Zhu Yichao
Professor Supervisor of Doctorate Candidates Supervisor of Master's Candidates
Gender:Male
Alma Mater:University of Oxford
Degree:Doctoral Degree
School/Department:Department of Engineering Mechanics
Discipline:Solid Mechanics. Applied Mathematics
Business Address:Room 523, 1st Lab Building
E-Mail:yichaozhu@dlut.edu.cn
Hits:
Indexed by:Journal Papers
Date of Publication:2020-08-01
Journal:INTERNATIONAL JOURNAL OF SOLIDS AND STRUCTURES
Included Journals:SCIE
Volume:198
Page Number:57-71
ISSN No.:0020-7683
Key Words:Dislocation; Pattern formation; Discrete-to-continuum transition; Machine learning; Asymptotic analysis
Abstract:The self-organisation behaviour of dislocation upon loading is still a mechanistically mysterious phenomenon. The present article aims to present a computationally tractable dislocation dynamics model, which can be used for helping analyse the mechanism behind dislocation pattern formation. With an integrated use of asymptotic analysis and machine learning tools, the discrete dislocation dynamics on two parallel slip planes is self-consistently reformulated at a coarse-grained level. For the present case, asymptotic analysis helps in 1) reformulating the original globally discrete problem by a continuum model underpinned by a database resolving the almost singular short-range elastic interaction of discrete dislocations; 2) identifying the proper input and output quantities for implementing machine learning tools; 3) digging out a hidden but explicit interrelation between mean-field quantities to reduce the dimensionality of the data space. Machine learning tools serve for 1) inferring the inherently implicit interrelationships between continuum quantities; 2) capturing a low-dimensional manifold in the data space corresponding to the local flow stress. The non-monotonically increasing profile of the flow stress - density relationship, as revealed by machine learning, is found to play a key role in the onset of dislocation patterning behaviour seen in the simulation results. A scaling law relating the applied stress to pattern wavelength is also derived, providing a rationale to the widely used empirical similitude relation. The experimentally observed swing in similitude coefficient value is attributed to the randomness in slip plane distributions. In a methodological viewpoint, the treatment of using asymptotic analysis to help design the curriculum for implementing machine learning tools, offers a paradigm for self-consistently upscaling more complicated multiscale systems. (C) 2020 Elsevier Ltd. All rights reserved.
Pre One:On speeding up an asymptotic-analysis-based homogenisation scheme for designing gradient porous structured materials using a zoning strategy
Next One:Generation of smoothly-varying infill configurations from a continuous menu of cell patterns and the asymptotic analysis of its mechanical behaviour
招生信息
招生类别：（计划2023、2024年秋季入学）
博士研究生1名、硕士研究2名。有意向者欢迎邮件联系：yichaozhu@dlut.edu.cn
导师信息：
朱一超，大连理工大学工程力学系教授，博士生导师，国家级人才项目青年项目入选者。本科毕业于复旦大学，于牛津大学获得博士学位。致力于“基于微观，预测宏观”的跨尺度建模分析研究。相关研究成果主要应用于核材料、3D打印、复合材料研发等国际前沿技术或国家重大需求领域。在固体力学旗舰期刊JMPS上发表论文近10篇，另有工作发表于PRL、CMAME、Scripta Materialia等物理学计算力学或材料学顶级期刊。同时与中国核动力研究设计院等单位深度合作，将研究成果应用于工程实践。
育人理念：
帮助学生完成从“学习者”到“研究者”的角色转换。注重学与思结合的科研方式，学生有充分的自由进行探索，同时安排定期讨论，答疑解惑；注重交流，承诺为博士生提供参加高水平国际会议或海内外顶尖学府（如牛津大学、约翰霍普金斯大学等）访问交流的机会，为所有学生提供在海内外学术会议介绍个人研究成果的机会；注重科研与生活的平衡，参考学部最高标准发放科研津贴，鼓励学生在工作方向明确的情况下自主分配科研-休假时间。
毕业愿景：
ü 之于博士生，帮助您在跨尺度建模领域形成国际前沿视角，在您努力的前提下，力争让本科直博/硕博连读生5年完成学业，已有硕士学位的学生3-4年完成学业，发表高水平第一作者论文3篇以上，为您未来找工作搭建高水平的平台；
ü 之于希望继续深造的硕士生，会按博士标准培养，发表科研论文，并全力推荐往海内外高校继续深造；
ü 之于希望去工业界的硕士生，将参与横向课题，向对口的国家级科研单位的推荐工作机会。
研究方向：
课题组致力于推动经典渐近分析方法与新兴机器学习算法深度融合，以微观机制视角分析材料宏观行为，发展高效保真数值模拟方法，为材料复杂跨尺度行为的预测提供扎实的理论保障与高效的仿真工具。具体包括：
a) 以晶体微结构演化视角发展辐照环境下材料力学行为仿真模型；
b) 合金材料内部元素演化与其宏观性能定量关系研究；
c) 3D打印复杂构型宏观物理性质建模分析；
d) 其它跨尺度系统（如复合材料等）之数学建模与模拟仿真研究。
一些要求：
a) 目标清晰，有科研热情，对编程不排斥
b) 力学、数学、物理、计算机专业优先
c) 先修课程：数学物理方法、弹性力学、计算方法
d) 有一定的英文基础
