Computational Mechanics最新文献_第5页

Data transfer within a finite cell remeshing approach applied to large deformation problems 应用于大变形问题的有限单元重网格方法中的数据传输

IF 4.1 2区工程技术

Computational Mechanics Pub Date : 2024-05-25 DOI: 10.1007/s00466-024-02486-0

Roman Sartorti, Alexander Düster

引用次数: 0

Accelerating aeroelastic UVLM simulations by inexact Newton algorithms 利用不精确牛顿算法加速气动弹性 UVLM 仿真

IF 4.1 2区工程技术

Computational Mechanics Pub Date : 2024-05-25 DOI: 10.1007/s00466-024-02484-2

Jenny Schubert, Marc C. Steinbach, Christian Hente, David Märtins, Daniel Schuster

{"title":"Accelerating aeroelastic UVLM simulations by inexact Newton algorithms","authors":"Jenny Schubert, Marc C. Steinbach, Christian Hente, David Märtins, Daniel Schuster","doi":"10.1007/s00466-024-02484-2","DOIUrl":"https://doi.org/10.1007/s00466-024-02484-2","url":null,"abstract":"We consider the aeroelastic simulation of flexible mechanical structures submerged in subsonic fluid flows at low Mach numbers. The nonlinear kinematics of flexible bodies are described in the total Lagrangian formulation and discretized by finite elements. The aerodynamic loads are computed using the unsteady vortex-lattice method wherein a free wake is tracked over time. Each implicit time step in the dynamic simulation then requires solving a nonlinear equation system in the structural variables with additional aerodynamic load terms. Our focus here is on the efficient numerical solution of this system by accelerating the Newton algorithm. The particular structure of the aeroelastic nonlinear system suggests the structural derivative as an approximation to the full derivative in the linear Newton system. We investigate and compare two promising algorithms based on this approximation, a quasi-Newton type algorithm and a novel inexact Newton algorithm. Numerical experiments are performed on a flexible plate and on a wind turbine. Our computational results show that the approximation can indeed accelerate the Newton algorithm substantially. Surprisingly, the theoretically preferable inexact Newton algorithm is much slower than the quasi-Newton algorithm, which motivates further research to speed up derivative evaluations.","PeriodicalId":55248,"journal":{"name":"Computational Mechanics","volume":"30 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2024-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141149482","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}

引用次数: 0

Graph-enhanced deep material network: multiscale materials modeling with microstructural informatics 图增强深度材料网络：利用微结构信息学进行多尺度材料建模

IF 4.1 2区工程技术

Computational Mechanics Pub Date : 2024-05-18 DOI: 10.1007/s00466-024-02493-1

Jimmy Gaspard Jean, Tung-Huan Su, Szu-Jui Huang, Cheng-Tang Wu, Chuin-Shan Chen

{"title":"Graph-enhanced deep material network: multiscale materials modeling with microstructural informatics","authors":"Jimmy Gaspard Jean, Tung-Huan Su, Szu-Jui Huang, Cheng-Tang Wu, Chuin-Shan Chen","doi":"10.1007/s00466-024-02493-1","DOIUrl":"https://doi.org/10.1007/s00466-024-02493-1","url":null,"abstract":"This study addresses the fundamental challenge of extending the deep material network (DMN) to accommodate multiple microstructures. DMN has gained significant attention due to its ability to be used for fast and accurate nonlinear multiscale modeling while being only trained on linear elastic data. Due to its limitation to a single microstructure, various works sought to generalize it based on the macroscopic description of microstructures. In this work, we utilize a mechanistic machine learning approach grounded instead in microstructural informatics, which can potentially be used for any family of microstructures. This is achieved by learning from the graph representation of microstructures through graph neural networks. Such an approach is a first in works related to DMN. We propose a mixed graph neural network (GNN)-DMN model that can single-handedly treat multiple microstructures and derive their DMN representations. Two examples are designed to demonstrate the validity and reliability of the approach, even when it comes to the prediction of nonlinear responses for microstructures unseen during training. Furthermore, the model trained on microstructures with complex topology accurately makes inferences on microstructures created under different and simpler assumptions. Our work opens the door for the possibility of unifying the multiscale modeling of many families of microstructures under a single model, as well as new possibilities in material design.","PeriodicalId":55248,"journal":{"name":"Computational Mechanics","volume":"26 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2024-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141062017","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}

引用次数: 0

A thermodynamic motivated RCCM damage interface model in an explicit transient dynamics framework 显式瞬态动力学框架下的热力学 RCCM 损伤界面模型

IF 4.1 2区工程技术

Computational Mechanics Pub Date : 2024-05-10 DOI: 10.1007/s00466-024-02489-x

Paul Larousse, David Dureisseix, Anthony Gravouil, Gabriel Georges

引用次数: 0

Digital twin of surface acoustic wave transceivers for a computational design of an optimal wave guiding layer thickness 计算设计最佳导波层厚度的表面声波收发器数字孪晶

IF 4.1 2区工程技术

Computational Mechanics Pub Date : 2024-05-10 DOI: 10.1007/s00466-024-02488-y

Ufuk Tan Baler, Ali Fethi Okyar, Bilen Emek Abali

引用次数: 0

Iterative method for large-scale Timoshenko beam models assessed on commercial-grade paperboard 在商业级纸板上评估大规模季莫申科梁模型的迭代法

IF 4.1 2区工程技术

Computational Mechanics Pub Date : 2024-05-10 DOI: 10.1007/s00466-024-02487-z

Morgan Görtz, Gustav Kettil, Axel Målqvist, Mats Fredlund, Fredrik Edelvik

{"title":"Iterative method for large-scale Timoshenko beam models assessed on commercial-grade paperboard","authors":"Morgan Görtz, Gustav Kettil, Axel Målqvist, Mats Fredlund, Fredrik Edelvik","doi":"10.1007/s00466-024-02487-z","DOIUrl":"https://doi.org/10.1007/s00466-024-02487-z","url":null,"abstract":"Large-scale structural simulations based on micro-mechanical models of paper products require extensive numerical resources and time. In such models, the fibrous material is often represented by connected beams. Whereas previous micro-mechanical simulations have been restricted to smaller sample problems, large-scale micro-mechanical models are considered here. These large-scale simulations are possible on a non-specialized desktop computer with 128GB of RAM using an iterative method developed for network models and based on domain decomposition. Moreover, this method is parallelizable and is also well-suited for computational clusters. In this work, the proposed memory-efficient iterative method is numerically validated for linear systems resulting from large networks of Timoshenko beams. Tensile stiffness and out-of-plane bending stiffness are simulated and validated for various commercial-grade three-ply paperboards consisting of layers composed of two different types of paper fibers. The results of these simulations show that a linear network model produces results consistent with theory and published experimental data","PeriodicalId":55248,"journal":{"name":"Computational Mechanics","volume":"42 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2024-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140937664","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}

引用次数: 0

Dynamic modeling of flexible multibody systems with complex geometry via finite cell method of absolute nodal coordinate formulation 通过有限单元法的绝对节点坐标公式对具有复杂几何形状的柔性多体系统进行动态建模

IF 4.1 2区工程技术

Computational Mechanics Pub Date : 2024-05-06 DOI: 10.1007/s00466-024-02482-4

Yue Feng, Jianqiao Guo, Qiang Tian, Haiyan Hu

引用次数: 0

Embedded symmetric positive semi-definite machine-learned elements for reduced-order modeling in finite-element simulations with application to threaded fasteners 嵌入式对称正半有限元机器学习元件用于有限元模拟中的降阶建模，并应用于螺纹紧固件

IF 4.1 2区工程技术

Computational Mechanics Pub Date : 2024-05-06 DOI: 10.1007/s00466-024-02481-5

Eric Parish, Payton Lindsay, Timothy Shelton, John Mersch

{"title":"Embedded symmetric positive semi-definite machine-learned elements for reduced-order modeling in finite-element simulations with application to threaded fasteners","authors":"Eric Parish, Payton Lindsay, Timothy Shelton, John Mersch","doi":"10.1007/s00466-024-02481-5","DOIUrl":"https://doi.org/10.1007/s00466-024-02481-5","url":null,"abstract":"We present a machine-learning strategy for finite element analysis of solid mechanics wherein we replace complex portions of a computational domain with a data-driven surrogate. In the proposed strategy, we decompose a computational domain into an “outer” coarse-scale domain that we resolve using a finite element method (FEM) and an “inner” fine-scale domain. We then develop a machine-learned (ML) model for the impact of the inner domain on the outer domain. In essence, for solid mechanics, our machine-learned surrogate performs static condensation of the inner domain degrees of freedom. This is achieved by learning the map from displacements on the inner-outer domain interface boundary to forces contributed by the inner domain to the outer domain on the same interface boundary. We consider two such mappings, one that directly maps from displacements to forces without constraints, and one that maps from displacements to forces by virtue of learning a symmetric positive semi-definite (SPSD) stiffness matrix. We demonstrate, in a simplified setting, that learning an SPSD stiffness matrix results in a coarse-scale problem that is well-posed with a unique solution. We present numerical experiments on several exemplars, ranging from finite deformations of a cube to finite deformations with contact of a fastener-bushing geometry. We demonstrate that enforcing an SPSD stiffness matrix drastically improves the robustness and accuracy of FEM–ML coupled simulations, and that the resulting methods can accurately characterize out-of-sample loading configurations with significant speedups over the standard FEM simulations.","PeriodicalId":55248,"journal":{"name":"Computational Mechanics","volume":"151 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2024-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140888976","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}

引用次数: 0

Viscoelasticty with physics-augmented neural networks: model formulation and training methods without prescribed internal variables 物理增强神经网络的粘弹性：无规定内部变量的模型制定和训练方法

IF 4.1 2区工程技术

Computational Mechanics Pub Date : 2024-05-06 DOI: 10.1007/s00466-024-02477-1

Max Rosenkranz, Karl A. Kalina, Jörg Brummund, WaiChing Sun, Markus Kästner

{"title":"Viscoelasticty with physics-augmented neural networks: model formulation and training methods without prescribed internal variables","authors":"Max Rosenkranz, Karl A. Kalina, Jörg Brummund, WaiChing Sun, Markus Kästner","doi":"10.1007/s00466-024-02477-1","DOIUrl":"https://doi.org/10.1007/s00466-024-02477-1","url":null,"abstract":"We present an approach for the data-driven modeling of nonlinear viscoelastic materials at small strains which is based on physics-augmented neural networks (NNs) and requires only stress and strain paths for training. The model is built on the concept of generalized standard materials and is therefore thermodynamically consistent by construction. It consists of a free energy and a dissipation potential, which can be either expressed by the components of their tensor arguments or by a suitable set of invariants. The two potentials are described by fully/partially input convex neural networks. For training of the NN model by paths of stress and strain, an efficient and flexible training method based on a long short-term memory cell is developed to automatically generate the internal variable(s) during the training process. The proposed method is benchmarked and thoroughly compared with existing approaches. Different databases with either ideal or noisy stress data are generated for training by using a conventional nonlinear viscoelastic reference model. The coordinate-based and the invariant-based formulation are compared and the advantages of the latter are demonstrated. Afterwards, the invariant-based model is calibrated by applying the three training methods using ideal or noisy stress data. All methods yield good results, but differ in computation time and usability for large data sets. The presented training method based on a recurrent cell turns out to be particularly robust and widely applicable. We show that the presented model together with the recurrent cell for training yield complete and accurate 3D constitutive models even for sparse bi- or uniaxial training data.","PeriodicalId":55248,"journal":{"name":"Computational Mechanics","volume":"30 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2024-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140889595","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}

引用次数: 0

A new unified arc-length method for damage mechanics problems 损伤力学问题的新统一弧长法

IF 4.1 2区工程技术

Computational Mechanics Pub Date : 2024-05-06 DOI: 10.1007/s00466-024-02473-5

Roshan Philip Saji, Panos Pantidis, Mostafa E. Mobasher

{"title":"A new unified arc-length method for damage mechanics problems","authors":"Roshan Philip Saji, Panos Pantidis, Mostafa E. Mobasher","doi":"10.1007/s00466-024-02473-5","DOIUrl":"https://doi.org/10.1007/s00466-024-02473-5","url":null,"abstract":"The numerical solution of continuum damage mechanics (CDM) problems suffers from convergence-related challenges during the material softening stage, and consequently existing iterative solvers are subject to a trade-off between computational expense and solution accuracy. In this work, we present a novel unified arc-length (UAL) method, and we derive the formulation of the analytical tangent matrix and governing system of equations for both local and non-local gradient damage problems. Unlike existing versions of arc-length solvers that monolithically scale the external force vector, the proposed method treats the latter as an independent variable and determines the position of the system on the equilibrium path based on all the nodal variations of the external force vector. This approach renders the proposed solver substantially more efficient and robust than existing solvers used in CDM problems. We demonstrate the considerable advantages of the proposed algorithm through several benchmark 1D problems with sharp snap-backs and 2D examples under various boundary conditions and loading scenarios. The proposed UAL approach exhibits a superior ability of overcoming critical increments along the equilibrium path. Moreover, in the presented examples, the proposed UAL method is 1–2 orders of magnitude faster than force-controlled arc-length and monolithic Newton–Raphson solvers.","PeriodicalId":55248,"journal":{"name":"Computational Mechanics","volume":"8 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2024-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140889503","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}

引用次数: 0