Computer Methods in Applied Mechanics and Engineering最新文献_第8页

Exploring the roles of numerical simulations and machine learning in multiscale paving materials analysis: Applications, challenges, best practices 探索数值模拟和机器学习在多尺度铺路材料分析中的作用：应用、挑战和最佳实践

IF 6.9 1区工程技术

Computer Methods in Applied Mechanics and Engineering Pub Date : 2024-10-28 DOI: 10.1016/j.cma.2024.117462

Mahmoud Khadijeh, Cor Kasbergen, Sandra Erkens, Aikaterini Varveri

{"title":"Exploring the roles of numerical simulations and machine learning in multiscale paving materials analysis: Applications, challenges, best practices","authors":"Mahmoud Khadijeh, Cor Kasbergen, Sandra Erkens, Aikaterini Varveri","doi":"10.1016/j.cma.2024.117462","DOIUrl":"10.1016/j.cma.2024.117462","url":null,"abstract":"<div><div>The complex structure of bituminous mixtures ranging from nanoscale binder components to macroscale pavement performance requires a comprehensive approach to material characterization and performance prediction. This paper provides a critical analysis of advanced techniques in paving materials modeling. It focuses on four main approaches: finite element method (FEM), discrete element method (DEM), phase field method (PFM), and artificial neural networks (ANNs). The review highlights how these computational methods enable more accurate predictions of material behavior, from asphalt binder rheology to mixture performance, while reducing reliance on extensive empirical testing. Key advances, such as the smooth integration of information across multiple scales and the emergence of physics-informed neural networks (PINNs), are discussed as promising avenues for enhancing model accuracy and computational efficiency. This review not only provides a comprehensive overview of current methodologies but also outlines future research directions aimed at developing more sustainable, cost-effective, and durable paving solutions through advanced multiscale modeling techniques.</div></div>","PeriodicalId":55222,"journal":{"name":"Computer Methods in Applied Mechanics and Engineering","volume":"433 ","pages":"Article 117462"},"PeriodicalIF":6.9,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142529319","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Quo vadis, wave? Dispersive-SUPG for direct van der Waals simulation (DVS) 何去何从，波浪？用于直接范德华模拟（DVS）的 Dispersive-SUPG

IF 6.9 1区工程技术

Computer Methods in Applied Mechanics and Engineering Pub Date : 2024-10-28 DOI: 10.1016/j.cma.2024.117471

Tianyi Hu, Hector Gomez

引用次数: 0

Sampling-based adaptive Bayesian quadrature for probabilistic model updating 基于采样的自适应贝叶斯正交法用于概率模型更新

IF 6.9 1区工程技术

Computer Methods in Applied Mechanics and Engineering Pub Date : 2024-10-25 DOI: 10.1016/j.cma.2024.117467

Jingwen Song , Zhanhua Liang , Pengfei Wei , Michael Beer

{"title":"Sampling-based adaptive Bayesian quadrature for probabilistic model updating","authors":"Jingwen Song , Zhanhua Liang , Pengfei Wei , Michael Beer","doi":"10.1016/j.cma.2024.117467","DOIUrl":"10.1016/j.cma.2024.117467","url":null,"abstract":"<div><div>Bayesian (probabilistic) model updating is a fundamental concept in computational science, allowing for the incorporation of prior beliefs with observed data to reduce prediction uncertainty of a computer simulator. However, the efficient evaluation of posterior probability density functions (PDFs) of model parameters poses challenges, particularly for computationally expansive simulators. This work presents a sampling-based adaptive Bayesian quadrature method to fill this gap. The method is based on approximating the simulator under investigation with a Gaussian process (GP) model, and then a conditional sampling procedure is introduced for generating sample paths, this way to infer a probability distribution for the evidence term. This inferred probability distribution indeed measures the prediction uncertainty of the evidence term, and thus based on which, an acquisition function is proposed to identify the site at which the prediction uncertainty of the GP model contributes the most to that of the evidence term. All the above ingredients finally form an adaptive algorithm for updating the posterior PDFs of model parameters with pre-specified accuracy tolerance. Case studies across numerical examples and engineering applications validate the ability of the proposed method to deal with multi-modal problems, and demonstrate its superiority in terms of computational efficiency and precision for estimating model evidence and posterior PDFs.</div></div>","PeriodicalId":55222,"journal":{"name":"Computer Methods in Applied Mechanics and Engineering","volume":"433 ","pages":"Article 117467"},"PeriodicalIF":6.9,"publicationDate":"2024-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142529233","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}

引用次数: 0

Neural differentiable modeling with diffusion-based super-resolution for two-dimensional spatiotemporal turbulence 针对二维时空湍流的基于扩散的超分辨率神经可微分建模

IF 6.9 1区工程技术

Computer Methods in Applied Mechanics and Engineering Pub Date : 2024-10-25 DOI: 10.1016/j.cma.2024.117478

Xiantao Fan , Deepak Akhare , Jian-Xun Wang

{"title":"Neural differentiable modeling with diffusion-based super-resolution for two-dimensional spatiotemporal turbulence","authors":"Xiantao Fan , Deepak Akhare , Jian-Xun Wang","doi":"10.1016/j.cma.2024.117478","DOIUrl":"10.1016/j.cma.2024.117478","url":null,"abstract":"<div><div>Simulating spatiotemporal turbulence with high fidelity remains a cornerstone challenge in computational fluid dynamics (CFD) due to its intricate multiscale nature and prohibitive computational demands. Traditional approaches typically employ closure models, which attempt to represent small-scale features in an unresolved manner. However, these methods often sacrifice accuracy and lose high-frequency/wavenumber information, especially in scenarios involving complex flow physics. In this paper, we introduce an innovative neural differentiable modeling framework designed to enhance the predictability and efficiency of spatiotemporal turbulence simulations. Our approach features differentiable hybrid modeling techniques that seamlessly integrate deep neural networks with numerical PDE solvers within a differentiable programming framework, synergizing deep learning with physics-based CFD modeling. Specifically, a hybrid differentiable neural solver is constructed on a coarser grid to capture large-scale turbulent phenomena, followed by the application of a Bayesian conditional diffusion model that generates small-scale turbulence conditioned on large-scale flow predictions. Two innovative hybrid architecture designs are studied, and their performance is evaluated through comparative analysis against conventional large eddy simulation techniques with physics-based subgrid-scale closures and purely data-driven neural solvers. The findings underscore the potential of the neural differentiable modeling framework to significantly enhance the accuracy and computational efficiency of turbulence simulations. This study not only demonstrates the efficacy of merging deep learning with physics-based numerical solvers but also sets a new precedent for advanced CFD modeling techniques, highlighting the transformative impact of differentiable programming in scientific computing.</div></div>","PeriodicalId":55222,"journal":{"name":"Computer Methods in Applied Mechanics and Engineering","volume":"433 ","pages":"Article 117478"},"PeriodicalIF":6.9,"publicationDate":"2024-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142529318","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}

引用次数: 0

Peridynamic topology optimization to improve fracture resistance of structures 优化围动力拓扑结构，提高结构的抗断裂能力

IF 6.9 1区工程技术

Computer Methods in Applied Mechanics and Engineering Pub Date : 2024-10-24 DOI: 10.1016/j.cma.2024.117455

Francisco S. Vieira, Aurélio L. Araújo

{"title":"Peridynamic topology optimization to improve fracture resistance of structures","authors":"Francisco S. Vieira, Aurélio L. Araújo","doi":"10.1016/j.cma.2024.117455","DOIUrl":"10.1016/j.cma.2024.117455","url":null,"abstract":"<div><div>In this work we propose a novel peridynamic topology optimization formulation to improve fracture resistance. The main strength of peridynamics is based on the straightforwardness in which crack propagation can be predicted, as a natural part of a peridynamic numerical simulation. This property can be leveraged in a topology optimization framework, in order to obtain fracture resistance designs. Hence, we formulate a meshfree density-based nonlocal topology optimization framework using a bond-based peridynamic formulation. As it is demonstrated in this paper, the classical compliance based solutions are far from optimal in terms of fracture resistance and the designs obtained with the proposed formulation can provide fracture resistant solutions while only reducing slightly the structural stiffness. The proposed formulation is presented along with all the details of the sensitivity analysis and additional numerical aspects of the implementation. Moreover, the peridynamic material model used is presented along with its numerical implementation. Numerical examples demonstrate the accuracy of the computed sensitivities and illustrate the impact and effectiveness of the presented formulation. A thorough study of the optimization parameters is presented and various optimization convergence studies are taken in order to obtain a stable optimization process. All the results are compared to classical compliance minimization designs to illustrate the advantages and capabilities of the proposed framework.</div></div>","PeriodicalId":55222,"journal":{"name":"Computer Methods in Applied Mechanics and Engineering","volume":"433 ","pages":"Article 117455"},"PeriodicalIF":6.9,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142529321","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An elasto-visco-plastic constitutive model for snow: Theory and finite element implementation 雪的弹性-粘弹性构成模型：理论与有限元实施

IF 6.9 1区工程技术

Computer Methods in Applied Mechanics and Engineering Pub Date : 2024-10-24 DOI: 10.1016/j.cma.2024.117465

Gianmarco Vallero, Monica Barbero, Fabrizio Barpi, Mauro Borri-Brunetto, Valerio De Biagi

引用次数: 0

Conditional score-based diffusion models for solving inverse elasticity problems 用于解决反弹性问题的基于条件分值的扩散模型

IF 6.9 1区工程技术

Computer Methods in Applied Mechanics and Engineering Pub Date : 2024-10-24 DOI: 10.1016/j.cma.2024.117425

Agnimitra Dasgupta , Harisankar Ramaswamy , Javier Murgoitio-Esandi , Ken Y. Foo , Runze Li , Qifa Zhou , Brendan F. Kennedy , Assad A. Oberai

{"title":"Conditional score-based diffusion models for solving inverse elasticity problems","authors":"Agnimitra Dasgupta , Harisankar Ramaswamy , Javier Murgoitio-Esandi , Ken Y. Foo , Runze Li , Qifa Zhou , Brendan F. Kennedy , Assad A. Oberai","doi":"10.1016/j.cma.2024.117425","DOIUrl":"10.1016/j.cma.2024.117425","url":null,"abstract":"<div><div>We propose a framework to perform Bayesian inference using conditional score-based diffusion models to solve a class of inverse problems in mechanics involving the inference of a specimen’s spatially varying material properties from noisy measurements of its mechanical response to loading. Conditional score-based diffusion models are generative models that learn to approximate the score function of a conditional distribution using samples from the joint distribution. More specifically, the score functions corresponding to multiple realizations of the measurement are approximated using a single neural network, the so-called score network, which is subsequently used to sample the posterior distribution using an appropriate Markov chain Monte Carlo scheme based on Langevin dynamics. Training the score network only requires simulating the forward model. Hence, the proposed approach can accommodate black-box forward models and complex measurement noise. Moreover, once the score network has been trained, it can be re-used to solve the inverse problem for different realizations of the measurements. We demonstrate the efficacy of the proposed approach on a suite of high-dimensional inverse problems in mechanics that involve inferring heterogeneous material properties from noisy measurements. Some examples we consider involve synthetic data, while others include data collected from actual elastography experiments. Further, our applications demonstrate that the proposed approach can handle different measurement modalities, complex patterns in the inferred quantities, non-Gaussian and non-additive noise models, and nonlinear black-box forward models. The results show that the proposed framework can solve large-scale physics-based inverse problems efficiently.</div></div>","PeriodicalId":55222,"journal":{"name":"Computer Methods in Applied Mechanics and Engineering","volume":"433 ","pages":"Article 117425"},"PeriodicalIF":6.9,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142529271","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}

引用次数: 0

Variational Bayesian optimal experimental design with normalizing flows 带有归一化流量的变异贝叶斯优化实验设计

IF 6.9 1区工程技术

Computer Methods in Applied Mechanics and Engineering Pub Date : 2024-10-24 DOI: 10.1016/j.cma.2024.117457

Jiayuan Dong , Christian Jacobsen , Mehdi Khalloufi , Maryam Akram , Wanjiao Liu , Karthik Duraisamy , Xun Huan

{"title":"Variational Bayesian optimal experimental design with normalizing flows","authors":"Jiayuan Dong , Christian Jacobsen , Mehdi Khalloufi , Maryam Akram , Wanjiao Liu , Karthik Duraisamy , Xun Huan","doi":"10.1016/j.cma.2024.117457","DOIUrl":"10.1016/j.cma.2024.117457","url":null,"abstract":"<div><div>Bayesian optimal experimental design (OED) seeks experiments that maximize the expected information gain (EIG) in model parameters. Directly estimating the EIG using nested Monte Carlo is computationally expensive and requires an explicit likelihood. Variational OED (vOED), in contrast, estimates a lower bound of the EIG without likelihood evaluations by approximating the posterior distributions with variational forms, and then tightens the bound by optimizing its variational parameters. We introduce the use of normalizing flows (NFs) for representing variational distributions in vOED; we call this approach vOED-NFs. Specifically, we adopt NFs with a conditional invertible neural network architecture built from compositions of coupling layers, and enhanced with a summary network for data dimension reduction. We present Monte Carlo estimators to the lower bound along with gradient expressions to enable a gradient-based simultaneous optimization of the variational parameters and the design variables. The vOED-NFs algorithm is then validated in two benchmark problems, and demonstrated on a partial differential equation-governed application of cathodic electrophoretic deposition and an implicit likelihood case with stochastic modeling of aphid population. The findings suggest that a composition of 4–5 coupling layers is able to achieve lower EIG estimation bias, under a fixed budget of forward model runs, compared to previous approaches. The resulting NFs produce approximate posteriors that agree well with the true posteriors, able to capture non-Gaussian and multi-modal features effectively.</div></div>","PeriodicalId":55222,"journal":{"name":"Computer Methods in Applied Mechanics and Engineering","volume":"433 ","pages":"Article 117457"},"PeriodicalIF":6.9,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142529269","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}

引用次数: 0

Particle Virtual Element Method (PVEM): an agglomeration technique for mesh optimization in explicit Lagrangian free-surface fluid modelling 粒子虚拟元素法（PVEM）：用于显式拉格朗日自由表面流体建模中网格优化的集聚技术

IF 6.9 1区工程技术

Computer Methods in Applied Mechanics and Engineering Pub Date : 2024-10-24 DOI: 10.1016/j.cma.2024.117461

Cheng Fu , Massimiliano Cremonesi , Umberto Perego , Blaž Hudobivnik , Peter Wriggers

{"title":"Particle Virtual Element Method (PVEM): an agglomeration technique for mesh optimization in explicit Lagrangian free-surface fluid modelling","authors":"Cheng Fu , Massimiliano Cremonesi , Umberto Perego , Blaž Hudobivnik , Peter Wriggers","doi":"10.1016/j.cma.2024.117461","DOIUrl":"10.1016/j.cma.2024.117461","url":null,"abstract":"<div><div>Explicit solvers are commonly used for simulating fast dynamic and highly nonlinear engineering problems. However, these solvers are only conditionally stable, requiring very small time-step increments determined by the characteristic length of the smallest, and often most distorted, element in the mesh. In the Lagrangian description of fluid motion, the computational mesh quickly deteriorates. To circumvent this problem, the Particle Finite Element Method (PFEM) creates a new mesh (e.g., through a Delaunay tessellation, based on node positions) when the current one becomes overly distorted. A fast and efficient remeshing technique is therefore of pivotal importance for an effective PFEM implementation in explicit dynamics. Unfortunately, the 3D Delaunay tessellation does not guarantee well-shaped elements, often generating zero- or near-zero-volume elements (slivers), which drastically reduce the stable time-step size. Available mesh optimization techniques have limited applicability due to their high computational cost when runtime remeshing is required. An innovative possibility to overcome this problem is the use of the Virtual Element Method (VEM), a variant of the finite element method that can make use of polyhedral elements of arbitrary shapes and number of nodes. This paper presents the formulation of a 3D first-order Particle Virtual Element Method (PVEM) for weakly compressible flows. Starting from a tetrahedral mesh, poorly shaped elements, such as slivers, are agglomerated to form polyhedral Virtual Elements (VEs) with a controlled characteristic length. This approach ensures full control over the minimum time-step size in explicit dynamics simulations, maintaining stability throughout the entire analysis.</div></div>","PeriodicalId":55222,"journal":{"name":"Computer Methods in Applied Mechanics and Engineering","volume":"433 ","pages":"Article 117461"},"PeriodicalIF":6.9,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142529268","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Uncertainty quantification of 3D acoustic shape sensitivities with generalized nth-order perturbation boundary element methods 用广义 nth 阶扰动边界元方法量化三维声学形状敏感性的不确定性

IF 6.9 1区工程技术

Computer Methods in Applied Mechanics and Engineering Pub Date : 2024-10-24 DOI: 10.1016/j.cma.2024.117464

Leilei Chen , Ruijin Huo , Haojie Lian , Bo Yu , Mengxi Zhang , Sundararajan Natarajan , Stéphane P.A. Bordas

{"title":"Uncertainty quantification of 3D acoustic shape sensitivities with generalized nth-order perturbation boundary element methods","authors":"Leilei Chen , Ruijin Huo , Haojie Lian , Bo Yu , Mengxi Zhang , Sundararajan Natarajan , Stéphane P.A. Bordas","doi":"10.1016/j.cma.2024.117464","DOIUrl":"10.1016/j.cma.2024.117464","url":null,"abstract":"<div><div>This paper presents a novel perburbation-based method for uncertainty quantification of acoustic fields and their shape sensitivities. In this work, the frequencies of impinging acoustic waves are regarded as random variables. Taylor’s series expansions of acoustic boundary integral equations are derived to obtain <span><math><mi>n</mi></math></span>th-order derivatives of acoustic state functions with respect to frequencies. Acoustic shape sensitivity is obtained by directly differentiating acoustic boundary integral equation with respect to shape design variables, and then the <span><math><mi>n</mi></math></span>th-order derivatives of shape sensitivity with respect to random frequencies are formulated with Taylor’s series expansions. Based on the <span><math><mi>n</mi></math></span>th-order perturbation theory, the statistical characteristics of acoustic state functions and their shape sensitivities can be evaluated. Numerical examples are presented to demonstrate the validity and effectiveness of the proposed algorithm.</div></div>","PeriodicalId":55222,"journal":{"name":"Computer Methods in Applied Mechanics and Engineering","volume":"433 ","pages":"Article 117464"},"PeriodicalIF":6.9,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142529272","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}

引用次数: 0