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Enhance Aircraft Performance and Optimisation through utilisation of Artificial Intelligence

Project description

Machine learning techniques for aircraft performance measurement

Current airline operations rely on the flight management system (FMS) to plan and manage flight trajectories. However, the FMS uses a single manufacturer’s performance model for each aircraft type and relies on pre-flight weather forecasts. This approach lacks accuracy and fails to provide precise measurements of aircraft performance. To address this issue, the EU-funded PERF-AI project aims to employ machine learning (ML) techniques on flight data. By doing so, it can accurately measure actual aircraft performance throughout its lifespan. The project will identify suitable ML algorithms, assess their accuracy for flight data analysis, and develop mathematical models to optimise real-flight trajectories.


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.


Net EU contribution
€ 318 675,00
75015 Paris

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The organization defined itself as SME (small and medium-sized enterprise) at the time the Grant Agreement was signed.

Ile-de-France Ile-de-France Paris
Activity type
Private for-profit entities (excluding Higher or Secondary Education Establishments)
Total cost
€ 455 250,00

Participants (1)