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Aerospace
Special Issues
Re-entry Analysis and Prediction
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Special Issue "Re-entry Analysis and Prediction"

A special issue of Aerospace (ISSN 2226-4310). This special issue belongs to the section "Astronautics & Space Science".

Deadline for manuscript submissions: closed (30 September 2023) | Viewed by 1906

Special Issue Editors

Dr. Mirko Trisolini

E-Mail Website
Guest Editor
Department of Aerospace Engineering, Politecnico di Milano, 20133 Milan, Italy
Interests: modeling and simulation; orbital dynamics; space debris; space sustainability; re-entry dynamics; fragmentation processes; uncertainty analysis; optimisation
Dr. Camilla Colombo

E-Mail Website
Guest Editor
Department of Aerospace Engineering, Politecnico di Milano, 20133 Milan, Italy
Interests: orbit propagation; trajectory design; mission analysis; space debris mitigation; end-of-life disposal; debris evolution models; space sustainability; planetary protection

Special Issue Information

Dear Colleagues,

We are pleased to announce a new open access Special Issue of the MDPI journal Aerospace dedicated to Re-entry Analysis and Prediction.

In the last decade, the number of artificial satellites launched by private companies and governmental agencies has increased exponentially. Such growth in space activities has largely benefited humankind with the availability of new technologies and services. However, it has also resulted in systematic congestion of the space environment with a higher number of inactive satellites.

To ensure the future sustainability of the space environment, international organizations have identified mitigation guidelines to help ensure the future accessibility of space. Among the mitigation guidelines, the re-entry of non-operational space object is regarded as one of the key elements to maintain operational orbits accessible in the future and limit the possibility of creating additional debris. The successful implementation of such mitigation strategies relies in our ability to effectively model the re-entry process, accurately propagate the trajectory of re-entering objects under the effect of perturbation and uncertainties, and design efficient and cost-effective disposal manoeuvres and technologies.

The re-entry of space objects can also pose a risk for people and properties on the ground, particularly if the re-entry is uncontrolled. Therefore, our capability to model the complex destructive re-entry process and develop design techniques to favour the demisability of re-entering objects is a key aspect, as is our capability to track these objects and predict their trajectory in the long and short term, also leveraging novel data-driven approaches that can benefit from the ever-growing number of satellites in orbit.

This Special Issue aims to collect contributions in a range of areas related to re-entry, with particular attention to tools and techniques for re-entry propagation and risk analysis, as well as advancement in re-entry prediction capabilities. Potential topics include, but are not limited to:

  • Re-entry modelling and propagation
  • Re-entry risk analysis and remediation techniques
  • Re-entry analysis tools and techniques
  • Spacecraft design-for-demise (design methodologies and testing)
  • Long- and short-term re-entry predictions
  • Sensor network for re-entry predictions
  • Machine learning applications to re-entry predictions
  • Uncertainty quantification and propagation for re-entry applications
  • Design of end-of-life manoeuvres
  • Development of disposal technologies (i.e., solar/drag sails, electrodynamic tethers, electric thrusters, etc.)

Dr. Mirko Trisolini
Dr. Camilla Colombo
Guest Editors

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 submissions that pass pre-check are 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 2400 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

  • re-entry predictions
  • re-entry modelling
  • destructive re-entry processes
  • design-for-demise
  • uncertainty propagation
  • end-of-life disposal
  • machine learning
  • re-entry analysis tools

Published Papers (1 paper)

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Research

25 pages, 2020 KiB  
Article
A Machine Learning and Feature Engineering Approach for the Prediction of the Uncontrolled Re-Entry of Space Objects
by
Francesco Salmaso
,
Mirko Trisolini
and
Camilla Colombo
Aerospace 2023, 10(3), 297; https://doi.org/10.3390/aerospace10030297 - 17 Mar 2023
Viewed by 1393
Abstract
The continuously growing number of objects orbiting around the Earth is expected to be accompanied by an increasing frequency of objects re-entering the Earth’s atmosphere. Many of these re-entries will be uncontrolled, making their prediction challenging and subject to several uncertainties. Traditionally, re-entry [...] Read more.
The continuously growing number of objects orbiting around the Earth is expected to be accompanied by an increasing frequency of objects re-entering the Earth’s atmosphere. Many of these re-entries will be uncontrolled, making their prediction challenging and subject to several uncertainties. Traditionally, re-entry predictions are based on the propagation of the object’s dynamics using state-of-the-art modelling techniques for the forces acting on the object. However, modelling errors, particularly related to the prediction of atmospheric drag, may result in poor prediction accuracies. In this context, we explored the possibility of performing a paradigm shift, from a physics-based approach to a data-driven approach. To this aim, we present the development of a deep learning model for the re-entry prediction of uncontrolled objects in Low Earth Orbit (LEO). The model is based on a modified version of the Sequence-to-Sequence architecture and is trained on the average altitude profile as derived from a set of Two-Line Element (TLE) data of over 400 bodies. The novelty of the work consists in introducing in the deep learning model, alongside the average altitude, and three new input features: a drag-like coefficient (B*), the average solar index, and the area-to-mass ratio of the object. The developed model was tested on a set of objects studied in the Inter-Agency Space Debris Coordination Committee (IADC) campaigns. The results show that the best performances are obtained on bodies characterised by the same drag-like coefficient and eccentricity distribution as the training set. Full article
(This article belongs to the Special Issue Re-entry Analysis and Prediction)
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