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

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.

Design Technology and AI-Based Decision Making Model for Digital Twin Engineering

“…The use and scope of digital twins is very wide. They are used not only in heavy engineering [14][15][16], in the automotive industry [17] and building [18], in the field of nuclear energy [19], in the aerospace [20,21] and oil and gas industries [22], in architectural design and creating smart cities [23], and agriculture, but they are also used to improve operational efficiency in the production of consumer goods [5], the accuracy of diagnostics and decision making in healthcare [24], to attract customers and customize services in the financial sector [25] and in retail [26], for organizing logistics processes and supply chains [27], and in regional and municipal management [28,29].…”

Industrial application of a low-cost structural health monitoring system in large-scale airframe tests

“…DIC has been used for damage detection in laboratory and industrial applications. [1][2][3][4] Similarly, infrared (IR) sensors are employed for NDE, with methods including passive and active****#thermography, and thermoelastic stress analysis (TSA). [5][6][7][8][9][10][11] As technology develops, visible and IR sensors are becoming smaller and cheaper.…”

“…DIC has been used for damage detection in laboratory and industrial applications. 1–4 Similarly, infrared (IR) sensors are employed for NDE, with methods including passive and active thermography, and thermoelastic stress analysis (TSA). 5–11…”

Validating out of the box: Identifying a campaign of physical tests

“…In a recent study, a procedure for evaluating the applicability of historical data in a validation process was developed. 11 The criteria for accepting historical data will usually be high, and little existing historical data will satisfy them; however, consideration of the criteria when specifying new physical tests will allow planned test data to become useful historical data. The FAIR data principles also have an important role to play to ensure that the data has a long and useful life.…”

“…This leads to questions about, for instance, the appropriateness and reliability of both measurement and simulation data acquired in prior studies, sometimes referred to as ‘historical data’ (e.g. Seaman et al 10 and Patterson et al 11 ), whereas the guidelines and standards recommend that physical tests should be designed and performed for the specific purpose of providing data for a validation process. In a recent study, a procedure for evaluating the applicability of historical data in a validation process was developed.…”