Enhance Aircraft Performance and Optimisation through utilisation of Artificial Intelligence

Projektinformationen

PERF-AI

ID Finanzhilfevereinbarung: 815914

DOI

10.3030/815914

Projekt abgeschlossen

EK-Unterschriftsdatum 10 Oktober 2018

Startdatum 1 November 2018

Enddatum 31 Oktober 2020

Finanziert unter

• SOCIETAL CHALLENGES - Smart, Green And Integrated Transport

Gesamtkosten

€ 705 125,00

EU-Beitrag

€ 568 550,00

Koordiniert durch

SAFETY LINE
France

Periodic Reporting for period 2 - PERF-AI (Enhance Aircraft Performance and Optimisation through utilisation of Artificial Intelligence)

Berichtszeitraum: 2019-11-01 bis 2020-10-31

Zusammenfassung vom Kontext und den Gesamtzielen des Projekts

For the Operation of Commercial aircraft, airlines use manufacturer data that are common to all aircraft from the same type. However, during its lifecycle, performances (fuel consumption, speed, etc) for each individual aircrat will change as engines are ageing, aircraftt strucrure can me modified, etc. Through PERF-AI, we apply machine learning technologies to flight data to identify the change in performance for each individual aircraft and provide airlines with updated data that in turn will allow to operate more efficiently. Safety Line has been working since several years on the optimization during climb phase using Artificial Intelligence (OptiClimb). Thanks to PERF-AI, we will be able to provide extended services to the airline industry and possibly to manufacturers. At the end of the project we were able to create tail specific performance model for individual aircraft and use actual data to propose optimized flight path to be flown by the pilots for new flights. This approach can reduce fuel consumption and CO2 emissions by a few percents, which is very significant at the industry level.

Arbeit, die ab Beginn des Projekts bis zum Ende des durch den Bericht erfassten Berichtszeitraums geleistet wurde, und die wichtigsten bis dahin erzielten Ergebnisse

The project started in November 2018 and performed the following tasks :

- decoding and pre-processing of aircraft flight data
- worked on the problem definition and data engineering
- identification of relevant machine learning techniques to issue performance data
- Issue performance tables compatible for the use of Flight Management System
- developed optimization algorithms based on the use of aircraft data

The updated performance data provide pilots and engineers with more accurate figures for flight preparation and execution. Through optimization, it is possible to achieve significant savings and reduce CO2 emissions.

Fortschritte, die über den aktuellen Stand der Technik hinausgehen und voraussichtliche potenzielle Auswirkungen (einschließlich der bis dato erzielten sozioökonomischen Auswirkungen und weiter gefassten gesellschaftlichen Auswirkungen des Projekts)

Due to the amount of data and the number of parameters that influences aircraft performances, it is a challenge to identify statistical models that fit real aircraft performances. New statistical approaches allowed to do so and statistical and algorithm techniques provided updated performance models. The results will allow to optimize flight trajectories according to actual performance and reduce fuel consumption and CO2 emissions generated by the aviation industry. This can add up to other ways to reduce aircraft emissions : lighter material, new engines and provide a few more percents of CO2 reduction that is highly needed in the aviation industry.

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