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Improvement in electric vehicle technology requires the lithium-ion battery system’s safe operations, protecting battery fire damage potential from road debris impact. In this research a design of sandwich panel construction with a lattice structure core is evaluated as the battery protection system. Additive manufacturing technology advancements have paved the way for lattice structure development. The sandwich protective structure designs are evaluated computationally using a non-linear dynamic finite element analysis for various geometry and material parameters. The lattice structure’s optimum shape was obtained based on the highest Specific Energy Absorption (SEA) parameter developed using the ANOVA and Taguchi robust design method. It is found that the octet-cross lattice structure with 40% relative density provided the best performance in terms of absorbing impact energy. Furthermore, the sandwich panel construction with two layers of lattice structure core performed very well in protecting the lithium-ion NCA battery in the ground impact loading conditions, which the impactor velocity is 42 m/s, representing vehicle velocity in highway, and weigh 0.77 kg. The battery shortening met the safety threshold of less than 3 mm deformation.

A methodology to predict key aspects of the structural response of masonry walls under blast loading using artificial neural networks (ANN) is presented in this paper. The failure patterns of masonry walls due to in and out-of-plane loading are complex due to the potential opening and sliding of the mortar joint interfaces between the masonry stones. To capture this response, advanced computational models can be developed requiring a significant amount of resources and computational effort. The article uses an advanced non-linear finite element model to capture the failure response of masonry walls under blast loads, introducing unilateral contact-friction laws between stones and damage mechanics laws for the stones. Parametric finite simulations are automatically conducted using commercial finite element software linked with MATLAB R2019a and Python. A dataset is then created and used to train an artificial neural network. The trained neural network is able to predict the out-of-plane response of the masonry wall for random properties of the blast load (standoff distance and weight). The results indicate that the accuracy of the proposed framework is satisfactory. A comparison of the computational time needed for a single finite element simulation and for a prediction of the out-of-plane response of the wall by the trained neural network highlights the benefits of the proposed machine learning approach in terms of computational time and resources. Therefore, the proposed approach can be used to substitute time consuming explicit dynamic finite element simulations and used as a reliable tool in the fast prediction of the masonry response under blast actions.

In the present paper, we propose a change in the representation of the discrete motion equations in structural non-linear dynamics to obtain an improvement in the stability of time numerical integrations. In particular, natural local state variables are indicated for a finite-element approach to beam problems. The results, relative to Newmark approximations for the variations in the displacement and velocity vectors, show a significant increase in the range of stability of the time-integration process and a reduction in the number of Newton iterations required in the time-integration steps. It is also shown that the proposed approach inherently obeys the law of conservation of energy.

Herein, state‐of‐the‐art multiscale modeling methods have been described. This research includes notable molecular, micro‐, meso‐, and macroscale models for hard (polymer, metal, yarn, fiber, fiber‐reinforced polymer, and polymer matrix composites) and soft (biological tissues such as brain white matter [BWM]) composite materials. These numerical models vary from molecular dynamics simulations to finite‐element (FE) analyses and machine learning/deep learning surrogate models. Constitutive material models are summarized, such as viscoelastic hyperelastic, and emerging models like fractional viscoelastic. Key challenges such as meshing, data variability and material nonlinearity‐driven uncertainty, limitations in terms of computational resources availability, model fidelity, and repeatability are outlined with state‐of‐the‐art models. Latest advancements in FE modeling involving meshless methods, hybrid ML and FE models, and nonlinear constitutive material (linear and nonlinear) models aim to provide readers with a clear outlook on futuristic trends in composite multiscale modeling research and development. The data‐driven models presented here are of varying length and time scales, developed using advanced mathematical, numerical, and huge volumes of experimental results as data for digital models. An in‐depth discussion on data‐driven models would provide researchers with the necessary tools to build real‐time composite structure monitoring and lifecycle prediction models.

A breathing crack is a typical form of structural damage attributed to long-term dynamic loads acting on engineering structures. Traditional linear damage identification methods suffer from the loss of valuable information when structural responses are essentially non-linear. To deal with this issue, bispectrum analysis is employed to study the non-linear dynamic characteristics of a beam structure containing a breathing crack, from the perspective of numerical simulation and experimental validation. A finite element model of a cantilever beam is built with contact elements to simulate a breathing crack. The effects of crack depth and location, excitation frequency and magnitude, and measurement noise on the non-linear behavior of the beam are studied systematically. The result demonstrates that bispectral analysis can effectively identify non-linear damage in different states with strong noise immunity. Compared with existing methods, the bispectral non-linear analysis can efficiently extract non-linear features of a breathing crack, and it can overcome the limitations of existing linear damage detection methods used for non-linear damage detection. This study’s outcome provides a theoretical basis and a paradigm for damage identification in cracked structures.

This work develops a simple finite element for the geometrically exact analysis of Bernoulli–Euler rods. Transversal shear deformation is not accounted for. Energetically conjugated cross-sectional stresses and strains are defined. A straight reference configuration is assumed for the rod. The cross-section undergoes a rigid body motion. A rotation tensor with the Rodrigues formula is used to describe the rotation, which makes the updating of the rotational variables very simple. A formula for the Rodrigues parameters in function of the displacements derivative and the torsion angle is for the first time settled down. The consistent connection between elements is thoroughly discussed, and an appropriate approach is developed. Cubic Hermitian interpolation for the displacements together with linear Lagrange interpolation for the torsion incremental angle were employed within the usual Finite Element Method, leading to adequate C 1 continuity. A set of numerical benchmark examples illustrates the usefulness of the formulation and numerical implementation.

Purpose The purpose of this paper is to present numerical simulations for magnetohydrodynamics natural convection of a nanofluid flow inside a cavity with an H-shaped obstacle based on combining artificial neural network (ANN) with the finite element method (FEM), and predict the heat transfer rate and system entropy. Design/methodology/approach The enclosure is assumed to be inclined. Changing the inclination angle results in a different obstacle shape, which affects the buoyancy force. Hence, different configurations of the contours of the fluid flow, isotherms and the entropy of the system are obtained. The outer walls of the cavity as well as the central part of the obstacle are kept adiabatic. The left vertical portion of the hindrance is cooled, whereas the right vertical part of the obstacle is a heated wall. Using dimensionless variables allows obtaining a dimensionless version of the governing system of equations that is solved via the consistency FEM. The coupled problem of pressure and velocity is overcome via the Increment Pressure Correction Scheme, which is known for its accuracy and stability for similar simple problems. A numerical computation is performed across a broad range of the governing parameters. A total of 304 data sets were used in the development of an ANN model. That data set was conducted from the numerical simulations. The data set underwent optimization, with 70% sets used for training the model, 15% for validation and another 15% for the testing phase. The training of the network model used the Levenberg–Marquardt training algorithm. Findings From the numerical simulations, it is concluded that the H-shaped obstacle boosts heat transfer rate in comparison with the I-shaped case. Also, raising the value of the inclination angle improves the entropy of the system presented by the Bejen number. Furthermore, strength heat transfer rate is obtained via decreasing the Hartmann number while this decrease decays the values of the Bejen number for both positive and negative amounts of the nonlinear Boussinesq parameter. Slower velocity and a better heat transfer rate characterize nanofluid compared with pure fluid. Leveraging the capabilities of the ANN, the developed model adeptly forecasts the values of both the average Nusselt and Bejen numbers with a high degree of accuracy. Originality/value A novel fusion of FEM and ANN has been tailored to forecast the heat transfer rate and system entropy of MHD natural convective flow within an inclined cavity containing an H-shaped obstacle, amid various physical influences.

Linear static response structural optimization has been developed fairly well by using the finite element method for linear static analysis. However, development is extremely slow for structural optimization where a non linear static analysis technique is required. Optimization methods using equivalent static loads (ESLs) have been proposed to solve various structural optimization disciplines. The disciplines include linear dynamic response optimization, structural optimization for multi-body dynamic systems, structural optimization for flexible multi-body dynamic systems, nonlinear static response optimization and nonlinear dynamic response optimization. The ESL is defined as the static load that generates the same displacement field by an analysis which is not linear static. An analysis that is not linear static is carried out to evaluate the displacement field. ESLs are evaluated from the displacement field, linear static response optimization is performed by using the ESLs, and the design is updated. This process proceeds in a cyclic manner. A variety of problems have been solved by the ESLs methods. In this paper, the methods are completely overviewed. Various case studies are demonstrated and future research of the methods is discussed.

Masonry buildings of historical centres are usually organized within aggregates, whose structural performance against seismic actions is challenging to predict and constitutes still an open issue. The SERA—AIMS (Seismic Testing of Adjacent Interacting Masonry Structures) project was developed to provide additional experimental data by testing a half-scale, two-unit stone masonry aggregate subjected to two horizontal components of dynamic excitation. In this context, this paper investigates the reliability of the modelling approach and the assumptions adopted to generate a three-dimensional continuum finite element model. The work involves two stages, namely a blind pre-diction and a post-diction phase, and proposes a series of simulation analyses including a strategy to shorten the actual records and save computation costs. The study was performed to investigate the extent of uncertainty in modelling for such masonry aggregates in relation to the experimental outcomes. Pre-diction results were proven to be not accurate in terms of predicted displacements and damage patterns. The upgrades introduced for the post-diction analyses, including the calibration of the elastic modulus and the introduction of a non-linear interface between the two units, allowed to improve the outcomes, with reasonable results in terms of predicted base shear force, displacements along Y-direction and damage pattern for the non-linear stage. The overall approach showed to be appropriate for the structural analysis of existing masonry aggregates, but the accurate modelling of this type of structure remains challenging due to the high level of uncertainties.

This work aims to analyze the effect of a new deformation measure in the response of 2D plates subjected to in-plane loads. The proposed formulation allows to clearly distinguish the energetic contribution of every involved deformation mechanism. The action functional is supposed to depend on a strain, a wryness and a new relative rotation tensor in the nonlinear hypothesis; therefore, the suggested approach seems to have a considerable potential to study granular materials. Some 2D samples characterized by complicated geometries are analyzed by means of the finite element method; parametric analyses are performed to test the role played by each material constant which appears in the new constitutive laws: both isotropic and orthotropic models are investigated.