.png){kind=icon}
返回
Machine learning-enabled self-consistent parametrically-upscaled crystal plasticity model for Ni-based superalloys
.png){kind=icon}
OA
AI
DOI：10.1016/j.cma.2022.115384.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
This paper introduces a concurrent multiscale modeling framework for developing parametrically-upscaled crystal plasticity models (PUCPM) for crystalline metals that are characterized by multiple phases in their intragranular microstructure. Specifically, Ni-based superalloys with distributions of gamma ' precipitates in the gamma matrix in their sub-grain microstructure are modeled in this study. The interaction of the gamma - gamma ' phases with nano-scale dislocation mechanisms determine the elasto-plastic behavior of the material across multiple scales. The PUCPM is designed to explicitly account for the morphological and configurational statistics of these gamma - gamma ' intragranular microstructures in its crystal plasticity constitutive coefficients. This approach provides a thermodynamically-consistent foundation to enable microstructure-aware material simulation at the higher scale. Establishing this multiscale characteristic requires an automated toolchain of computational methods to generate and embed heterogeneous, statistically-equivalent representative volume elements (SERVEs) into the concurrent multiscale simulation domains for self-consistent homogenization. The self-consistency condition is enforced through an optimization strategy invoking a series of coupled nonlinear finite element solutions. Supervised and unsupervised machine learning methods are integrated with the physics-based modeling at all stages of model development to overcome computational limitations and to provide the final, connecting map between PUCPM constitutive coefficients and gamma - gamma ' microstructural descriptors. The resulting PUCPM benefits from orders of magnitudes of speedup compared to the equivalent explicit representation of the lower scale microstructure. This advantage enables unique model capabilities for the multiscale analysis of deformation and failure in materials and location-specific design.(c) 2022 Elsevier B.V. All rights reserved.
Keyword:
Parametric upscaling
Ni-based superalloy
Self-consistent homogenization
Concurrent multiscale model
Machine learning
Nonlinear finite element method
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
7.3
论文数:
1.3W
被引数:
5.6W
机构
引用论文
On the Development and Applications of Cellulosic Nanofibrillar and Nanocrystalline Materials纤维素纳米原纤和纳米晶材料的发展与应用
PREAI
分享收藏Finite strain mean-field homogenization of composite materials with hyperelastic-plastic constituents具有超弹塑性成分的复合材料的有限应变平均场均匀化
PREAI
分享收藏Single-cell dissection of cellular components and interactions shaping the tumor immune phenotypes in ovarian cancer细胞成分的单细胞解剖和相互作用塑造卵巢癌的肿瘤免疫表型.png)
Cancer CellOAAI
Cancer CellIF0
OAAI分享收藏分享收藏A multi-level computational model for multi-scale damage analysis in composite and porous materials
PREAI
分享收藏
查看更多内容