{"title":"基于有限元集群的异质材料晃动分析基础缩减法","authors":"Xiuchen Gong, Yinghao Nie, Gengdong Cheng","doi":"10.1007/s00466-024-02470-8","DOIUrl":null,"url":null,"abstract":"<p>Shakedown analysis with Melan’s theorem is an important approach to predicting the ultimate load-bearing capacity of heterogeneous materials under varying loads. However, this approach entails dealing with a large-scale nonlinear mathematical programming problem with numerous element-wise yielding constraints and unknown time-independent beneficial residual stress variables, resulting in a substantial computational burden. The well-known basis reduction method expresses the unknown time-independent beneficial residual stress as a linear combination of a set of self-equilibrium stress (SES) bases, and the corresponding coefficients are the unknowns. This method is effective only if the set of SES basis vectors is small and easily available. Based on the representative volume element (RVE) and FEM-cluster based analysis (FCA) method, this paper proposes a FEM cluster-based basis reduction method to fast predict the shakedown domain of heterogeneous materials. The novel data-driven clustering method is introduced to divide the RVE into several clusters. The SES basis is constructed by applying the cluster eigenstrain to RVE under periodic boundary conditions. Numerical experiments show that the unknown time-independent beneficial residual stress can be well represented with this small set of SES basis vectors. In this way, the unknown variables are reduced dramatically. In addition, to further reduce the number of nonlinear constraints, a constraint reduction strategy based on the reduced-order model of FCA is implemented to remove the element-wise yielding constraints for the elements far from yielding. Several numerical examples demonstrate its efficiency and accuracy.</p>","PeriodicalId":55248,"journal":{"name":"Computational Mechanics","volume":"13 1","pages":""},"PeriodicalIF":3.7000,"publicationDate":"2024-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A FEM cluster-based basis reduction method for shakedown analysis of heterogeneous materials\",\"authors\":\"Xiuchen Gong, Yinghao Nie, Gengdong Cheng\",\"doi\":\"10.1007/s00466-024-02470-8\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Shakedown analysis with Melan’s theorem is an important approach to predicting the ultimate load-bearing capacity of heterogeneous materials under varying loads. However, this approach entails dealing with a large-scale nonlinear mathematical programming problem with numerous element-wise yielding constraints and unknown time-independent beneficial residual stress variables, resulting in a substantial computational burden. The well-known basis reduction method expresses the unknown time-independent beneficial residual stress as a linear combination of a set of self-equilibrium stress (SES) bases, and the corresponding coefficients are the unknowns. This method is effective only if the set of SES basis vectors is small and easily available. Based on the representative volume element (RVE) and FEM-cluster based analysis (FCA) method, this paper proposes a FEM cluster-based basis reduction method to fast predict the shakedown domain of heterogeneous materials. The novel data-driven clustering method is introduced to divide the RVE into several clusters. The SES basis is constructed by applying the cluster eigenstrain to RVE under periodic boundary conditions. Numerical experiments show that the unknown time-independent beneficial residual stress can be well represented with this small set of SES basis vectors. In this way, the unknown variables are reduced dramatically. In addition, to further reduce the number of nonlinear constraints, a constraint reduction strategy based on the reduced-order model of FCA is implemented to remove the element-wise yielding constraints for the elements far from yielding. Several numerical examples demonstrate its efficiency and accuracy.</p>\",\"PeriodicalId\":55248,\"journal\":{\"name\":\"Computational Mechanics\",\"volume\":\"13 1\",\"pages\":\"\"},\"PeriodicalIF\":3.7000,\"publicationDate\":\"2024-04-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Computational Mechanics\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1007/s00466-024-02470-8\",\"RegionNum\":2,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"MATHEMATICS, INTERDISCIPLINARY APPLICATIONS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computational Mechanics","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1007/s00466-024-02470-8","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MATHEMATICS, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 0
摘要
利用梅兰定理进行动摇分析是预测异质材料在不同荷载下最终承载能力的重要方法。然而,这种方法需要处理一个大规模的非线性数学编程问题,其中包含大量元素屈服约束和未知的与时间无关的有益残余应力变量,从而造成了巨大的计算负担。著名的碱基还原法将未知的与时间无关的有益残余应力表示为一组自平衡应力(SES)碱基的线性组合,相应的系数为未知数。这种方法只有在自平衡应力基向量集较小且易于获得时才有效。本文在代表体积元素(RVE)和基于有限元簇的分析(FCA)方法的基础上,提出了一种基于有限元簇的基还原方法,用于快速预测异质材料的振动域。本文引入了新颖的数据驱动聚类方法,将 RVE 分成多个簇。在周期性边界条件下,通过对 RVE 应用簇特征应变来构建 SES 基础。数值实验表明,与时间无关的未知有益残余应力可以用这一小组 SES 基向量很好地表示。这样一来,未知变量就大大减少了。此外,为了进一步减少非线性约束的数量,还采用了一种基于 FCA 降阶模型的约束缩减策略,以去除远离屈服的元素的元素屈服约束。几个数值实例证明了该方法的高效性和准确性。
A FEM cluster-based basis reduction method for shakedown analysis of heterogeneous materials
Shakedown analysis with Melan’s theorem is an important approach to predicting the ultimate load-bearing capacity of heterogeneous materials under varying loads. However, this approach entails dealing with a large-scale nonlinear mathematical programming problem with numerous element-wise yielding constraints and unknown time-independent beneficial residual stress variables, resulting in a substantial computational burden. The well-known basis reduction method expresses the unknown time-independent beneficial residual stress as a linear combination of a set of self-equilibrium stress (SES) bases, and the corresponding coefficients are the unknowns. This method is effective only if the set of SES basis vectors is small and easily available. Based on the representative volume element (RVE) and FEM-cluster based analysis (FCA) method, this paper proposes a FEM cluster-based basis reduction method to fast predict the shakedown domain of heterogeneous materials. The novel data-driven clustering method is introduced to divide the RVE into several clusters. The SES basis is constructed by applying the cluster eigenstrain to RVE under periodic boundary conditions. Numerical experiments show that the unknown time-independent beneficial residual stress can be well represented with this small set of SES basis vectors. In this way, the unknown variables are reduced dramatically. In addition, to further reduce the number of nonlinear constraints, a constraint reduction strategy based on the reduced-order model of FCA is implemented to remove the element-wise yielding constraints for the elements far from yielding. Several numerical examples demonstrate its efficiency and accuracy.
期刊介绍:
The journal reports original research of scholarly value in computational engineering and sciences. It focuses on areas that involve and enrich the application of mechanics, mathematics and numerical methods. It covers new methods and computationally-challenging technologies.
Areas covered include method development in solid, fluid mechanics and materials simulations with application to biomechanics and mechanics in medicine, multiphysics, fracture mechanics, multiscale mechanics, particle and meshfree methods. Additionally, manuscripts including simulation and method development of synthesis of material systems are encouraged.
Manuscripts reporting results obtained with established methods, unless they involve challenging computations, and manuscripts that report computations using commercial software packages are not encouraged.