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Applied Sciences
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Aerospace Vehicle Design under Uncertainties
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Aerospace Vehicle Design under Uncertainties

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Aerospace Science and Engineering".

Deadline for manuscript submissions: closed (20 February 2023) | Viewed by 6275

Special Issue Editors

Dr. Mathieu Balesdent

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Guest Editor
ONERA-The French Aerospace Lab, 91120 Palaiseau, France
Interests: the design of aerospace systems; multidisciplinary design optimization; uncertainty quantification; reliability-based design optimization
Special Issues, Collections and Topics in MDPI journals
Dr. Loïc Brevault

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Guest Editor
ONERA-The French Aerospace Lab, 91120 Palaiseau, France
Interests: multidisciplinary design optimization; uncertainty quantification; machine learning for the design of complex systems; mixed discrete/continuous optimization; aerospace vehicle design
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Jérôme Morio

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Guest Editor
ONERA/DTIS, Université de Toulouse, 31000 Toulouse, France
Interests: safety engineering; uncertainty management in complex aerospace systems (reliability, sensitivity analysis, surrogate modeling, etc.)
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear colleagues,

The design of aerospace vehicles is a complex process involving numerous disciplines (aerodynamics, propulsion, trajectory, structure, etc.). These disciplines have to be tightly coupled into a multidisciplinary design process to handle their interactions. Moreover, the design of such systems usually involves different phases, from early design up to detailed design. These phases are characterized by their own level of knowledge (e.g., fidelity of the models, maturity of the technologies, system environment and specifications). This results in  the need to handle different types of uncertainties (epistemic and aleatory) at the different design phases to provide robust and reliable aerospace concepts. The handling of uncertainty in the design of aerospace vehicles is an open research area that requires developments in key topics such as uncertainty modeling, uncertainty propagation, reliability analysis or optimization under uncertainty. This Special Issue covers these research fields as well as innovative applications of uncertainty quantification techniques to aerospace vehicles.

Dr. Mathieu Balesdent
Dr. Loïc Brevault
Prof. Dr. Jérôme Morio
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. Applied Sciences is an international peer-reviewed open access semimonthly 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

  • multidisciplinary design optimization
  • reliability analysis
  • uncertainty quantification
  • reliability-based design optimization
  • design under uncertainty
  • machine learning
  • multifidelity modeling
  • uncertainty propagation
  • sensitivity analysis
  • nonprobabilistic methods

Published Papers (4 papers)

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Research

18 pages, 4366 KiB  
Article
Hierarchical Model Updating Method for Vector Electric-Propulsion Satellites
by Xueqian Wu and Yunfeng Dong
Appl. Sci. 2023, 13(8), 4980; https://doi.org/10.3390/app13084980 - 15 Apr 2023
Cited by 2 | Viewed by 871
Abstract
Electric propulsion is of great significance to the development of high-efficiency and long-life satellites, and digital twins have gradually become a powerful tool for satellite engineering. Being affected by uncertainty factors such as the complexity and variability of the space environment and the [...] Read more.
Electric propulsion is of great significance to the development of high-efficiency and long-life satellites, and digital twins have gradually become a powerful tool for satellite engineering. Being affected by uncertainty factors such as the complexity and variability of the space environment and the satellite system, the digital twin model cannot accurately reflect the real physical properties. Therefore, it is crucial to update the satellite model to improve prediction accuracy. However, the complex structure and multi-physics process coupling of vector electric-propulsion satellites bring great challenges to model updating. According to the characteristics of the vector electric-propulsion satellite, this paper establishes mathematical models of the whole satellite. Additionally, a hierarchical model updating method is proposed and applied to the model updating case of a satellite with multiple subsystems. The simulation results show that the method is suitable for the model updating of the vector electric-propulsion satellite. Through multiple iterations of closed-loop cycles, the residual errors between the simulation values and the telemetry values can be decreased, and the errors between the estimated values and the true values of state variables can also be decreased by an order of magnitude. Full article
(This article belongs to the Special Issue Aerospace Vehicle Design under Uncertainties)
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21 pages, 1373 KiB  
Article
Cramér–Rao Lower Bounds on 3D Position and Orientation Estimation in Distributed Ranging Systems
by Sharanya Srinivas, Samuel Welker, Andrew Herschfelt and Daniel W. Bliss
Appl. Sci. 2023, 13(3), 2008; https://doi.org/10.3390/app13032008 - 03 Feb 2023
Viewed by 2037
Abstract
As radio frequency (RF) hardware continues to improve, many technologies that were traditionally impractical have suddenly become viable alternatives to legacy systems. Two-way ranging (TWR) is often considered a poor positioning solution for airborne and other vehicular navigation systems due to its low [...] Read more.
As radio frequency (RF) hardware continues to improve, many technologies that were traditionally impractical have suddenly become viable alternatives to legacy systems. Two-way ranging (TWR) is often considered a poor positioning solution for airborne and other vehicular navigation systems due to its low precision, poor angular resolution, and precise timing requirements. With the advent of modern RF hardware and advanced processing techniques, however, modern studies have experimentally demonstrated TWR systems with an unprecedented, sub-centimeter ranging precision with low size, weight, power, and cost (SWaP-C) consumer-grade hardware. This technique enables a new class of positioning, navigation, and timing (PNT) capabilities for urban and commercial aircraft but also instigates new system design challenges such as antenna placement, installation of new electronics, and design of supporting infrastructure. To inform these aircraft design decisions, we derive 2D and 3D Cramér–Rao lower bounds (CRLBs) on position and orientation estimation in a multi-antenna TWR system. We specifically formulate these bounds as a function of the number of antennas, platform geometry, and geometric dilution of precision (GDoP) to inform aircraft design decisions under different mission requirements. We simulate the performance of several classic position and orientation estimators in this context to validate these bounds and to graphically depict the expected performance with respect to these design considerations. To improve the accessibility of these highly theoretical results, we also present a simplified discussion of how these bounds may be applied to common airborne applications and suggest best practices for using them to inform aircraft design decisions. Full article
(This article belongs to the Special Issue Aerospace Vehicle Design under Uncertainties)
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22 pages, 7200 KiB  
Article
AC Electric Powertrain without Power Electronics for Future Hybrid Electric Aircrafts: Architecture, Design and Stability Analysis
by Alexandre Richard, Xavier Roboam, Florent Rougier, Nicolas Roux and Hubert Piquet
Appl. Sci. 2023, 13(1), 672; https://doi.org/10.3390/app13010672 - 03 Jan 2023
Cited by 2 | Viewed by 1558
Abstract
This paper proposes an electric powertrain architecture for future hybrid electric aircrafts which structure is only composed of permanent magnet synchronous machines for both generators (PMSG) and motors (PMSM). The direct connection through an AC bus of a PMSG with one or several [...] Read more.
This paper proposes an electric powertrain architecture for future hybrid electric aircrafts which structure is only composed of permanent magnet synchronous machines for both generators (PMSG) and motors (PMSM). The direct connection through an AC bus of a PMSG with one or several PMSMs involves the suppression of power electronics usually embedded in electric or hybrid electric powertrains. The idea is clearly to simplify the architecture and to significantly reduce the weight of propulsive device, “weight being the prime enemy in aeronautics”. However, the connection between power generation and propulsion devices through power electronics converters offers degrees of freedom allowing to control and stabilize the whole system. Contrarily, the direct connection between synchronous machines (PMSG-PMSM) sets a rigid link with non-linear behavior between both devices, causing complex stability issues that are analyzed. For that purpose, after having discussed the advantages and drawbacks of this powertrain by comparison with classical architectures, including power electronics, a set of models (analytic and simulation) and analysis tools (root locus, transient time simulation) is proposed. They are used in a theoretical approach to emphasize the stability issue and to assess parameter sensitivity. A reduced power scale test bench with a single-motor AC powertrain is presented: together with circuit simulation models, it is used to compare and validate the theoretical analysis results. Full article
(This article belongs to the Special Issue Aerospace Vehicle Design under Uncertainties)
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24 pages, 4000 KiB  
Article
Active Learning Strategy for Surrogate-Based Quantile Estimation of Field Function
by Loïc Brevault, Mathieu Balesdent and Jorge-Luis Valderrama-Zapata
Appl. Sci. 2022, 12(19), 10027; https://doi.org/10.3390/app121910027 - 06 Oct 2022
Cited by 1 | Viewed by 1188
Abstract
Uncertainty quantification is widely used in engineering domains to provide confidence measures on complex systems. It often requires to accurately estimate extreme statistics on computationally intensive black-box models. In case of spatially or temporally distributed model outputs, one valuable metric results in the [...] Read more.
Uncertainty quantification is widely used in engineering domains to provide confidence measures on complex systems. It often requires to accurately estimate extreme statistics on computationally intensive black-box models. In case of spatially or temporally distributed model outputs, one valuable metric results in the estimation of extreme quantile of the output stochastic field. In this paper, a novel active learning surrogate-based method is proposed to determine the quantile of an unidimensional output stochastic process with a confidence measure. This allows to control the error on the estimation of a extreme quantile measure of a stochastic process. The proposed approach combines dimension reduction techniques, Gaussian process and an adaptive refinement strategy to enrich the surrogate model and control the accuracy of the quantile estimation. The proposed methodology is applied on an analytical test case and a realistic aerospace problem for which the estimation of a flight envelop is of prime importance for launch safety reasons in the space industry. Full article
(This article belongs to the Special Issue Aerospace Vehicle Design under Uncertainties)
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