Efficient high-dimensional material reliability analysis with explicit voxel-level stochastic microstructure representation

Yi Gao; Yang Jiao; Yongming Liu

首页> 外文期刊>Applied Mathematical Modelling >Efficient high-dimensional material reliability analysis with explicit voxel-level stochastic microstructure representation

【24h】

Efficient high-dimensional material reliability analysis with explicit voxel-level stochastic microstructure representation

机译：高效高维材料可靠性分析，具有明确的体素级随机微结构表示

获取原文

获取原文并翻译 | 示例

掌桥外文数据库（机构版） >>

开具论文收录证明 >>

文献代查 >>

页面导航

摘要
著录项
相似文献
相关主题

摘要

A novel efficient methodology for probabilistic material reliability analysis considering fine-scale microstructure stochasticity is proposed in this paper. Integrated computational material engineering requires efficient multiscale computational capabilities to enable computational design and validation. Two critical challenges are identified: handling uncertainties from microstructures and material properties; and handling the "curse of dimensionality" for probabilistic solvers. The proposed study addresses these two critical challenges. First, an analytical and hierarchical uncertainty quantification method is proposed for the explicit stochastic microstructure representation at the voxel-level. The hierarchy of uncertainties from both phase maps and uncertainties within each phase is modeled using an explicit Gaussian mixture random field. Analytical approximation for the arbitrary non-Gaussian random field is derived, which can facilitate the computation of gradient information in optimization. Following this, an efficient probabilistic solver using adjoint first-order reliability method combining the importance sampling is derived by formulating the material reliability analysis as a constrained optimization problem. The adjoint method is used to efficiently evaluate the responses and exact gradients with the help of the analytical Gaussian mixture random field. Several numerical examples for material reliability calculation with high-dimensional (voxel-level) random fields are subsequently employed to demonstrate and validate the proposed methodology. The results of the proposed method are quantitatively compared to those obtained via the classical first-order reliability method, direct Monte Carlo simulation, subset simulation, and the sequential importance sampling method. The comparisons indicate that the proposed method possesses high efficiency for high-dimensional material reliability problems.

机译：本文提出了考虑微观微观结构随机性的概率材料可靠性分析的新型高效方法。集成计算材料工程需要有效的多尺度计算能力来实现计算设计和验证。确定了两个关键挑战：处理微观结构和材料特性的不确定性;处理概率求解器的“维度诅咒”。拟议的研究解决了这两个关键挑战。首先，提出了分析和分层不确定性定量方法，用于体素水平的显式随机微结构表示。使用显式高斯混合随机字段建模来自每个阶段的相位映射和不确定性的不确定性的层次。推导出任意非高斯随机场的分析近似，这可以促进优化中的梯度信息的计算。在此之后，通过将材料可靠性分析作为约束优化问题的材料可靠性分析来导出使用伴随的一阶可靠性方法的有效概率求解器。伴随方法用于有效地评估借助于分析高斯混合物随机场的响应和精确梯度。随后采用具有高维（体素级）随机字段的材料可靠性计算的几个数值例子来证明和验证所提出的方法。所提出的方法的结果与通过经典一阶可靠性方法，直接蒙特卡罗模拟，子集模拟和顺序重视采样方法获得的那些。比较表明，该方法具有高效率的高维材料可靠性问题。

著录项

来源
《Applied Mathematical Modelling》 |2021年第3期|1117-1140|共24页
作者
Yi Gao; Yang Jiao; Yongming Liu;
展开▼
作者单位

School for Engineering of Matter Transport and Energy Arizona State University Tempe AZ 85287 United States;

School for Engineering of Matter Transport and Energy Arizona State University Tempe AZ 85287 United States;

School for Engineering of Matter Transport and Energy Arizona State University Tempe AZ 85287 United States;

展开▼
收录信息美国《科学引文索引》(SCI);美国《工程索引》(EI);
原文格式 PDF
正文语种 eng
中图分类
关键词
Non-Gaussian random field; Karhunen-Loeve expansion; Microstructure; Reliability analysis;

机译：非高斯随机场;Karhunen-Loeve扩张;微观结构;可靠性分析;

相似文献

外文文献
中文文献
专利

1. Confidence Bounds on Design Variables Using High-Dimensional Model Representation-Based Inverse Reliability Analysis [J] . A. S. Balu, B. N. Rao Journal of structural engineering . 2013,第6期

机译：基于高维模型表示的逆可靠性分析对设计变量的置信界
2. High-dimensional model representation for structural reliability analysis: Authors' reply to comments by S. Rahman and H. Xu [J] . Rajib Chowdhury, B. N. Rao, A. Meher Prasad Communications in numerical methods in engineering . 2011,第10期

机译：用于结构可靠性分析的高维模型表示：作者对S. Rahman和H. Xu的评论的答复
3. Comments on 'High-dimensional model representation for structural reliability analysis' [J] . S. Rahman, H.Xu Communications in numerical methods in engineering . 2011,第10期

机译：关于“用于结构可靠性分析的高维模型表示”的评论
4. Accurate and efficient stochastic reliability analysis of composite services using their compact Markov reward model representations [C] . Sato, N., Trivedi, . 2007

机译：使用紧凑型马尔可夫奖励模型表示的复合服务准确而有效的随机可靠性分析
5. Efficient stochastic finite element method for the reliability analysis of nonlinear frame structures. [D] . Zhou, Yiguang. 1992

机译：非线性框架结构可靠性分析的有效随机有限元方法。
6. Correction: Li Q. and Liang S.Y. Microstructure Images Restoration of Metallic Materials Based upon KSVD and Smoothing Penalty Sparse Representation Approach. Materials 2018 11 637 [O] . Qing Li, Steven Y. Liang 2020

机译：更正：李问和梁世Y基于KSVD和平滑罚分稀疏表示方法的金属材料微结构图像复原。材料201811637
7. Figure 6: (A) Tree resulted the standard MP analysis of the 98-loci supermatrix of the Great Apes (Hominidae, Primates, Mammalia) constructed using the modified summary of Lehtonen et al. (2011) (see “Materials and Methods” for the details) and a posteriori rooted relatively Papio (length = 12,092, CI = 0.7877, RI = 0.5597); 34,022 characters are constant, and the number of the parsimony-informative characters is equal to 4,311. MP BS values are provided below branches. The only characters with non-ambiguous value of the outgroup (Papio) have been saved within concatenated alignment; (B) the average consensus tree of the score 0.00084 resulted the analysis of the Hennigian forest of the 5,507 trees derived from the binary representation of the 98-loci supermatrix of the Great Apes (Hominidae, Primates, Mammalia) (see “Materials and Methods” for the details); Papio is assumed to be the best all-plesiomorphic group. “Additional…” FORESTER’s output tree file (see “Materials and Methods” for the details) selected for future analyses; (C) the average consensus tree of the score 0.00090 resulted the analysis of the Hennigian forest of the 5,339 trees derived from the modified binary representation of the 98-loci supermatrix of the Great Apes (Hominidae, Primates, Mammalia) (see 6.I. and “Materials and Methods” for the details), but with all 168 trees, which contain the clade (Homo plus Pan) have been removed from the input forest. “Additional…” FORESTER’s output tree file (see “Materials and Methods”) selected for future analyses. [O] . -1

机译：图6：（A）树导致的标准MP分析的98-基因座的类人猿（人科，灵长类动物，哺乳动物），使用莱赫托宁等人的修改的摘要构成的超级矩阵。（2011）（参见细节“材料和方法”）以及相对植根狒狒后验概率（长度= 12092，CI = 0.7877，RI = 0.5597）; 34022个字符是恒定的，并且简约信息的字符数等于4311。下面提供分支MP BS值。与外类群（狒狒）的非模糊值的唯一的字符已被保存的级联对准内; （B）的得分0.00084的平均一致树导致从巨猿（原始人类，灵长类动物，哺乳动物）的98座超级矩阵的二进制表示获得的5507棵的Hennigian森林的分析（见“材料和方法”为细节）;狒狒被认为是最好的全祖征群。 “其他...” FORESTER的输出树文件（请参阅详细信息“材料和方法”），用于进一步的分析; （C）的分数0.00090的平均一致树导致从98个位点的类人猿（人科，灵长类动物，哺乳动物）的超级矩阵（见6.I的改性二进制表示导出的5339种树木的Hennigian森林的分析以及“材料和方法”的详细说明），但所有168棵，其中包含的分支（智人加PAN）已经从输入林中删除。 “其他...” FORESTER的输出树文件（参见“材料和方法”），用于进一步的分析。
8. Efficient Numerical Approximations of Tracking Statistical Quantities of Interest From the Solution of High-Dimensional Stochastic Partial Differential Equations. [R] . Trenchea, C. 2012

机译：基于高维随机偏微分方程解的跟踪统计量的有效数值逼近。

Efficient high-dimensional material reliability analysis with explicit voxel-level stochastic microstructure representation

摘要

著录项

相似文献

相关主题

期刊订阅