International Journal for Numerical Methods in Engineering最新文献

A novel Adaptive Mesh Refinement (AMR) strategy is developed and evaluated for transonic high-speed flows using Ansys Fluent. The algorithm for marking cells for adaptation is designed to systematically reduce local truncation errors based on the curvature of the primitive vector field. The algorithm for marking cells for adaptation is described in sufficient detail to be portable to other flow solvers that offer AMR. The relative importance of each primitive vector variable within the scheme is evaluated using both equal-weighting and optimized-weighting approaches. Variations of the proposed algorithm that use flow gradients or limit adaptation regionally are also investigated. The negative consequences of adaptation without enforcing the original smooth surface shape are demonstrated. An equal-weighted, primitive vector curvature-based strategy is shown to typically produce near-grid-independent results with an order of magnitude less grid required than classic grid refinement.

The proposed study presents a finite element direct formulation of limit analysis, allowing the application of the classical method of limit design to complex structures in two and three-dimensional environments. The main result of the proposed method is to recognize the admissible stress in a structure as a function of a finite number of nodal parameters that allow defining the optimal program to implement the lower bound theorem in the Melàn form. Direct limit analysis, devoted to calculating the collapse load, does not depend on the detailed knowledge of the load time history that is generally unknown. Specific formulation for masonry structure is described, focusing on the two-step approach at the constituent scale, mortar and brick fabric, and at the structural scale. Namely, at first, limit analysis has been applied to Representative Volume Elements describing the masonry fabric and has been used to calculate the limit strength along the principal axes of the material anisotropy. A second step involves applying the procedure to a structure composed of the material obtained from the previous analysis. The proposed examples describe a typical masonry building where the anisotropic behavior of the material, both in the elastic and plastic ranges, has been considered, highlighting the feasibility of the method and the accuracy of the obtained results. Finally, a literature case, the Prestwood bridge, whose experimental collapse load is obtained from a full-scale test by Page, has been analyzed to compare the method with experiment and different numerical approaches, confirming its feasibility and robustness.

A novel finite element particle method (FEPM) is proposed in this paper for modeling extreme deformation problems. In a material domain, regions undergoing small deformation are discretized using elements of the finite element method (FEM), while zones subject to extreme deformation are represented with particles of the material point method (MPM). A background grid, covering the entire computational domain, is employed to solve the momentum equations. To circumvent mesh distortion, distorted elements under extreme deformation are adaptively converted into particles during the simulation. The seamless coupling between elements and particles is naturally achieved through the background grid's single-valued velocity field. Furthermore, a contact method is introduced to handle interactions between distinct material domains. Several numerical examples, including symmetric rod impact, soil collapse, soil collapse with a base, penetration of a plate by a long rod projectile, and penetration of a plate by an explosively formed projectile, are studied using the proposed FEPM. The numerical results exhibit good agreement with published literature data and experimental results, demonstrating the effectiveness of FEPM in simulating extreme deformation scenarios.

The initial value problem under dense initial conditions (D-IVP) is a critical challenge in aerospace engineering, such as debris tracking and orbital uncertainty propagation. Finite-difference-based methods are the most commonly used methods for solving this type of problem; however, they suffer from low computational efficiency when dealing with large-scale D-IVPs due to their reliance on small integration step sizes to maintain accuracy. To address this issue, this paper proposes an efficient semi-analytical method that combines polynomial approximation with parallel large-step computation. Using Taylor expansion in the spatial domain, the proposed method expresses the solutions of the D-IVP as a polynomial with temporally varying coefficients, whose computational cost is independent of the problem's scale. In particular, these coefficients are efficiently derived through parallel integral iteration in large steps, bypassing the limitations of finite-difference methods. The method's performance is validated through three classical dynamic problems, and the computational results demonstrate that its efficiency advantage over conventional methods increases with the scale of the D-IVP.

This study presents a numerical investigation into the fracture behaviour of composite materials—an essential consideration for the reliable and sustainable design of lightweight structural components. To accurately capture their complex failure mechanisms, a phase-field approach integrated with an elasto-plastic material model was employed. The numerical framework utilised user-defined subroutines (UMAT and UEL) within ABAQUS, complemented by additional simulations performed on the open-source FEniCS platform. Deep Neural Network (DNN) is used to solve the phase field equations, and the results are compared to the standard FEA framework. Characteristic composite fracture features like crack deflection, kinking and nonlinearity are well presented. Given the inherent anisotropy and heterogeneity of composite materials, modelling their fracture behaviour remains challenging. Benchmark simulations, including force–displacement responses, were validated against published literature and demonstrated strong agreement. The diffuse nature of crack propagation—characteristic of the length-scale-dependent, nonlocal continuum mechanics underpinning the phase-field method—was effectively captured and illustrated through plots showing phase-field evolution relative to the distance from the crack centre.

This paper presents a topology optimization method based on peridynamics, considering the entire fracture process under quasi-static loadings. By simulating the complete crack propagation behavior under quasi-static loadings and employing the unique “bond” concept from peridynamics, the fracture performance is effectively quantified through a damage factor. The improvement of fracture resistance is achieved by directly controlling the elongation rate of the bonds. However, under quasi-static loading conditions, repeated sensitivity analysis is required which demands prohibitively high computational cost. To deal with the problem, a preset actual crack is introduced in each of the optimization iteration steps during the entire loading process. Consequently, the repeated sensitivity calculation of functionals whose values are dependent on displacement history become unnecessary. This approximation technique maintains computational efficiency while ensuring analytical accuracy. Numerical examples verify the effectiveness of the proposed method, and the results obtained demonstrate better fracture resistance performance than that from static analysis.

Interval-based multi-objective robust optimization aims to achieve high-performance solutions insensitive to uncertainty, has garnered significant attention. However, efficiently analyzing the maximum fluctuations of solutions in both objective and constraint functions to assess their robustness remains challenging. To address this issue, this article proposes an adaptive coevolution method, which can evaluate the maximum fluctuations of multiple candidate solutions with respect to a function in a single run. This method is integrated with the multi-objective evolutionary algorithm (MOEA) to develop a framework termed adaptive coevolution-based multi-objective robust optimization (AC-MORO) for solving multi-objective robust optimization problems. To evaluate the performance of AC-MORO, it is compared with MODE-RO on a set of benchmark problems; meanwhile, a performance metric is proposed to test the accuracy of the adaptive coevolution method in analyzing the robustness of solutions. The impact of various parameter settings on the efficiency of the proposed method is also investigated. Subsequently, a variant of the adaptive coevolution method is explored to further enhance the performance of AC-MORO. Finally, AC-MORO is applied to address robust optimization problems in practical engineering.

The study of debris-fluid-structure interaction (DFSI) poses challenges for engineers and animators alike due to its complex nature involving multiple materials, multiple phases, constitutive nonlinearity, and large deformations across varying scales. Current numerical methods frequently overlook critical aspects of DFSI, can be overly complicated to implement, and require excessive computational resources for practical applications. To alleviate this problem, this paper introduces a flexible and explicit Material Point Method (MPM) that achieves a 100-fold improvement over traditional MPM formulations in terms of CPU-based computation. The key improvement results from the implementation of computer graphics techniques (MLS-MPM, APIC, ASFLIP, Simple F-Bar) and hardware (Multiple Graphics Processing Units). However, while computer graphics prioritizes qualitative realism, engineering needs quantitative accuracy. Therefore, this paper concentrates on a series of DFSI validation benchmarks using an enhanced graphics tool for engineering applications. Carefully chosen examples highlight critical aspects of DFSI. To show stability and favorability for next-generation scales, we simulate 100,000 to 1,000,000,000 particles within hours for all benchmarks. Accuracy relative to experiments, analytical equations, and alternative numerical models is demonstrated.