The successful deployment of structural damage prognosis for aircrafts requires reliable forecasting of future structural loading; however, little work in this area has been done. This project will address this need and develop a viable technique for predicting multiple and correlated aircraft flight parameters. Flight profiles are extracted from historic data, and then a flight dynamics model-based flight simulator is employed to simulate these profiles. By correlating the obtained outputs with field data, optimization will be performed iteratively to validate and improve the simulation model. Sensitivity analysis will be conducted to identify the impact of flight parameter uncertainty on predicted loading. Uncertainty will then be modeled and integrated with the forecasting process. The final outcome of this project will be random realizations of flight profiles that can be used for probabilistic life assessment.
Advances in computational tools and capabilities have made it possible for the U.S. Air Force to plan for a digital twin for every aircraft platform. This digital twin would be subjected to the same flight spectrum as the physical system. Therefore, analytical capabilities that enable integration of advanced damage initiation and propagation models into the digital twin become critical. This project develops a framework that can provide estimates of probabilistic damage states of the aircraft at any given time. The proposed framework consists of four modules. The first module is the load forecasting module, which will use past load history to forecast random flight loads. When the digital twin is subjected to these random flight loads, damage initiates and propagates based on the material strength characteristics. Therefore, the second module is the development of damage models (cohesive zone model). These models are strategically inserted into the structural model at critical locations. These locations will be identified using a failure mode effects and criticality analysis based approach. These inputs to the structural system will enable determination of the probabilistic damage state of the airframe at any given time during the loading history. Technology developed in this project can easily be transferred to industry and applied to any structural system.