Computer Methods in Applied Mechanics and Engineering最新文献

{"title":"Evolutionary topology optimization with stress control for composite laminates using Tsai-Wu criterion","authors":"Xubo Zhang, Yiyi Zhou, Liang Xia, Yi Min Xie, Minger Wu, Yue Li","doi":"10.1016/j.cma.2024.117570","DOIUrl":"https://doi.org/10.1016/j.cma.2024.117570","url":null,"abstract":"In this study, a topology optimization technique with stress control is proposed for the composite laminates. The bi-directional evolutionary structural optimization (BESO) method is selected to avoid the stress singularity. The technique expresses the failure index based on the Tsai-Wu criterion, thereby ensuring a comprehensive consideration of the anisotropy. To address the local nature of stress and multi-layer structure of laminates, a nested <ce:italic>p</ce:italic>-norm is employed, providing a global approximation of the local maximum failure index. To mitigate the highly non-linear stress behaviors, filter schemes are applied to both sensitivity numbers and design variables. Sensitivity numbers are derived via adjoint sensitivity analysis and Lagrange multipliers to address stress-based and stress-constrained problems. The proposed framework is validated through systematic numerical studies across various examples and conditions, offering insights into the topological behaviors of composite laminates. This study underscores the importance of considering distinct tensile and compressive strength in composite materials, which represents a key innovation. Additionally, the relationships among stiffness-based, stress-constrained, and stress-based designs are explored in depth.","PeriodicalId":55222,"journal":{"name":"Computer Methods in Applied Mechanics and Engineering","volume":"15 1","pages":""},"PeriodicalIF":7.2,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142684302","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Non-intrusive parametric hyper-reduction for nonlinear structural finite element formulations","authors":"Davide Fleres, Daniel De Gregoriis, Onur Atak, Frank Naets","doi":"10.1016/j.cma.2024.117532","DOIUrl":"https://doi.org/10.1016/j.cma.2024.117532","url":null,"abstract":"Model Order Reduction (MOR) is a core technology for the creation of comprehensive executable Digital Twins, since it efficiently reduces the computational burden of high-fidelity models. When dealing with nonlinear structural Finite Element analyses, several Hyper-Reduction (HR) approaches have been developed to reduce the computational cost. Nonetheless, HR approaches are typically intrusive in nature, posing challenges when it comes to integration into existing (commercial) software. Recently, data driven Non-Intrusive MOR methodologies have been proposed. However, these techniques often suffer from overfitting and violate key physics properties, leading to unstable behavior. This work proposes to use Scientific Machine Learning to reintegrate critical stability-preserving physics properties. It introduces a data-driven, physics-augmented, parametric approach that combines Proper Orthogonal Decomposition (POD) with a Partially Input Convex Neural Network (PICNN) architecture. The proposed method effectively reduces the computational burden associated with parametric static nonlinear elastic structural problems while retaining material consistency, hyper-elasticity, and material stability properties in the Reduced Order Model. Numerical validation on several structural models subjected to geometrical and material nonlinearities under static loading conditions demonstrates the effectiveness of the POD-PICNN approach. Additionally, three different sampling strategies have been compared to assess their impact on the method performance. The results emphasize that physics-augmentation is required, as it inherently embeds essential physical constraints into the neural network architecture, ensuring stable and consistent behavior, while highlighting its potential for dynamic and multiphysics applications.","PeriodicalId":55222,"journal":{"name":"Computer Methods in Applied Mechanics and Engineering","volume":"71 1","pages":""},"PeriodicalIF":7.2,"publicationDate":"2024-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142684303","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 composite Bayesian optimisation framework for material and structural design","authors":"R.P. Cardoso Coelho, A. Francisca Carvalho Alves, T.M. Nogueira Pires, F.M. Andrade Pires","doi":"10.1016/j.cma.2024.117516","DOIUrl":"https://doi.org/10.1016/j.cma.2024.117516","url":null,"abstract":"In this contribution, a new design framework leveraging Bayesian optimisation is developed to enhance the efficiency and quality of material and structural design processes. The proposed framework comprises two main steps. The first step involves efficiently exploring the design space with a minimum number of sampled points to mitigate computational costs. In the subsequent step, a composite Bayesian optimisation strategy is employed to evaluate the objective function and identify the next candidate for sampling. By building a surrogate model for numerical simulation responses in a fixed-size latent response space and using techniques like Principal Component Analysis for dimensionality reduction, the framework effectively exploits the composition aspect of the objective function. Unlike traditional methods that rely on random sampling across the design space, our Bayesian optimisation approach uses a dynamic, adaptive sampling strategy. This method significantly reduces the number of required experiments while effectively managing uncertainty. We evaluate the framework’s performance across various design scenarios and conduct a critical comparative analysis against well-established data-driven approaches. These scenarios include linear and nonlinear material and structural behaviours, addressing multi-objective optimisation and data variability. Our findings demonstrate substantial improvements in performance and quality, particularly in nonlinear settings. This underscores the framework’s potential to advance design methodologies in material and structural engineering.","PeriodicalId":55222,"journal":{"name":"Computer Methods in Applied Mechanics and Engineering","volume":"64 1","pages":""},"PeriodicalIF":7.2,"publicationDate":"2024-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142684301","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 novel SCTBEM with inversion-free Padé series expansion for 3D transient heat transfer analysis in FGMs","authors":"Ruijiang Jing, Bo Yu, Shanhong Ren, Weian Yao","doi":"10.1016/j.cma.2024.117546","DOIUrl":"https://doi.org/10.1016/j.cma.2024.117546","url":null,"abstract":"In this study, a novel scaled coordinate transformation boundary element method (SCTBEM) is proposed to solve the transient heat transfer problem of three-dimensional (3D) functionally gradient materials. In order to compute the coefficient matrix only once when solving transient problems, the fundamental solution of Laplace operator is used to derive the boundary-domain integral equation. To maintain advantages of the boundary element method in dimensionality reduction, this study adopts the SCT technique proposed by Yu et al., to transform the domain integral into the boundary integral. With the aim of determining high precision heat flux, the dual interpolation technique is introduced for deriving integral equations only from the internal nodes of the surface element, which unifies the corner problem and achieves the coalescence of degrees of freedom. It is noteworthy that this study establishes the precise integration solution of the first order ordinary differential equation by means of Padé expansions without matrices inversion to improve the accuracy and efficiency of the solution. Numerical results show that both temperature and heat flux of 3D functionally gradient materials are highly accurate and stable, even for complex multi-connection models.","PeriodicalId":55222,"journal":{"name":"Computer Methods in Applied Mechanics and Engineering","volume":"18 1","pages":""},"PeriodicalIF":7.2,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142684331","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 active learning reliability analysis: A multi-point look-ahead paradigm","authors":"Tong Zhou, Tong Guo, Chao Dang, Lei Jia, You Dong","doi":"10.1016/j.cma.2024.117524","DOIUrl":"https://doi.org/10.1016/j.cma.2024.117524","url":null,"abstract":"To alleviate the intensive computational burden of reliability analysis, a new parallel active learning reliability method is proposed from the multi-point look-ahead paradigm. First, in the framework of probability density evolution method, a global measure of epistemic uncertainty about Kriging-based failure probability estimation, referred to as the targeted integrated mean squared error (TIMSE), is defined and well proved. Then, three key ingredients are developed in the workflow of parallel active learning reliability analysis: (i) A look-ahead learning function called <mml:math altimg=\"si387.svg\" display=\"inline\"><mml:mi>k</mml:mi></mml:math>-point targeted integrated mean square error reduction (<mml:math altimg=\"si387.svg\" display=\"inline\"><mml:mi>k</mml:mi></mml:math>-TIMSER) is deduced in closed form, quantifying explicitly the reduction of TIMSE induced by adding a batch of <mml:math altimg=\"si3.svg\" display=\"inline\"><mml:mrow><mml:mi>k</mml:mi><mml:mrow><mml:mo>(</mml:mo><mml:mo>≥</mml:mo><mml:mn>1</mml:mn><mml:mo>)</mml:mo></mml:mrow></mml:mrow></mml:math> new points in expectation. (ii) A hybrid convergence criterion is specified according to the actual reduction of TIMSE at each iteration. (iii) Both prescribed scheme and adaptive scheme are devised to identify the rational size of batch of new points added per iteration. The most distinctive feature of the proposed approach lies in that the multi-point enrichment process is fully guided by the learning function <mml:math altimg=\"si387.svg\" display=\"inline\"><mml:mi>k</mml:mi></mml:math>-TIMSER itself, without resorting to additional batch selection strategies. Hence, it is much more theoretically elegant and easy to implement. The effectiveness of the proposed approach is testified on three examples, and comparisons are made against several existing reliability methods. The results show that the proposed method achieves fair superiority over other existing ones in terms of the accuracy of failure probability estimate and the number of iterations. Particularly, the advantage of the total computational time becomes very evident in the proposed method, when computationally-expensive reliability problems are considered.","PeriodicalId":55222,"journal":{"name":"Computer Methods in Applied Mechanics and Engineering","volume":"61 1","pages":""},"PeriodicalIF":7.2,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142684329","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Neurodevelopmental disorders modeling using isogeometric analysis, dynamic domain expansion and local refinement","authors":"Kuanren Qian, Genesis Omana Suarez, Toshihiko Nambara, Takahisa Kanekiyo, Ashlee S. Liao, Victoria A. Webster-Wood, Yongjie Jessica Zhang","doi":"10.1016/j.cma.2024.117534","DOIUrl":"https://doi.org/10.1016/j.cma.2024.117534","url":null,"abstract":"Neurodevelopmental disorders (NDDs) have arisen as one of the most prevailing chronic diseases within the US. Often associated with severe adverse impacts on the formation of vital central and peripheral nervous systems during the neurodevelopmental process, NDDs are comprised of a broad spectrum of disorders, such as autism spectrum disorder, attention deficit hyperactivity disorder, and epilepsy, characterized by progressive and pervasive detriments to cognitive, speech, memory, motor, and other neurological functions in patients. However, the heterogeneous nature of NDDs poses a significant roadblock to identifying the exact pathogenesis, impeding accurate diagnosis and the development of targeted treatment planning. A computational NDDs model holds immense potential in enhancing our understanding of the multifaceted factors involved and could assist in identifying the root causes to expedite treatment development. To tackle this challenge, we introduce optimal neurotrophin concentration to the driving force and degradation of neurotrophin to the synaptogenesis process of a 2D phase field neuron growth model using isogeometric analysis to simulate neurite retraction and atrophy. The optimal neurotrophin concentration effectively captures the inverse relationship between neurotrophin levels and neuron survival, while its degradation regulates concentration levels. Leveraging dynamic domain expansion, the model efficiently expands the domain based on outgrowth patterns to minimize degrees of freedom. Based on truncated T-splines, our model simulates the evolving process of complex neurite structures by applying local refinement adaptively to the cell/neurite boundary. Furthermore, a thorough parameter investigation is conducted with detailed comparisons against neuron cell cultures in experiments, enhancing our fundamental understanding of the possible mechanisms underlying NDDs.","PeriodicalId":55222,"journal":{"name":"Computer Methods in Applied Mechanics and Engineering","volume":"23 1","pages":""},"PeriodicalIF":7.2,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142684330","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 physics-informed deep learning framework for solving forward and inverse problems based on Kolmogorov–Arnold Networks","authors":"Yizheng Wang, Jia Sun, Jinshuai Bai, Cosmin Anitescu, Mohammad Sadegh Eshaghi, Xiaoying Zhuang, Timon Rabczuk, Yinghua Liu","doi":"10.1016/j.cma.2024.117518","DOIUrl":"https://doi.org/10.1016/j.cma.2024.117518","url":null,"abstract":"AI for partial differential equations (PDEs) has garnered significant attention, particularly with the emergence of Physics-informed neural networks (PINNs). The recent advent of Kolmogorov–Arnold Network (KAN) indicates that there is potential to revisit and enhance the previously MLP-based PINNs. Compared to MLPs, KANs offer interpretability and require fewer parameters. PDEs can be described in various forms, such as strong form, energy form, and inverse form. While mathematically equivalent, these forms are not computationally equivalent, making the exploration of different PDE formulations significant in computational physics. Thus, we propose different PDE forms based on KAN instead of MLP, termed Kolmogorov–Arnold-Informed Neural Network (KINN) for solving forward and inverse problems. We systematically compare MLP and KAN in various numerical examples of PDEs, including multi-scale, singularity, stress concentration, nonlinear hyperelasticity, heterogeneous, and complex geometry problems. Our results demonstrate that KINN significantly outperforms MLP regarding accuracy and convergence speed for numerous PDEs in computational solid mechanics, except for the complex geometry problem. This highlights KINN’s potential for more efficient and accurate PDE solutions in AI for PDEs.","PeriodicalId":55222,"journal":{"name":"Computer Methods in Applied Mechanics and Engineering","volume":"26 1","pages":""},"PeriodicalIF":7.2,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142684332","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 physical-information-flow-constrained temporal graph neural network-based simulator for granular materials","authors":"Shiwei Zhao, Hao Chen, Jidong Zhao","doi":"10.1016/j.cma.2024.117536","DOIUrl":"https://doi.org/10.1016/j.cma.2024.117536","url":null,"abstract":"This paper introduces the Temporal Graph Neural Network-based Simulator (TGNNS), a novel physical-information-flow-constrained deep learning-based simulator for granular material modeling. The TGNNS leverages a series of frames, each representing material point positions, enabling particle dynamics to propagate through the sequence, resulting in a more physically grounded architecture for granular flow learning. The TGNNS has been thoroughly trained, validated, and tested using simulation data derived from a hierarchical multiscale modeling approach, DEMPM, which combines the Material Point Method (MPM) and the Discrete Element Method (DEM). Results demonstrate that the TGNNS performs robustly with previously unseen datasets of varying granular column sizes, even under manually incorporated barrier boundary conditions. Remarkably, the TGNNS operates at a speed 100 times faster than direct numerical simulation using the state-of-the-art GPU-based DEMPM. Employing a unique deep learning architecture that is constrained by the flow of physical information, the TGNNS offers a pioneering learning paradigm for multiscale emerging behaviors of granular materials and provides a potential solution to physics-based modeling in digital twins involving granular materials.","PeriodicalId":55222,"journal":{"name":"Computer Methods in Applied Mechanics and Engineering","volume":"3 1","pages":""},"PeriodicalIF":7.2,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142684333","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fatigue-constrained topology optimization method for orthotropic materials based on an expanded Tsai-Hill criterion","authors":"Hongling Ye,&nbsp;Yang Xiao,&nbsp;Yongjia Dong,&nbsp;Jialin Xie","doi":"10.1016/j.cma.2024.117542","DOIUrl":"10.1016/j.cma.2024.117542","url":null,"abstract":"<div><div>Fatigue-constrained topology optimization (FCTO) is a currently research hotspot, and its fatigue constraints have material property dependency, highly nonlinear, and local features, which lead to challenges for the algorithm stability, computational efficiency, and different material application of FCTO. This research provides a FCTO method for structures subjected to variable-amplitude fatigue loading, incorporating the potential orthotropic behavior of materials. Firstly, a fatigue failure function derived from the constitutive model of orthotropic materials and the polynomial form in the Tsai-Hill criterion is proposed to predict multiaxial fatigue failure with a given loading spectrum. Secondly, a FCTO model minimizing structural weight is established based on the independent continuous mapping (ICM) method and constrained by a filtered, scaled, and aggregated fatigue failure function to enhance stability and convergence speed. Thirdly, the sensitivities of objective and constraint in the FCTO model are analyzed, and the optimal model is solved using convolutional filters and the globally convergent method of moving asymptotes (GCMMA) to generate manufacturable design. Finally, numerical examples demonstrate the feasibility of the method for 2D and 3D structures with varying material properties, load spectrums, and design domains. The developed method aims to facilitate the creation of lightweight designs capable of withstanding fatigue loads and to provide a framework and references for the advancement of integrated material-structure-performance designs.</div></div>","PeriodicalId":55222,"journal":{"name":"Computer Methods in Applied Mechanics and Engineering","volume":"433 ","pages":"Article 117542"},"PeriodicalIF":6.9,"publicationDate":"2024-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142658578","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Anisotropic variational mesh adaptation for embedded finite element methods","authors":"Saman Rahmani, Joan Baiges, Javier Principe","doi":"10.1016/j.cma.2024.117504","DOIUrl":"https://doi.org/10.1016/j.cma.2024.117504","url":null,"abstract":"Embedded or immersed boundary methods (IBM) are powerful mesh-based techniques that permit to solve partial differential equations (PDEs) in complex geometries circumventing the need of generating a mesh that fits the domain boundary, which is indeed very difficult and has been the main bottleneck of the simulation pipeline for decades. Embedded methods exploit a simple background mesh that covers the domain and the difficulties are (1) the imposition of boundary conditions, (2) the ill-conditioning generated by poorly intersected elements and (3) the lack of resolution required in boundary layers. Whereas several methods are available in the literature to address the first two difficulties, the third one still deserves attention. Meshless methods, Chimera grids or adaptive h or p-refinement strategies have been proposed but none of them include alignment techniques.","PeriodicalId":55222,"journal":{"name":"Computer Methods in Applied Mechanics and Engineering","volume":"254 1","pages":""},"PeriodicalIF":7.2,"publicationDate":"2024-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142684334","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}