Computer Methods in Applied Mechanics and Engineering最新文献

{"title":"Orientation-aware interaction-based deep material network in polycrystalline materials modeling","authors":"Ting-Ju Wei ,&nbsp;Tung-Huan Su ,&nbsp;Chuin-Shan Chen","doi":"10.1016/j.cma.2025.117977","DOIUrl":"10.1016/j.cma.2025.117977","url":null,"abstract":"<div><div>Multiscale simulations are indispensable for connecting microstructural features to the macroscopic behavior of polycrystalline materials, but their high computational demands limit their practicality. Deep material networks (DMNs) have been proposed as efficient surrogate models, yet they fall short of capturing texture evolution. To address this limitation, we propose the orientation-aware interaction-based deep material network (ODMN), which incorporates an orientation-aware mechanism and an interaction mechanism grounded in the Hill–Mandel principle. The orientation-aware mechanism learns the crystallographic textures, while the interaction mechanism captures stress-equilibrium directions among representative volume element (RVE) subregions, offering insight into internal microstructural mechanics. Notably, ODMN requires only linear elastic data for training yet generalizes effectively to complex nonlinear and anisotropic responses. Our results show that ODMN accurately predicts both mechanical responses and texture evolution under complex plastic deformation, thus expanding the applicability of DMNs to polycrystalline materials. By balancing computational efficiency with predictive fidelity, ODMN provides a robust framework for multiscale simulations of polycrystalline materials.</div></div>","PeriodicalId":55222,"journal":{"name":"Computer Methods in Applied Mechanics and Engineering","volume":"441 ","pages":"Article 117977"},"PeriodicalIF":6.9,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143814873","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 hybrid discrete and continuum framework for multiscale modeling of granular media","authors":"Maytee Chantharayukhonthorn ,&nbsp;Peter Yichen Chen ,&nbsp;Yonghao Yue ,&nbsp;Eitan Grinspun ,&nbsp;Ken Kamrin","doi":"10.1016/j.cma.2025.117936","DOIUrl":"10.1016/j.cma.2025.117936","url":null,"abstract":"<div><div>This work provides key advancements to a nascent simulation approach (Yue et al., 2018; Chen et al., 2021), which hybridizes two common simulation methodologies: discrete element methods and continuum methods. Discrete element methods (DEM), commonly used in granular media simulation, model every single micro-constituent and are thus accurate; however, in light of the enormous number of particles frequently required, they scale poorly. By contrast, continuum methods can be faster by greatly reducing the degrees of freedom represented. However, they can lose accuracy due to constitutive modeling assumptions of system behavior. The hybrid method is a multiscale approach utilizing a discrete representation in regions where flow behavior is complex and a continuum representation in larger-scale regions where behavior is simpler. The method adaptively determines these subregions, and can homogenize discrete grains into continuum material points, enrich continuum regions into discrete grains, and then couple these systems in a thin hybrid zone. This study presents work on all components of the hybrid method to expand its accuracy and robustness, resolving several known problems that occur in the existing hybrid method. We first introduce new granular packing methods capable of generating ad hoc granular assemblies that can meet user-defined criteria, so as to better match the underlying continuum representation during enrichment. Second, we discuss new enrichment and homogenization operators that conserve mass and momentum while also preserving higher-order packing properties such as fabric. Finally, we discuss a higher-order hybrid zone coupling, which better represents the two disparate simulation methods at the grid level. With these updates to the hybrid method, we subsequently demonstrate the ability to accurately simulate large length- and time-scale granular systems in geometries of geomechanical and industrial relevance. The results of the hybrid method compare favorably to purely discrete simulations albeit with much faster computation times.</div></div>","PeriodicalId":55222,"journal":{"name":"Computer Methods in Applied Mechanics and Engineering","volume":"441 ","pages":"Article 117936"},"PeriodicalIF":6.9,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143814877","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":"Efficient Bayesian inversion for simultaneous estimation of geometry and spatial field using the Karhunen-Loève expansion","authors":"Tatsuya Shibata ,&nbsp;Michael C. Koch ,&nbsp;Iason Papaioannou ,&nbsp;Kazunori Fujisawa","doi":"10.1016/j.cma.2025.117960","DOIUrl":"10.1016/j.cma.2025.117960","url":null,"abstract":"<div><div>Detection of abrupt spatial changes in physical properties representing unique geometric features such as buried objects, cavities, and fractures is an important problem in geophysics and many engineering disciplines. In this context, simultaneous spatial field and geometry estimation methods that explicitly parameterize the background spatial field and the geometry of the embedded anomalies are of great interest. This paper introduces an advanced inversion procedure for simultaneous estimation using the domain independence property of the Karhunen-Loève (K-L) expansion. Previous methods pursuing this strategy face significant computational challenges. The associated integral eigenvalue problem (IEVP) needs to be solved repeatedly on evolving domains, and the shape derivatives in gradient-based algorithms require costly computations of the Moore–Penrose inverse. Leveraging the domain independence property of the K-L expansion, the proposed method avoids both of these bottlenecks, and the IEVP is solved only once on a fixed bounding domain. Comparative studies demonstrate that our approach yields two orders of magnitude improvement in K-L expansion gradient computation time. Inversion studies on one-dimensional and two-dimensional seepage flow problems highlight the benefits of incorporating geometry parameters along with spatial field parameters. The proposed method captures abrupt changes in hydraulic conductivity with a lower number of parameters and provides accurate estimates of boundary and spatial-field uncertainties, outperforming spatial-field-only estimation methods.</div></div>","PeriodicalId":55222,"journal":{"name":"Computer Methods in Applied Mechanics and Engineering","volume":"441 ","pages":"Article 117960"},"PeriodicalIF":6.9,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143814876","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":"Space–time isogeometric topology optimization with additive manufacturing constraints","authors":"Li Yang ,&nbsp;Weiming Wang ,&nbsp;Ye Ji ,&nbsp;Chun-Gang Zhu ,&nbsp;Charlie C.L. Wang","doi":"10.1016/j.cma.2025.117976","DOIUrl":"10.1016/j.cma.2025.117976","url":null,"abstract":"<div><div>This paper presents a novel space–time isogeometric topology optimization (ITO) framework for additive manufacturing, enabling concurrent optimization of structural shape and fabrication sequence with accurate geometric representation. The method integrates a density distribution function with a pseudo-time function to optimize build sequences for complex structures, with an objective function that minimizes compliance under external loads and accounts for self-weight effects during fabrication. Density values and virtual heat conduction coefficients are defined at B-spline control points to serve as design variables. A heat conduction-based formulation is employed to generate the pseudo-time function so that prevents the generation of isolated or floating material regions. A layer thickness constraint, defined by the pseudo-time gradient, further enhances manufacturability. The approach has been validated in 2D and 3D examples, demonstrating its effectiveness in managing objectives of entire structure’s stiffness and self-weight of intermediate structures.</div></div>","PeriodicalId":55222,"journal":{"name":"Computer Methods in Applied Mechanics and Engineering","volume":"441 ","pages":""},"PeriodicalIF":6.9,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143807179","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}

{"title":"A stabilized LSMPS for three-dimensional complex free-surface flow with moving wall","authors":"Xiaoxing Liu ,&nbsp;Siwei He ,&nbsp;Wei Huang ,&nbsp;Xi Wang","doi":"10.1016/j.cma.2025.117998","DOIUrl":"10.1016/j.cma.2025.117998","url":null,"abstract":"<div><div>This study proposes a stabilized LSMPS method for simulating free surface flows with moving wall. In this stabilized LSMPS method, a novel wall velocity model is proposed for velocity divergence calculation and velocity interpolation. The proposed method also combines type-A and type-B LSMPS to enhance both accuracy and stability. Type-A LSMPS is applied to internal particles, while type-B scheme is used for free-surface and sub-free-surface particles. Additionally, type-B LSMPS is employed to interpolate the velocity after particle shifting. The stability of both type-A and type-B schemes is enhanced by the incorporation of positive values into the diagonal element of the least-squares matrix. To verify the developed LSMPS method, several three-dimensional numerical simulations are conducted.</div></div>","PeriodicalId":55222,"journal":{"name":"Computer Methods in Applied Mechanics and Engineering","volume":"441 ","pages":"Article 117998"},"PeriodicalIF":6.9,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143807813","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":"Stochastic fracture and fatigue analysis in elasto-plastic materials via virtual modelling techniques","authors":"Yiyang Liu ,&nbsp;Yuan Feng ,&nbsp;Di Wu ,&nbsp;Xiaojun Chen ,&nbsp;Chengwei Yang ,&nbsp;Wei Gao","doi":"10.1016/j.cma.2025.117997","DOIUrl":"10.1016/j.cma.2025.117997","url":null,"abstract":"<div><div>This study presents a stochastic elasto-plastic fracture and fatigue analysis framework, leveraging the phase field method to address the complex fatigue phenomenon. Fatigue behaviour in elasto-plastic materials, governed by the intricate process of plastic damage accumulation, remains pivotal for precisely evaluating structural load-bearing capacities. Transitioning from static fracture analysis to fatigue presents additional challenges, particularly in capturing the cyclic loading effects and progressive damage accumulation, which significantly increase computational demands. These challenges are further exacerbated by system uncertainties, including variations in geometric configurations, material properties, and external loads. To address these difficulties, the proposed framework adopts the predictive capabilities of virtual modelling to systematically elucidate the interdependencies between fatigue responses and uncertain system inputs, providing an efficient alternative to conventional time-consuming numerical simulations. The integration of S-spline polynomial kernel into the extended support vector regression model enhances the training process of the virtual model and demonstrates unparalleled robustness in tackling the challenges of elasto-plastic fracture and fatigue phenomena. Comprehensive numerical investigations, benchmarked against experimental and numerical data, validate the framework's exceptional accuracy and computational efficiency, establishing its versatility for safety and reliability evaluations across static and fatigue scenarios.</div></div>","PeriodicalId":55222,"journal":{"name":"Computer Methods in Applied Mechanics and Engineering","volume":"441 ","pages":"Article 117997"},"PeriodicalIF":6.9,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143799207","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}

{"title":"An integral method for Reliability-Based design Optimization","authors":"Zhenzhong Chen ,&nbsp;Wenhao Wang ,&nbsp;Qianghua Pan ,&nbsp;Guangming Guo ,&nbsp;Xiaoke Li ,&nbsp;Ge Chen ,&nbsp;Xuehui Gan","doi":"10.1016/j.cma.2025.118000","DOIUrl":"10.1016/j.cma.2025.118000","url":null,"abstract":"<div><div>In Reliability-Based design Optimization (RBDO), the aim is to develop an optimal design characterized by high reliability through fulfilling design requirements at the targeted probability threshold. The goal of reliability optimization is to obtain excellent algorithms by focusing on evaluation and optimization. In RBDO, due to the selection of evaluation methods and the problem of updating reliable point methods, it is often impossible to obtain accurate failure probability results with less computation, and the results are inaccurate, or the computation amount is increased. The integral-based reliability analysis method: Hyperspherical cap area integral method (HCAIM) can achieve an accuracy close to that of the MCS method while having a very low computational load, thus enabling the efficient and precise calculation of failure probabilities. Therefore, by introducing a reliability evaluation method based on Integral Method for Reliability Optimization (IMRO), as a decoupling method, high accuracy failure probability calculation results can be obtained with relatively small calculation amount, and then optimization results can be obtained by IMRO and sequence optimization methods. First, a reliability analysis example is utilized to demonstrate the accuracy of the reliability analysis part of IMRO. Then, a set of nonlinear challenges and diverse engineering case studies were utilized to assess the algorithm's performance. The calculation results after comparison prove that IMRO is more accurate in dealing with nonlinear problems and engineering examples, and can better meet the requirements.</div></div>","PeriodicalId":55222,"journal":{"name":"Computer Methods in Applied Mechanics and Engineering","volume":"441 ","pages":""},"PeriodicalIF":6.9,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143807111","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 hybrid deep learning approach for the design of 2D Auxetic Metamaterials","authors":"Chonghui Zhang,&nbsp;Yaoyao Fiona Zhao","doi":"10.1016/j.cma.2025.117972","DOIUrl":"10.1016/j.cma.2025.117972","url":null,"abstract":"<div><div>Mechanical metamaterials feature unique and complex architectures that produce properties not present in their base materials. Traditional design methods often fall short in exploring the vast 2D design space efficiently, necessitating advanced techniques that can accommodate the design of these metamaterials. This paper presents a comprehensive framework for the design and evaluation of 2D metamaterials by integrating data enhancement technology and two novel machine learning (ML) models for design generation and field prediction. One of the primary challenges in designing mechanical metamaterials is the scarcity of data, particularly for non-linear behaviors. To enhance non-linear data, the framework employs data enhancement techniques including domain adaptation (Low-Rank Adaptation (LoRA) and fine-tuning) to adapt knowledge from data-rich linear to non-linear scenarios, and ensemble learning to label designs for generative models. With the enhanced data, a novel hybrid generation model of conditional Variational Autoencoder (CVAE) and Denoising Diffusion Probabilistic Model (DDPM) is introduced. The proposed hybrid model not only achieves high-fidelity design generation but also incorporates a guidance mask module, enabling users to influence the generation process actively and align the output with specific design requirements. Then, to evaluate the generated designs effectively, a novel graph-enhanced convolutional neural network (CNN) model is introduced for field prediction tasks, which has been tested on stress and displacement field prediction. This model excels in predicting stress fields at a nodal level, especially in high-stress regions, and improves the prediction of displacement fields through embedded topological consistency, enhancing both physical fidelity and training efficiency. Based on the predicted stress field, radial basis function (RBF) optimization techniques are applied to fine-tune the designs, particularly at high-stress points, ensuring optimal stress distribution and improved mechanical performance. The results demonstrate that the data enhancement techniques significantly contributed to developing the ML models for non-linear behavior. The proposed CVAE-DDPM hybrid model shows substantial improvements in design robustness and accuracy,compared to the individual CVAE and DDPM models. Additionally, the graph-enhanced CNN outperforms other field prediction models, and the subsequent RBF optimization effectively reduces the maximum von Mises stress in the design, based on predictions from the graph-enhanced CNN.</div></div>","PeriodicalId":55222,"journal":{"name":"Computer Methods in Applied Mechanics and Engineering","volume":"441 ","pages":"Article 117972"},"PeriodicalIF":6.9,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143807815","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":"Stochastic reconstruction of multiphase composite microstructures using statistics-encoded neural network for poro/micro-mechanical modelling","authors":"Jinlong Fu,&nbsp;Wei Tan","doi":"10.1016/j.cma.2025.117986","DOIUrl":"10.1016/j.cma.2025.117986","url":null,"abstract":"<div><div>A fundamental understanding of the microstructure–property relationships (MPRs) is crucial for optimising the performances and functionality of multiphase composites. Image-based poro/micro-mechanical modelling offers a powerful non-invasive method to explore MPRs, but the inherent randomness in multiphase composites often necessitates extensive datasets of 3D digital microstructures for reliable statistical analysis. This paper presents a cost-effective machine learning-based framework to efficiently reconstruct numerous virtual 3D microstructures from a limited number of 2D real exemplars, bypassing the prohibitive costs associated with volumetric microscopy for opaque composites. This innovative framework leverages feedforward neural networks to encode morphological statistics in 2D exemplars, referred to as the statistics-encoded neural network (SENN), providing an accurate statistical characterisation of complex multiphase microstructures. Utilising the SENN-based characterisation, 3D morphological statistics can be inferred from 2D measurements through a 2D-to-3D morphology integration scheme, and then statistically equivalent 3D microstructures are synthesised via Gibbs sampling. This framework further incorporates hierarchical characterisation and multi-level reconstruction approaches, allowing for the seamless capture of local, regional, and global microstructural features across multiple length scales. Validation studies are conducted on three representative multiphase composites, and morphological similarity between the reconstructed and reference 3D microstructures is evaluated by comparing a series of morphological descriptors. Additionally, image-based meshing and pore/micro-scale simulations are performed on these digital microstructures to compute effective macroscopic properties, including stiffness, permeability, effective diffusivity, and thermal conductivity tensors. Results reveal strong statistical equivalence between the reconstructed and reference 3D microstructures in both morphology and physical properties, confirming the SENN-based framework is a high-fidelity tool to reconstruct multiphase microstructures for image-based poro/micro-mechanical analysis.</div></div>","PeriodicalId":55222,"journal":{"name":"Computer Methods in Applied Mechanics and Engineering","volume":"441 ","pages":"Article 117986"},"PeriodicalIF":6.9,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143807814","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":"Invariant-domain preserving and locally mass conservative approximation of the Lagrangian hydrodynamics equations","authors":"Jean-Luc Guermond ,&nbsp;Bojan Popov ,&nbsp;Laura Saavedra ,&nbsp;Madison Sheridan","doi":"10.1016/j.cma.2025.117927","DOIUrl":"10.1016/j.cma.2025.117927","url":null,"abstract":"<div><div>In this paper we construct an explicit approximation for the Lagrangian hydrodynamics equations equipped with an arbitrary equation of state. The approximation of the state variable is done with piecewise constant finite elements and the approximation of the mesh motion is done with higher-order continuous finite elements. The method is invariant-domain preserving and locally mass conservative. The purpose of this method is to be used in combination with higher-order methods to make them invariant domain preserving as well.</div></div>","PeriodicalId":55222,"journal":{"name":"Computer Methods in Applied Mechanics and Engineering","volume":"441 ","pages":"Article 117927"},"PeriodicalIF":6.9,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143799206","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}