An Ultrafast Crack Growth Lifing Model to Support Digital Twin, Virtual Testing, and Probabilistic Damage Tolerance Applications

New aeronautical technologies like the Airframe Digital Twin, Virtual Fatigue Testing, and Probabilistic Damage Tolerance Analysis require a very large number of crack growth evaluations with a comprehensive number of random variables in order to accurately predict the fatigue life, the structural risk, or the remaining useful life of a structure. Current state-of-the-art crack growth methodologies and probabilistic methods do not make these new technologies possible due to limitations on computational speed, number of random variables, and statistical tools. In this work, a new computational strategy is developed and demonstrated such that several random variables directly affecting the crack growth analysis can be considered. This approach provides the opportunity for a more comprehensive and accurate digital twin evaluation, virtual testing prediction, and risk assessment, hence, improving aircraft design, safety, and reliability.