Descrizione del progetto
Potenziare il collaudo e la progettazione degli aerogiri grazie all’analisi dei megadati e all’intelligenza artificiale
La strumentazione e i sensori di bordo generano grandi quantità di dati durante i voli di prova dei prototipi, che non è possibile sottoporre a una valutazione adeguata utilizzando metodi e piattaforme tradizionali. Questo impedisce di sfruttare le nuove tecnologie digitali per potenziare e migliorare la progettazione delle prove di volo. Inoltre, il potenziale dei nuovi algoritmi di intelligenza artificiale (IA) e della valutazione statistica non è pienamente sfruttato nell’ambito delle prove di volo. Il progetto ADMITTED, finanziato dall’UE, mette a punto e in funzione una piattaforma avanzata, permettendo l’analisi di una grande quantità di dati raccolti dai voli di prova, tramite una complessa architettura hardware e software a sostegno dell’analisi dei megadati. Inoltre, il progetto adotterà algoritmi specifici di IA per favorire il collegamento dei dati, la gestione delle serie temporali e l’analisi statistica.
Obiettivo
Flight testing is an important phase during the development of an aircraft to validate the design. During flight, data is gathered and design problems are identified and solved. The collected data are fundamental for the analysis and Aircraft are properly instrumented to generate large amounts of information. Such huge amount of data needs to be properly evaluated and traditional methods and platforms are no more effective.
Flight testing is a significant cost contributor to the aircraft production life cycle and is still extensively deployed. Flight test programmes take several years and more prototypes are built to reduce lead times. Strong adherence to rigour safety and certification requirements and generally unchanged circular advisories inhibit the potential improvement of flight test designs. Innovative algorithms and statistical estimation are not achieving its full potential in the industrialized flight testing environment.
The methods in this proposal increase the quality and productivity of an experiment, leading to a required test point reduction or increased predictive capabilities. The purpose of this project is to define and implement a state-of-the-art platform able to support data analysis. This is achieved by adopting a complex hardware architecture to support big data analysis and implementing specific algorithms to support data correlation, time series management and statistical analysis.
Furthermore, to support flight test engineers, novel approaches based on machine learning are provided to support the technicians in detecting specific flight conditions. The same platform is also adapted to support the development of the Next Generation Civil Tilt Rotor Technology Demonstrator.
Campo scientifico
- natural sciencescomputer and information sciencesdata sciencebig data
- engineering and technologymechanical engineeringvehicle engineeringaerospace engineeringaircraft
- social scienceseconomics and businesseconomicsproduction economicsproductivity
- natural sciencescomputer and information sciencesartificial intelligencemachine learning
Parole chiave
Programma(i)
Argomento(i)
Meccanismo di finanziamento
RIA - Research and Innovation actionCoordinatore
20093 Cologno Monzese
Italia