Opendata, web and dolomites

PERF-AI SIGNED

Enhance Aircraft Performance and Optimisation through utilisation of Artificial Intelligence

Total Cost €

0

EC-Contrib. €

0

Partnership

0

Views

0

Project "PERF-AI" data sheet

The following table provides information about the project.

Coordinator
SAFETY LINE 

Organization address
address: 130 RUE DE LOURMEL
city: PARIS
postcode: 75015
website: n.a.

contact info
title: n.a.
name: n.a.
surname: n.a.
function: n.a.
email: n.a.
telephone: n.a.
fax: n.a.

 Coordinator Country France [FR]
 Total cost 705˙125 €
 EC max contribution 568˙550 € (81%)
 Programme 1. H2020-EU.3.4.5.6. (ITD Systems)
 Code Call H2020-CS2-CFP07-2017-02
 Funding Scheme CS2-IA
 Starting year 2018
 Duration (year-month-day) from 2018-11-01   to  2020-10-31

 Partnership

Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 
1    SAFETY LINE FR (PARIS) coordinator 318˙675.00
2    INSTITUT NATIONAL DE RECHERCHE ENINFORMATIQUE ET AUTOMATIQUE FR (LE CHESNAY CEDEX) participant 249˙875.00

Map

 Project objective

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.

 Deliverables

List of deliverables.
Report on Communication, Dissemination and Exploitation of project results 1 Documents, reports 2020-04-02 08:52:00

Take a look to the deliverables list in detail:  detailed list of PERF-AI deliverables.

Are you the coordinator (or a participant) of this project? Plaese send me more information about the "PERF-AI" project.

For instance: the website url (it has not provided by EU-opendata yet), the logo, a more detailed description of the project (in plain text as a rtf file or a word file), some pictures (as picture files, not embedded into any word file), twitter account, linkedin page, etc.

Send me an  email (fabio@fabiodisconzi.com) and I put them in your project's page as son as possible.

Thanks. And then put a link of this page into your project's website.

The information about "PERF-AI" are provided by the European Opendata Portal: CORDIS opendata.

More projects from the same programme (H2020-EU.3.4.5.6.)

2021 GRIP (2019)

Crouzet Next Grip Generation

Read More  

vACCINE (2019)

AeronautiCal Cyber INtrusion dEtection mechanism

Read More  

FAVIT (2019)

FEASIBILITY ANALYSIS OF INNOVATIVE PRACTICES IN VIRTUAL TESTING METHODS FOR AIRCRAFT CERTIFICATION

Read More