Composites: Made Faster - Rapid, physics-based simulation tools for composite manufacture

复合材料在交通和能源领域的轻量化解决方案中变得越来越重要。通过减少燃料消耗，以及促进电力和氢燃料等新技术的发展，降低结构重量，提高机械性能，对于实现航空航天和汽车的可持续发展目标至关重要。然而，纤维增强复合材料的性质使它们在制造的不同阶段极易发生变化。这可能导致其机械性能和设计公差的显著降低，通过要求“过度设计”来降低其减重优势。建模方法能够模拟复合材料制造中涉及的不同过程，为在设计阶段早期帮助缓解这些问题提供了强大的工具。实现良好模拟的一个主要挑战是考虑材料和制造过程固有的可变性，以便考虑可能结果的统计分布，而不是单一的确定性结果。为了实现这一目标，需要一个概率建模框架，这就需要快速的数值工具来模拟复合材料制造过程中的每个步骤。该项目专注于纺织复合材料，将开发一种新的定制求解器，其方法可以模拟复合材料制造过程中的预制件创建、预制件沉积和最后的预制件压实三个关键步骤。在新的和正在开发的处理器架构的帮助下，这种定制的求解器将提供独特的快速而准确的仿真能力。为每个过程开发的方法将通过系统的概率敏感性分析进行询问，以减少其复杂性，同时保留其预测能力。其目的是在预测能力和运行时效率之间找到平衡。这将最终提供一种工具，该工具在数值上足够有效，可以运行足够的迭代，以捕获每个纺织复合材料制造过程中存在的显著随机变化，即使是在大的组件规模上。然后将该框架应用于与工业有关的问题。考虑到现实世界的可变性，这些工具将用于优化设计过程，并进一步探索制造过程的优化。与项目的工业合作伙伴密切合作，并访问他们的演示器和生产制造数据，将确保创建的工具与行业相关，并可以集成到当前的设计过程中，以实现即时影响。这将使制造工程师能够通过更少的试验、更少的浪费和更快的上市时间来达成可接受的解决方案，从而为目前正在工业中发生的数字革命做出贡献。

Composite materials are becoming increasingly important for light-weight solutions in the transport and energy sectors. Reduced structural weight, with improved mechanical performance is essential to achieve aerospace and automotive's sustainability objectives, through reduced fuel-burn, as well as facilitating new technologies such as electric and hydrogen fuels. The nature of fibre reinforced composite materials however makes them highly susceptible to variation during the different stages of their manufacture. This can result in significant reductions in their mechanical performance and design tolerances not being met, reducing their weight saving advantages through requiring "over design". Modelling methods able to simulate the different processes involved in composite manufacture offer a powerful tool to help mitigate these issues early in the design stage. A major challenge in achieving good simulations is to consider the variability, inherent to both the material and the manufacturing processes, so that the statistical spread of possible outcomes is considered rather than a single deterministic result. To achieve this, a probabilistic modelling framework is required, which necessitates rapid numerical tools for modelling each step in the composite manufacturing process.Focussing specifically on textile composites, this project will develop a new bespoke solver, with methods to simulate preform creation, preform deposition and finally, preform compaction, three key steps of the composite manufacturing process. Aided by new and developing processor architectures, this bespoke solver will deliver a uniquely fast, yet accurate simulation capability. The methods developed for each process will be interrogated through systematic probabilistic sensitivity analyses to reduce their complexity while retaining their predictive capability. The aim being to find a balance between predictive capability and run-time efficiency. This will ultimately provide a tool that is numerically efficient enough to run sufficient iterations to capture the significant stochastic variation present in each of the textile composite manufacturing processes, even at large, component scale.The framework will then be applied to industrially relevant problems. Accounting for real-world variability, the tools will be used to optimise the processes for use in design and to further to explore the optimising of manufacturing processes.Close collaboration with the project's industrial partners and access to their demonstrator and production manufacturing data will ensure that the tools created are industry relevant and can be integrated within current design processes to achieve immediate impact. This will enable a step change in manufacturing engineers' ability to reach an acceptable solution with significantly fewer trials, less waste and faster time to market, contributing to the digital revolution that is now taking place in industry.