arXiv subjects

Xavier Olive

Publications and source records attributed to Xavier Olive.

5 recordsLinked to original sources

arXiv2025

A Neural ODE Approach to Aircraft Flight Dynamics Modelling

Accurate aircraft trajectory prediction is critical for air traffic management, airline operations, and environmental assessment. This paper introduces NODE-FDM, a Neural Ordinary Differential Equations-based Flight Dynamics Model trained on Quick Access Recorder (QAR) data. By combining analytical kinematic relations with data-driven components, NODE-FDM achieves a more accurate reproduction of recorded trajectories than state-of-the-art models such as a BADA-based trajectory generation methodology (BADA4 performance model combined with trajectory control routines), particularly in the descent phase of the flight. The analysis demonstrates marked improvements across altitude, speed, and mass dynamics. Despite current limitations, including limited physical constraints and the limited availability of QAR data, the results demonstrate the potential of physics-informed neural ordinary differential equations as a high-fidelity, data-driven approach to aircraft performance modelling. Future work will extend the framework to incorporate a full modelling of the lateral dynamics of the aircraft.

cs.LG↗

arXiv2025

OpenSky Report 2025: Improving Crowdsourced Flight Trajectories with ADS-C Data

The OpenSky Network has been collecting and providing crowdsourced air traffic surveillance data since 2013. The network has primarily focused on Automatic Dependent Surveillance--Broadcast (ADS-B) data, which provides high-frequency position updates over terrestrial areas. However, the ADS-B signals are limited over oceans and remote regions, where ground-based receivers are scarce. To address these coverage gaps, the OpenSky Network has begun incorporating data from the Automatic Dependent Surveillance--Contract (ADS-C) system, which uses satellite communication to track aircraft positions over oceanic regions and remote areas. In this paper, we analyze a dataset of over 720,000 ADS-C messages collected in 2024 from around 2,600 unique aircraft via the Alphasat satellite, covering Europe, Africa, and parts of the Atlantic Ocean. We present our approach to combining ADS-B and ADS-C data to construct detailed long-haul flight paths, particularly for transatlantic and African routes. Our findings demonstrate that this integration significantly improves trajectory reconstruction accuracy, allowing for better fuel consumption and emissions estimates. We illustrate how combined data captures flight patterns across previously underrepresented regions across Africa. Despite coverage limitations, this work marks an important advancement in providing open access to global flight trajectory data, enabling new research opportunities in air traffic management, environmental impact assessment, and aviation safety.

cs.SI↗

arXiv2025

Contrail, or not contrail, that is the question: the "feasibility" of climate-optimal routing

The environmental impact of aviation has been a focus of significant research for several decades. While there is a broad consensus among stakeholders on reducing carbon emissions, leading to efforts to improve route efficiency in air traffic management, the impact of non-carbon emissions like contrails has sparked a different debate. Some organizations have already moved to pre-operational trials for contrail avoidance through flight re-routing, with recent industry projects fast-tracking these operational strategies. This paper addresses the practical challenges of implementing contrail-aware routing. Building on our previous research, which enables the generation of wind-optimal 4D trajectories using grid-like cost functions, we utilize the TOP tool and the OpenAP aircraft performance model to analyze the trade-offs between fuel consumption and contrail avoidance. We also examine the impact of weather forecast uncertainty on contrail mitigation, the effects on airspace capacity and network operations, and the implications for aviation regulatory frameworks. To investigate these challenges, we reconstructed a dataset from OpenSky data, encompassing all flight trajectories over Europe on a day with significant contrail potential. Our data-driven analysis highlights the potential difficulties in implementing contrail-optimal routing in practice, particularly concerning uncertainties in weather forecasts, impacts on airspace capacity, and questions of responsibility for optimal routing. Overall, we argue that contrail-optimal routing should be approached with caution, as its implementation may not be as straightforward as some stakeholders suggest.

physics.soc-ph↗

arXiv2023

A Benchmark on Uncertainty Quantification for Deep Learning Prognostics

Reliable uncertainty quantification on RUL prediction is crucial for informative decision-making in predictive maintenance. In this context, we assess some of the latest developments in the field of uncertainty quantification for prognostics deep learning. This includes the state-of-the-art variational inference algorithms for Bayesian neural networks (BNN) as well as popular alternatives such as Monte Carlo Dropout (MCD), deep ensembles (DE) and heteroscedastic neural networks (HNN). All the inference techniques share the same inception deep learning architecture as a functional model. We performed hyperparameter search to optimize the main variational and learning parameters of the algorithms. The performance of the methods is evaluated on a subset of the large NASA NCMAPSS dataset for aircraft engines. The assessment includes RUL prediction accuracy, the quality of predictive uncertainty, and the possibility to break down the total predictive uncertainty into its aleatoric and epistemic parts. The results show no method clearly outperforms the others in all the situations. Although all methods are close in terms of accuracy, we find differences in the way they estimate uncertainty. Thus, DE and MCD generally provide more conservative predictive uncertainty than BNN. Surprisingly, HNN can achieve strong results without the added training complexity and extra parameters of the BNN. For tasks like active learning where a separation of epistemic and aleatoric uncertainty is required, radial BNN and MCD seem the best options.

cs.LG↗

arXiv2019

Quantitative Assessments of Runway Excursion Precursors using Mode S data

A way to assess rare aircraft incidents (e.g., runway excursion) is to identify contributing factors (e.g., late braking, long landing, inappropriate flare, unstable approach) and to build a dependency tree (e.g., long landing may be the result of an unstable approach not followed by a go around) that describes the causality between these factors. Probabilities are then fed into such models in order to evaluate the assessed risk. When estimating such probabilities, many sources can be of interest. Airlines have access to the comprehensive flight data records of their fleet; manufacturers push to collect data for the aircraft they build; air traffic control log radar tracks. Albeit not as complete as other flight data records, Mode S data is very attractive, esp. for academics, as the data is open, may be published without obfuscation and offers reproducible results to the community. Mode S also provides an indiscriminate source of information (not limited to an airline or to an aircraft type) that is of great help for putting in context flights matching unusual patterns. We propose to discuss the advantages and limitations of an analysis based only on Mode S data with a case study around the runway excursion risk assessment.

physics.soc-ph↗