Introduction

There is a paradox in aerospace manufacturing. The aim is to design an aircraft that has a very small probability of failing. Yet to remain commercially viable, a manufacturer can afford only a few tests of the fully assembled plane. How can engineers confidently predict the failure of a low-probability event? This research will develop novel, unified AI methods that intelligently fuse models and data enabling industry to slash conservatism in engineering design, leading to faster, lighter, more sustainable aircraft.

Explaining the science

The test pyramid

The engineering solution to building a safe aircraft is the test pyramid. Variability in structural performance is quantified from thousands of small tests of the material (a coupon or element test), hundreds of tests at the intermediate length scales (called details and components) and a handful of tests of the full system. Sequentially, the lower length scales derive ‘design allowables’ for tests at the higher levels. Yet, such derivation is accompanied by significant uncertainty. 

In practice, this uncertainty means that ad hoc ‘engineering safety factors’ have to be applied at all length scales. As we seek a more sustainable aviation industry, the compound effect of these safety factors leads to significant overdesign of structures. The overdesign results in extra weight, which severely limits the efficiency of modern aircraft. A clear route to flying lighter, more efficient aircraft is to better quantify the uncertainties inherent in the engineering test pyramid, which will allow the industry to confidently trim excessive safety factors.

Uncertainties

New high-fidelity simulation capabilities in composites now mean that mathematical models can supplement limited experimental data at the higher length scales. Yet, with this comes additional uncertainties from the models themselves. Existing experimental tests, particularly at the lower length scales, can be used to reduce and quantify uncertainty in the models. The typical statistical approach is Bayesian, in which the distribution of a model’s parameters is learned to fit the data probabilistically. However, existing capabilities are notoriously computationally expensive, often limited to small-scale applications and simplified experimental data sets. For real engineering test pyramids, the available data and models are more heterogeneous and their connections are complex, driving the need for new fundamental research.

Project aims

This research will develop a novel, unified AI framework that intelligently fuses models and data at all levels of the engineering test pyramid, remains computationally feasible and is sufficiently robust to support ethical engineering decision-making in order to slash conservatism in engineering design, leading to faster, lighter, more sustainable aircraft.

Recent updates

October 2019

Working with the Data-Centric Engineering Programme, researchers have reformulated 'peridynamics' (a novel, deterministic, meshless method for modelling fracture in brittle materials), into a probablisitic setting. 'Probabilistic peridyanmics' gives a much richer framework for modelling real world fracture problems, since it provides a distribution and uncertainty in the failure behaviour, and can be readily integrated with data for given a 'data-adjusted' material model. Preprint to follow shortly.