Smarter Testing Technique Development for Aerospace Inspection

Challenges

The testing process for novel aircraft wings takes several years and is dominated by undertaking full-scale wing loading tests. Currently, a significant portion of this time is taken up by downtime, in which a problem is detected in the test, some inspection is undertaken to characterise the problem and corrective action is taken. Often, the issue is non-critical and does not justify stopping the test, meaning all the time spent halting, inspecting and restarting the test was wasted.

The challenge is thus to be able to better understand from the sensors used in test the magnitude of an issue and therefore what action to take. This should in turn reduce downtime in the test and the overall test duration, reducing the time to market for new aircraft.

Solution

The wings tested in this process are assemblies of thousands of components which each have a set of tolerances. These tolerances compound together to produce uncertainties in the exact properties of the wing being tested. Typically, some simulation is undertaken to understand the likely variation in performance. However, existing models are constrained by a lack of
parameterisation and run time which limits the number of models that can be executed.

In this project, we developed fully parameterised models of the test using the MOOSE framework which could be automatically created, executed and analysed using high performance compute. Sensitivity analysis was also applied to the results, identifying which parameters of the test are most significant.

Impact

This work enabled 1,000,000 structural finite element simulations of the wing to be evaluated in under three weeks. This provides a much deeper characterisation of the expected variability, better informing engineers about the expected behaviour and therefore allowing them to make more intelligent decisions.

A better understanding of the correlation between sensor measurements and the impact of defects on the reading has been enabled. Through sensitivity analysis, the key drivers of test performance were identified, providing insight into where best to expend effort measuring and characterising the test. Altogether, this work is aiding the reduction of uncertainty in the test, increasing efficiency and reducing execution time.