Roadmap for a European open science alliance for ATM research

We here propose an open science alliance for ATM, advocating for open data in such a framework. The benefits of adopting an open science approach are to be found, inter alia, through the independent verification and validation of reported impacts/results and achieved performance levels, i.e., through reproducibility. We consider that this can only be achieved through: (1) open access to scientific methods and data utilised; (2) open access to (analytical) code and methods; (3) open review of reported analyses/research. The proper application of such practices will reduce the innovation cycles in ATM, which is much needed by industry and society. Steps for forming an open science alliance for ATM are described. We propose further initial, specific recommendations for supporting open data and improved access for research

Probabilistic Characterization of Operational Uncertainties in Transport Aircraft using OpenSky

The aerodynamic design of transonic wings is already a mature field, and the use of aerodynamic shape optimization is a well-established discipline in industrial setting. Aircraft manufacturers design configurations by considering a representative but limited set of flight conditions. In practice, airlines do not always fly at the conditions they were designed to operate. Flight altitude, airspeed and aircraft weight are affected by operational requirements and environmental uncertainties. As a result, aircraft altitude, Mach number and lift coefficient, three of the most important parameters when performing aerodynamic design, can not be treated as single deterministic values in the design process. A full probabilistic approach is required to better characterize the real performance of the aircraft. However, there is a lack of aircraft operational data necessary to characterize uncertainty sources in flight. The objective of this paper is the characterization and quantification of operational uncertainty sources based on aircraft surveillance data. The definition of these uncertainties will be essential for the robust design of the next generation of commercial aircraft. To understand the variability in operating conditions of a representative aircraft fleet, surveillance data from the OpenSky network is gathered. The Mach number is directly obtained from the BDS-60 codes, while the altitude is provided by the ADS-B. The lift coefficient of the aircraft at each instant is roughly estimated according to the Breguet equation and the initial and final fuel weights. These are determined by the distance between departure and arriving airports. After the Mach, lift coefficient and altitude are obtained for each individual flight, they are filtered for cruise conditions (level flight). A Kernel Density Estimation is used to obtain the full probability distribution function. This methodology enables the accurate characterization of operational uncertainties that will be required for the aerodynamic robust design of the next generation of aircraft. The design will be tailored to the airliners operations. This framework can also be used by designers and operators to understand how aircraft are operated in reality

Predictive maintenance using deep learning

Treballs Finals de Grau d'Enginyeria Informàtica, Facultat de Matemàtiques, Universitat de Barcelona, Any: 2021, Director: Simone Balocco[en] The goal of this study is to demonstrate if failures reported in an aircraft can be related to the environmental conditions during operation time. The current study is the first step of a long-term predictive maintenance project driven by the company DMD Solutions. First of all, the concepts of reliability and predictive maintenance are introduced. Furthermore, the fundamentals of machine learning and the state of the art are detailed. Gathering quality data was a complex process, since the available data was incomplete, noisy and unbalanced. The analysis proposes and compares several solutions. Two different approaches were carried out: the first one consisted of the prediction of failure (binary classification), and the second one, more ambitious, the prediction of the time before the next defect using time intervals (multi-class classification). Both approaches were designed using an iterative process that improved quality of both models and data at each stage of the study. The obtained results were promising and encourage further research

Integrating pyModeS and OpenSky Historical Database

A large quantity of Mode S data is being gathered by the OpenSky receiver network every day. Information regarding common flight states, such as position, ground speed, and the vertical rate is broadcast by ADS-B and has already been decoded and made available for researchers via the OpenSky historical database. However, there is still a large amount of Mode S communication data that has not yet been fully explored. Specifically, the information contained in Enhanced Mode S Surveillance downlink messages can be utilized to better support ATM research. The challenge of decoding such information lies in the implicit inference process for Mode S Comm-B messages. This paper presents a new open library, pymodes-opensky, which connects the existing open-source pyModeS decoder to the raw Mode S messages from the OpenSky historical database through the Impala shell. It also presents a convenient workflow that can be used to obtain additional information regarding airspeeds, flight intentions, and meteorological conditions of a given flight from the OpenSky database. An analysis based on a global dataset from OpenSky is conducted, and the associated Mode S interrogation statistics in different regions are shown.Control & SimulationControl & Operation