Netherlands, MARKNESSE
Internship: Physics-informed machine learning for variable amplitude loading in fatigue crack growth - Op locatie - - Marknesse , Flevoland , Nederland - Aerospace Vehicles
Vacature details Solliciteren Functieomschrijving
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:
• Preliminary assessment of available PIML for variable amplitude (VA) fatigue crack modelling.
• A literature review to analyse the application of the PIML framework within the context of PHM to support aircraft health management.
• Designing and implementing a PIML framework to model VA fatigue crack growth or the use of machine learning to understand load interaction effects by finding relationships between different terms in a physical model for VA fatigue crack growth.
• Validating the framework using multiple real-world datasets and benchmarking its performance against state-of-the-art methods.
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.
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