Hybrid Deterministic/Statistical Multi-scale Modelling Techniques for 3D Woven Composites

3D 编织复合材料的混合确定性/统计多尺度建模技术

• 批准号: EP/V050591/1
• 负责人: Bassam El Said
• 金额: $ 32.45万
• 依托单位: University of Bristol
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FV050591%2F1
• 关键词: Hybrid; Deterministic; Statistical; Multi; scale

Today, composite materials are at the forefront of an engineering revolution targeting lighter, more reliable, and more fuel-efficient aerospace structures. Advanced composites are made from layers of long fibres bound together using a matrix to form the structure. The most common fibre type used in aerospace applications are Carbon Fibres combined with an Epoxy matrix. More recently other types of fibres/matrix are being introduced, such as: ceramics matrix composites for high temperature applications and metal matrix composites for abrasion/ impact resistance. However, something common between all types of composites is that they are based on fibre layers. By definition, layers are 2D. As a result, all conventional composite materials struggle with direct loading in the third direction. While 2D composites provide designers with clear advantages coming from the superior properties of the fibres and the flexibility of tailoring fibre directions or combining different fibre types, through thickness performance remains an Achilles heel that have limited their full potential.3D Composites is a viable solution to these issues as they are made from fibres woven in all three dimensions. These materials show a lot of promise as they can carry direct load through thickness and can resist impact events. However, there are a set of modelling challenges that come with using 3D composites, which have prevented engineers from taking full advantage of these materials. Traditionally, to understand a new material behaviour, engineers and scientists test samples of the material to characterise its behaviour. Then this characteristic behaviour is included in the mathematical models that can predict the behaviour of structures made from this material. These structure models are what is used as design tool. This conventional approach does not work for 3D composites. During manufacturing, the 3D network of woven fibres deforms around corners and other structural features to conform to the structure geometry. This in turn means that the fibre network will have a different architecture for each part of the structure and consequently will have its own characteristic behaviour. As a result, simple material testing is no longer descriptive of the material behaviour and an alternative approach is needed.This project aims to train models to detected repeating patterns that exist in a 3D woven network of fibres across a structure. These repeatable patterns will be characterised using highly detailed models to understand how each pattern behaves under different loading conditions and as part of multiple structures. Using this approach, a parameterised database containing thousands of these repeatable patterns and their behaviour will be built using unsupervised machine learning. On the structure scale, the behaviour of a full structure can be assembled from the behaviour of the repeating patterns forming it regardless of its geometry. This approach will allow engineers, for the first time, to design both the structure and the 3D fibre network forming it simultaneously. Achieving this goal allows us to build aerospace structures that are lighter, consume less fuel to fly, cheaper and faster to produce. The concept of using statistical models for describing structural behaviour have been around for some time. However, these approaches have always been proposed as a black box solution that can give an answer regarding what will happen to a structural/material but not why it happened. In this project, a hybrid approach is used, which combines statistical models with physically based deterministic models. The hybrid approach provides information about the mechanical performance, as well as the underlying physical reasons regarding why a given behaviour happens. This will allow engineers and scientist to understand 3D composites behaviour at a much deeper level than is currently possible by the statistical or deterministic models alone.

今天，复合材料处于工程革命的最前沿，目标是更轻，更可靠，更省油的航空航天结构。先进的复合材料是由长纤维层结合在一起，使用矩阵形成的结构。航空航天应用中最常见的纤维类型是碳纤维与环氧树脂基体的结合。最近，其他类型的纤维/基质也被引入，例如：用于高温应用的陶瓷基质复合材料和用于耐磨/抗冲击的金属基质复合材料。然而，所有类型的复合材料之间的共同点是它们都基于纤维层。根据定义，层是2D的。因此，所有传统的复合材料都难以承受第三方向的直接载荷。虽然2D复合材料为设计人员提供了明显的优势，来自纤维的上级性能和定制纤维方向或组合不同纤维类型的灵活性，但厚度性能仍然是限制其全部潜力的致命弱点。3D复合材料是解决这些问题的可行方案，因为它们是由所有三个维度的纤维编织而成。这些材料显示出很大的希望，因为它们可以通过厚度承载直接载荷，并且可以抵抗冲击事件。然而，使用3D复合材料带来了一系列建模挑战，这阻碍了工程师充分利用这些材料。传统上，为了了解一种新材料的行为，工程师和科学家会测试材料的样品以验证其行为。然后，这种特性行为被包括在数学模型中，该模型可以预测由这种材料制成的结构的行为。这些结构模型是用来作为设计工具。这种传统方法不适用于3D复合材料。在制造过程中，编织纤维的3D网络围绕拐角和其他结构特征变形，以符合结构几何形状。这又意味着光纤网络将为结构的每个部分具有不同的架构，因此将具有其自身的特性行为。因此，简单的材料测试不再能描述材料的行为，需要一种替代方法。该项目旨在训练模型，以检测存在于结构中的3D纤维编织网络中的重复图案。这些可重复的模式将使用高度详细的模型来表征，以了解每个模式在不同的负载条件下以及作为多个结构的一部分时的行为。使用这种方法，将使用无监督机器学习构建一个包含数千个可重复模式及其行为的参数化数据库。在结构尺度上，一个完整结构的行为可以从形成它的重复模式的行为中组装出来，而不管它的几何形状如何。这种方法将首次允许工程师同时设计结构和形成它的3D纤维网络。实现这一目标使我们能够建造更轻、消耗更少燃料的航空航天结构，更便宜、更快地生产。使用统计模型来描述结构行为的概念已经存在了一段时间。然而，这些方法一直被认为是一种黑箱解决方案，可以回答结构/材料会发生什么，但不能回答为什么会发生。在这个项目中，使用了一种混合方法，它结合了统计模型与基于物理的确定性模型。混合方法提供了有关机械性能的信息，以及关于为什么发生给定行为的潜在物理原因。这将使工程师和科学家能够在比目前仅通过统计或确定性模型更深的层次上理解3D复合材料的行为。

项目成果

SECOND-ORDER COMPUTATIONAL HOMOGENISATION FOR THICK SHELL MODELS: APPLICATION TO NON-LINEAR MULTISCALE ANALYSIS OF COMPOSITES

厚壳模型的二阶计算均匀化：在复合材料非线性多尺度分析中的应用

• 发表时间: 2021
• 作者: Hii AKW

A Deep Learning Approach for Predicting the Architecture of 3d Textile Fabrics

预测 3D 纺织面料结构的深度学习方法

• DOI: 10.2139/ssrn.4625867
• 发表时间: 2023
• 作者: Koptelov A

Predicting the Non-linear Response of Composite Materials using Deep Recurrent Convolutional Neural Networks

• DOI: 10.1016/j.ijsolstr.2023.112334
• 发表时间: 2023-05
• 期刊: International Journal of Solids and Structures
• 影响因子: 3.6
• 作者: Bassam El Said

A kinematically consistent second-order computational homogenisation framework for thick shell models

厚壳模型运动学一致的二阶计算均质化框架

• DOI: 10.1016/j.cma.2022.115136
• 发表时间: 2022
• 期刊: Computer Methods in Applied Mechanics and Engineering
• 影响因子: 7.2
• 作者: Hii A

Multiscale modelling of strongly heterogeneous materials using geometry informed clustering

• DOI: 10.1016/j.ijsolstr.2023.112369
• 发表时间: 2023-09
• 期刊: International Journal of Solids and Structures
• 影响因子: 3.6
• 作者: J. Selvaraj;Bassam El Said