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5th Anniversary of Aerospace Science and Engineering Section—Recent Advances in Aerospace

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

Deadline for manuscript submissions: 31 December 2025 | Viewed by 3169

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Special Issue Editors

Prof. Dr. Dongsheng Wen

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Guest Editor

Institute of Thermodynamics, Technical University of Munich, 85748 Garching, Germany
Interests: nanotechnology; enhanced oil recovery; solar energy; multiphase flow; multiscale modelling
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Dr. Rosario Pecora

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Guest Editor

Department of Industrial Engineering—Aerospace Division, University of Naples “Federico II”, Via Claudio, 21, 80125 Napoli, NA, Italy
Interests: smart structures; smart aircraft technologies; morphing structures; structural dynamics; vibration control; dynamic aeroelasticity; non-linear dynamics; mechanics and experimental dynamics
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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

Dr. Jin Zhao

E-Mail Website
Guest Editor

School of Aeronautic Science and Engineering, Beihang University, Beijing 100191, China
Interests: thermoaerodynamics; high speed flow; thermal protection system; AI-CFD; AI-heat transfer
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Special Issue Information

Dear Colleagues,

To celebrate the 5th Anniversary of Aerospace Science and Engineering Section, Applied Sciences is pleased to announce a Special Issue titled "5th Anniversary of Aerospace Science and Engineering Section—Recent Advances in Aerospace". This Special Issue aims to showcase cutting-edge research, innovative technologies, and transformative developments in the field of aerospace science and engineering. We invite researchers, engineers, and industry professionals to contribute their original research articles, reviews, and case studies that highlight recent advancements and future directions in aerospace.

This Special Issue will provide a platform for disseminating high-quality research that addresses the challenges and opportunities in aerospace science and technology. We welcome submissions across a broad range of topics, including but not limited to the following:

Aircraft Design and Manufacturing
- Advanced materials and manufacturing techniques
- Lightweight structures and composites
- Additive manufacturing in aerospace
- Sustainable and eco-friendly aircraft design
Jet Engine and Propulsion
- Next-generation propulsion systems
- Turbomachinery and combustion technologies
- Hybrid and electric propulsion
- Noise reduction and emission control
Aerodynamics and Design
- Computational fluid dynamics (CFD) and experimental aerodynamics
- High-speed and hypersonic aerodynamics
- Flow control and optimization techniques
- Aerodynamic design of UAVs and drones
Structure and Properties
- Structural health monitoring and damage detection
- Fatigue, fracture, and failure analysis
- Smart materials and adaptive structures
- Thermal protection systems
Environmental Control and Life Protection Systems
- Cabin air quality and pressurization systems
- Thermal management in extreme environments
- Life-support systems for space exploration
- Radiation shielding and protection
Flight Control and Dynamics
- Autonomous flight systems
- Guidance, navigation, and control (GNC)
- Stability and control of aircrafts and spacecrafts
- Human–machine interface and cockpit systems
Astrodynamics
- Orbital mechanics and trajectory optimization
- Spacecraft dynamics and control
- Mission design and analysis
- Space debris mitigation and management
Space Science and Engineering
- Satellite technology and applications
- Space exploration and planetary science
- Spacecraft design and system engineering
- Lunar and Martian exploration technologies
Artificial Intelligence in Aerospace Applications
- Machine learning for aerospace systems
- AI-driven design and optimization
- Autonomous systems and robotics in aerospace
- Predictive maintenance and fault diagnosis

Prof. Dr. Dongsheng Wen
Dr. Rosario Pecora
Prof. Dr. Jérôme Morio
Dr. Jin Zhao
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

aircraft
engine
propulsion
aerodynamics
flight control
astrodynamics
space science
artificial intelligence

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Published Papers (5 papers)

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Research

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23 pages, 4240 KiB

Open AccessArticle

Heliocentric Orbital Repositioning of a Sun-Facing Diffractive Sail with Controlled Binary Metamaterial Arrayed Grating

by Alessandro A. Quarta

Appl. Sci. 2025, 15(15), 8755; https://doi.org/10.3390/app15158755 - 7 Aug 2025

Viewed by 217

Abstract

This paper investigates the performance of a spacecraft equipped with a diffractive sail in a heliocentric mission scenario that requires phasing along a prescribed elliptical orbit. The diffractive sail represents an evolution of the more traditional reflective solar sail, which converts solar radiation pressure into thrust using a large reflective surface typically coated with a thin metallic film. In contrast, the diffractive sail proposed by Swartzlander leverages the properties of an advanced metamaterial-based film to generate a net transverse thrust even when the sail is Sun-facing, i.e., in a configuration that can be passively maintained by a suitably designed spacecraft. Specifically, this study considers a sail membrane covered with a set of electro-optically controlled diffractive panels. These panels employ a (controlled) binary metamaterial arrayed grating to steer the direction of photons exiting the diffractive film. This control technique has recently been applied to achieve a circle-to-circle interplanetary transfer using a Sun-facing diffractive sail. In this work, an optimal control law is employed to execute a rapid phasing maneuver along an elliptical heliocentric orbit with specified characteristics, such as those of Earth and Mercury. The analysis also includes a limiting case involving a circular heliocentric orbit. For this latter scenario, a simplified and elegant control law is proposed based on a linearized form of the equations of motion to describe the heliocentric dynamics of the diffractive sail-based spacecraft during the phasing maneuver. Full article

(This article belongs to the Special Issue 5th Anniversary of Aerospace Science and Engineering Section—Recent Advances in Aerospace)

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18 pages, 1814 KiB

Open AccessArticle

Student’s t Kernel-Based Maximum Correntropy Criterion Extended Kalman Filter for GPS Navigation

by Dah-Jing Jwo, Yi Chang, Yun-Han Hsu and Amita Biswal

Appl. Sci. 2025, 15(15), 8645; https://doi.org/10.3390/app15158645 - 5 Aug 2025

Viewed by 304

Abstract

Global Navigation Satellite System (GNSS) receivers may produce measurement outliers in real-world applications owing to various circumstances, including poor signal quality, multipath effects, data loss, satellite signal loss, or electromagnetic interference. This can lead to a noise distribution that is non-Gaussian heavy-tailed, affecting the effectiveness of satellite navigation filters. This paper presents a robust Extended Kalman Filter (EKF) based on the Maximum Correntropy Criterion with a Student’s t kernel (STMCCEKF) for GPS navigation under non-Gaussian noise. Unlike traditional EKF and Gaussian-kernel MCCEKF, the proposed method enhances robustness by leveraging the heavy-tailed Student’s t kernel, which effectively suppresses outliers and dynamic observation noise. A fixed-point iterative algorithm is used for state update, and a new posterior error covariance expression is derived. The simulation results demonstrate that STMCCEKF outperforms conventional filters in positioning accuracy and robustness, particularly in environments with impulsive noise and multipath interference. The Student’s t-distribution kernel efficiently mitigates heavy-tailed non-Gaussian noise, while it adaptively adjusts process and measurement noise covariances, leading to improved estimation performance. A detailed explanation of several key concepts along with practical examples are discussed to aid in understanding and applying the Global Positioning System (GPS) navigation filter. By integrating cutting-edge reinforcement learning with robust statistical approaches, this work advances adaptive signal processing and estimation, offering a significant contribution to the field. Full article

(This article belongs to the Special Issue 5th Anniversary of Aerospace Science and Engineering Section—Recent Advances in Aerospace)

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25 pages, 4844 KiB

Open AccessArticle

Numerical Investigations and Optimized Design of the Active Cooling Performance with Phase Change for Aircraft Rudder Shaft

by Xiangchun Sun, Kaiyan Jin, Kuan Zhao, Hexuan Zhang, Guice Yao and Dongsheng Wen

Appl. Sci. 2025, 15(14), 8105; https://doi.org/10.3390/app15148105 - 21 Jul 2025

Viewed by 257

Abstract

During hypersonic flight, the air rudder shaft can undergo huge aerodynamic heating load, where it is necessary to design the thermal protection system of the air rudder shaft. Aiming to prevent the rudder shaft from thermal failure due to the heat endurance limit of materials, numerical investigations are conducted systemically to predict the active cooling performance of the rudder shaft with liquid water considering phase change. The validation of the numerical simulation method considering phase-change heat transfer is further investigated by experiments. The effect of coolant injection flow velocity on the active cooling performance is further analyzed for both the steady state and transient state. Finally, to achieve better cooling performance, an optimized design of the cooling channels is performed in this work. The results of the transient numerical simulation show that, employing the initial cooling structures, it may undergo the heat transfer deterioration phenomenon under the coolant injection velocity below 0.2 m/s. For the rudder shaft with an optimized structure, the heat transfer deterioration can be significantly reduced, which significantly reduces the risk of thermal failure. Moreover, the total pressure drop of the optimized rudder shaft under the same coolant injection condition can be reduced by about 19% compared with the initial structure. This study provides a valuable contribution to the thermal protection performance for the rudder shaft, as a key component of aircraft under the aero heating process. Full article

(This article belongs to the Special Issue 5th Anniversary of Aerospace Science and Engineering Section—Recent Advances in Aerospace)

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22 pages, 1644 KiB

Open AccessArticle

Machine Learning Prediction of Airfoil Aerodynamic Performance Using Neural Network Ensembles

by Diana-Andreea Sterpu, Daniel Măriuța, Grigore Cican, Ciprian-Marius Larco and Lucian-Teodor Grigorie

Appl. Sci. 2025, 15(14), 7720; https://doi.org/10.3390/app15147720 - 9 Jul 2025

Viewed by 581

Abstract

Reliable aerodynamic performance estimation is essential for both preliminary design and optimization in various aeronautical applications. In this study, a hybrid deep learning model is proposed, combining convolutional neural networks (CNNs) and operating directly on raw airfoil geometry, with parallel branches of fully connected deep neural networks (DNNs) that process operational parameters and engineered features. The model is trained on an extensive database of NACA four-digit airfoils, covering angles of attack ranging from −5° to 14° and ten Reynolds numbers increasing in steps of 500,000 from 500,000 up to 5,000,000. As a novel contribution, this work investigates the impact of random seed initialization on model accuracy and reproducibility and introduces a seed-based ensemble strategy to enhance generalization. The best-performing single-seed model tested (seed 0) achieves a mean absolute percentage error (MAPE) of 1.1% with an

R^{2}

of 0.9998 for the lift coefficient prediction and 0.57% with an

R^{2}

of 0.9954 for the drag coefficient prediction. In comparison, the best ensemble model tested (seeds 610, 987, and 75025) achieves a lift coefficient MAPE of 1.43%, corresponding to

R^{2}

0.9999, and a drag coefficient MAPE of 1.19%, corresponding to

R^{2}

= 0.9968. All the tested seed dependencies in this paper (ten single seeds and five ensembles) demonstrate an overall

R^{2}

greater than 0.97, which reflects the model architecture’s strong foundation. The novelty of this study lies in the demonstration that the same machine learning model, trained on identical data and architecture, can exhibit up to 250% variation in prediction error solely due to differences in random seed selection. This finding highlights the often-overlooked impact of seed initialization on model performance and highlights the necessity of treating seed choice as an active design parameter in ML aerodynamic predictions. Full article

(This article belongs to the Special Issue 5th Anniversary of Aerospace Science and Engineering Section—Recent Advances in Aerospace)

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Review

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25 pages, 8133 KiB

Open AccessReview

Hydrogen-Powered Aviation: Insights from a Cross-Sectional Scientometric and Thematic Analysis of Patent Claims

by Raj Bridgelall

Appl. Sci. 2025, 15(10), 5555; https://doi.org/10.3390/app15105555 - 15 May 2025

Cited by 1 | Viewed by 1405

Abstract

Hydrogen-powered aviation is gaining momentum as a sustainable alternative to fossil-fueled flight, yet the field faces complex technological and operational challenges. To better understand commercial innovation pathways, this study analyzes the claims sections of 166 hydrogen aviation patents issued between 2018 and 2024. Unlike prior studies that focused on patent titles or abstracts, this approach reveals the protected technical content driving commercialization. The study classifies innovations into seven domains: fuel storage, fuel delivery, fuel management, turbine enhancement, fuel cell integration, hybrid propulsion, and safety enhancement. Thematic word clouds and term co-occurrence networks based on natural language processing techniques validate these classifications and highlight core technical themes. Scientometric analyses uncover rapid patent growth, rising international participation, and strong engagement from both established aerospace firms and young companies. The findings provide stakeholders with a structured view of the innovation landscape, helping to identify technological gaps, emerging trends, and areas for strategic investment and policymaking. This claims-based method offers a scalable framework to track progress in hydrogen aviation and is adaptable to other emerging technologies. Full article

(This article belongs to the Special Issue 5th Anniversary of Aerospace Science and Engineering Section—Recent Advances in Aerospace)

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