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Data-driven material homogenisation, selection and design

数据驱动的材料均质化、选择和设计

基本信息

批准号：
2442202
负责人：
金额：
--
依托单位：
Swansea University
依托单位国家：
英国
项目类别：
Studentship
财政年份：
2020
资助国家：
英国
起止时间：
2020 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=studentship-2442202
关键词：
Data driven material homogenisation selection

项目摘要

Data-driven homogenisation techniques present an innovative and highly promising approach that will enable material modelling to match the fast pace of the current advances in material design and engineering. With such modelling strategies, the challenging and often tedious task of formulating the appropriate constitutive equations for new material designs is superseded by a neural network surrogate model constructed through a learning phase on the appropriately designed Representative Volume Element (RVE) of material. As a result the computer modelling of material behaviour and design becomes more straightforward and much more efficient. This is particularly important in the context of advanced micro-structure design and novel additive layer manufacturing techniques as well as the for the understanding and prediction of material degradation and life cycle studies. This project will integrate significant recent developments in multi-scale modelling, computational homogenisation and machine learning, thus providing a powerful digital twin based on the latest advances in material engineering with the aim of accelerating the material research, development and design.

数据驱动的均化技术提供了一种创新的和非常有前途的方法，将使材料建模能够与当前材料设计和工程领域的快速发展相匹配。利用这种建模策略，为新材料设计建立适当本构方程的具有挑战性且往往乏味的任务被通过适当设计的材料的代表性体积单元(RVE)的学习阶段构建的神经网络代理模型所取代。因此，材料行为和设计的计算机建模变得更加直接和高效。在先进的微结构设计和新的添加剂层制造技术的背景下，这对于了解和预测材料降解和生命周期研究尤其重要。该项目将整合多尺度建模、计算均质化和机器学习方面的重大最新发展，从而基于材料工程的最新进展提供强大的数字孪生兄弟，旨在加速材料的研究、开发和设计。

项目成果

期刊论文数量（3）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

A framework for neural network based constitutive modelling of inelastic materials

DOI：
10.1016/j.cma.2023.116672
发表时间：
2024-02
期刊：
Computer Methods in Applied Mechanics and Engineering
影响因子：
7.2
作者：
W. Dettmer;Eugenio J. Muttio;R. Alhayki;Djordje Perić
通讯作者：
W. Dettmer;Eugenio J. Muttio;R. Alhayki;Djordje Perić

Neural Network-Based Constitutive Model for Solid Materials

DOI：
10.23967/wccm-apcom.2022.079
发表时间：
2022
期刊：
15th World Congress on Computational Mechanics (WCCM-XV) and 8th Asian Pacific Congress on Computational Mechanics (APCOM-VIII)
影响因子：
0
作者：
R. Alhayki;E. Muttio-Zavala;W. Dettmer;D. Peric
通讯作者：
R. Alhayki;E. Muttio-Zavala;W. Dettmer;D. Peric

A supervised parallel optimisation framework for metaheuristic algorithms