Project description
Machine learning and nanoparticles for competitive aircraft
Increased competition and strict environmental regulations are driving the aerospace industry to explore methods to reduce weight, decrease fuel consumption and mitigate the environmental impact of flight. Stiffened composite panels in which nanoparticles are introduced at critical damage-prone regions offer a lot of potential compared to using mechanical fasteners. However, there are no certified stiffened panels with nanoparticles yet. Furthermore, damage mechanisms are complex and predictive methods for damage propagation and life-cycle estimation are not yet fully established. The EU-funded MACADAMIA project envisages approaching the problem combining nanoparticles and physics-based patterns to detect strength and damage evolution in stiffened panels and using machine learning to perfect their life-cycle estimation.
Objective
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.
Fields of science
- 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
Programme(s)
Funding Scheme
MSCA-IF - Marie Skłodowska-Curie Individual Fellowships (IF)Coordinator
2628 CN Delft
Netherlands