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Aerospace
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Advances in Aerospace Software Engineering
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Advances in Aerospace Software Engineering

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

Deadline for manuscript submissions: closed (31 December 2022) | Viewed by 7269

Special Issue Editors

Prof. Dr. Umut Durak

E-Mail Website
Guest Editor
German Aerospace Center (DLR), Institute of Flight Systems, Braunschweig, Germany
Interests: model-based engineering and simulation-based verification of airborne software-intensive systems
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Harro von Viebahn

E-Mail Website
Guest Editor
German Aerospace Center (DLR), Institute of Flight Systems, Braunschweig, Germany
Interests: avionics systems engineering

Special Issue Information

Dear Colleagues,

The last two decades in software engineering have been characterized by a fast pace of change in almost all aspects, from lifecycle models to tooling and from requirements engineering to testing.

On one hand, software engineering is evolving toward removing disconnects among its activities by employing continuous practices to achieve agile processes. Test-driven development (TDD), continuous integration (CI) and continuous deployment (CD), behavior-driven development (BDD), and DevOps/DevSecOps are established as standard practices. We are moving toward Everything as Code (EaC), where we are trying to manage all aspects of development, delivery, and deployment with code. On the other hand, the advances in model-based approaches have been phenomenal. Particularly in cyberphysical systems (CPS) domains, models have become the central asset, used for code generation, verification, and validation purposes.

In addition to others, emerging segments such as advanced air mobility (AAM) and urban air mobility (UAM) are asking for higher levels of automation, and even autonomy. Artificial Intelligence (AI) is becoming a core technology. We now require new approaches for engineering software for AI-based systems.

Moreover, the increasing demand for embedded high-performance computing is pushing hardware to change. In addition to heterogenous and multi-core targets, the utilization of vector processor is ramping up. This emphasizes new software engineering approaches.

Furthermore, aerospace software engineering is a highly regulated domain. Complexity, certification, fault isolation, and fault tolerance have always been the main drivers. These introduce major challenges to adopting new technologies, using new target platforms, and addressing the emerging requirements of AI-based systems.

This Special Issue aims to highlight recent advances in aerospace software engineering and encourages authors to submit full research articles and review manuscripts addressing (but not limited to) agile methods, model-based software engineering, AI software engineering, and software for heterogeneous and multi-core target platforms.

Prof. Dr. Umut Durak
Prof. Dr. Harro von Viebahn
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.

Published Papers (3 papers)

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Research

20 pages, 1802 KiB  
Article
UltraFLoads: A Simulation Suite and Framework for High-Fidelity Flight Loads
by Johan Feldwisch and Martin Bauer
Aerospace 2023, 10(3), 273; https://doi.org/10.3390/aerospace10030273 - 10 Mar 2023
Cited by 1 | Viewed by 1485
Abstract
With the progress of high-performance computing, computationally expensive high-fidelity methods can be applied early in the design process of an aircraft. This enables Computational Fluid Dynamics (CFD) for the assesment of flight performance, handling qualities, flight loads due to manoeuver or gusts, and [...] Read more.
With the progress of high-performance computing, computationally expensive high-fidelity methods can be applied early in the design process of an aircraft. This enables Computational Fluid Dynamics (CFD) for the assesment of flight performance, handling qualities, flight loads due to manoeuver or gusts, and stability such as flutter. Those aeroealstic analyses require coupling of disciplines to adequately address the interplay of flexibility, aerodynamic forces and inertia forces. Previous work at the German Aerospace Center have demonstrated the proof of concept for those multidisciplinary analyses. Now, UltraFLoads is a simulation suite in the FlowSimulator ecosystem to offer standardized, multidisciplinary scenarios. UltraFLoads can be used as a tool, as a library and as a framework. This work describes the architecture, the integration layers, which scenarios are available and which equations are used. Three different applications are presented. The first case involves a steady fluid–structure-coupled simulation with geometrically nonlinear deformation. The second case compares the deformation of a simulated elastic, free-flying aircraft with actual flight test data. The last application demonstrates the flight dynamic capabilities with a bank to bank maneuver. Full article
(This article belongs to the Special Issue Advances in Aerospace Software Engineering)
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21 pages, 432 KiB  
Article
Design of High-Performance and General-Purpose Satellite Management Unit Based on Rad-Hard Multi-Core SoCand Linux
by Lu Li, Junwang He, Dongxiao Xu, Wen Chen, Jinpei Yu and Huawang Li
Aerospace 2023, 10(2), 201; https://doi.org/10.3390/aerospace10020201 - 20 Feb 2023
Viewed by 1719
Abstract
Since deep space exploration tasks, such as space gravitational wave detection, put forward increasingly higher requirements for the satellite platform, the scale and complexity of the satellite management unit (SMU) software are also increasing, and the trend of intelligentization is showing. It is [...] Read more.
Since deep space exploration tasks, such as space gravitational wave detection, put forward increasingly higher requirements for the satellite platform, the scale and complexity of the satellite management unit (SMU) software are also increasing, and the trend of intelligentization is showing. It is difficult for the traditional SMU based on single-core system on chip (SoC) to meet the various requirements brought by the above trends. This paper presents a high-performance general-purpose SMU design. Based on rad-hard multi-core SoC, we configure and tailor Linux, and design an SMU software architecture with three modes. It has the characteristics of high performance, high reliability, general purpose and scalability, which can meet the needs of the SMU of future complex satellites. Finally, through the application experiment in the background of the space gravitational wave detection project, the performance and application prospect of our proposed SMU are demonstrated. Full article
(This article belongs to the Special Issue Advances in Aerospace Software Engineering)
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19 pages, 803 KiB  
Article
Automated Model Hardening with Reinforcement Learning for On-Orbit Object Detectors with Convolutional Neural Networks
by Qi Shi, Lu Li, Jiaqi Feng, Wen Chen and Jinpei Yu
Aerospace 2023, 10(1), 88; https://doi.org/10.3390/aerospace10010088 - 16 Jan 2023
Cited by 1 | Viewed by 2489
Abstract
On-orbit object detection has received extensive attention in the field of artificial intelligence (AI) in space research. Deep-learning-based object-detection algorithms are often computationally intensive and rely on high-performance devices to run. However, those devices usually lack space-qualified versions, and they can hardly meet [...] Read more.
On-orbit object detection has received extensive attention in the field of artificial intelligence (AI) in space research. Deep-learning-based object-detection algorithms are often computationally intensive and rely on high-performance devices to run. However, those devices usually lack space-qualified versions, and they can hardly meet the reliability requirement if directly deployed on a satellite platform, due to software errors induced by the space environment. In this paper, we evaluated the impact of space-environment-induced software errors on object-detection algorithms through large-scale fault injection tests. Aside from silent data corruption (SDC), we propose an extended criterial SDC-0.1 to better quantify the effect of the transient faults on the object-detection algorithms. Considering that a bit-flip error could cause severe detection result corruption in many cases, we propose a novel automated model hardening with reinforcement learning (AMHR) framework to solve this problem. AMHR searches for error-sensitive kernels in a convolutional neural network (CNN) through trial and error with a deep deterministic policy gradient (DDPG) agent and has fine-grained modular-level redundancy to increase the fault tolerance of the CNN-based object detectors. Compared to other selective hardening methods, AMHR achieved the lowest SDC-0.1 rates for various detectors and could tremendously improve the mean average precision (mAP) of the SSD detector by 28.8 in the presence of multiple errors. Full article
(This article belongs to the Special Issue Advances in Aerospace Software Engineering)
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