Details of Grant 

EPSRC Reference:	EP/K031368/1
Title:	Multiscale Modelling of Aerospace Composites: Increasing Quality, Reducing Empiricism and Challenging Conservatism
Principal Investigator:	Butler, Professor R
Other Investigators:	Scheichl, Professor R
Researcher Co-Investigators:	Professor T J Dodwell
Project Partners:	GKN
Department:	Mechanical Engineering
Organisation:	University of Bath
Scheme:	Standard Research
Starts:	06 January 2014	Ends:	05 February 2018	Value (£):	396,002
EPSRC Research Topic Classifications:	Manufacturing Machine & Plant Numerical Analysis Statistics & Appl. Probability
EPSRC Industrial Sector Classifications:	Manufacturing
Related Grants:
Panel History:	Panel Date Panel Name Outcome 19 Feb 2013 Maths-Manufacturing Call Prioritisation Panel Announced
Summary on Grant Application Form
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.
Key Findings
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Organisation Website:	http://www.bath.ac.uk