alternative for overcoming these drawbacks is through the use of Physics-Informed Neural Networks—PINNs, in which we approximate the problem’s solution using neural networks (NNs) while incorporating the known data and physical laws when training it and also easily enabling us to take advantage of computational resources like GPUs. Here, we show a Level-Set PINN-based framework for reconstructing the velocity field for bubble flows. Given only the bubble position, we apply the framework to reconstruct gas bubbles rising in viscous liquid problems. We use synthetic data generated by adaptive mesh refinement and coarsening simulations with a different method, a phase-field approach. The only data provided is a set of snapshots containing the bubble position, i.e., the phase field, from which we try to infer the velocities. Our approach does not require any reinitialization scheme, as is usual when using a level-set approach and traditional numerical methods. Such a scheme can reconstruct the flow quantities with reasonable accuracy, and it is straightforward to parallelize when using a data-parallel approach.

A robust method for uncertainty quantification is undeniably leading to a greater certainty in simulation results and more sustainable designs. The inherent uncertainties of the world around us render everything stochastic, from material parameters, over geometries, up to forces. Consequently, the results of engineering simulations should reflect this randomness. Many methods have been developed for uncertainty quantification for linear elastic material behavior. However, real-life structure often exhibit inelastic material behavior such as visco-plasticity. Inelastic material behavior is described by additional internal variables with accompanying differential equations. This increases the complexity for the computation of stochastic quantities, e.g., expectation and standard deviation, drastically. The time-separated stochastic mechanics is a novel method for the uncertainty quantification of inelastic materials. It is based on a separation of all fields into a sum of products of time-dependent but deterministic and stochastic but time-independent terms. Only a low number of deterministic finite element simulations are then required to track the effect of (in)homogeneous material fluctuations on stress and internal variables. Despite the low computational effort the results are often indistinguishable from reference Monte Carlo simulations for a variety of boundary conditions and loading scenarios.