Special Issue "Aeronautical Informatics"

A special issue of Aerospace (ISSN 2226-4310). This special issue belongs to the section "Aeronautics".

Deadline for manuscript submissions: 30 June 2021.

Special Issue Editor

Dr. Umut Durak
E-Mail Website
Guest Editor
1. Institute of Flight Systems, German Aerospace Center (DLR), Braunschweig, Germany
2. Department of Informatics, Clausthal University of Technology, Clausthal-Zellerfeld, Germany
Interests: modeling- and simulation-based approaches in aeronautics

Special Issue Information

Dear Colleagues,

The recent advances in Information and Communication Technologies (ICT) have been phenomenal. Through various disruptive innovations, they have brought us to the digitalization era that is characterized by the keywords “smart” and “connected”. While all previous efforts were intended to automate individual systems, today’s focus is on the integration of all systems within a value chain into digital ecosystems.

After realizing far-reaching automation on aircraft, the aeronautics domain is now looking at the next generation of flight. Aeronautical informatics is here the key applied field of research that focuses on understanding, applying, and enhancing advancement of ICT in aeronautics. This multidisciplinary field is involved in information processing and engineering of information systems in relation to the science or practice of building or flying aircraft.

This Special Issue aims to highlight recent aeronautical informatics research and encourages authors to submit full research articles and review manuscripts that address (but are not limited to) advances in Software Engineering, Cyber-Physical Systems, Internet of Things (IoT), Service-Oriented Architecture (SOA), Digital Twin, Big Data and Data Analytics, Artificial Intelligence, Reconfigurable Computing, and Wireless/Cellular Networking applied to aeronautics.

Dr. Umut Durak
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Aerospace is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Software Engineering
  • Cyber-Physical Systems
  • Internet of Things (IoT)
  • Service-Oriented Architecture (SOA)
  • Digital Twin
  • Big Data and Data Analytics
  • Artificial Intelligence
  • Reconfigurable Computing
  • Wireless/Cellular Networking

Published Papers (2 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

Article
Physics Guided Deep Learning for Data-Driven Aircraft Fuel Consumption Modeling
Aerospace 2021, 8(2), 44; https://doi.org/10.3390/aerospace8020044 - 08 Feb 2021
Viewed by 1001
Abstract
This paper presents a physics-guided deep neural network framework to estimate fuel consumption of an aircraft. The framework aims to improve data-driven models’ consistency in flight regimes that are not covered by data. In particular, we guide the neural network with the equations [...] Read more.
This paper presents a physics-guided deep neural network framework to estimate fuel consumption of an aircraft. The framework aims to improve data-driven models’ consistency in flight regimes that are not covered by data. In particular, we guide the neural network with the equations that represent fuel flow dynamics. In addition to the empirical error, we embed this physical knowledge as several extra loss terms. Results show that our proposed model accomplishes correct predictions on the labeled test set, as well as assuring physical consistency in unseen flight regimes. The results indicate that our model, while being applicable to the aircraft’s complete flight envelope, yields lower fuel consumption error measures compared to the model-based approaches and other supervised learning techniques utilizing the same training data sets. In addition, our deep learning model produces fuel consumption trends similar to the BADA4 aircraft performance model, which is widely utilized in real-world operations, in unseen and untrained flight regimes. In contrast, the other supervised learning techniques fail to produce meaningful results. Overall, the proposed methodology enhances the explainability of data-driven models without deteriorating accuracy. Full article
(This article belongs to the Special Issue Aeronautical Informatics)
Show Figures

Figure 1

Article
Toward ATM Resiliency: A Deep CNN to Predict Number of Delayed Flights and ATFM Delay
Aerospace 2021, 8(2), 28; https://doi.org/10.3390/aerospace8020028 - 25 Jan 2021
Viewed by 727
Abstract
The European Air Traffic Management Network (EATMN) is comprised of various stakeholders and actors. Accordingly, the operations within EATMN are planned up to six months ahead of target date (tactical phase). However, stochastic events and the built-in operational flexibility (robustness), along with other [...] Read more.
The European Air Traffic Management Network (EATMN) is comprised of various stakeholders and actors. Accordingly, the operations within EATMN are planned up to six months ahead of target date (tactical phase). However, stochastic events and the built-in operational flexibility (robustness), along with other factors, result in demand and capacity imbalances that lead to delayed flights. The size of the EATMN and its complexity challenge the prediction of the total network delay using analytical methods or optimization approaches. We face this challenge by proposing a deep convolutional neural network (DCNN), which takes capacity regulations as the input. DCNN architecture successfully improves the prediction results by 50 percent (compared to random forest as the baseline model). In fact, the trained model on 2016 and 2017 data is able to predict 2018 with a mean absolute percentage error of 22% and 14% for the delay and delayed traffic, respectively. This study presents a method to provide more accurate situational awareness, which is a must for the topic of network resiliency. Full article
(This article belongs to the Special Issue Aeronautical Informatics)
Show Figures

Figure 1

Back to TopTop