Multiscale Modelling of Aerospace Composites: Increasing Quality, Reducing Empiricism and Challenging Conservatism

虽然碳纤维增强塑料等复合材料层压板的基本优势得到了很好的证明，但由于多种缺陷，尤其是在航空航天应用中的复杂部件中，它们往往会受到高成本、长时间开发和质量差的影响。在安全至上的航空航天行业，改变设计需要在各种长度尺度上进行昂贵的经验测试，即所谓的“测试金字塔”。这个复杂的工程系统的一个重要目标是将认证测试失败的可能性降至最低。建模技术和开发不同阶段的测试都是朝着这一目标精心安排的，这是在过去几十年里试探性地开发的，没有清楚地了解每个参与者如何为减少不确定性做出贡献。该项目将聘请一个由工程师和数学家组成的多学科小组来开发新的数学建模工具来解决这一问题。嵌入的大学和行业合作伙伴关系将致力于创建具有基础基础研究的新能力，以减少机身和航空发动机制造的设计到制造时间并提高质量，这对英国航空航天部门的国际地位至关重要。我们将系统地开发随机模型，将模拟和经验测试(在测试金字塔的不同阶段)的不确定性整合在一起，并量化它们在系统中的传播，为高质量的碳纤维制造提供有效和可靠的质量控制。新的、经过充分验证的层压设计将被开发出来，挑战固有的保守性和昂贵的行业标准，该行业标准主要使用经验测试来进行结构完整性认证。该项目的一个中心主题是飞机部件结构层次内多个尺度的复杂相互作用。各方面的相互作用对本方案涉及的三个研究领域都有很大影响。最近在层状地质结构中的褶皱建模方面获得的专业知识将被用来研究在层叠零件的自动化制造过程中物理上类似的缺陷形成。多尺度结构性能模型将利用新的数值放大技术来预测包含微尺度内部缺陷的大型航空部件的强度。新的概率不确定性量化工具，如多层蒙特卡罗和多层蒙特卡罗马尔可夫链，将被用于整个子组件的性能分析。这些模型的数据将直接从使用计算X射线断层扫描(CT)获得的图像中推断出来。将借调团队成员到位于国家复合材料中心的GKN航空航天公司了解制造实践，以探索行业技术目标和业务目标之间的相互作用，帮助研究人员使用新的建模工具，并选择最佳制造解决方案。目标部件将是翼梁、蒙皮纵梁和发动机风扇叶片。用于故障预测的新型随机方法的开发和应用将在来自佛罗里达大学和加州劳伦斯利弗莫尔国家实验室的访问研究人员的专家指导下进行。我们的愿景是使从设计到制造的时间减少50%以上，同时确保可预测的产品改进，相当于显著(&gt；10%)的组件重量节省。

Whilst the basic advantages of composite laminates, such as carbon fibre-reinforced plastic, are well proven, they are often compromised by high cost, long development time and poor quality due to multiple defects, particularly in complex parts such as those found in aerospace applications. Within the aerospace industry, where safety is paramount, design changes require expensive programmes of empirical testing over a variety of length scales, the so-called "test pyramid". An important objective of this complex engineering system is to minimize the probability of failing the certification test. Modelling technologies and testing at various stages of development are all orchestrated toward this objective, which has been heuristically developed over the last decades without a clear understanding of how each player contributes to uncertainty reduction. This project will engage a multidisciplinary team of engineers and mathematicians to develop novel mathematical modelling tools to address this issue. An embedded university-industry partnership will focus effort on creation of new capability with underlying fundamental research to reduce design-to-manufacture time and increase quality in airframe and aero-engine manufacture, critically important to the international standing of the UK aerospace sector. We will systematically develop stochastic models that integrate uncertainties from simulations and empirical testing (at different stages of the test pyramid) and quantify their propagation through the system to provide effective and reliable quality control for high-quality carbon fibre manufacture. New and fully-validated, laminate designs will be developed that challenge the inherent conservatism and the expensive industry standard which predominantly uses empirical testing for structural integrity certification. A central theme to the project is the complex interaction of multiple scales within the structural hierarchy of an aircraft component. Interaction over all the scales strongly influences each of the three research areas addressed within this programme. Recently gained expertise in the modelling of folding in layered geological structures will be exploited to study the physically analogous formation of defects during automated manufacture of laminated parts. Multiscale structural performance models will draw upon novel numerical upscaling techniques to predict the strength of large aerospace components containing microscale internal defects. Novel probabilistic uncertainty quantification tools, such as multilevel Monte Carlo and multilevel Monte Carlo Markov Chain, will be brought to bear in performance analyses of entire sub-components. The data for these models will be inferred directly from images obtained using Computational X-ray Tomography (CT). Manufacturing practices will be informed by seconding team members to GKN Aerospace, located at the National Composites Centre, to explore the interaction between the technical and business objectives of the industry, assisting researchers in the use of the new modelling tools, and in the selection of optimal manufacturing solutions. Target components will be wing spars, skin-stringer panels, and engine fan blades. The development and application of the novel stochastic methods for failure prediction will be undertaken with expert guidance of visiting researchers from the University of Florida and Lawrence Livermore National Laboratory, CA. Our vision is to enable a greater than 50% reduction in design-to-manufacture time whilst ensuring predictable product improvement, amounting to significant (>10%) component weight saving.