Descrizione del progetto
Apprendimento automatico e nanoparticelle per aeromobili competitivi
La maggiore concorrenza e i regolamenti ambientali più severi stanno portando l’industria aerospaziale a esplorare metodi per ridurre il peso, diminuire il consumo di carburante e mitigare l’impatto ambientale degli aeromobili. I pannelli compositi irrigiditi in cui vengono introdotte le nanoparticelle in regioni critiche soggette a danni offrono un elevato potenziale rispetto all’utilizzo di dispositivi di fissaggio meccanici. Tuttavia, non sono attualmente presenti pannelli irrigiditi certificati con nanoparticelle. Inoltre, i meccanismi di danneggiamento sono complessi e i metodi predittivi per la propagazione del danno e la stima del ciclo di vita non sono ancora pienamente stabiliti. Il progetto MACADAMIA, finanziato dall’UE, prevede di approcciare il problema combinando nanoparticelle e modelli basati sulla fisica per individuare forza ed evoluzione dei danni nei pannelli irrigiditi e di utilizzare l’apprendimento automatico per perfezionare la stima del loro ciclo di vita.
Obiettivo
MACADAMIA ambitiously seeks a seamless integration of machine learning concepts with physics-based models to optimise aerospace stiffened panels for damage tolerance. As an innovative strategy to delay damage, nanoparticles will be added in failure-prone hot-spots of composite stiffened panels to serve as damage arrest features. The efficacy of machine learning when used in conjunction with advanced computational methods for data classification and prediction will be smartly leveraged to classify and predict damage mechanisms in aircraft structures, the understanding of which is critical to their safe implementation.
In aircraft, stiffened composite panels are popular alternatives to structures with mechanical fasteners because they retain strength while reducing weight and part count; but cost and weight savings cannot be fully realized until stiffened panels are certified without fasteners in primary load-bearing structures. It is estimated that a one-pound weight reduction on each aircraft in a commercial fleet would result in fuel savings of 14000 gallons/year, which also mitigates the environmental impact of flight. To strengthen the competitiveness of European aerospace technologies in compliance with evolving environmental regulations, it is vital to work towards accelerated certification of fastener-free composite panels. Major challenges to this goal are: i) damage mechanisms in stiffened panels are complex and coupled, making the evaluation of strength and durability difficult; ii) predictive models for life-cycle estimation have large uncertainty. MACADAMIA envisions an approach with carefully designed experiments for nanoparticle inclusion along with physics-based models to investigate strength and damage evolution in stiffened panels, and machine learning to further optimise them for longer useful life. Multidisciplinary concepts of structural mechanics, computational physics, nanotechnology and machine learning will be used to accomplish research plan.
Campo scientifico
- engineering and technologymaterials engineeringcomposites
- engineering and technologymechanical engineeringvehicle engineeringaerospace engineeringaircraft
- engineering and technologynanotechnologynano-materials
- engineering and technologyenvironmental engineeringenergy and fuels
- natural sciencescomputer and information sciencesartificial intelligencemachine learning
Parole chiave
Programma(i)
Argomento(i)
Meccanismo di finanziamento
MSCA-IF - Marie Skłodowska-Curie Individual Fellowships (IF)Coordinatore
2628 CN Delft
Paesi Bassi