A hybrid Deep Learning-assisted Finite Element technique to predict dynamic failure evolution in advanced ceramics (DeLFE)

用于预测先进陶瓷动态失效演化的混合深度学习辅助有限元技术 (DeLFE)

• 批准号: EP/Y004671/1
• 负责人: Sathiskumar Anusuya Ponnusami
• 金额: $ 20.41万
• 依托单位: City, University of London
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2024
• 资助国家: 英国
• 起止时间: 2024 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FY004671%2F1
• 关键词: hybrid; Deep; Learning; assisted; Finite

Engineers use computer tools to design complex structural components, be they airplane wings or high-rise buildings. The most common technique underneath such computer tools is finite element analysis (FEA). For not-so-demanding structural applications, simple linear elastic finite element models are sufficient to conduct the design process. However, when it comes to advanced structural applications such as gas turbine blades or nuclear reactor components, they experience severe loading conditions, which are high temperatures and/or high-pressure dynamic loads (impact). For such high-temperature applications, advanced structural ceramics and their composites are the natural choices of materials. Designing these structures made of ceramics against impact is non-trivial as it involves complex damage evolution mechanisms. Hence, engineers conduct a variety of experiments and large-scale computational (FEA) simulations to understand their behaviour. However, these experiments and simulations are time-consuming and costly. Most often, they have to conduct simple linear elastic FEA models which are far less time-consuming but do not account for complex damage mechanisms. Such a limitation naturally results in a sub-optimal design of these advanced structural components. One of the ways to overcome this challenge is to exploit the potential of artificial intelligence (AI) techniques to see if finite element computer simulations can be accelerated and yet reliably predict complex damage mechanisms. The proposed research addresses this industry-relevant problem and aims to develop an AI-driven accelerated FEA tool to simulate dynamic brittle fracture evolution in advanced structural ceramics. When we use AI models for such physical problems, the inherent problem is their reliability as they are often termed as 'black box modes' leading to uncertainties in their predictions. In this perspective, the proposed project takes a hybrid approach whereby data-driven deep learning models are adaptively combined with physics-based traditional FEA models in an integrated framework, thoroughly validated using experimental testing. Such a hybrid strategy aids in achieving accelerated, yet reliable simulations for complex physical problems such as the dynamic damage evolution in structures made of brittle ceramic materials. More specifically, engineers or end-users can specify the level of the desired accuracy depending on the design stage of the structural component. The adaptive simulation framework will then appropriately hybridise deep learning-based predictor and traditional FEA, resulting in an optimal damage prediction balancing the computational costs and accuracy. The global objective is to equip engineers with the necessary simulation tools whereby both accuracy and speed are ensured. This helps in transitioning the current industrial approach of using simplistic models into adopting AI-driven models that captures complex physical mechanisms, ultimately leading to the efficient and safe design of advanced structural components.

工程师使用计算机工具设计复杂的结构部件，无论是飞机机翼还是高层建筑。此类计算机工具最常用的技术是有限元分析 (FEA)。对于要求不那么高的结构应用，简单的线弹性有限元模型足以进行设计过程。然而，当涉及燃气轮机叶片或核反应堆部件等先进结构应用时，它们会经历严峻的载荷条件，即高温和/或高压动态载荷（冲击）。对于此类高温应用，先进结构陶瓷及其复合材料是材料的自然选择。设计这些由陶瓷制成的抗冲击结构并非易事，因为它涉及复杂的损伤演化机制。因此，工程师进行各种实验和大规模计算 (FEA) 模拟来了解它们的行为。然而，这些实验和模拟既耗时又昂贵。大多数情况下，他们必须建立简单的线弹性有限元分析模型，这种模型耗时少得多，但无法考虑复杂的损坏机制。这种限制自然会导致这些先进结构部件的设计不理想。克服这一挑战的方法之一是利用人工智能 (AI) 技术的潜力，看看是否可以加速有限元计算机模拟，并可靠地预测复杂的损坏机制。拟议的研究解决了这一行业相关问题，旨在开发一种人工智能驱动的加速有限元分析工具来模拟先进结构陶瓷的动态脆性断裂演化。当我们使用人工智能模型来解决此类物理问题时，固有的问题是它们的可靠性，因为它们通常被称为“黑匣子模式”，导致其预测存在不确定性。从这个角度来看，拟议的项目采用了一种混合方法，将数据驱动的深度学习模型与基于物理的传统有限元分析模型自适应地结合在一个集成框架中，并通过实验测试进行彻底验证。这种混合策略有助于对复杂的物理问题（例如脆性陶瓷材料结构的动态损伤演化）实现加速且可靠的模拟。更具体地说，工程师或最终用户可以根据结构部件的设计阶段指定所需精度的水平。然后，自适应模拟框架将适当地混合基于深度学习的预测器和传统的有限元分析，从而产生平衡计算成本和准确性的最佳损伤预测。全球目标是为工程师配备必要的仿真工具，从而确保准确性和速度。这有助于将当前使用简单模型的工业方法转变为采用人工智能驱动的模型来捕获复杂的物理机制，最终实现先进结构组件的高效和安全设计。