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 (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
- engineering and technology mechanical engineering vehicle engineering aerospace engineering aircraft
- natural sciences computer and information sciences computational science
- engineering and technology nanotechnology nano-materials
- engineering and technology environmental engineering energy and fuels
- natural sciences computer and information sciences artificial intelligence machine learning
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Keywords
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Programme(s)
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
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H2020-EU.1.3. - EXCELLENT SCIENCE - Marie Skłodowska-Curie Actions
MAIN PROGRAMME
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H2020-EU.1.3.2. - Nurturing excellence by means of cross-border and cross-sector mobility
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Topic(s)
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Funding Scheme
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
MSCA-IF - Marie Skłodowska-Curie Individual Fellowships (IF)
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Call for proposal
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
(opens in new window) H2020-MSCA-IF-2018
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Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.
2628 CN DELFT
Netherlands
The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.