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Enhance Aircraft Performance and Optimisation through utilisation of Artificial Intelligence

Descrizione del progetto

Tecniche di apprendimento automatico per misurare le prestazioni degli aeromobili

Attualmente, le compagnie aeree si affidano al sistema di pilotaggio (FMS, Flight Management System) per pianificare e gestire le traiettorie di volo. Tuttavia, l’FMS utilizza il modello di prestazioni di un solo produttore per ogni tipo di aeromobile e si basa sulle previsioni meteorologiche antecedenti al volo. Questo approccio manca di accuratezza e non offre misure precise delle prestazioni del velivolo. Per affrontare il problema, il progetto PERF-AI, finanziato dall’UE, si prefigge di applicare tecniche di apprendimento automatico ai dati di volo. In tal modo, sarà possibile misurare con precisione le prestazioni effettive dell’aeromobile per tutta la sua vita utile. Il progetto individuerà gli algoritmi di apprendimento automatico adatti, ne valuterà l’accuratezza per l’analisi dei dati di volo e svilupperà modelli matematici per ottimizzare le traiettorie di voli reali.

Obiettivo

PERF-AI will apply Machine Learning techniques on flight data (parametric & non-parametric approaches) to accurately measure actual aircraft performance throughout its lifecycle.

Within current airline operations, both at flight preparation (on-ground) & at flight management (in-air) levels, the trajectory is first planned, then managed by the Flight Management System (FMS) using a single manufacturer’s performance model that is the same for every aircraft of the same type, & also on weather forecast that is computed long before the flight. It induces a lack of accuracy during the planning phase with a flight route pre-established at specific altitudes & speeds to optimize fuel burn, from take-off to landing using aircraft performances that are not those of the real aircraft. Also, the actual flight will usually shift from the original plan because of Air Traffic Control (ATC) constraints, adverse weather, wind changes & tactical re-routing, without possibility for the flight crew, either using the FMS or through connected services to tactically recompute the trajectory in order to continuously optimize the flight path. This is in particular due to the limitations of the performance databases that the current systems are using.

Hence, PERF-AI is focusing on identifying adequate machine learning algorithms, testing their accuracy & capability to perform flight data statistical analysis & developing mathematical models to optimize real flight trajectories with respect to the actual aircraft performance, thus, minimizing fuel consumption throughout the flight.

The consortium consists of Safety-Line (FR) & INRIA (FR), having full expertise at Aircraft Performance & Data Science, hence, able to fully propose, test & validate different statistical models that will allow to accurately solve some optimization challenges & implement them in an operational environment.

PERF-AI total grant request to the CSJU is 568 550€ with total project duration of 24 months.

Meccanismo di finanziamento

CS2-IA - Innovation action

Coordinatore

SAFETY LINE
Contribution nette de l'UE
€ 318 675,00
Indirizzo
ETAGE 11 TOUR MONTPARNASSE 33 AVENUE DU MAINE
75015 Paris
Francia

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PMI

L’organizzazione si è definita una PMI (piccola e media impresa) al momento della firma dell’accordo di sovvenzione.

Regione
Ile-de-France Ile-de-France Paris
Tipo di attività
Private for-profit entities (excluding Higher or Secondary Education Establishments)
Collegamenti
Costo totale
€ 455 250,00

Partecipanti (1)