Demonstration of an Airframe Digital Twin Framework Using a CF-188 Full-Scale Component Test

Abstract

The airframe digital twin (ADT) framework is a potential game-changing fleet management concept recently proposed by the United States Air Force to allow proactive and cost-effective decisions on an individual aircraft basis. The National Research Council of Canada is currently demonstrating the ADT framework using a CF–188 full-scale component test to assess the adaptability of this approach for Royal Canadian Air Force (RCAF) fleets. An in-house analysis tool is being developed to perform quantitative risk assessment (QRA) based on the Bayesian inference method using individual aircraft tracking and non-destructive inspection data. The modular components of the ADT tool, currently being validated, include load and crack size distribution updating, material initial discontinuity state, residual stress effects, load transfer functions, crack tip stress intensity factor calculations, and crack growth predictions. Test cases analysed to verify and validate these modules showed the benefits of the Bayesian updating approach for performing QRA with inputs that are initially scarce and become less uncertain throughout the service life of the aircraft. Short-term benefits expected from the application of the ADT approach in the RCAF fleet management include a better use of the IAT data and an improvement in fatigue life estimation. In the longer term, a higher return on investment is foreseen in terms of improved life cycle management, increased fleet availability, and reduced total fleet ownership costs.