Computers & Structures最新文献

{"title":"Density-based topology optimization using a deformable mesh","authors":"Kyusoon Jung ,&nbsp;Do-Nyun Kim","doi":"10.1016/j.compstruc.2025.107879","DOIUrl":"10.1016/j.compstruc.2025.107879","url":null,"abstract":"<div><div>Conventional density-based topology optimization suffers from blurred or stair-stepped boundaries of the optimized structure, difficulty in converting outputs to usable designs, and a significant increase in computational cost when fine meshes are used. To address these challenges, we propose a density-based topology optimization method that allows the deformation of the reference mesh by incorporating nodal displacements as additional design variables, which offers higher design flexibility even with coarse meshes. This approach improves resolution without the burden of dense discretization and eliminates post-processing by directly producing a binarized density distribution with smooth boundaries. Demonstrated on benchmark problems, our method is expected to streamline the design-to-fabrication process and holds promise for more efficient and accurate structural optimization.</div></div>","PeriodicalId":50626,"journal":{"name":"Computers & Structures","volume":"316 ","pages":"Article 107879"},"PeriodicalIF":4.4,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144516931","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Triangular DSPM15 shell element based on the Reissner-Naghdi model, an assumed orthogonal bending energy, and mixed transverse shear strains","authors":"Andi Makarim Katili ,&nbsp;Antonia Larese ,&nbsp;Roland Wüchner ,&nbsp;Kai-Uwe Bletzinger ,&nbsp;Irwan Katili","doi":"10.1016/j.compstruc.2025.107877","DOIUrl":"10.1016/j.compstruc.2025.107877","url":null,"abstract":"<div><div>This paper presents a 3-node triangular shell element based on the Reissner-Naghdi shell theory. The proposed shell element, DSPM15, has 5 DOFs per node. The element uses linear functions for translation displacements and incomplete quadratic functions for rotations. Adopting the concept of Bergan’s free formulation, the coupling of lower and higher modes in bending is assumed to be zero. The discrete shear projection method defines the mixed transverse shear strains to avoid the phenomenon of shear locking. Standard benchmark problems and comparisons with existing elements are used to evaluate the element’s performance. The new shell element is shear-locking free, has no spurious modes, and passes the standard benchmark tests for thick and thin shells with excellent convergence behaviour, reliability, and precision.</div></div>","PeriodicalId":50626,"journal":{"name":"Computers & Structures","volume":"316 ","pages":"Article 107877"},"PeriodicalIF":4.4,"publicationDate":"2025-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144510777","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Attention-enhanced convolutional neural network for predicting the full‐profile seismic response of buildings","authors":"Shuang Li,&nbsp;Changqing Li,&nbsp;Yicheng Chen,&nbsp;Chenyu Zhang,&nbsp;Changhai Zhai","doi":"10.1016/j.compstruc.2025.107874","DOIUrl":"10.1016/j.compstruc.2025.107874","url":null,"abstract":"<div><div>Data-driven surrogate models can predict the seismic response of buildings in near real-time. However, the current surrogate models for predicting the buildings’ full-profile seismic response (the peak seismic response of all floors) are only applicable to buildings with a specific number of floors due to their output dimensions are immutable. To solve this problem, a data pre-processing method based on spline interpolation is proposed to unify the data dimensions of the full-profile seismic response of different buildings. Then, the attention-enhanced convolutional neural network was established to predict the full-profile seismic response of buildings with arbitrary number of floors. The channel and spatial channel attention mechanism is adopted in the proposed surrogate model to fully extract the time–frequency features of ground motions as supplementary input of seismic intensity measures and building parameters to ensure the prediction accuracy and generalization capability. The excellent performance on numerical simulation and field sensing cases validates the effectiveness of the proposed method, and it is not necessary to retrain the surrogate model when predicting the full-profile seismic response of the new buildings under new earthquakes.</div></div>","PeriodicalId":50626,"journal":{"name":"Computers & Structures","volume":"316 ","pages":"Article 107874"},"PeriodicalIF":4.4,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144481139","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Bayesian meta-optimisation of variable stiffness composite cylinders for mass minimisation with manufacturing constraints","authors":"José Humberto S. Almeida Jr. ,&nbsp;Aravind Ashok ,&nbsp;Muhammad Uzair ,&nbsp;Saullo G.P. Castro","doi":"10.1016/j.compstruc.2025.107868","DOIUrl":"10.1016/j.compstruc.2025.107868","url":null,"abstract":"<div><div>This study presents a Bayesian Optimisation (BO) framework for the mass minimisation of variable-stiffness (VS) composite cylinders under multiple buckling constraints, incorporating manufacturing limitations derived from filament winding processes. A computationally efficient single-curvature finite element model is used to evaluate the linear buckling response of multilayered shells. BO simultaneously optimises fibre paths, number of layers, and thickness distribution, achieving comparable or improved performance relative to a Genetic Algorithm (GA) while reducing simulation time by up to 70 %. Across most design loads, BO delivers structurally efficient solutions with smooth thickness transitions and local stiffness tailoring. Although GA outperformed BO in the highest load case in terms of weight and buckling capacity, BO retained competitive performance and demonstrated higher modal richness. Buckling mode analyses revealed that BO designs support mixed-mode instabilities with greater circumferential complexity, enhancing structural adaptability. In contrast, GA designs exhibited more uniform fibre paths and axial-dominated modes, reflecting conservative reinforcement strategies. These findings highlight the capability of BO to exploit complex design spaces more effectively, offering a scalable and data-efficient alternative to traditional optimisation methods. The proposed framework is particularly well suited for high-fidelity, simulation-driven design of advanced composite structures where computational cost and manufacturability are critical constraints.</div></div>","PeriodicalId":50626,"journal":{"name":"Computers & Structures","volume":"316 ","pages":"Article 107868"},"PeriodicalIF":4.4,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144471419","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Geometrically nonlinear isogeometric topology optimization via extended finite element method","authors":"Jianli Liu ,&nbsp;Haobo Zhang ,&nbsp;Qiang Min ,&nbsp;Peng Li ,&nbsp;Jianzhong Yang ,&nbsp;Zhaohui Xia","doi":"10.1016/j.compstruc.2025.107871","DOIUrl":"10.1016/j.compstruc.2025.107871","url":null,"abstract":"<div><div>Geometric nonlinearity can accurately simulate and predict the large deformation behavior of structures, which is crucial for ensuring the safety and reliability of engineering designs. To address the limitations of existing linear isogeometric topology optimization methods in accurately describing the nonlinear behavior of boundary elements, this paper proposes a geometric nonlinear isogeometric topology optimization method based on the extended finite element method. Firstly, to accurately capture the nonlinear behavior of boundary elements, Gaussian integration points and weights of boundary elements are regenerated during the optimization process using the extended finite element method. Secondly, an isogeometric topology optimization model is developed based on the principle of minimum complementary work and the bi-directional evolutionary structural optimization method. A geometric nonlinear isogeometric analysis in the total Lagrangian formulation is proposed, and the incremental form of the Newton-Raphson method is employed to solve the nonlinear equilibrium equations, improving the accuracy and stability of structural response analysis. Lastly, the effectiveness and necessity of isogeometric topology optimization considering geometric nonlinearity are demonstrated through 2D and 3D examples. The results show that structures experience significant deformation under full load conditions, leading to the failure of linearly optimized structures, while geometrically nonlinear optimized structures exhibit superior performance.</div></div>","PeriodicalId":50626,"journal":{"name":"Computers & Structures","volume":"316 ","pages":"Article 107871"},"PeriodicalIF":4.4,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144470756","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Newton-Krylov-based implicit integration algorithm for single and multisurface plasticity","authors":"Rafael Abreu ,&nbsp;Cristian Mejia ,&nbsp;Deane Roehl","doi":"10.1016/j.compstruc.2025.107872","DOIUrl":"10.1016/j.compstruc.2025.107872","url":null,"abstract":"<div><div>Robust integration schemes for plasticity models allow accurate and efficient numerical simulations of materials such as metals, concrete, soils, and rocks. The Newton-Raphson method is a popular choice for solving systems of nonlinear equations in the context of plasticity problems. However, this method requires calculating the Jacobian matrix of the system of equations defined by the flow rule, the hardening/softening law, and the Karush-Kuhn-Tucker conditions. This task can be cumbersome, especially for complex and multisurface plasticity models. Therefore, this work proposes a novel numerical implicit integration scheme for multisurface plasticity based on a Jacobian-free Newton-Krylov method. Notably, the Karush-Kuhn-Tucker conditions are implemented based on well-established smooth complementary functions to consider multiple yield surfaces properly. The proposed algorithm is vastly versatile since it can be easily applied to diverse single and multisurface models under different stress conditions, including the plane stress condition. The computational efficiency of the proposed method is compared to other common integration schemes, focusing on evaluating different smooth complementary functions. The results highlight the effectiveness of the Jacobian-free Newton-Krylov method for integrating multisurface plasticity equations and its ability to handle challenging finite element problems.</div></div>","PeriodicalId":50626,"journal":{"name":"Computers & Structures","volume":"316 ","pages":"Article 107872"},"PeriodicalIF":4.4,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144481138","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A high-precision surrogate model for seismic vehicle-track-bridge system based on hybrid deep learning for nonlinear structural restoring forces","authors":"Kang Peng ,&nbsp;Zhipeng Lai ,&nbsp;Lizhong Jiang ,&nbsp;Wangbao Zhou ,&nbsp;Yuxi Xie ,&nbsp;Lei Xu","doi":"10.1016/j.compstruc.2025.107870","DOIUrl":"10.1016/j.compstruc.2025.107870","url":null,"abstract":"<div><div>This paper presents a novel high-precision surrogate model for seismic vehicle-bridge interaction, leveraging hybrid deep learning techniques to predict nonlinear structural restoring forces. By integrating deep learning predictions within a coupled high-speed vehicle-track-bridge (VTB) system model, this approach offers a significant advancement in simulating the complex nonlinear hysteretic behaviour of critical track-bridge components during seismic events. The innovative surrogate model effectively replaces traditional finite element-based nonlinear components with a machine learning-driven solution, thereby enhancing both computational efficiency and accuracy. Extensive evaluations under varying seismic intensities confirm the model’s precision in capturing structural and vehicular responses, as well as performance metrics related to vehicle derailment during earthquakes. The results demonstrate the robustness of the hybrid deep learning approach in accurately predicting dynamic responses and mitigating the risks of high-speed train derailments on seismically impacted bridges, making it a valuable tool for safety assessments in high-speed rail infrastructure. The methodology and code implementation are publicly available at <span><span>https://github.com/kanepro1998/Surrogate-Model</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50626,"journal":{"name":"Computers & Structures","volume":"316 ","pages":"Article 107870"},"PeriodicalIF":4.4,"publicationDate":"2025-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144365804","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Parallel reduced-order modeling for digital twins using high-performance computing workflows","authors":"S. Ares de Parga ,&nbsp;J.R. Bravo ,&nbsp;N. Sibuet ,&nbsp;J.A. Hernandez ,&nbsp;R. Rossi ,&nbsp;Stefan Boschert ,&nbsp;Enrique S. Quintana-Ortí ,&nbsp;Andrés E. Tomás ,&nbsp;Cristian Cătălin Tatu ,&nbsp;Fernando Vázquez-Novoa ,&nbsp;Jorge Ejarque ,&nbsp;Rosa M. Badia","doi":"10.1016/j.compstruc.2025.107867","DOIUrl":"10.1016/j.compstruc.2025.107867","url":null,"abstract":"<div><div>The integration of reduced-order models with high-performance computing is critical for developing digital twins, particularly for real-time monitoring and predictive maintenance of industrial systems. This paper presents a comprehensive, high-performance computing-enabled workflow for developing and deploying projection-based reduced-order models for large-scale mechanical simulations. We use PyCOMPSs’ parallel framework to efficiently execute reduced-order model training simulations, employing parallel singular value decomposition algorithms such as randomized singular value decomposition, Lanczos singular value decomposition, and full singular value decomposition based on tall-skinny QR. Moreover, we introduce a partitioned version of the hyperreduction scheme known as the Empirical Cubature Method to further enhance computational efficiency in projection-based reduced-order models for mechanical systems. Despite the widespread use of high-performance computing for projection-based reduced-order models, there is a significant lack of publications detailing comprehensive workflows for building and deploying end-to-end projection-based reduced-order models in high-performance computing environments. Our workflow is validated through a case study focusing on the thermal dynamics of a motor, a multiphysics problem involving convective heat transfer and mechanical components. The projection-based reduced-order model is designed to deliver a real-time prognosis tool that could enable rapid and safe motor restarts post-emergency shutdowns under different operating conditions, demonstrating its potential impact on the practice of simulations in engineering mechanics. To facilitate deployment, we use the High-Performance Computing Workflow as a Service strategy and Functional Mock-Up Units to ensure compatibility and ease of integration across high-performance computing, edge, and cloud environments. The outcomes illustrate the efficacy of combining projection-based reduced-order models and high-performance computing, establishing a precedent for scalable, real-time digital twin applications in computational mechanics across multiple industries.</div></div>","PeriodicalId":50626,"journal":{"name":"Computers & Structures","volume":"316 ","pages":"Article 107867"},"PeriodicalIF":4.4,"publicationDate":"2025-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144471418","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A k-medoids-based partitioned method for computational homogenization of heterogeneous materials","authors":"Modesar Shakoor","doi":"10.1016/j.compstruc.2025.107875","DOIUrl":"10.1016/j.compstruc.2025.107875","url":null,"abstract":"<div><div>Although they are very interesting for structural simulations involving heterogeneous materials, Finite Element (FE) squared approaches, often coined FE², are well-known to require great computational resources. The main challenge is that, for each integration point of the <em>coarse</em> structure, a so-called <em>fine</em> scale problem should be solved. In this work, a k-medoids-based partitioned FE² approach is proposed to directly tackle this challenge by effectively reducing the number of fine scale solves. At each coarse scale nonlinear iteration, coarse scale integration points are partitioned <em>a priori</em> based on the current coarse scale displacement gradient and fine scale internal variables using the k-medoids-based clustering algorithm. Stresses and tangent moduli are computed only for cluster medoids, and are then extended to the remaining non-medoid coarse scale integration points. Results with nonlinear material behavior such as hyperelasticity and elasto-plasticity show that the proposed method is a promising candidate for reducing the computational cost of FE² simulations.</div></div>","PeriodicalId":50626,"journal":{"name":"Computers & Structures","volume":"316 ","pages":"Article 107875"},"PeriodicalIF":4.4,"publicationDate":"2025-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144365805","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development of a coupled matrix-free LU-SGS solver for hypersonic laminar diatomic gas flows with decoupled vibrational energy mode","authors":"Adam Tater ,&nbsp;Jiří Holman","doi":"10.1016/j.compstruc.2025.107864","DOIUrl":"10.1016/j.compstruc.2025.107864","url":null,"abstract":"<div><div>This study presents a newly developed implicit solver for steady-state, thermally non-equilibrium, hypersonic laminar diatomic gas flows. The algorithm focuses on cases where the gas is highly vibrationally excited, necessitating the consideration of decoupled internal energy modes. This phenomenon is modeled using the Navier–Stokes equations, along with an additional transport equation for vibrational energy. Implemented within the OpenFOAM framework, the solver employs the finite volume method with an approximate HLLC Riemann solver, enhanced with a low-Mach correction. Furthermore, limited piecewise linear reconstructions are used for convective fluxes, while viscous fluxes are approximated using a central scheme. Time marching is performed using the first-order backward differentiation formula. The resulting equations are solved using a matrix-free lower-upper symmetric Gauss–Seidel scheme, enabling efficient simulations with a low memory footprint. The developed solver is applied to solve two distinct axisymmetric hypersonic flow problems: hollow-cylinder-flare and double-cone flows, both computed for two different sets of free-stream and wall conditions. The results are compared with experimental data and those obtained from equilibrium simulations. Finally, a comparison with a well-known explicit non-equilibrium solver and simulation data from literature is provided, focusing on accuracy and performance.</div></div>","PeriodicalId":50626,"journal":{"name":"Computers & Structures","volume":"316 ","pages":"Article 107864"},"PeriodicalIF":4.4,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144364558","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}