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Predictive Modelling of the Fundamentals of Failure in Metals

金属失效基本原理的预测模型

基本信息

批准号：
EP/P002188/1
负责人：
James Kermode
金额：
$ 12.83万
依托单位：
University of Warwick
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2016
资助国家：
英国
起止时间：
2016 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=EP%2FP002188%2F1
关键词：
Predictive Modelling Fundamentals Failure Metals

项目摘要

Our lack of detailed understanding of the atomic scale mechanisms which lead to failure of metals through processes such as cracking, creep, embrittlement or fatigue is surprising, given the significant technological and economic impact that such understanding could generate. Examples of what could be achieved include designing stronger, lighter turbine blades for aeroplane engines, improved lightweight alloys for the automobile industry or improved radiation shields for the nuclear industry.Progress to date has been limited partly because the current generation of continuum models for metal failure rely heavily on empirical methods. The overarching aim of this proposal is to develop new models to enable continuum-scale modelling of failure processes, in particular crack growth, by incorporating pre-computed first-principles information. Adding reliable probabilistic "error bars'' which incorporate the effects of model error, limited data, epistemic uncertainty and coarse-graining would help to address one of the major barriers holding back wider adoption of materials modelling in industry (cf. Innovate UK/KTN special interest group on Uncertainty Quantification and Management for High Value Manufacturing).Realising these long-term aims first requires developing (i) accurate atomic scale models for `slow' failure processes in metals and (ii) a rigorous model reduction procedure to capture information lost during coarse graining, allowing complex microstructures to be modelled. This project addresses (i) in detail by developing new methodology to compute energy barriers with QM accuracy in systems large enough to capture stress concentration, with application to dislocation motion and crack growth in technologically relevant but still structurally simple single crystal model systems (nickel, aluminium and tungsten). Requirement (ii) will be explored via a case study to be further developed in future proposals.The project is aligned with research areas in which the UK is a world leader: condensed matter (electronic structure), materials engineering (metals and alloys) and numerical analysis.

考虑到这种理解可能产生的重大技术和经济影响，我们对原子尺度机制缺乏详细的了解，这些机制导致金属通过裂纹、蠕变、脆化或疲劳等过程而失效，这是令人惊讶的。可以实现的例子包括为飞机发动机设计更强、更轻的涡轮叶片，为汽车工业改进轻质合金，或为核工业改进辐射屏蔽。迄今为止，进展有限，部分原因是当前一代金属失效连续模型严重依赖经验方法。该提案的总体目标是开发新模型，通过结合预先计算的第一原理信息，对失效过程（特别是裂纹扩展）进行连续尺度建模。添加可靠的概率“误差线”，其中包含模型误差、有限数据、认知不确定性和粗粒度的影响，将有助于解决阻碍材料建模在工业中更广泛采用的主要障碍之一（参见创新英国/KTN高价值制造不确定性量化和管理特别兴趣小组）。实现这些长期目标首先需要开发（i）针对“缓慢”故障过程的精确原子尺度模型 (ii) 严格的模型简化程序，以捕获粗粒化过程中丢失的信息，从而对复杂的微观结构进行建模。该项目通过开发新的方法来详细解决（i），在足够大的系统中以 QM 精度计算能垒，以捕获应力集中，并应用于技术相关但结构仍然简单的单晶模型系统（镍、铝和钨）中的位错运动和裂纹生长。要求 (ii) 将通过案例研究进行探索，并在未来的提案中进一步发展。该项目与英国处于世界领先地位的研究领域保持一致：凝聚态物质（电子结构）、材料工程（金属和合金）和数值分析。

项目成果

期刊论文数量（10）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Machine learning unifies the modeling of materials and molecules.

DOI：
10.1126/sciadv.1701816
发表时间：
2017-12
期刊：
Science advances
影响因子：
13.6
作者：
Bartók AP;De S;Poelking C;Bernstein N;Kermode JR;Csányi G;Ceriotti M
通讯作者：
Ceriotti M

Atomistic QM/MM simulations of the strength of covalent interfaces in carbon nanotube-polymer composites.

碳纳米管-聚合物复合材料中共价界面强度的原子 QM/MM 模拟。

DOI：
10.1039/d0cp01841d
发表时间：
2020
期刊：
PCCP
影响因子：
0
作者：
Golebiowski JR
通讯作者：
Golebiowski JR

Exploiting Machine Learning in Multiscale Modelling of Materials

DOI：
10.1007/s40033-022-00424-z
发表时间：
2022-11
期刊：
Journal of The Institution of Engineers (India): Series D
影响因子：
0
作者：
G. Anand;Swarnava Ghosh;Liwei Zhang;Angesh Anupam;Colin L. Freeman;C. Ortner;M. Eisenbach;J. Kermode
通讯作者：
G. Anand;Swarnava Ghosh;Liwei Zhang;Angesh Anupam;Colin L. Freeman;C. Ortner;M. Eisenbach;J. Kermode

Machine learning a general purpose interatomic potential for silicon

机器学习硅的通用原子间势