Fatigue damage prognosis of aircraft wing structure using time-based subcycle formulation and hybrid learning

Yang Yu, Karthik Rajan Venkatesan, Yongming Liu

Research output: Chapter in Book/Report/Conference proceedingConference contribution


The aircraft wing structures are prone to fatigue during flight operations as they undergo complex loading conditions. The classical cycle-based formulation for fatigue crack growth has intrinsic difficulties in dealing with these complex loadings since they often cannot be described as cyclic. In this study, a time-based subcycle formulation for fatigue crack growth is adopted to address this difficulty. Meanwhile, real-time fatigue damage prognosis requires efficient prediction of aircraft dynamical responses. In order to reduce the computational costs, this study proposes a hybrid learning method to simulate the aircraft dynamics. The hybrid learning method integrates the underlying physics of the dynamical system into learning models such as neural networks to reduce the training and computational costs. For demonstration, the aircraft wing structure is modeled as a cantilever beam and the proposed method is adopted to conduct the fatigue damage prognosis.

Original languageEnglish (US)
Title of host publicationAIAA Scitech 2019 Forum
PublisherAmerican Institute of Aeronautics and Astronautics Inc, AIAA
ISBN (Print)9781624105784
StatePublished - 2019
EventAIAA Scitech Forum, 2019 - San Diego, United States
Duration: Jan 7 2019Jan 11 2019

Publication series

NameAIAA Scitech 2019 Forum


ConferenceAIAA Scitech Forum, 2019
Country/TerritoryUnited States
CitySan Diego

ASJC Scopus subject areas

  • Aerospace Engineering


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