Internship: Physics-informed machine learning for variable amplitude loading in fatigue crack growth

Job description

Physics-informed machine learning for variable amplitude loading in fatigue crack growth Marknesse Master Thesis: Background: Fatigue in metallic structures is still considered a major threat to the continuing airworthiness of aircraft. Existing models to predict the fatigue life of aircraft structural components under variable loading conditions have limitations due to a limited understanding of the interaction between load cycles. Current maintenance programmes are therefore conservative to account for the limitations in predictions. The aim of this project is to enhance current physical models for predicting fatigue life under variable operational conditions by exploring the application of Physics-Informed Machine Learning (PIML) within the context of Prognostics and Health Management (PHM). Assignment: The assignment will include the following tasks: Available model and VA fatigue crack growth datasets NLR has created physical model for VA fatigue crack growth, but it is unclear how VA loading changes the interaction between different terms in the equation that is validated for constant amplitude data. - Preliminary assessment of available PIML for variable amplitude (VA) fatig...