A Data-Driven Digital Twin Approach for the Aging Prediction of Airworthiness of Aircraft Composite Components Accounting for Flight and Environmental

The main objective of this SBIR effort is two-fold. First, to develop a digital twin of an aircraft structure and its composite components capable of accurate aging predictions for any mission-specific loading and environmental variability, in a manner that enables the assessment of the structural airworthiness of the aircraft and its composite components from a damage tolerance perspective. Second, to identify potential multiphysics trade-offs to enable accelerated testing. To this end, the detailed technical objectives for Phase II are to: Extend the multiphysics MDD (multiphysics data-driven modeling approach) developed during Phase I to enable the increased fidelity associated with capturing aging-induced damage. Generalize the MDD -NPM (nonparametric probabilistic method for model-form uncertainty (MFU)) computational framework developed in Phase I to an arbitrary number of levels (coupon, element, subcomponent, component, aircraft), to minimize MFU and test uncertainty introduced at each level, thereby leading to the construction of a robust digital twin instance of a structural system that can replace the current role of the building block approach (BBA) for qualification and sustainment operations. Validate this multiphysics framework using laboratory tests that simulate the in-service loading environment for different blocks of the BBA. Demonstrate that the MDD-NPM framework is functional with actual data from multiaxial testing at the coupon, subcomponent, and component levels. Equip the MDD-NPM computational framework with a fast approach for identifying potential multiphysics trade-offs to enable accelerated testing. Develop ASLM (aircraft structural life management), a set of minimally intrusive software modules for this computational framework and a universal interface between this software end product and third-party CFD, FEA, and coupled CFD-FEA solvers to ease the commercialization of ASLM.