Validation of a structural model of an aircraft cockpit panel: An industrial case study

Eann A Patterson,Thorsten Siebert,Ioannis Diamantakos,Richard J Greene,Erwin Hack,George Lampeas,Marek Lomnitz,Ksenija Dvurecenska

doi:10.1177/03093247211059084

Abstract

Computational models of structures are widely used to inform decisions about design, maintenance and operational life of engineering infrastructure, including airplanes. Confidence in the predictions from models is provided via validation processes that assess the extent to which predictions represent the real world, where the real world is often characterised by measurements made in experiments of varying sophistication dependent on the importance of the decision that the predictions will inform. There has been steady progress in developing validation processes that compare fields of predictions and measurements in a quantitative manner using the uncertainty in measurements as a basis for assessing the importance of differences between the fields of data. In this case study, three recent advances in a validation process, which was evaluated in an inter-laboratory study 5 years ago, are implemented using a ground-test on a fuselage at the aircraft manufacturer’s site for the first time. The results show that the advances successfully address the issues raised by the inter-laboratory study, that the enhanced validation process can be implemented in an industrial environment on a complex structure, and that the model was an excellent representation of the measurements made using digital image correlation.

Highlights

Validation is an essential step in any modelling process when the predictions from the model are to be used to inform decisions
In the context of computational solid mechanics, the American Society of Mechanical Engineers (ASME) produced a guide for verification and validation in 20064 which has recently been revised into a standard.[1]
The purpose of this case study was to demonstrate, at a large scale and in an industrial environment, the effectiveness of the solutions to the issues raised by the inter-laboratory study[7] conducted in 2016 using the Comite Europeen de Normalisation (CEN) workshop agreement on the validation of computational solid mechanics models.[5]. These issues were: (i) the need for a measure of the quality of predictions, which has been addressed by the validation metric developed by Dvurecenska et al.[8] and used to generate the plot in Figure 10; (ii) the requirement to reliably match regions of interest in the fields of measurements and predictions, which has been addressed by using QR decomposition to process irregularly-shaped regions of interest, as described by Christian et al.[9] and implemented in a specially-written programme, THEON and (iii) the importance of designing experiments for the specific purpose of performing a validation process

Summary

Introduction

Validation is an essential step in any modelling process when the predictions from the model are to be used to inform decisions. There is an extensive literature on the subject of model validation which has recently been reviewed by Sargent and Balci[2] and by Roungas et al.[3] In the context of computational solid mechanics, the American Society of Mechanical Engineers (ASME) produced a guide for verification and validation in 20064 which has recently been revised into a standard.[1] While the European standards organisation, Comite Europeen de Normalisation (CEN) published a workshop agreement on validation in 2014.5 The former provides principles and comprehensive definitions while the latter describes a practical approach to making quantitative comparisons between fields of measurements and predictions These efforts are largely based on observations in research laboratories, and there are relatively few reports of the application of validation processes in industrial environments. This is important because Barlas and Carpenter[6] observed that

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