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Aerospace
Volume 11
Issue 6
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Aerospace, Volume 11, Issue 6 (June 2024) – 86 articles

Cover Story (view full-size image): UAVs need to traverse multiple essential target points in the shortest possible time in smart city and modern military scenarios. Such missions require UAVs to improve mission execution efficiency by optimizing trajectories. Multi-waypoint trajectory optimization is one of the key technologies for UAVs to perform this kind of complex task, and this paper discusses this technical field in depth, covering the core contents of trajectory planning and time optimization. By adopting the advanced Constrained Parameterized Differential Dynamic Programming (C-PDDP) algorithm, the UAV is able to optimize the flight time of each trajectory segment while meeting the path constraints, which significantly shortens the mission time and improves mission efficiency. View this paper
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13 pages, 4041 KiB  
Article
An Efficient Analysis Method of Aluminum Alloy Helicopter Fuselage Projectile Damage Based on Projectile Breakdown Theory
by Xiaobing Xing, Jianfeng Tan, Yuxiao Yang, Tian Yong, Linjun Yu and Yun Zhou
Aerospace 2024, 11(6), 500; https://doi.org/10.3390/aerospace11060500 - 20 Jun 2024
Viewed by 596
Abstract
The shape and structure of helicopter fuselage are complex, and it is difficult for a finite element method (FEM) to investigate helicopter fuselage projectile damage in a multi-projectile environment. An efficient analysis method for helicopter fuselage projectile damage is then proposed by coupling [...] Read more.
The shape and structure of helicopter fuselage are complex, and it is difficult for a finite element method (FEM) to investigate helicopter fuselage projectile damage in a multi-projectile environment. An efficient analysis method for helicopter fuselage projectile damage is then proposed by coupling the projectile breakdown theory, which is derived from the existing empirical formula for penetrating ductile metals, and the model of projectile impact. Then, the characteristics of the helicopter fuselage impact coordinate, residual velocity, and fuselage damage area under a multi-projectile attack are investigated. The results show that the predicted residual velocities agree with the experiments. The maximum errors of the residual velocity and the fuselage damage area are 4.7% and 9.56%, respectively. Compared with the FEM, the computational time of the proposed method is reduced by 92.1%, and its efficiency is obviously improved. As the incidence angle increases, the residual velocity decreases, and the damage area increases. When the incidence angle of the projectile body is large, the projectile body cannot penetrate the surface of the fuselage, resulting in flume skid damage and a large area of damage. Full article
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<p>Projectile model.</p> Full article ">Figure 2
<p>Experiment model.</p> Full article ">Figure 3
<p>Comparison of predicted residual velocity with experiment.</p> Full article ">Figure 4
<p>Fifty-projectile points in 3D space.</p> Full article ">Figure 5
<p>Strike-simulation.</p> Full article ">Figure 6
<p>Three-dimensional model of helicopter.</p> Full article ">Figure 7
<p>Helicopter fuselage grid.</p> Full article ">Figure 8
<p>Contrast curve of residual velocity and damage area.</p> Full article ">Figure 9
<p>Comparison of computation time.</p> Full article ">Figure 10
<p>Stress nephogram for six impact points.</p> Full article ">Figure 11
<p>Maximum stress and residual velocity at different angles.</p> Full article ">Figure 12
<p>Residual velocity with time.</p> Full article ">
32 pages, 32908 KiB  
Article
An Analytical Reentry Solution Based Online Time-Coordinated A* Path Planning Method for Hypersonic Gliding Vehicles Considering No-Fly-Zone Constraint
by Zihan Xie, Changzhu Wei, Naigang Cui and Yingzi Guan
Aerospace 2024, 11(6), 499; https://doi.org/10.3390/aerospace11060499 - 20 Jun 2024
Viewed by 680
Abstract
To meet the time-coordinated requirement of hypersonic gliding vehicles to reach a single target simultaneously in the presence of no-fly-zone constraints, this paper proposes a time-coordinated A* path planning method considering multiple constraints. The path planning method is designed based on an analytical [...] Read more.
To meet the time-coordinated requirement of hypersonic gliding vehicles to reach a single target simultaneously in the presence of no-fly-zone constraints, this paper proposes a time-coordinated A* path planning method considering multiple constraints. The path planning method is designed based on an analytical steady gliding path model and the framework of the A* algorithm. Firstly, an analytical steady gliding path model is designed based on a quadratic function-type altitude-velocity profile. It can derive the control commands explicitly according to the desired terminal altitude and velocity, thus establishing a mapping between the terminal states and the control commands. Secondly, the node extension method of the A* algorithm is improved based on the mapping. Taking the terminal states as new design variables, a feasible path-node set is produced by a one-step integration using the control commands derived according to different terminal states. This node extension method ensures the feasibility of the path nodes while satisfying terminal constraints. Next, the path evaluation function of the A* algorithm is modified by introducing a heuristic switching term to select the most proper node as a waypoint, aiming to minimize the arrival time deviation. Meanwhile, introducing the penalty items into the path evaluation function satisfies the no-fly-zone constraints, process constraints, and control variable constraints. Finally, an online time-coordinated method is proposed to determine a commonly desired arrival time for several hypersonic gliding vehicles. It eliminates the need to specify the arrival time in advance and improves the capability to deal with sudden threats, increasing the path planning method’s online application capability. The proposed method can achieve online time-coordinated multi-constraint path planning for several hypersonic gliding vehicles, whose effectiveness and superiority are verified by simulations. Full article
(This article belongs to the Special Issue Dynamics, Guidance and Control of Aerospace Vehicles)
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Figure 1
<p>Definition of the states.</p> Full article ">Figure 2
<p>Multi-HGV cooperative path planning scenario.</p> Full article ">Figure 3
<p>Schematic diagram of the <span class="html-italic">H</span>-<span class="html-italic">V</span> profile.</p> Full article ">Figure 4
<p>Schematic diagram of the path planning process.</p> Full article ">Figure 5
<p>A* algorithm path planning schematic.</p> Full article ">Figure 6
<p>Schematic of the time-coordinated A* path planning method.</p> Full article ">Figure 7
<p>Schematic of the node extension method.</p> Full article ">Figure 8
<p>Heading angle error corridor.</p> Full article ">Figure 9
<p>Dealing with no-fly zone constraints.</p> Full article ">Figure 10
<p>Arrival-time online coordination method.</p> Full article ">Figure 11
<p>Ground tracks in Case 1.</p> Full article ">Figure 12
<p>Ground tracks in Case 2.</p> Full article ">Figure 13
<p>Ground tracks in Case 3.</p> Full article ">Figure 14
<p>Altitudes in Case 3.</p> Full article ">Figure 15
<p>Velocities in Case 3.</p> Full article ">Figure 16
<p>Flight-path angles in Case 3.</p> Full article ">Figure 17
<p>Angles of attack in Case 3.</p> Full article ">Figure 18
<p>Bank angles in Case 3.</p> Full article ">Figure 19
<p>Heading angle errors in Case 3.</p> Full article ">Figure 20
<p>Dynamic pressures in Case 3.</p> Full article ">Figure 21
<p>Heating rates in Case 3.</p> Full article ">Figure 22
<p>Aerodynamic loads in Case 3.</p> Full article ">Figure 23
<p>Altitude-velocity profile in Case 3.</p> Full article ">Figure 24
<p>Time-to-go estimation in Case 3.</p> Full article ">Figure 25
<p>Ground tracks in Case 4.</p> Full article ">Figure 26
<p>Altitudes in Case 4.</p> Full article ">Figure 27
<p>Velocities in Case 4.</p> Full article ">Figure 28
<p>Flight-path angles in Case 4.</p> Full article ">Figure 29
<p>Angles of attack in Case 4.</p> Full article ">Figure 30
<p>Bank angles in Case 4.</p> Full article ">Figure 31
<p>Heading angle errors in Case 4.</p> Full article ">Figure 32
<p>Dynamic pressures in Case 4.</p> Full article ">Figure 33
<p>Heating rates in Case 4.</p> Full article ">Figure 34
<p>Aerodynamic loads in Case 4.</p> Full article ">Figure 35
<p>Ground tracks in Case 5.</p> Full article ">Figure 36
<p>Altitudes in Case 5.</p> Full article ">Figure 37
<p>Velocities in Case 5.</p> Full article ">Figure 38
<p>Heading angle errors in Case 5.</p> Full article ">Figure 39
<p>Angles of attack in Case 5.</p> Full article ">Figure 40
<p>Bank angles in Case 5.</p> Full article ">Figure 41
<p>Ground tracks in Case 6.</p> Full article ">Figure 42
<p>Altitudes in Case 6.</p> Full article ">Figure 43
<p>Velocities in Case 6.</p> Full article ">Figure 44
<p>Flight-path angles in Case 6.</p> Full article ">Figure 45
<p>Angles of attack in Case 6.</p> Full article ">Figure 46
<p>Bank angles in Case 6.</p> Full article ">Figure 47
<p>Heading angle errors in Case 6.</p> Full article ">Figure 48
<p>Dynamic pressures in Case 6.</p> Full article ">Figure 49
<p>Heating rates in Case 6.</p> Full article ">Figure 50
<p>Aerodynamic loads in Case 6.</p> Full article ">Figure 51
<p>Ground tracks.</p> Full article ">Figure 52
<p>Terminal altitude.</p> Full article ">Figure 53
<p>Terminal velocity.</p> Full article ">Figure 54
<p>Arrival time error.</p> Full article ">
18 pages, 2320 KiB  
Article
Comprehensive Measurement of Position and Velocity in the Transverse Direction Using the Crab Pulsar
by Yuan Feng, Huanzi Zhang, Jianfeng Chen, Jin Liu and Xin Ma
Aerospace 2024, 11(6), 498; https://doi.org/10.3390/aerospace11060498 - 20 Jun 2024
Viewed by 627
Abstract
Traditional X-ray pulsar ranging and velocity measurement methods only estimate the radial position and velocity information of the pulsar. For non-linear orbits, errors in the transverse position and velocity of the pulsar lead to errors in the radial velocity of the pulsar, leading [...] Read more.
Traditional X-ray pulsar ranging and velocity measurement methods only estimate the radial position and velocity information of the pulsar. For non-linear orbits, errors in the transverse position and velocity of the pulsar lead to errors in the radial velocity of the pulsar, leading to distortion of the X-ray pulsar profile. Based on this, we propose using the distortion of the pulsar profile to infer the transverse position and velocity information of the pulsar. First, a model of the distortion of the pulsar profile due to errors in the transverse position and velocity is established, and the observable directions of the transverse position and velocity are given separately. Then, considering that the distortions in the pulsar profile caused by errors in the transverse position and velocity are indistinguishable, we establish a reactive motion state measure related to the observable directions for the transverse position and velocity errors as a new observable measure in X-ray pulsar navigation. The experimental results show that the precision of the reactive motion state measure reaches 0.57, equivalent to a position error of 284.50 m or a velocity error of 0.57 m/s. Full article
(This article belongs to the Special Issue Space Navigation and Control Technologies)
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<p>A schematic diagram of the reactive direction in the transverse plane.</p> Full article ">Figure 2
<p>Indistinguishability of the offsets in position and velocity.</p> Full article ">Figure 3
<p>The standard deviation graph for different initial velocity errors and initial position errors (normalized).</p> Full article ">Figure 4
<p>Chart of the comprehensive measurement of position and velocity.</p> Full article ">Figure 5
<p>The orbital recursive error of combined errors under different ratios of position and velocity.</p> Full article ">Figure 6
<p>The orbital recursive error for various reactive state measures.</p> Full article ">Figure 7
<p>Position and velocity insensitivity direction errors.</p> Full article ">Figure 8
<p>The precision of comprehensive measurement for different semi-major axes.</p> Full article ">
29 pages, 2080 KiB  
Article
Summary of Lunar Constellation Navigation and Orbit Determination Technology
by Xiao Zhang, Zhaowei Sun, Xiao Chen, Linxin Pan and Yubin Zhong
Aerospace 2024, 11(6), 497; https://doi.org/10.3390/aerospace11060497 - 20 Jun 2024
Viewed by 962
Abstract
The Moon is the closest celestial body to the Earth. Its rich unique resources are an important supplement to the Earth’s resources and have a profound impact on the sustainable development of human society. As large-scale exploration missions gradually progress, demands for communication, [...] Read more.
The Moon is the closest celestial body to the Earth. Its rich unique resources are an important supplement to the Earth’s resources and have a profound impact on the sustainable development of human society. As large-scale exploration missions gradually progress, demands for communication, navigation, surveying and other services of lunar-space probes have significantly increased. Constellation navigation and orbit determination technology will become an indispensable part of future lunar exploration infrastructure. This article systematically analyzes the current status of lunar relay navigation satellite networks at home and abroad, summarizes the technical principles of single-satellite and constellation navigation and orbit determination, discusses the technical difficulties in lunar navigation constellation orbit determination and navigation, and analyzes possible solutions. Finally, the development trend of research on high-precision orbit determination and navigation methods for lunar navigation constellations is proposed. Full article
(This article belongs to the Special Issue Space Navigation and Control Technologies)
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<p>Schematic diagram of radio interferometry.</p> Full article ">Figure 2
<p>Schematic diagram of angle measurement navigation.</p> Full article ">Figure 3
<p>Schematic diagram of ranging navigation.</p> Full article ">Figure 4
<p>Schematic diagram of velocity measurement navigation.</p> Full article ">Figure 5
<p>Schematic diagram of laser ranging.</p> Full article ">Figure 6
<p>Basic principles of spaceborne GNSS orbit determination.</p> Full article ">
18 pages, 5724 KiB  
Article
Pixel-Wise and Class-Wise Semantic Cues for Few-Shot Segmentation in Astronaut Working Scenes
by Qingwei Sun, Jiangang Chao, Wanhong Lin, Dongyang Wang, Wei Chen, Zhenying Xu and Shaoli Xie
Aerospace 2024, 11(6), 496; https://doi.org/10.3390/aerospace11060496 - 20 Jun 2024
Viewed by 603
Abstract
Few-shot segmentation (FSS) is a cutting-edge technology that can meet requirements using a small workload. With the development of China Aerospace Engineering, FSS plays a fundamental role in astronaut working scene (AWS) intelligent parsing. Although mainstream FSS methods have made considerable breakthroughs in [...] Read more.
Few-shot segmentation (FSS) is a cutting-edge technology that can meet requirements using a small workload. With the development of China Aerospace Engineering, FSS plays a fundamental role in astronaut working scene (AWS) intelligent parsing. Although mainstream FSS methods have made considerable breakthroughs in natural data, they are not suitable for AWSs. AWSs are characterized by a similar foreground (FG) and background (BG), indistinguishable categories, and the strong influence of light, all of which place higher demands on FSS methods. We design a pixel-wise and class-wise network (PCNet) to match support and query features using pixel-wise and class-wise semantic cues. Specifically, PCNet extracts pixel-wise semantic information at each layer of the backbone using novel cross-attention. Dense prototypes are further utilized to extract class-wise semantic cues as a supplement. In addition, the deep prototype is distilled in reverse to the shallow layer to improve its quality. Furthermore, we customize a dataset for AWSs and conduct abundant experiments. The results indicate that PCNet outperforms the published best method by 4.34% and 5.15% in accuracy under one-shot and five-shot settings, respectively. Moreover, PCNet compares favorably with the traditional semantic segmentation model under the 13-shot setting. Full article
(This article belongs to the Section Astronautics & Space Science)
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<p>The overall architecture of the proposed PCNet, including the shared backbone, PCC block, fusion block, RPD module, and a simple decoder. Different colors in the backbone indicate different blocks. Dashed arcs indicate inter-layer RPDs (short arcs) or inter-block RPDs (long arcs). <math display="inline"><semantics> <mrow> <mo>⨁</mo> </mrow> </semantics></math> is the pixel-wise addition.</p> Full article ">Figure 2
<p>False segmentation due to the similarity of FG-BG. The <span style="color:#00B0F0">blue</span> line indicates that the query BG is correlated with the support FG and is the cause of incorrect segmentation. The <span style="color:red">red</span> line indicates the correct correlation.</p> Full article ">Figure 3
<p>Cross-attention in our method. The <span style="color:#00B0F0">blue</span> line indicates the false correlation. The <span style="color:red">red</span> line indicates correct correlations. The thicker the line, the stronger the correlation. Correlations calculated between support and query features are transferred to the support mask to predict the query mask.</p> Full article ">Figure 4
<p>PCC block modified from the 5th block of ResNet50. Darker colors indicate deeper layers. MAP denotes the masked average pooling. <math display="inline"><semantics> <mrow> <mo>⨂</mo> </mrow> </semantics></math> means the Hadamard product. M is the support mask. P is the prototype. RPD stands for reverse prototype distill. Conv includes convolution, group normalization, and ReLu.</p> Full article ">Figure 5
<p>The calculation process of 1-shot and 5-shot settings. (<b>a</b>) The 1-shot setting. (<b>b</b>) The 5-shot setting.</p> Full article ">Figure 6
<p>Characteristics of AWSs. (<b>a</b>) Samples of the dataset. (<b>b</b>) Labels corresponding to the samples. (<b>c</b>) Some predictions using the method from HDMNet [<a href="#B12-aerospace-11-00496" class="html-bibr">12</a>].</p> Full article ">Figure 7
<p>Comparison of qualitative results between HDMNet and PCNet.</p> Full article ">Figure 8
<p>Comparison of qualitative results with PCNet under different settings. (<b>a</b>) Results under the 1-shot setting. (<b>b</b>) Results under the 5-shot setting.</p> Full article ">Figure 8 Cont.
<p>Comparison of qualitative results with PCNet under different settings. (<b>a</b>) Results under the 1-shot setting. (<b>b</b>) Results under the 5-shot setting.</p> Full article ">Figure 9
<p>Ablation studies of different forms of cross-attention.</p> Full article ">Figure 10
<p>Average mIoU over four splits under different settings. The red dotted line is the result of PSPNet. The blue line shows the results for PCNet with a different amount of support data.</p> Full article ">
9 pages, 188 KiB  
Article
Air Traffic Controllers’ Rostering: Sleep Quality, Vigilance, Mental Workload, and Boredom: A Report of Two Case Studies
by Michela Terenzi, Giorgia Tempestini and Francesco Di Nocera
Aerospace 2024, 11(6), 495; https://doi.org/10.3390/aerospace11060495 - 20 Jun 2024
Viewed by 772
Abstract
Fatigue in air traffic management (ATM) is a well-recognized safety concern. International organizations like ICAO and EASA have responded by advocating for fatigue risk management systems (FRMSs). EU Regulation 2017/373, implemented in January 2020, mandates specific requirements for air traffic service providers (ANSPs) [...] Read more.
Fatigue in air traffic management (ATM) is a well-recognized safety concern. International organizations like ICAO and EASA have responded by advocating for fatigue risk management systems (FRMSs). EU Regulation 2017/373, implemented in January 2020, mandates specific requirements for air traffic service providers (ANSPs) regarding controller fatigue, stress, and rostering practices. These regulations are part of broader safety management protocols. Despite ongoing efforts to raise awareness about fatigue in ATC, standardized operational requirements remain elusive. To address this gap, Eurocontrol recently published “Guidelines on fatigue management in ATC rostering systems” (23 April 2024). This initiative aims to facilitate the adoption of common fatigue management standards across operations. However, neither EU Regulation 2017/373 nor existing documentation provides definitive rostering criteria. ANSPs typically derive these criteria from a combination of scientific research, best practices, historical data, and legal and operational constraints. Assessing and monitoring fatigue in the real-world ATC setting is complex. The multifaceted nature of fatigue makes it difficult to study, as it is influenced by many factors including sleep quality, circadian rhythms, psychosocial stressors, individual differences, and environmental conditions. Long-term studies are often required to fully understand these complex interactions. This paper presents two case studies that attempt to create an evidence-based protocol for fatigue risk monitoring in ATC operations. These studies utilize a non-invasive approach and collect multidimensional data. The cases involved en-route and tower (TWR) controllers from different ATC centers. The results highlight the importance of fatigue assessment in ATC and shed light on the challenges of implementing fatigue monitoring systems within operational environments. Full article
(This article belongs to the Special Issue Human Factors during Flight Operations)
23 pages, 4538 KiB  
Review
The U.S. Air Force Next-Generation Air-Refueling System: A Resurgence of the Blended Wing Body?
by Guilherme Fernandes and Victor Maldonado
Aerospace 2024, 11(6), 494; https://doi.org/10.3390/aerospace11060494 - 20 Jun 2024
Cited by 1 | Viewed by 1254
Abstract
The interest in flying wings dates as far as the early years of the aviation age. Early investigations of the feasibility of the concept demonstrated increased aerodynamic efficiency and reduced fuel consumption. However, structural, engine integration, and stability and control issues prevented further [...] Read more.
The interest in flying wings dates as far as the early years of the aviation age. Early investigations of the feasibility of the concept demonstrated increased aerodynamic efficiency and reduced fuel consumption. However, structural, engine integration, and stability and control issues prevented further development. In the 1990s, a new concept, the blended wing body (BWB), was created to alleviate some of the concerns of flying wings while maintaining increased efficiency and adding further benefits, such as reduced pollutant and noise emissions. Despite the promise, technical hurdles once again proved to be a deal breaker and, as of 2024, the only successful flying wing is the B-2 Spirit, an extremely complex and expensive aircraft. Nowadays, with the world quickly transitioning towards cleaner energy, the interest in the BWB has been renewed. The latest technological advancements in the aerospace industry should make its development more plausible; however, passenger comfort issues remain. Surprisingly, the BWB development may come from an unexpected application, as a tanker aircraft. As the U.S. Air Force is seeking a replacement to hundreds of aging tankers, a startup company was recently funded to develop the concept and build a prototype. In this study, we explore the history of blended designs from its early days, highlighting its opportunities and challenges—and why the design is an intriguing fit for application as a tanker aircraft. Full article
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<p>Northrop N-1M “Jeep” flying wing (Courtesy of Smithsonian National Air and Space Museum. Available at <a href="https://airandspace.si.edu/collection-media/NASM-NASM2015-04014" target="_blank">https://airandspace.si.edu/collection-media/NASM-NASM2015-04014</a> (accessed on 25 May 2024).</p> Full article ">Figure 2
<p>The Ho 229 in flight (public domain).</p> Full article ">Figure 3
<p>B-21 Raider stealth bomber. Courtesy of the U.S. Air Force.</p> Full article ">Figure 4
<p>X-48C flight test model (Courtesy of NASA).</p> Full article ">Figure 5
<p>Artist impression of a blended wing body tanker aircraft (Courtesy of JetZero).</p> Full article ">Figure 6
<p>Different design lift distributions for a BWB design. Reprinted from [<a href="#B43-aerospace-11-00494" class="html-bibr">43</a>], with permission from Elsevier.</p> Full article ">Figure 7
<p>FEA analysis of the multi-bubble concept fuselage (MBF) loaded with cabin pressure and simulated aerodynamic loadings [<a href="#B48-aerospace-11-00494" class="html-bibr">48</a>].</p> Full article ">Figure 8
<p>Y-braced box fuselage for BWB vehicles [<a href="#B48-aerospace-11-00494" class="html-bibr">48</a>].</p> Full article ">Figure 9
<p>Control surface arrangement for early BWB design [<a href="#B17-aerospace-11-00494" class="html-bibr">17</a>].</p> Full article ">Figure 10
<p>Moment behavior of select blended wing body configurations. Reprinted from [<a href="#B30-aerospace-11-00494" class="html-bibr">30</a>], with permission from Elsevier.</p> Full article ">Figure 11
<p>Split drag rudders on B-2 Spirit. Courtesy of the U.S. Air Force.</p> Full article ">Figure 12
<p>Artist conception of the NGAS during mission. (Courtesy of JetZero).</p> Full article ">
26 pages, 7019 KiB  
Article
A Smart Wing Model: From Design to Testing in a Wind Tunnel with a Turbulence Generator
by Ioan Ursu, George Tecuceanu, Daniela Enciu, Adrian Toader, Ilinca Nastase, Minodor Arghir and Manuela Calcea
Aerospace 2024, 11(6), 493; https://doi.org/10.3390/aerospace11060493 - 19 Jun 2024
Cited by 1 | Viewed by 844
Abstract
The paper concerns the technology of the design, realization, and testing of a flexible smart wing in a wind tunnel equipped with a turbulence generator. The system of smart wing, described in detail, consists mainly of: a physical model of the wing with [...] Read more.
The paper concerns the technology of the design, realization, and testing of a flexible smart wing in a wind tunnel equipped with a turbulence generator. The system of smart wing, described in detail, consists mainly of: a physical model of the wing with an aileron; an electric servomotor of broadband with a connecting rod-crank mechanism for converting the rectilinear motion of the servoactuator into the aileron deflection; two transducers: an encoder for measuring the deflection of the control aileron and an accelerometer mounted on the wing to measure its bending and torsional vibrations; a procedure for determining the mathematical model of the wing by experimental identification; a turbulence generator in the wind tunnel; implemented ℋ∞ and LQG algorithms for active control of vibrations. The attenuation experimentally obtained for the aeroelastic vibrations of the wing, but also for those accentuated by the turbulence, reaches values of up to 50%. Full article
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<p><b>Up</b>: <b>left</b>: elastic smart wing with aileron and encapsulated electric actuator and accelerometer; middle: turbulence generator (TG) mounted in the wind tunnel and right: flutter test in WT; <b>down</b>: layout and TG dimensions; <b>right</b>: hybrid model for the study of turbulent flow: Stress-Blended Eddy Simulations [<a href="#B38-aerospace-11-00493" class="html-bibr">38</a>].</p> Full article ">Figure 2
<p><b>Up</b>: CATIA modal analysis; in the <b>middle</b>: laboratory modal tests (<b>left</b>: wing longeron; middle: instrumentation setup for modal tests; <b>right</b>: wing); <b>down</b>; spectral analysis of wing.</p> Full article ">Figure 3
<p>The connecting rod-crank mechanism (CR-CM). <span class="html-italic">Up</span>: details with the actuator installed; <span class="html-italic">middle left</span>: actuator-CR-CM assembly: rod 1; crank 2; actuator shaft 3; actuator 4; <span class="html-italic">middle right</span>: connecting rod assembly with crank rollers 1 (translational slider); contact point “K” between the driving roller and the driven rollers; <span class="html-italic">down left</span>: the mechanism of transforming linear motion into rotation; <span class="html-italic">down right</span> actuator assembly (CATIA).</p> Full article ">Figure 4
<p>Block diagram of the controlled smart wing; <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="normal">δ</mi> <mi>c</mi> </msub> </mrow> </semantics></math>—the servo actuator input signal generated by the implemented control; <math display="inline"><semantics> <mrow> <mi mathvariant="normal">δ</mi> </mrow> </semantics></math>—the deflection of the wing aileron; <math display="inline"><semantics> <mrow> <mi mathvariant="normal">C</mi> </mrow> </semantics></math>—implemented Control, <math display="inline"><semantics> <mrow> <mi>PD</mi> </mrow> </semantics></math>—Proportional-Derivative, <math display="inline"><semantics> <mrow> <mi>CS</mi> </mrow> </semantics></math>—Controlled System. <math display="inline"><semantics> <mrow> <msub> <mi>H</mi> <mrow> <mi mathvariant="normal">δ</mi> <mover accent="true"> <mi>u</mi> <mo stretchy="false">˜</mo> </mover> </mrow> </msub> <mo stretchy="false">(</mo> <mi>s</mi> <mo stretchy="false">)</mo> </mrow> </semantics></math> incorporates LCAM and CR-CM; see <a href="#aerospace-11-00493-f003" class="html-fig">Figure 3</a>.</p> Full article ">Figure 5
<p>Sketch for calculating the transfer matrix of CR-CM.</p> Full article ">Figure 6
<p>Algorithm for tuning the PD controller.</p> Full article ">Figure 7
<p>Aileron response in the frequency domain (<span class="html-italic">V</span> = 25 m/s); bandwidth of about 36.6 Hz.</p> Full article ">Figure 8
<p><b>Left</b> and <b>middle</b>: the evolution of the degree of turbulence mediated in the transversal planes, along the flow; <b>right</b>: the turbulence intensity curve as a function of the distance to the grid; the grid is positioned at the coordinate <math display="inline"><semantics> <mrow> <mi>x</mi> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math>.</p> Full article ">Figure 9
<p>Experimental and identified transfer function <math display="inline"><semantics> <mrow> <mi>u</mi> <mo>→</mo> <mi>y</mi> <mo>,</mo> </mrow> </semantics></math> see <a href="#aerospace-11-00493-f004" class="html-fig">Figure 4</a>.</p> Full article ">Figure 10
<p>The accuracy of transfer function estimation <math display="inline"><semantics> <mrow> <msub> <mi>H</mi> <mrow> <mi>y</mi> <msub> <mi mathvariant="normal">δ</mi> <mi>c</mi> </msub> <mo>;</mo> <mi mathvariant="bold">idt</mi> </mrow> </msub> <mrow> <mo>(</mo> <mrow> <mi mathvariant="normal">i</mi> <mi mathvariant="normal">ω</mi> </mrow> <mo>)</mo> </mrow> <mo>≅</mo> <msub> <mi>H</mi> <mrow> <mi>y</mi> <msub> <mi mathvariant="normal">δ</mi> <mi>c</mi> </msub> <mo>;</mo> <mi>exp</mi> </mrow> </msub> <mrow> <mo>(</mo> <mrow> <mi mathvariant="normal">i</mi> <mi mathvariant="normal">ω</mi> </mrow> <mo>)</mo> </mrow> </mrow> </semantics></math>.</p> Full article ">Figure 11
<p>Equipment connection in the experimental setup: (1) computer; (2) PXI-1082; (3) PXI-6225; (4) connector SC-68; (5) source TR9158; (6) amplifier LCAM 5/15 H2W; (7) linear actuator H2W Technology NCC05-18-060-2PBS with Winkel MOT 13 encoder; (8) accelerometer 4394-S capacitive; (9) load amplifier TYPE 2635; (10) wing model.</p> Full article ">Figure 12
<p>Block diagram of the augmented system, static weights. CS–controlled system.</p> Full article ">Figure 13
<p>Attenuation of about 18 dB at 5 Hz, <math display="inline"><semantics> <mrow> <msub> <mo>ℋ</mo> <mo>∞</mo> </msub> </mrow> </semantics></math>, <b>left</b> and LQG, <b>right</b>, <span class="html-italic">V</span> = 25 m/s.</p> Full article ">Figure 14
<p>Recording of 2 superimposed regimes (for visual comparison) of vibration at <span class="html-italic">V</span> = 25 m/s; <b>left:</b> <math display="inline"><semantics> <mi>y</mi> </semantics></math> displacement, without control versus with active control (AC), LQG, pure air; <b>middle</b>: similar, with turbulence, <math display="inline"><semantics> <mrow> <msub> <mo>ℋ</mo> <mo>∞</mo> </msub> </mrow> </semantics></math> “superstrong”; <b>right</b>: turbulent mode, <math display="inline"><semantics> <mrow> <msub> <mo>ℋ</mo> <mo>∞</mo> </msub> </mrow> </semantics></math> “superstrong” <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="normal">δ</mi> <mi>c</mi> </msub> <mo>:</mo> <mo>=</mo> <mi>u</mi> </mrow> </semantics></math> and aileron displacement <math display="inline"><semantics> <mi mathvariant="normal">δ</mi> </semantics></math>.</p> Full article ">Figure 15
<p><b>Left:</b> comparative graphs of some overloaded vibration regimes, UC and AC, followed by damping after extinguishing the excitation; <b>right</b>: zoom on the chart above.</p> Full article ">
22 pages, 1070 KiB  
Article
Analytic Solutions for Volume, Mass, Center of Gravity, and Inertia of Wing Segments and Rotors of Constant Density
by Benjamin C. Moulton and Douglas F. Hunsaker
Aerospace 2024, 11(6), 492; https://doi.org/10.3390/aerospace11060492 - 19 Jun 2024
Viewed by 859
Abstract
In the preliminary design of aircraft lifting surfaces, accurate mass and inertia properties can be difficult to obtain. Typically, such methods as computer-aided design or statistical processes are used to determine these properties. These methods require significant time and effort to implement. The [...] Read more.
In the preliminary design of aircraft lifting surfaces, accurate mass and inertia properties can be difficult to obtain. Typically, such methods as computer-aided design or statistical processes are used to determine these properties. These methods require significant time and effort to implement. The present paper presents an exact analytic method for calculating the volume, mass, center of gravity, and inertia properties of wing segments and rotors of constant density. The influence of taper, spanwise thickness distribution, airfoil geometry, and sweep are included. The utility of the method is presented, and the accuracy is evaluated with various test cases via percent difference with a corresponding computer-aided design model. These case studies demonstrate the present method to be accurate to within about 1% for typical wing geometries and within about 1.3% for typical propeller geometries. Full article
(This article belongs to the Section Aeronautics)
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Figure 1
<p>Wing-segment geometry definitions.</p> Full article ">Figure 2
<p>Rotor geometry definitions.</p> Full article ">Figure 3
<p>Comparison of volumes computed from each method.</p> Full article ">Figure 4
<p>Comparison of CG location computed from each method.</p> Full article ">Figure 5
<p>Comparison of moments of inertia about the CG computed from each method.</p> Full article ">Figure 6
<p>Comparison of products of inertia about the CG computed from each method.</p> Full article ">Figure 7
<p>Validation CAD models of the 5-bladed propeller.</p> Full article ">Figure 8
<p>Example application of the present method with various wing segment structural components.</p> Full article ">
28 pages, 21291 KiB  
Article
Electrostatic Signal Self-Adaptive Denoising Method Combined with CEEMDAN and Wavelet Threshold
by Yan Liu, Hongfu Zuo, Zhenzhen Liu, Yu Fu, James Jiusi Jia and Jaspreet S. Dhupia
Aerospace 2024, 11(6), 491; https://doi.org/10.3390/aerospace11060491 - 19 Jun 2024
Viewed by 825
Abstract
A novel low-pass filtering self-adaptive (LPFA) denoising method combining complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and a wavelet threshold (WT) strategy is proposed to solve the problem of the aero-engine gas-path electrostatic signal noise, which challenges the gas-path component condition [...] Read more.
A novel low-pass filtering self-adaptive (LPFA) denoising method combining complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and a wavelet threshold (WT) strategy is proposed to solve the problem of the aero-engine gas-path electrostatic signal noise, which challenges the gas-path component condition monitoring and feature extraction techniques. Firstly, the integration of CEEMDAN addresses modal aliasing and intermittent signal challenges, while the proposed low-pass filtering method autonomously selects valuable signal components. Additionally, the application of the WT in the unselected components enhances the extraction of useful information, presenting a unique and advanced approach to electrostatic signal denoising. Moreover, the proposed method is applied to simulated signals with different input signal-to-noise ratios and experimental fault electrostatic signals of a micro-turbojet engine. The comparison with several traditional approaches in a denoising test for the simulated signals and experimental signals reveals that the proposed method performs better in extracting the effective components of the signal and eliminating noise. Full article
(This article belongs to the Section Aeronautics)
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Figure 1
<p>The flow of the proposed noise reduction algorithm.</p> Full article ">Figure 2
<p>The simulated electrostatic signal.</p> Full article ">Figure 3
<p>The EMD results of the simulation signal <span class="html-italic">x</span>(<span class="html-italic">t</span>).</p> Full article ">Figure 4
<p>The CEEMDAN results of the simulation signal <span class="html-italic">x</span>(<span class="html-italic">t</span>).</p> Full article ">Figure 5
<p>The ACF of IMF components decomposed by EMD.</p> Full article ">Figure 6
<p>The ACF of IMFs components decomposed by CEEMDAN.</p> Full article ">Figure 7
<p>The selection of IMFs by the LPFA: (<b>a</b>) EMD; (<b>b</b>) CEEMDAN.</p> Full article ">Figure 8
<p>(<b>a</b>) Reconstructed signal by CEEMDAN + LPFA; (<b>b</b>) reconstructed signal by CEEMDAN + LPFA + WT; (<b>c</b>) reconstruction errors by CEEMDAN + LPFA and useful information extracted by WT.</p> Full article ">Figure 9
<p>The results of denoising performance with different parameters. (<b>a</b>–<b>d</b>) Box plots of <span class="html-italic">SNR<sub>out</sub></span>, <span class="html-italic">MSE</span>, <span class="html-italic">NCC</span>, and <span class="html-italic">Time</span> values in different wavelet functions. (<b>e</b>–<b>h</b>) Box plots of <span class="html-italic">SNR<sub>out</sub></span>, <span class="html-italic">MSE</span>, <span class="html-italic">NCC</span>, and <span class="html-italic">Time</span> values in different influence factors. (<b>i</b>–<b>l</b>) Box plots of <span class="html-italic">SNR<sub>out</sub></span>, <span class="html-italic">MSE</span>, <span class="html-italic">NCC</span>, and <span class="html-italic">Time</span> values in different realization numbers.</p> Full article ">Figure 10
<p>The normalized comparison of denoising performance. (<b>a</b>,<b>b</b>) <span class="html-italic">Z-Score</span> and <span class="html-italic">Score</span> values in different wavelet functions. (<b>c</b>,<b>d</b>) <span class="html-italic">Z-Score</span> and <span class="html-italic">Score</span> values in different influence factors. (<b>e</b>,<b>f</b>) <span class="html-italic">Z-Score</span> and <span class="html-italic">Score</span> values in different realization numbers.</p> Full article ">Figure 11
<p>Simulated denoising signal comparisons: (<b>a</b>) pure original signal; (<b>b</b>) the proposed method; (<b>c</b>) WT; (<b>d</b>) EMD + ACF; (<b>e</b>) CEEMDAN + ACF; (<b>f</b>) EMD + LPFA; (<b>g</b>) CEEMDAN + LPFA; (<b>h</b>) EMD + ACF + WT; (<b>i</b>) CEEMDAN + ACF + WT; (<b>j</b>) EMD + LPFA + WT.</p> Full article ">Figure 12
<p>Comparisons with different <span class="html-italic">SNR<sub>in</sub>.</span></p> Full article ">Figure 13
<p>The schematic of the experimental system.</p> Full article ">Figure 14
<p>The physical representation of the experimental setup.</p> Full article ">Figure 15
<p>Block diagram of the measurement system.</p> Full article ">Figure 16
<p>Fault electrostatic signals of micro-turbojet engine.</p> Full article ">Figure 17
<p>Fault signal decomposed by EMD.</p> Full article ">Figure 18
<p>The first 12 IMFs of the fault signal decomposed by CEEMDAN.</p> Full article ">Figure 19
<p>Comparison of the five denoising methods: (<b>a</b>) original signal; (<b>b</b>) WT; (<b>c</b>) EMD + LPFA; (<b>d</b>) CEEMDAN + LPFA; (<b>e</b>) CEEMDAN + ACF + WT; (<b>f</b>) the proposed method (CEEMDAN + LPFA + WT).</p> Full article ">Figure 20
<p>Comparison of state-of-the-art methods: (<b>a</b>) EMD-IMF1-IMF2-IMF3; (<b>b</b>) EMD + Highest Energy Values; (<b>c</b>) VMD + Kurtosis + Permutation Entropy; (<b>d</b>) TVD; (<b>e</b>) EMD + ACF; (<b>f</b>) the proposed method (CEEMDAN + LPFA + WT).</p> Full article ">
19 pages, 1217 KiB  
Article
Optimal Guidance for Heliocentric Orbit Cranking with E-Sail-Propelled Spacecraft
by Alessandro A. Quarta
Aerospace 2024, 11(6), 490; https://doi.org/10.3390/aerospace11060490 - 19 Jun 2024
Cited by 1 | Viewed by 803
Abstract
In astrodynamics, orbit cranking is usually referred to as an interplanetary transfer strategy that exploits multiple gravity-assist maneuvers to change both the inclination and eccentricity of the spacecraft osculating orbit without changing the specific mechanical energy, that is, the semimajor axis. In the [...] Read more.
In astrodynamics, orbit cranking is usually referred to as an interplanetary transfer strategy that exploits multiple gravity-assist maneuvers to change both the inclination and eccentricity of the spacecraft osculating orbit without changing the specific mechanical energy, that is, the semimajor axis. In the context of a solar sail-based mission, however, the concept of orbit cranking is typically referred to as a suitable guidance law that is able to (optimally) change the orbital inclination of a circular orbit of an assigned radius in a general heliocentric three-dimensional scenario. In fact, varying the orbital inclination is a challenging maneuver from the point of view of the velocity change, so orbit cranking is an interesting mission application for a propellantless propulsion system. The aim of this paper is to analyze the performance of a spacecraft equipped with an Electric Solar Wind Sail in a cranking maneuver of a heliocentric circular orbit. The maneuver performance is calculated in an optimal framework considering spacecraft dynamics described by modified equinoctial orbital elements. In this context, the paper presents an analytical version of the three-dimensional optimal guidance laws obtained by using the classical Pontryagin’s maximum principle. The set of (analytical) optimal control laws is a new contribution to the Electric Solar Wind Sail-related literature. Full article
(This article belongs to the Special Issue Advances in CubeSat Sails and Tethers (2nd Edition))
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<p>The radial–transverse–normal (RTN) reference frame and E-sail cone (<math display="inline"><semantics> <mi>α</mi> </semantics></math>) and clock (<math display="inline"><semantics> <mi>δ</mi> </semantics></math>) angles. The red surface indicates the plane of the osculating orbit, while the green surface indicates the local vertical plane.</p> Full article ">Figure 2
<p>The minimum flight time <math display="inline"><semantics> <msub> <mi>t</mi> <mi>f</mi> </msub> </semantics></math>, in terms of multiples of the circular orbit period <math display="inline"><semantics> <msub> <mi>T</mi> <mn>0</mn> </msub> </semantics></math>, as a function of the change in orbital inclination <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi>i</mi> </mrow> </semantics></math> in an orbit cranking maneuver when the dimensionless (reference) propulsive acceleration magnitude is <math display="inline"><semantics> <mrow> <msub> <mover accent="true"> <mi>a</mi> <mo stretchy="false">˜</mo> </mover> <mn>0</mn> </msub> <mo>=</mo> <mn>0.1</mn> </mrow> </semantics></math>.</p> Full article ">Figure 3
<p>The final value of the spacecraft true longitude <math display="inline"><semantics> <msub> <mi>L</mi> <mi>f</mi> </msub> </semantics></math> as a function of the change in orbital inclination <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi>i</mi> </mrow> </semantics></math> in an orbit cranking maneuver when the dimensionless (reference) propulsive acceleration magnitude is <math display="inline"><semantics> <mrow> <msub> <mover accent="true"> <mi>a</mi> <mo stretchy="false">˜</mo> </mover> <mn>0</mn> </msub> <mo>=</mo> <mn>0.1</mn> </mrow> </semantics></math>.</p> Full article ">Figure 4
<p>The dimensionless value of the velocity change <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi>V</mi> </mrow> </semantics></math> as a function of <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi>i</mi> </mrow> </semantics></math> in circular orbit cranking obtained with a single impulsive maneuver.</p> Full article ">Figure 5
<p>The optimal transfer trajectory (black line) in a heliocentric ecliptic Cartesian reference frame when <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi>i</mi> <mo>=</mo> <mn>60</mn> <mspace width="0.166667em"/> <mi>deg</mi> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mover accent="true"> <mi>a</mi> <mo stretchy="false">˜</mo> </mover> <mn>0</mn> </msub> <mo>=</mo> <mn>0.1</mn> </mrow> </semantics></math>. Blue line → circular parking orbit; red line → circular final orbit; blue circle → start point; red square → arrival point; yellow circle → the Sun.</p> Full article ">Figure 6
<p>Time variation in the osculating orbit characteristics <math display="inline"><semantics> <mrow> <mo>{</mo> <mi>i</mi> <mo>,</mo> <mspace width="0.166667em"/> <mi>e</mi> <mo>}</mo> </mrow> </semantics></math> and the magnitude of the spacecraft position (<span class="html-italic">r</span>) and velocity (<span class="html-italic">v</span>) vector when <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi>i</mi> <mo>=</mo> <mn>60</mn> <mspace width="0.166667em"/> <mi>deg</mi> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mover accent="true"> <mi>a</mi> <mo stretchy="false">˜</mo> </mover> <mn>0</mn> </msub> <mo>=</mo> <mn>0.1</mn> </mrow> </semantics></math>. Blue circle → start point; red square → arrival point.</p> Full article ">Figure 7
<p>Time variation in the two thrust vector angles <math display="inline"><semantics> <mrow> <mo>{</mo> <mi>α</mi> <mo>,</mo> <mspace width="0.166667em"/> <mi>δ</mi> <mo>}</mo> </mrow> </semantics></math> and the on/off parameter <math display="inline"><semantics> <mi>τ</mi> </semantics></math> when <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi>i</mi> <mo>=</mo> <mn>60</mn> <mspace width="0.166667em"/> <mi>deg</mi> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mover accent="true"> <mi>a</mi> <mo stretchy="false">˜</mo> </mover> <mn>0</mn> </msub> <mo>=</mo> <mn>0.1</mn> </mrow> </semantics></math>. Blue circle → start point; red square → arrival point.</p> Full article ">Figure 8
<p>Time variation in the two thrust vector angles <math display="inline"><semantics> <mrow> <mo>{</mo> <mi>α</mi> <mo>,</mo> <mspace width="0.166667em"/> <mi>δ</mi> <mo>}</mo> </mrow> </semantics></math> and the on/off parameter <math display="inline"><semantics> <mi>τ</mi> </semantics></math> when the change in orbital inclination is equal to <math display="inline"><semantics> <mrow> <mn>10</mn> <mspace width="0.166667em"/> <mi>deg</mi> </mrow> </semantics></math> and the dimensionless performance term is <math display="inline"><semantics> <mrow> <msub> <mover accent="true"> <mi>a</mi> <mo stretchy="false">˜</mo> </mover> <mn>0</mn> </msub> <mo>=</mo> <mn>0.1</mn> </mrow> </semantics></math>. Blue circle → start point; red square → arrival point.</p> Full article ">Figure 9
<p>Time variation in the spacecraft osculating orbit characteristics when the change in orbital inclination is equal to <math display="inline"><semantics> <mrow> <mn>10</mn> <mspace width="0.166667em"/> <mi>deg</mi> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mover accent="true"> <mi>a</mi> <mo stretchy="false">˜</mo> </mover> <mn>0</mn> </msub> <mo>=</mo> <mn>0.1</mn> </mrow> </semantics></math>. Blue circle → start point; red square → arrival point.</p> Full article ">Figure 10
<p>Time variation in the spacecraft osculating orbit characteristics in a near-optimal “patched” orbit cranking with <math display="inline"><semantics> <mrow> <mo>Δ</mo> <msup> <mi>i</mi> <mrow> <mo>(</mo> <mi>p</mi> <mo>)</mo> </mrow> </msup> <mo>=</mo> <mn>10</mn> <mspace width="0.166667em"/> <mi>deg</mi> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi>i</mi> <mo>=</mo> <mn>60</mn> <mspace width="0.166667em"/> <mi>deg</mi> </mrow> </semantics></math>, and <math display="inline"><semantics> <mrow> <msub> <mover accent="true"> <mi>a</mi> <mo stretchy="false">˜</mo> </mover> <mn>0</mn> </msub> <mo>=</mo> <mn>0.1</mn> </mrow> </semantics></math>. Blue circle → start point; red square → arrival point.</p> Full article ">Figure A1
<p>Time variation in the osculating orbit characteristics when the change in orbital inclination is equal to <math display="inline"><semantics> <mrow> <mn>90</mn> <mspace width="0.166667em"/> <mi>deg</mi> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mover accent="true"> <mi>a</mi> <mo stretchy="false">˜</mo> </mover> <mn>0</mn> </msub> <mo>=</mo> <mn>0.4</mn> </mrow> </semantics></math>. Blue circle → start point; red square → arrival point.</p> Full article ">Figure A2
<p>The transfer trajectory (black line) when <math display="inline"><semantics> <mrow> <msub> <mover accent="true"> <mi>a</mi> <mo stretchy="false">˜</mo> </mover> <mn>0</mn> </msub> <mo>=</mo> <mn>0.4</mn> </mrow> </semantics></math> as a function of the required value of <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi>i</mi> </mrow> </semantics></math>. Blue line → circular parking orbit; red line → circular final orbit; blue circle → start point; red square → arrival point; yellow circle → the Sun.</p> Full article ">
23 pages, 7915 KiB  
Article
Aircraft Wake Evolution Prediction Based on Parallel Hybrid Neural Network Model
by Leilei Deng, Weijun Pan, Yuhao Wang, Tian Luan and Yuanfei Leng
Aerospace 2024, 11(6), 489; https://doi.org/10.3390/aerospace11060489 - 19 Jun 2024
Viewed by 927
Abstract
To overcome the time-consuming drawbacks of Computational Fluid Dynamics (CFD) numerical simulations, this paper proposes a hybrid model named PA-TLA (parallel architecture combining a TCN, LSTM, and an attention mechanism) based on the concept of intelligent aerodynamics and a parallel architecture. This model [...] Read more.
To overcome the time-consuming drawbacks of Computational Fluid Dynamics (CFD) numerical simulations, this paper proposes a hybrid model named PA-TLA (parallel architecture combining a TCN, LSTM, and an attention mechanism) based on the concept of intelligent aerodynamics and a parallel architecture. This model utilizes CFD data to drive efficient predictions of aircraft wake evolution at different initial altitudes during the approach phase. Initially, CFD simulations of continuous initial altitudes during the approach phase are used to generate aircraft wake evolution data, which are then validated against real-world LIDAR data to verify their reliability. The PA-TLA model is designed based on a parallel architecture, combining Long Short-Term Memory (LSTM) networks, Temporal Convolutional Networks (TCNs), and a tensor concatenation module based on the attention mechanism, which ensures computational efficiency while fully leveraging the advantages of each component in a parallel processing framework. The study results show that the PA-TLA model outperforms both the LSTM and TCN models in predicting the three characteristic parameters of aircraft wake: vorticity, circulation, and Q-criterion. Compared to the serially structured TCN-LSTM, PA-TLA achieves an average reduction in mean squared error (MSE) of 6.80%, in mean absolute error (MAE) of 7.70%, and in root mean square error (RMSE) of 4.47%, with an average increase in the coefficient of determination (R2) of 0.36% and a 35% improvement in prediction efficiency. Lastly, this study combines numerical simulations and the PA-TLA deep learning architecture to analyze the near-ground wake vortex evolution. The results indicate that the ground effect increases air resistance and turbulence as vortices approach the ground, thereby slowing the decay rate of the wake vortex strength at lower altitudes. The ground effect also accelerates the dissipation and movement of vortex centers, causing more pronounced changes in vortex spacing at lower altitudes. Additionally, the vortex center height at lower altitudes initially decreases and then increases, unlike the continuous decrease observed at higher altitudes. Full article
(This article belongs to the Section Aeronautics)
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<p>PA-TCN-LSTM-Attention model flowchart.</p> Full article ">Figure 2
<p>Schematic diagram of aircraft wake vortex formation.</p> Full article ">Figure 3
<p>A330-200 aircraft wing model.</p> Full article ">Figure 4
<p>Computational domain of A330-200.</p> Full article ">Figure 5
<p>Correlation coefficient heatmap.</p> Full article ">Figure 6
<p>PA-TLA model architecture diagram.</p> Full article ">Figure 7
<p>An expanded causal convolution with dilation factors d = 1, 2, 4 and filter size k = 3. (<math display="inline"><semantics> <mrow> <msub> <mrow> <mi>x</mi> </mrow> <mrow> <mi>T</mi> </mrow> </msub> </mrow> </semantics></math> is the input at time step <math display="inline"><semantics> <mrow> <mi mathvariant="normal">T</mi> </mrow> </semantics></math>, and <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>h</mi> </mrow> <mrow> <mi>r</mi> </mrow> </msub> </mrow> </semantics></math> is the output vector at time step <math display="inline"><semantics> <mrow> <mi mathvariant="normal">T</mi> </mrow> </semantics></math>).</p> Full article ">Figure 8
<p>The TCN residual block. (A 1 × 1 convolution is added when residual input and output have different dimensions.)</p> Full article ">Figure 9
<p>LSTM network structure diagram.</p> Full article ">Figure 10
<p>Aircraft wake vortex vorticity cloud diagram at decision height under calm wind conditions.</p> Full article ">Figure 11
<p>Comparison of CFD numerical simulation data and LIDAR wake detection data.</p> Full article ">Figure 12
<p>Comparison of the wake vortex core position of the A330-200 between CFD simulation results and LIDAR data.</p> Full article ">Figure 13
<p>The loss changes in PA-TLA on the training set and validation set with different epochs.</p> Full article ">Figure 14
<p>Comparison of the predictive performance of three models on aircraft wake vorticity ((<b>a</b>) for 50 m, (<b>b</b>) for 100 m, (<b>c</b>) for 150 m, (<b>d</b>) for 200 m, (<b>e</b>) for 250 m, (<b>f</b>) for 300 m).</p> Full article ">Figure 15
<p>Relative error in vorticity prediction for three models ((<b>a</b>) for 50 m, (<b>b</b>) for 100 m, (<b>c</b>) for 150 m, (<b>d</b>) for 200 m, (<b>e</b>) for 250 m, (<b>f</b>) for 300 m).</p> Full article ">Figure 16
<p>Evolution curve of aircraft wake circulation at different heights.</p> Full article ">Figure 17
<p>Evolution of vortex core position of aircraft wake at different heights ((<b>a</b>) represents the three-dimensional variation curve of the vortex center of the aircraft wake, (<b>b</b>) represents the evolution of the longitudinal position of the vortex center, and (<b>c</b>) represents the evolution of the distance between the two vortex centers).</p> Full article ">
21 pages, 5940 KiB  
Article
Improved YOLOv5 Network for Aviation Plug Defect Detection
by Li Ji and Chaohang Huang
Aerospace 2024, 11(6), 488; https://doi.org/10.3390/aerospace11060488 - 19 Jun 2024
Viewed by 837
Abstract
Ensuring the integrity of aviation plug components is crucial for maintaining the safety and functionality of the aerospace industry. Traditional methods for detecting surface defects often show low detection probabilities, highlighting the need for more advanced automated detection systems. This paper enhances the [...] Read more.
Ensuring the integrity of aviation plug components is crucial for maintaining the safety and functionality of the aerospace industry. Traditional methods for detecting surface defects often show low detection probabilities, highlighting the need for more advanced automated detection systems. This paper enhances the YOLOv5 model by integrating the Generalized Efficient Layer Aggregation Network (GELAN), which optimizes feature aggregation and boosts model robustness, replacing the conventional Convolutional Block Attention Module (CBAM). The upgraded YOLOv5 architecture, incorporating GELAN, effectively aggregates multi-scale and multi-layer features, thus preserving essential information across the network’s depth. This capability is vital for maintaining high-fidelity feature representations, critical for detecting minute and complex defects. Additionally, the Focal EIOU loss function effectively tackles class imbalance and concentrates the model’s attention on difficult detection areas, thus significantly improving its sensitivity and overall accuracy in identifying defects. Replacing the traditional coupled head with a lightweight decoupled head improves the separation of localization and classification tasks, enhancing both accuracy and convergence speed. The lightweight decoupled head also reduces computational load without compromising detection efficiency. Experimental results demonstrate that the enhanced YOLOv5 architecture significantly improves detection probability, achieving a detection rate of 78.5%. This improvement occurs with only a minor increase in inference time per image, underscoring the efficiency of the proposed model. The optimized YOLOv5 model with GELAN proves highly effective, offering significant benefits for the precision and reliability required in aviation component inspections. Full article
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<p>Examples of aviation plug defects.</p> Full article ">Figure 2
<p>The example of image augmentation for defect detection. (<b>a</b>) Original image. (<b>b</b>) Image graying. (<b>c</b>) Image blurring. (<b>d</b>) Horizontal flipping. (<b>e</b>) HSV jittering. (<b>f</b>) Composite mosaic image.</p> Full article ">Figure 3
<p>The architecture of CBAM.</p> Full article ">Figure 4
<p>The architecture of GELAN: (<b>a</b>) CSPNet [<a href="#B46-aerospace-11-00488" class="html-bibr">46</a>], (<b>b</b>) ELAN [<a href="#B47-aerospace-11-00488" class="html-bibr">47</a>], and (<b>c</b>) GELAN.</p> Full article ">Figure 5
<p>Grad-CAM visualizations show that the heatmap color change from cool to warm represents increasing attention: (<b>a</b>) the original image of the defect; (<b>b</b>) the image processed by the CBAM module; (<b>c</b>) the image processed by GELAN.</p> Full article ">Figure 6
<p>Varying cases of overlap with identical IOU values.</p> Full article ">Figure 7
<p>Illustration of the coupled head and decoupled head.</p> Full article ">Figure 8
<p>The structure of improved YOLOv5 algorithm.</p> Full article ">Figure 9
<p>Statistics on bounding box counts within the training dataset.</p> Full article ">Figure 10
<p>P-R curve of the YOLOv5.</p> Full article ">Figure 11
<p>P-R curve of the enhanced algorithm.</p> Full article ">Figure 12
<p>Comparison of detection effect before (<b>left</b>) and after (<b>right</b>) network enhancement.</p> Full article ">Figure 13
<p>Visual outcomes of our method across varied lighting conditions: (<b>a</b>) low light; (<b>b</b>) high light.</p> Full article ">Figure 14
<p>YOLOv5 algorithm results.</p> Full article ">Figure 15
<p>Improved YOLOv5 algorithm results.</p> Full article ">
22 pages, 11923 KiB  
Article
Numerical Study on the Cooling Method of Phase Change Heat Exchange Unit with Layered Porous Media
by Ruo-Ji Zhang, Jing-Yang Zhang and Jing-Zhou Zhang
Aerospace 2024, 11(6), 487; https://doi.org/10.3390/aerospace11060487 - 19 Jun 2024
Viewed by 624
Abstract
The implementation of heat sinks in high-power pulse electronic devices within hypersonic aircraft cabins has been facilitated by the emergence of innovative phase change materials (PCMs) characterized by excellent thermal conductivity and high latent heat. In this study, a representative material, layered porous [...] Read more.
The implementation of heat sinks in high-power pulse electronic devices within hypersonic aircraft cabins has been facilitated by the emergence of innovative phase change materials (PCMs) characterized by excellent thermal conductivity and high latent heat. In this study, a representative material, layered porous media filled with paraffin wax, was utilized, and a three-dimensional numerical model based on the enthalpy-porosity approach was employed. A thermal response research was conducted on the Phase Change Heat Exchange Unit with Layered Porous Media (PCHEU-LPM) with different cooling methods. The results indicate that water cooling proved to be suitable for the PCHEU-LPM with a heat flux of 50,000 W/m2. Additionally, parametric studies were performed to determine the optimal cooling conditions, considering the inlet temperature and velocity of the cooling flow. The results revealed that the most suitable conditions were strongly influenced by the coolant inlet parameters, along with the position of the PCM interface. Finally, the identification of the parameter combination that minimizes temperature fluctuations was achieved through the Response Surface Analysis method (RSA). Subsequent verification through simulation further reinforced the reliability of the proposed optimal parameters. Full article
(This article belongs to the Section Aeronautics)
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<p>Thermodynamic Cycle Layout of MR2 Aircraft.</p> Full article ">Figure 2
<p>Schematic diagram of the heat transfer process with phase change. (<b>a</b>) Three-dimensional heat transfer process; (<b>b</b>) Two-dimensional cross-section diagram in the direction of heat transfer process.</p> Full article ">Figure 3
<p>Structured grid division for PCHEU-LPM and water channels.</p> Full article ">Figure 4
<p>Grid independence test results.</p> Full article ">Figure 5
<p>Experimental setup for the validation of PCHEU-LPM model effectiveness.</p> Full article ">Figure 6
<p>Photographs of components inside the test section.</p> Full article ">Figure 7
<p>Comparison between experimental temperature results and numerical results. (<b>a</b>) Constant heating mode; (<b>b</b>) Pulse heating mode.</p> Full article ">Figure 8
<p>Influence of different heat dissipation methods on the hot end temperature of PCHEU-LPM.</p> Full article ">Figure 9
<p>Temperature field distribution under different boundary condition combinations. (<b>a</b>) Constant heat flux with constant cold flow; (<b>b</b>) Constant heat flux with coolant flow; (<b>c</b>) Pulsed heat flux with coolant flow.</p> Full article ">Figure 10
<p>Velocity field distribution under different boundary condition combinations. (<b>a</b>) Constant heat flux with constant cold flow; (<b>b</b>) Constant heat flux with coolant flow; (<b>c</b>) Pulsed heat flux with coolant flow.</p> Full article ">Figure 11
<p>Response curves of the heating surface interface temperature <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>T</mi> </mrow> <mrow> <mi mathvariant="normal">h</mi> </mrow> </msub> </mrow> </semantics></math> for different incoming flow temperatures.</p> Full article ">Figure 12
<p>Maximum temperature values <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>T</mi> </mrow> <mrow> <mi mathvariant="normal">h</mi> <mo>,</mo> <mi mathvariant="normal">m</mi> <mi mathvariant="normal">a</mi> <mi mathvariant="normal">x</mi> </mrow> </msub> </mrow> </semantics></math> and temperature fluctuations <math display="inline"><semantics> <mrow> <msub> <mrow> <mo>∆</mo> <mi>T</mi> </mrow> <mrow> <mi mathvariant="normal">h</mi> </mrow> </msub> </mrow> </semantics></math> at the interface for different incoming flow temperatures.</p> Full article ">Figure 13
<p>Maximum gradient of interface temperature <math display="inline"><semantics> <mrow> <msub> <mrow> <mo>∇</mo> <mi>T</mi> </mrow> <mrow> <mi>h</mi> <mo>,</mo> <mi>m</mi> <mi>a</mi> <mi>x</mi> </mrow> </msub> </mrow> </semantics></math> with different <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>T</mi> </mrow> <mrow> <mi>f</mi> </mrow> </msub> </mrow> </semantics></math>.</p> Full article ">Figure 14
<p>Velocity profiles and phase field contours at 450 s of the pulse cycle, considering various incoming flow temperatures.</p> Full article ">Figure 15
<p>The variation of non-dimensional phase interface position with different <span class="html-italic">T<sub>f</sub></span>.</p> Full article ">Figure 16
<p>Response curves of the heating surface interface temperature <span class="html-italic">T</span><sub>h</sub> for different incoming flow velocities.</p> Full article ">Figure 17
<p>Maximum temperature values <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>T</mi> </mrow> <mrow> <mi mathvariant="normal">h</mi> <mo>,</mo> <mi mathvariant="normal">m</mi> <mi mathvariant="normal">a</mi> <mi mathvariant="normal">x</mi> </mrow> </msub> </mrow> </semantics></math> with different <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mi>f</mi> </mrow> </msub> </mrow> </semantics></math> <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>T</mi> </mrow> <mrow> <mi mathvariant="normal">h</mi> </mrow> </msub> </mrow> </semantics></math> and temperature fluctuations <math display="inline"><semantics> <mrow> <msub> <mrow> <mo>∆</mo> <mi>T</mi> </mrow> <mrow> <mi mathvariant="normal">h</mi> </mrow> </msub> </mrow> </semantics></math> at the interface for different incoming flow velocities.</p> Full article ">Figure 18
<p>Maximum gradient of interface temperature <math display="inline"><semantics> <mrow> <msub> <mrow> <mo>∇</mo> <mi>T</mi> </mrow> <mrow> <mi>h</mi> <mo>,</mo> <mi>m</mi> <mi>a</mi> <mi>x</mi> </mrow> </msub> </mrow> </semantics></math> with different <span class="html-italic">U<sub>f</sub></span>.</p> Full article ">Figure 19
<p>Velocity profiles and phase field contours at 450 s of the pulse cycle, considering various incoming flow velocities.</p> Full article ">Figure 20
<p>The variation of non-dimensional phase interface position with different <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mi>f</mi> </mrow> </msub> </mrow> </semantics></math>.</p> Full article ">Figure 21
<p>Parameter sensitivity analysis chart.</p> Full article ">Figure 22
<p>Response Surface Analysis results.</p> Full article ">Figure 23
<p>Comparison of interface temperature results between optimizing parameters with benchmark parameters.</p> Full article ">
15 pages, 1059 KiB  
Article
Coaxial Helicopter Attitude Control System Design by Advanced Model Predictive Control under Disturbance
by Zhi Chen, Xiangyu Lin and Wanyue Jiang
Aerospace 2024, 11(6), 486; https://doi.org/10.3390/aerospace11060486 - 19 Jun 2024
Viewed by 820
Abstract
This paper proposes an advanced model predictive control (MPC) scheme for the attitude tracking of coaxial drones under wind disturbances. Unlike most existing MPC setups, this scheme embeds steady-input, steady-output, and steady-state conditions into the optimization problem as decision variables. Consequently, the coaxial [...] Read more.
This paper proposes an advanced model predictive control (MPC) scheme for the attitude tracking of coaxial drones under wind disturbances. Unlike most existing MPC setups, this scheme embeds steady-input, steady-output, and steady-state conditions into the optimization problem as decision variables. Consequently, the coaxial drone’s attitude can slide along the state manifold composed of a series of steady states. This allows it to move toward the optimal reachable equilibrium. To address disturbances that are difficult to accurately measure, an extended state observer is employed to estimate the disturbances in the prediction model. This design ensures that the algorithm maintains recursive stability even in the presence of disturbances. Finally, numerical simulations and flight tests are provided to confirm the effectiveness of the proposed method through comparison with other control algorithms. Full article
(This article belongs to the Section Aeronautics)
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<p>(<b>a</b>) Coaxial drone; (<b>b</b>) diagram of coordinate systems on coaxial drone.</p> Full article ">Figure 2
<p>A schematic of the proposed attitude control system.</p> Full article ">Figure 3
<p>The roll channel variable under different controllers: (<b>a</b>) the roll angle; (<b>b</b>) the tracking error of the roll; (<b>c</b>) the attitude velocity of the roll channel; (<b>d</b>) the input of the roll channel.</p> Full article ">Figure 4
<p>The pitch channel variable under different controllers: (<b>a</b>) the pitch angle; (<b>b</b>) the tracking error of the pitch; (<b>c</b>) the attitude velocity of the pitch channel; (<b>d</b>) the input of the pitch channel.</p> Full article ">Figure 5
<p>The yaw channel variable under different controllers: (<b>a</b>) the yaw angle; (<b>b</b>) the tracking error of the yaw; (<b>c</b>) the attitude velocity of the yaw channel; (<b>d</b>) the input of the yaw channel.</p> Full article ">Figure 6
<p>The attitude angle tracking curve of the UAV after changing different initial states: (<b>a</b>) roll; (<b>b</b>) pitch; (<b>c</b>) yaw.</p> Full article ">Figure 7
<p>The velocity curve of the UAV after changing different initial states: (<b>a</b>) p; (<b>b</b>) q; (<b>c</b>) r.</p> Full article ">Figure 8
<p>The input curve of the UAV after changing different initial states: (<b>a</b>) Ux; (<b>b</b>) Uy; (<b>c</b>) Uz.</p> Full article ">Figure 9
<p>The actual state change of the drone roll angle channel: (<b>a</b>) the roll; (<b>b</b>) the rate for the roll channel; (<b>c</b>) the PWM output values for the roll channel. Green dotted lines are the actual angle, rate and pwm output. The blue dotted lines are the expectations.</p> Full article ">
16 pages, 5387 KiB  
Article
Investigation on Accelerated Initiation of Oblique Detonation Wave Induced by Laser-Heating Hot-Spot
by Yirong Xin, Jiahao Shang, Gaoxiang Xiang and Qiu Wang
Aerospace 2024, 11(6), 485; https://doi.org/10.3390/aerospace11060485 - 19 Jun 2024
Cited by 1 | Viewed by 956
Abstract
A reliable initiation of oblique detonation is critical in oblique detonation engines, especially for oblique detonation engines under extreme conditions such as a high altitude and low Mach number, which may lead to excessive length of the induction zone and even the phenomenon [...] Read more.
A reliable initiation of oblique detonation is critical in oblique detonation engines, especially for oblique detonation engines under extreme conditions such as a high altitude and low Mach number, which may lead to excessive length of the induction zone and even the phenomenon of extinction. In this paper, surface ignition was applied to the initiation of oblique detonation, and a high-temperature region was set on the wedge to simulate the presence of a hot-spot induced by the laser heating. The two-dimensional multi-component Navier–Stokes equations considering a detailed H2 combustion mechanism are solved, and the oblique detonation wave accelerated by a hot-spot is studied. In this paper, hot-spots in the induction zone on the wedge, are introduced to explore the possibility of hot-spot initiation, providing a potential method for initiation control. Results show that these methods can effectively promote the accelerated initiation of the oblique detonation. Furthermore, the hot-spot temperature, size and position are varied to analyze their effects on the initiation position. Increasing the temperature and size of the hot-spot both can accelerate initiation, but from the perspective of energy consumption, a small hot-spot at a high temperature is preferable for accelerating ODW initiation than a large hot-spot at a low temperature. The initiated position of the oblique detonation is sensitive to the position of the hot-spots; if a 2000 K hotspot is at the beginning of the wedge, then the ODW’s initiation distance will be reduced to about 30% of that without hotspot acceleration. Full article
(This article belongs to the Special Issue Advances in Detonative Propulsion)
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<p>The (<b>a</b>) ODE schematic and (<b>b</b>) computation domain.</p> Full article ">Figure 2
<p>Schematic of the laser heating material.</p> Full article ">Figure 3
<p>Temperature distribution of the tungsten surface after 1 s irradiation by laser with Q<sub>0</sub> = 19,000 W/cm<sup>2</sup>.</p> Full article ">Figure 4
<p>(<b>a</b>) Pressure contours of ODW and (<b>b</b>) pressure distributed along the line of <span class="html-italic">y</span> = 0 mm and <span class="html-italic">y</span> = 5 mm with three scales for <span class="html-italic">T</span><sub>0</sub> = 300 K, <span class="html-italic">P</span><sub>0</sub> = 101,325.0 Pa, and <span class="html-italic">M</span><sub>0</sub> = 7.</p> Full article ">Figure 5
<p>(<b>a</b>) Temperature fields of ODWs and (<b>b</b>) mass fraction (H<sub>2</sub>) and temperature along the line of <span class="html-italic">y</span> = 0.05 mm without and with a hot-spot for <span class="html-italic">M</span><sub>0</sub> = 7.0, <span class="html-italic">T</span> = 2000 K, <span class="html-italic">δ</span> = 2 mm, <span class="html-italic">x</span><sub>hot-spot</sub> = 2.06 mm.</p> Full article ">Figure 6
<p>The position of the oblique shock-detonation transition <span class="html-italic">L</span><sub>I</sub> as a function of inflow Mach number with and without a hot-spot.</p> Full article ">Figure 7
<p>Temperature fields for a hot-spot-controlled ODW for <span class="html-italic">M</span><sub>0</sub> = 7.0, <span class="html-italic">δ</span> = 2 mm, <span class="html-italic">x</span><sub>hot-spot</sub> = 5.15 mm and <span class="html-italic">T</span> = 2000 K (<b>upper</b>), <span class="html-italic">T</span> = 1500 K (<b>lower</b>).</p> Full article ">Figure 8
<p>Temperature and mass fraction of H<sub>2</sub> along the line of <span class="html-italic">y</span> = 0.05 mm in the ODW without and with a hot-spot for <span class="html-italic">M</span><sub>0</sub> = 7.0, <span class="html-italic">T</span> = 2000 K, <span class="html-italic">T</span> = 1500 K, <span class="html-italic">δ</span> = 2 mm, and <span class="html-italic">x</span><sub>hot-spot</sub> = 5.15 mm.</p> Full article ">Figure 9
<p>The characteristic length of the induction zone as a function of hot-spot temperature (<span class="html-italic">M</span><sub>0</sub> = 7.0, <span class="html-italic">δ</span> = 2 mm and <span class="html-italic">x</span><sub>hot-spot</sub> = 5.15 mm).</p> Full article ">Figure 10
<p>Temperature fields for a hot-spot-controlled ODW in two sizes of hot-spot (<span class="html-italic">δ</span> = 1 mm and <span class="html-italic">δ</span> = 10 mm) for <span class="html-italic">M</span><sub>0</sub> = 7.0, <span class="html-italic">x</span><sub>hot-spot</sub> = 5.15 mm, <span class="html-italic">T</span> = 1500 K.</p> Full article ">Figure 11
<p>The characteristic length of the induction zone as a function of hot-spot size at three Mach numbers, <span class="html-italic">x</span><sub>hot-spot</sub> = 5.15 mm, <span class="html-italic">T</span> = 1500 K.</p> Full article ">Figure 12
<p>The temperature along the line of <span class="html-italic">y</span> = 0.05 mm in the ODW without and with a hot-spot at different <span class="html-italic">δ</span>, <span class="html-italic">M</span><sub>0</sub> = 7.0, <span class="html-italic">x</span><sub>hot-spot</sub> = 5.15 mm, <span class="html-italic">T</span> = 1500 K.</p> Full article ">Figure 13
<p>Temperature fields overlapped with pressure contours in the cases of <span class="html-italic">M</span><sub>0</sub> = 7.0, <span class="html-italic">T</span> = 2000 K, <span class="html-italic">δ</span> = 2 mm and without hot-spot (<b>a</b>), <span class="html-italic">x</span><sub>hot-spot</sub> = 10.30 mm (<b>b</b>), 5.15 mm (<b>c</b>), 0 mm (<b>d</b>).</p> Full article ">Figure 14
<p>The characteristic length of the induction zone as a function of the position of the hot-spot, <span class="html-italic">M</span><sub>0</sub> = 7.0, <span class="html-italic">T</span> = 2000 K.</p> Full article ">
21 pages, 8115 KiB  
Article
Evaluation of Sonic Boom Shock Wave Generation with CFD Methods
by Samuele Graziani, Francesco Petrosino, Jacob Jäschke, Antimo Glorioso, Roberta Fusaro and Nicole Viola
Aerospace 2024, 11(6), 484; https://doi.org/10.3390/aerospace11060484 - 19 Jun 2024
Viewed by 1294
Abstract
Over the past two decades, there has been a renewed interest in the development of a new generation of supersonic aircraft for civil purposes that could potentially succeed Concorde. However, the noise annoyance is still considered one of the hampering factors to meet [...] Read more.
Over the past two decades, there has been a renewed interest in the development of a new generation of supersonic aircraft for civil purposes that could potentially succeed Concorde. However, the noise annoyance is still considered one of the hampering factors to meet public consensus. This paper aims at revealing the potential of numerical simulations to predict sonic boom signature in Near Field at early design stages. In particular, the paper further demonstrates the applicability of the numerical approach proposed by NASA and other partners during the Sonic Boom Prediction Workshops held between 2014 and 2021, to compute the pressure signature of aircraft in the zone close to it. The results highlight the suitability of the approach (1) to capture the impact of aircraft flight condition variations on the sonic boom signature, (2) to enable the characterization of novel aircraft layout, including Mach 5 waverider configuration, (3) to provide near-field shock wave noise predictions that can be used to evaluate shock propagation, on-ground signature analyses, and annoyance assessment. Full article
(This article belongs to the Special Issue Sustainable Civil Supersonic and Hypersonic Aircraft: Main Challenges and Latest Developments)
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<p>Schematic view of the near-field and far-field domain for sonic boom simulation.</p> Full article ">Figure 2
<p>CAD geometries: CS1 (Mach 2) on the left and CS3 (Mach 5) on the right.</p> Full article ">Figure 3
<p>Hybrid grid approach: inner unstructured mesh (pink) and outer structured inclined mesh (blue).</p> Full article ">Figure 4
<p>CS1 reference aircraft, 3 views.</p> Full article ">Figure 5
<p>CS1 mission profile and angle of attack variation.</p> Full article ">Figure 6
<p>CS1 computational grid, overall grid domain (<b>left</b>), and particular cells around the aircraft (<b>right</b>).</p> Full article ">Figure 7
<p>CS1 mesh grid, comparison between aeroacoustic (<b>left</b>) and aerodynamic (<b>right</b>) mesh construction approaches.</p> Full article ">Figure 8
<p>CS3 reference aircraft.</p> Full article ">Figure 9
<p>CS3 mission profile and angle of attack variation.</p> Full article ">Figure 10
<p>CS3 computational domain (<b>left</b>) and detail of the mesh grid around the aircraft (<b>right</b>).</p> Full article ">Figure 11
<p>Azimuth angles for pressure extraction around an aircraft.</p> Full article ">Figure 12
<p>CS1 mach number field for mission point 1, Mach 1.5, AoA 4.0, altitude 17,500 m.</p> Full article ">Figure 13
<p>CS1 pressure signature at <math display="inline"><semantics> <mrow> <mi>H</mi> <mo>/</mo> <mi>L</mi> <mo>=</mo> <mn>3</mn> </mrow> </semantics></math> for mission point 1.</p> Full article ">Figure 14
<p>CS1 pressure signature in radial position around the aircraft at <math display="inline"><semantics> <mrow> <mi>H</mi> <mo>/</mo> <mi>L</mi> <mo>=</mo> <mn>3</mn> </mrow> </semantics></math> on the left, values in dB on the right, for mission point 1.</p> Full article ">Figure 15
<p>CS1 mach number field for mission point 2, Mach 1.5, AoA 4.5, altitude 15,000 m.</p> Full article ">Figure 16
<p>CS1 pressure signature at <math display="inline"><semantics> <mrow> <mi>H</mi> <mo>/</mo> <mi>L</mi> <mo>=</mo> <mn>3</mn> </mrow> </semantics></math> for mission point 2.</p> Full article ">Figure 17
<p>CS1 pressure signature in radial position around the aircraft at <math display="inline"><semantics> <mrow> <mi>H</mi> <mo>/</mo> <mi>L</mi> <mo>=</mo> <mn>3</mn> </mrow> </semantics></math> on the left, values in dB on the right, for mission point 2.</p> Full article ">Figure 18
<p>CS1 mach number field for mission point 3.</p> Full article ">Figure 19
<p>CS1 pressure signature at <math display="inline"><semantics> <mrow> <mi>H</mi> <mo>/</mo> <mi>L</mi> <mo>=</mo> <mn>3</mn> </mrow> </semantics></math> for mission point 3, Mach 2.0, AoA 3.5, altitude 18,500 m.</p> Full article ">Figure 20
<p>CS1 pressure signature in radial position around the aircraft at <math display="inline"><semantics> <mrow> <mi>H</mi> <mo>/</mo> <mi>L</mi> <mo>=</mo> <mn>3</mn> </mrow> </semantics></math> on the left, values in dB on the right, for mission point 3.</p> Full article ">Figure 21
<p>CS3 Mach number (<b>left</b>) and delta pressure (<b>right</b>) contours for mission point 6.</p> Full article ">Figure 22
<p>CS3 pressure distribution, radial evaluation at <math display="inline"><semantics> <mrow> <mi>H</mi> <mo>/</mo> <mi>L</mi> <mo>=</mo> <mn>3</mn> </mrow> </semantics></math>.</p> Full article ">Figure 23
<p>CS3 wave peak pressure levels, radial evaluation at <math display="inline"><semantics> <mrow> <mi>H</mi> <mo>/</mo> <mi>L</mi> <mo>=</mo> <mn>3</mn> </mrow> </semantics></math>.</p> Full article ">Figure 24
<p>CS3 sonic boom pressure signatures with Mach number sensitivity at <math display="inline"><semantics> <mrow> <mi>H</mi> <mo>/</mo> <mi>L</mi> <mo>=</mo> <mn>3</mn> </mrow> </semantics></math>.</p> Full article ">Figure 25
<p>CS3 sonic boom pressure signatures with different altitudes at <math display="inline"><semantics> <mrow> <mi>H</mi> <mo>/</mo> <mi>L</mi> <mo>=</mo> <mn>3</mn> </mrow> </semantics></math>.</p> Full article ">Figure 26
<p>CS3 sonic boom pressure signature at Mach 5, 28 km altitude, <math display="inline"><semantics> <mrow> <mi>H</mi> <mo>/</mo> <mi>L</mi> <mo>=</mo> <mn>5</mn> </mrow> </semantics></math>.</p> Full article ">Figure 27
<p>CS3 comparison of pressure signature using different numerical schemes and grid size.</p> Full article ">
15 pages, 58652 KiB  
Article
Research on the Heat Dissipation in Aviation-Integrated Communication Equipment Based on Graphene Films
by Jingyi Qian, Min Liu, Quan Zhao, Shimiao Luo, Feng Xia and Yunfeng Bai
Aerospace 2024, 11(6), 483; https://doi.org/10.3390/aerospace11060483 - 18 Jun 2024
Viewed by 862
Abstract
Aviation-integrated communication equipment is integral to modern aircraft to ensure its performance and safety. The heat dissipation problems of equipment have become increasingly prominent for the high electronic integration and system power consumption. To solve the above problem, the heat dissipation performance of [...] Read more.
Aviation-integrated communication equipment is integral to modern aircraft to ensure its performance and safety. The heat dissipation problems of equipment have become increasingly prominent for the high electronic integration and system power consumption. To solve the above problem, the heat dissipation performance of aviation-integrated communication equipment based on graphene films is deeply studied. This paper establishes a three-dimensional model of aviation-integrated communication equipment to simulate the distribution of temperature fields. The influence between aluminum alloy and graphene films on the surface of magnesium alloy on the heat dissipation performance of aviation-integrated communication equipment is studied. The simulation results show that the heat balance time of the equipment using graphene films on the surface of magnesium alloy is reduced from 3600 s to 800 s, representing an approximately 77.78% improvement; the measured equipment exhibited a reduction in its overall thermal equilibrium temperature, decreasing from 68.1 °C to 66.3 °C, representing an improvement of approximately 2.64%. Full article
(This article belongs to the Special Issue Design and Analysis of Advanced Composites and Structures in Aerospace)
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<p>Aviation-integrated communication equipment Composition Diagram.</p> Full article ">Figure 2
<p>Aviation-integrated communication equipment Mechanical Structure 2D Drawing.</p> Full article ">Figure 3
<p>Aviation-integrated communication equipment Mechanical Structure Explosion 3D Drawing.</p> Full article ">Figure 4
<p>Aviation-integrated communication equipment Heat Dissipation Path Drawing.</p> Full article ">Figure 5
<p>Aviation-integrated communication equipment thermal simulation model.</p> Full article ">
23 pages, 8853 KiB  
Article
Fluid–Structure Interactions between Oblique Shock Trains and Thin-Walled Structures in Isolators
by Xianzong Meng, Ruoshuai Zhao, Qiaochu Wang, Zebin Zhang and Junlei Wang
Aerospace 2024, 11(6), 482; https://doi.org/10.3390/aerospace11060482 - 18 Jun 2024
Viewed by 583
Abstract
Understanding aeroelastic issues related to isolators is pivotal for the structural design and flow control of scramjets. However, research on fluid–structure interactions (FSIs) between thin-walled structures and the isolator flow remains limited. This study delves into the FSIs between thin-walled panels and the [...] Read more.
Understanding aeroelastic issues related to isolators is pivotal for the structural design and flow control of scramjets. However, research on fluid–structure interactions (FSIs) between thin-walled structures and the isolator flow remains limited. This study delves into the FSIs between thin-walled panels and the isolator flow, as characterized by an oblique shock train, by quantitatively analyzing 11 flow parameters assessing the structural response, separation zones, shock structures, flow symmetry, and performance. The results reveal that an FSI triggers panel flutter under oblique shock train conditions, with the panel shapes exhibiting a combination of first- and second-mode responses, peaking at 0.75 of the panel length. Compared to rigid wall conditions, isolators with a flexible panel at the bottom wall experience downstream movement of the separation zones and shock structures, reduced flow symmetry, and minor changes in performance. Transient fluctuations occur due to the panel flutter. Two flexible panels at the top and bottom walls have a comparatively lesser influence on the averaged parameters but exhibit more violent transient fluctuations. Furthermore, the FSI effects under oblique shock train conditions are contrasted with those under normal shock train conditions. The flutter response under normal shock train conditions is more pronounced, with a larger amplitude and higher frequency, driven by the heightened participation of the first-mode response. The effects of FSIs under normal shock train conditions on the averaged parameters are the opposite (with a larger influence) to those under oblique shock train conditions, with significantly more drastic transient fluctuations. Overall, this study sheds light on the complex and substantial influence of FSIs on the isolator flow, emphasizing the necessity of considering FSIs in future isolator design and development endeavors. Full article
(This article belongs to the Special Issue Aeroelasticity, Volume IV)
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<p>Comparisons between experiments and simulations. (<b>a</b>) Flow field schlieren. (<b>b</b>) Wall pressure distribution on the bottom wall.</p> Full article ">Figure 2
<p>Panel flutter in supersonic flow. (<b>a</b>) Structural response at 0.75<span class="html-italic">l</span> (centerline) under λ = 800. (<b>b</b>) Vibration amplitude at 0.75<span class="html-italic">l</span> (centerline) under different <math display="inline"><semantics> <mi>λ</mi> </semantics></math>.</p> Full article ">Figure 3
<p>Schematic of the computational configuration.</p> Full article ">Figure 4
<p>Bottom wall pressure distributions of the different grids.</p> Full article ">Figure 5
<p>The computational grid.</p> Full article ">Figure 6
<p>The time-averaged flow structures of the oblique shock train under rigid wall conditions. (<b>a</b>) Mach contour. (<b>b</b>) Density gradient contour with streamlines.</p> Full article ">Figure 7
<p>Mach number contours of two aeroelastic cases. (<b>a</b>) Case 1: One thin-walled panel at the bottom wall. (<b>b</b>) Case 2: Two thin-walled panels at the top and the bottom wall.</p> Full article ">Figure 7 Cont.
<p>Mach number contours of two aeroelastic cases. (<b>a</b>) Case 1: One thin-walled panel at the bottom wall. (<b>b</b>) Case 2: Two thin-walled panels at the top and the bottom wall.</p> Full article ">Figure 8
<p>Dynamic responses of the structural and flow parameters for Case 1. (<b>a</b>) The transient response of structural displacement at different locations. (<b>b</b>) The transient response of the pressure recovery coefficient.</p> Full article ">Figure 9
<p>Dynamic responses of the structural and flow parameters for Case 2. (<b>a</b>) The transient response of structural displacement. (<b>b</b>) The transient response of the pressure recovery coefficient.</p> Full article ">Figure 10
<p>Schematic diagram of the monitored parameters.</p> Full article ">Figure 11
<p>The influence of FSIs on panel structures. (<b>a</b>) Panel shapes (Case 1) during one LCO period. (<b>b</b>) Panel shapes of the panel at the bottom wall (Case 2) during one LCO period. (<b>c</b>) Panel shapes of the panel at the top wall (Case 2) during one LCO period. (<b>d</b>) The change in structural displacement at 0.75<span class="html-italic">l</span>.</p> Full article ">Figure 12
<p>The influence of the FSI on the separation zones. (<b>a</b>) The locations of the separation zones. (<b>b</b>) The lengths of the separation zones at the top wall. (<b>c</b>) The lengths of the separation zones at the bottom wall.</p> Full article ">Figure 12 Cont.
<p>The influence of the FSI on the separation zones. (<b>a</b>) The locations of the separation zones. (<b>b</b>) The lengths of the separation zones at the top wall. (<b>c</b>) The lengths of the separation zones at the bottom wall.</p> Full article ">Figure 13
<p>The influence of the FSI on the shock structures. (<b>a</b>) The location of the shock train front. (<b>b</b>) The distance between the first two shocks.</p> Full article ">Figure 14
<p>The influence of the FSI on the flow symmetry level. (<b>a</b>) The local flow symmetry factor. (<b>b</b>) The lift coefficient.</p> Full article ">Figure 15
<p>The influence of the FSI on the performance of the isolator. (<b>a</b>) The total pressure recovery coefficient. (<b>b</b>) The flow distortion index.</p> Full article ">Figure 16
<p>Mach contours of two types of shock train under the influence of FSIs. (<b>a</b>) FSI between the thin-walled panel and the normal shock train at t = 0.5 s. (<b>b</b>) FSI between the thin-walled panel and the oblique shock train at t = 0.5 s.</p> Full article ">Figure 16 Cont.
<p>Mach contours of two types of shock train under the influence of FSIs. (<b>a</b>) FSI between the thin-walled panel and the normal shock train at t = 0.5 s. (<b>b</b>) FSI between the thin-walled panel and the oblique shock train at t = 0.5 s.</p> Full article ">Figure 17
<p>Comparisons of the effect of FSIs on the structures under different shock train conditions. (<b>a</b>) Panel shapes (FSI of normal shock train) in one LCO period. (<b>b</b>) The change in structural displacement at 0.75<span class="html-italic">l</span>.</p> Full article ">Figure 18
<p>Comparisons of the effect of FSIs on the separation zones under different shock train conditions. (<b>a</b>) The locations of the separation zones at the top wall. (<b>b</b>) The lengths of the separation zones at the top wall. (<b>c</b>) The locations of the separation zones at the bottom wall. (<b>d</b>) The lengths of the separation zones at the bottom wall.</p> Full article ">Figure 18 Cont.
<p>Comparisons of the effect of FSIs on the separation zones under different shock train conditions. (<b>a</b>) The locations of the separation zones at the top wall. (<b>b</b>) The lengths of the separation zones at the top wall. (<b>c</b>) The locations of the separation zones at the bottom wall. (<b>d</b>) The lengths of the separation zones at the bottom wall.</p> Full article ">Figure 19
<p>Comparisons of the effect of FSIs on the shock structures under different shock train conditions. (<b>a</b>) The location of the shock train front. (<b>b</b>) The distance between the first two shocks.</p> Full article ">Figure 20
<p>Comparisons of the effect of FSIs on the shock structures under different shock train conditions. (<b>a</b>) The local flow symmetry. (<b>b</b>) The overall flow symmetry.</p> Full article ">Figure 21
<p>Comparisons of the effect of FSIs on the performance under different shock train conditions. (<b>a</b>) The total pressure recovery coefficient. (<b>b</b>) The flow distortion index.</p> Full article ">
26 pages, 28425 KiB  
Article
The Electrostatic Induction Characteristics of SiC/SiC Particles in Aero-Engine Exhaust Gases: A Simulated Experiment and Analysis
by Yan Liu, Zhenzhen Liu, Fang Bai, Hongfu Zuo, Zezhong Guo and Xin Li
Aerospace 2024, 11(6), 481; https://doi.org/10.3390/aerospace11060481 - 17 Jun 2024
Viewed by 775
Abstract
This study investigates the electrostatic induction characteristics of silicon carbide-fiber-reinforced silicon carbide (SiC/SiC) particles within aero-engine exhaust gases using a dedicated J20 turbojet engine experimental platform. Our comprehensive experiments explored the electrostatic properties of SiC/SiC particles under varying engine operational states—specifically focusing on [...] Read more.
This study investigates the electrostatic induction characteristics of silicon carbide-fiber-reinforced silicon carbide (SiC/SiC) particles within aero-engine exhaust gases using a dedicated J20 turbojet engine experimental platform. Our comprehensive experiments explored the electrostatic properties of SiC/SiC particles under varying engine operational states—specifically focusing on different thermal conditions, particle mass concentrations, particle sizes, and exhaust gas velocities compared to those of common engine exhaust constituents like carbon (C) and iron (Fe) particles. The results demonstrate that SiC/SiC particles consistently maintain a stable positive charge across varied temperatures, significantly diverging from the behaviors of carbon (C) and iron (Fe) particles. Additionally, our findings reveal that higher mass concentrations of SiC/SiC particles, smaller particle sizes within a certain range, and greater exhaust gas velocities of the aero-engine all lead to increased particle charge and more pronounced electrostatic induction characteristics. This study highlights the potential of electrostatic sensors for the early detection and diagnosis of failures in aero-engines, offering crucial insights into the development of more resilient real-time aero-engine health monitoring systems. Full article
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<p>Illustration of the electrostatic sensing mechanism.</p> Full article ">Figure 2
<p>Physical structure of the electrostatic sensor, and the 1#, 2#, 3#, …, 6# represent sensor number.</p> Full article ">Figure 3
<p>Schematic of the induced electric field by charged particles.</p> Full article ">Figure 4
<p>Schematic of the engine exhaust gas electrostatic monitoring system.</p> Full article ">Figure 5
<p>A schematic of the experimental system.</p> Full article ">Figure 6
<p>The physical representation of the experimental setup.</p> Full article ">Figure 7
<p>Process of experiment program.</p> Full article ">Figure 8
<p>Three types of material particles used in the wear simulation experiments.</p> Full article ">Figure 9
<p>Microscopic images of SiC/SiC particles at different sizes: (<b>a</b>) 50 µm particles, (<b>b</b>) 75 µm particles, (<b>c</b>) 150 µm particles, (<b>d</b>) single 50 µm particle magnified 5 times, (<b>e</b>) single 75 µm particle magnified 5 times, (<b>f</b>) single 150 µm particle magnified 5 times, 20 times, and 50 times.</p> Full article ">Figure 10
<p>Block diagram of the measurement system.</p> Full article ">Figure 11
<p>The raw data and the denoised data obtained through signal processing methodology: (<b>a</b>) The raw data; (<b>b</b>) The denoised data.</p> Full article ">Figure 12
<p>Electrostatic induction simulation signals: (<b>a</b>) positive charge induction signal, (<b>b</b>) negative charge induction signal.</p> Full article ">Figure 13
<p>Hysteresis in electrostatic signals during aero-engine operation [<a href="#B52-aerospace-11-00481" class="html-bibr">52</a>]: (<b>a</b>) simulated test electrostatic signal, (<b>b</b>) actual test-run electrostatic signal.</p> Full article ">Figure 14
<p>The induction signal from Sensor 1 and its characteristic parameters in the low-temperature experiment with particle sizes of 75 μm and 2 g: (<b>a</b>) the denoised electrostatic signals, (<b>b</b>) RMS of the fault signals, (<b>c</b>) PER of the fault signals, (<b>d</b>) NER of the fault signals.</p> Full article ">Figure 15
<p>Under low-temperature conditions, different materials’ peak values, RMS, and polarity indices (average of six sensors).</p> Full article ">Figure 16
<p>The induction signal from Sensor 1 and the characteristic parameters of the high-temperature experiment with particle sizes if 75 μm and 2 g: (<b>a</b>) the denoised electrostatic signals, (<b>b</b>) RMS of the fault signals, (<b>c</b>) PER of the fault signals, (<b>d</b>) NER of the fault signals.</p> Full article ">Figure 17
<p>Under high-temperature conditions, different materials’ peak values, RMS, and polarity indices (average of six sensors).</p> Full article ">Figure 18
<p>The induction signal from Sensor 1 and the characteristic parameters of different mass concentrations with particle sizes of 75 μm: (<b>a</b>) the denoised electrostatic signals, (<b>b</b>) RMS of the fault signals, (<b>c</b>) PER of the fault signals, (<b>d</b>) NER of the fault signals.</p> Full article ">Figure 19
<p>Comparison of electrostatic parameters for 75 μm SiC/SiC particles at different mass concentrations: peak values, RMS, and polarity indices (average of six sensors).</p> Full article ">Figure 20
<p>The induction signal from Sensor 1 and the characteristic parameters of different particle sizes: (<b>a</b>) the denoised electrostatic signals, (<b>b</b>) RMS of the fault signals, (<b>c</b>) PER of the fault signals, (<b>d</b>) NER of the fault signals.</p> Full article ">Figure 21
<p>Comparison of electrostatic parameters for SiC/SiC particles of different sizes: peak values, RMS, and polarity indices (average of six sensors).</p> Full article ">Figure 22
<p>The induction signal from Sensor 1 and the characteristic parameters of different exhaust gas velocities: (<b>a</b>) the denoised electrostatic signals, (<b>b</b>) RMS of the fault signals, (<b>c</b>) PER of the fault signals, (<b>d</b>) NER of the fault signals.</p> Full article ">Figure 23
<p>Comparison of electrostatic parameters for SiC/SiC particles at different exhaust gas velocities: peak values, RMS, and polarity indices (average of six sensors).</p> Full article ">
13 pages, 3843 KiB  
Article
Decomposing Carbon Intensity Trends in China’s Civil Aviation: A Comprehensive Analysis from 1998 to 2019
by Jinglei Yu, Mengyuan Lu, Kaifeng Wang, Jinmei Ge, Zan Tao, Zheng Xu and Longfei Chen
Aerospace 2024, 11(6), 480; https://doi.org/10.3390/aerospace11060480 - 17 Jun 2024
Viewed by 776
Abstract
Carbon emission intensity is an important index reflecting an entity’s low-carbon competitiveness. This paper presents an extended logarithmic mean divisia index (LMDI) model to dissect carbon intensity within China’s civil aviation from 1998 to 2019, revealing a significant reduction in CO2 emissions [...] Read more.
Carbon emission intensity is an important index reflecting an entity’s low-carbon competitiveness. This paper presents an extended logarithmic mean divisia index (LMDI) model to dissect carbon intensity within China’s civil aviation from 1998 to 2019, revealing a significant reduction in CO2 emissions per air transport revenue. It attributes this decrease to technological advancements, optimized fleet structures, and improved operational efficiencies, highlighting the impact of larger, more efficient aircraft and enhanced load factors. The study also explores economic factors influencing carbon efficiency, suggesting a comprehensive approach encompassing technological innovation and strategic operational improvements for sustainable aviation development. Full article
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<p>Trend of carbon emission intensity of China civil aviation during 1998–2019.</p> Full article ">Figure 2
<p>The contribution of the varying effects on CO<sub>2</sub> emission per transport revenue.</p> Full article ">Figure 3
<p>Trend of total transport turnover, total carbon emission, and carbon intensity of China civil aviation during 1998–2019.</p> Full article ">Figure 4
<p>Average load factor for civil aviation in China.</p> Full article ">Figure 5
<p>The proportion of large- and medium-sized aircraft in China’s fleet.</p> Full article ">Figure 6
<p>Average transport distance for civil aviation in China.</p> Full article ">Figure 7
<p>Annual takeoffs per aircraft in China’s civil aviation.</p> Full article ">Figure 8
<p>Ration of aircraft number to total operating cost for civil aviation in China.</p> Full article ">Figure 9
<p>Ratio of total transport cost to total transport revenue in China’s civil aviation.</p> Full article ">
15 pages, 5067 KiB  
Article
High-Temperature DIC Deformation Measurement under High-Intensity Blackbody Radiation
by Seng Min Han and Nam Seo Goo
Aerospace 2024, 11(6), 479; https://doi.org/10.3390/aerospace11060479 - 17 Jun 2024
Viewed by 713
Abstract
During the high-speed flight of a vehicle in the atmosphere, surface friction with the air generates aerodynamic heating. The aerodynamic heating phenomenon can create extremely high temperatures near the surface. These high temperatures impact material properties and the structure of the aircraft, so [...] Read more.
During the high-speed flight of a vehicle in the atmosphere, surface friction with the air generates aerodynamic heating. The aerodynamic heating phenomenon can create extremely high temperatures near the surface. These high temperatures impact material properties and the structure of the aircraft, so thermal deformation measurement is essential in aerospace engineering. This paper revisits high-temperature deformation measurement using the digital image correlation (DIC) technique under high-intensity blackbody radiation with a precise speckle pattern fabrication and a heat haze reduction method. The effects of the speckle pattern on the DIC measurement have been thoroughly studied at room temperature, but high-temperature measurement studies have not reported such effects so far. We found that the commonly used methods to reduce the heat haze effect could produce incorrect results. Hence, we propose a new method to mitigate heat haze effects. An infrared radiation heater was employed to make an experimental setup that could heat a specimen up to 950 °C. First, we mitigated image saturation using a short-wavelength bandpass filter with blue light illumination, a standard procedure for high-temperature DIC deformation measurement. Second, we studied how to determine the proper size of the speckle pattern in a high-temperature environment. Third, we devised a reduction method for the heat haze effect. As proof of the effectiveness of our developed experimental method, we successfully measured the deformation of stainless steel 304 specimens from 25 °C to 800 °C. The results confirmed that this method can be applied to the research and development of thermal protection systems in the aerospace field. Full article
(This article belongs to the Special Issue Current Trend of High Temperature and Pressure Materials in Hypersonic Vehicles)
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<p>Image saturation phenomenon: (<b>a</b>) spectral radiance versus wavelength in terms of surface temperature; (<b>b</b>) spectral response of a CCD camera sensor.</p> Full article ">Figure 2
<p>Experimental setup: (<b>a</b>) front view; (<b>b</b>) side view.</p> Full article ">Figure 3
<p>Captured images using the same CCD camera at various temperatures without a bandpass filter and blue light illumination.</p> Full article ">Figure 4
<p>A bandpass filter to mitigate image saturation: (<b>a</b>) the schematic of a bandpass filter and (<b>b</b>) the transmission vs. wavelength graph of the BP-470 filter.</p> Full article ">Figure 5
<p>Captured images using the same CCD camera at various temperatures with a bandpass filter and blue light illumination.</p> Full article ">Figure 6
<p>Nine randomly selected points in an area (appropriate dot size).</p> Full article ">Figure 7
<p>Nine randomly selected points in an area (inappropriate dot size).</p> Full article ">Figure 8
<p>3D measuring view in ARAMIS<sup>®</sup> 2018 software in an 800 °C environment: (<b>a</b>) appropriate dot size: smooth and flat, and (<b>b</b>) inappropriate dot size: missing area.</p> Full article ">Figure 9
<p>An enlarged view of the speckle pattern.</p> Full article ">Figure 10
<p>The specimen with a high-contrast speckle pattern.</p> Full article ">Figure 11
<p>High-temperature-resistant speckle pattern variation at different temperatures.</p> Full article ">Figure 12
<p>The displacement field pattern (<b>a</b>) without heat haze effect and (<b>b</b>) with heat haze effect.</p> Full article ">Figure 13
<p>The experimental setup with an electric fan.</p> Full article ">Figure 14
<p>The experimental setup with insulation.</p> Full article ">Figure 15
<p>Selected region on the specimen.</p> Full article ">Figure 16
<p>The temperature curve when using (<b>a</b>) insulation and (<b>b</b>) a fan.</p> Full article ">Figure 17
<p>The temperature difference between the two setups during the heating process.</p> Full article ">Figure 18
<p>Displacement field at 800 °C. (<b>a</b>) X-direction. (<b>b</b>) Y-direction.</p> Full article ">Figure 19
<p>The quadratic fitting curve of strain data.</p> Full article ">
14 pages, 2634 KiB  
Article
Research on the Movement Speed of Situational Map Symbols Based on User Dynamic Preference Perception
by Mu Tong, Shanguang Chen, Xinyue Wang and Chengqi Xue
Aerospace 2024, 11(6), 478; https://doi.org/10.3390/aerospace11060478 - 17 Jun 2024
Viewed by 676
Abstract
When designing situational maps, selecting distinct and visually comfortable movement speeds for dynamic elements is an ongoing challenge for designers. This study addresses this issue by conducting two experiments to measure the human eye’s ability to discern moving speeds on a screen and [...] Read more.
When designing situational maps, selecting distinct and visually comfortable movement speeds for dynamic elements is an ongoing challenge for designers. This study addresses this issue by conducting two experiments to measure the human eye’s ability to discern moving speeds on a screen and examines how symbol movement speeds within situational maps affect users’ subjective experiences, task performance, and visual comfort. The first experiment measured participants’ speed discrimination capabilities for Landolt Ring of varying sizes moving at 0–256°/s, yielding speed discrimination thresholds of 7–23% and a sensitive velocity range of 1–64°/s. The second experiment evaluated observers’ visual perceptions of moving elements within a cognitive task across the same range of 1–64°/s, identifying three significant benchmarks—8°/s, 16°/s, and 32°/s. These can be utilized to categorize slow-, moderate-, and fast-moving symbols in situational maps. The findings can aid in designing human–machine interface environments with improved viewer experience and visual comfort for both Air Traffic Control interfaces and situational maps. Full article
(This article belongs to the Topic Uncertainty Quantification in Design, Manufacturing and Maintenance of Complex Systems)
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<p>Design of stimuli and description of their movement on the screen.</p> Full article ">Figure 2
<p>The trend of JNDS changing with movement speed.</p> Full article ">Figure 3
<p>Experimental material descriptions.</p> Full article ">Figure 4
<p>Questionnaire for assessing visual comfort.</p> Full article ">Figure 5
<p>Explanation of the experimental procedure.</p> Full article ">Figure 6
<p>The scores for visual comfort across various symptoms.</p> Full article ">
25 pages, 4683 KiB  
Article
Concept Evaluation of Radical Short–Medium-Range Aircraft with Turbo-Electric Propulsion
by W. J. Vankan, W. F. Lammen, E. Scheers, P. J. Dewitte and Sebastien Defoort
Aerospace 2024, 11(6), 477; https://doi.org/10.3390/aerospace11060477 - 17 Jun 2024
Viewed by 977
Abstract
Ambitious targets for the coming decades have been set for further reductions in aviation greenhouse gas emissions. Hybrid electric propulsion (HEP) concepts offer potential for the mitigation of these aviation emissions. To investigate this potential in an adequate level of detail, the European [...] Read more.
Ambitious targets for the coming decades have been set for further reductions in aviation greenhouse gas emissions. Hybrid electric propulsion (HEP) concepts offer potential for the mitigation of these aviation emissions. To investigate this potential in an adequate level of detail, the European research project IMOTHEP (Investigation and Maturation of Technologies for Hybrid Electric Propulsion) explores key technologies for HEP in close relation with developments of aircraft missions and configuration. This paper presents conceptual-level design investigations on radical HEP aircraft configurations for short–medium-range (SMR) missions. In particular, a blended-wing-body (BWB) configuration with a turbo-electric powertrain and distributed electric propulsion is investigated using NLR’s aircraft evaluation tool MASS. For the aircraft and powertrain design, representative top-level aircraft requirements have been defined in IMOTHEP, and the reference aircraft for the assessment of potential benefits is based on the Airbus A320neo aircraft. The models and data developed in IMOTHEP and presented in this paper show that the turbo-electric BWB configuration has potential for reduced fuel consumption in comparison to the reference aircraft. But in comparison to advanced turbofan-powered BWB configurations, which have the same benefits of the BWB airframe and advanced technology assumptions, this potential is limited. Full article
(This article belongs to the Special Issue Multidisciplinary Design Optimization for Climate-Neutral Transport Aviation)
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<p>Global overview of the IMOTHEP project, illustrating the interrelation between the integrated design at aircraft vehicle level and the development of HEP component technologies.</p> Full article ">Figure 2
<p>Implementation of the IMOTHEP design logic for SMR-RAD.</p> Full article ">Figure 3
<p>Illustration of the modeling and analysis process in MASS [<a href="#B8-aerospace-11-00477" class="html-bibr">8</a>] for parallel HEP architecture.</p> Full article ">Figure 4
<p>Illustration of the BWB SMILE aircraft geometry based on an ONERA concept study [<a href="#B16-aerospace-11-00477" class="html-bibr">16</a>]. This geometry is the basis for the SMR-0HEP configuration. The figure presents the 3D shape with two CFM-LEAP-1A engines mounted onto the rear center body (<b>left</b> picture) and the approximate planform (<b>right</b> picture, orange contour, in comparison with A320neo approximate planform in blue contour).</p> Full article ">Figure 5
<p>Illustration of the drag polars for the BWB clean configuration (left), showing <span class="html-italic">C<sub>L</sub></span> versus α (the angle of attack) and versus <span class="html-italic">C<sub>D</sub></span> (the aircraft level drag coefficient) and based on a reference area S_ref = 268.6 m<sup>2</sup>. Also, the maximum values of α are indicated by the gray circles; the data for higher values of α have been excluded from further processing. The speed–altitude combinations that are representative for the considered mission are listed on the right, expressed by the Mach number and altitude in ISA.</p> Full article ">Figure 6
<p>Schematic representation of a kerosene-powered turbo-electric powertrain configuration. Switches, circuit breakers, gearboxes, etc., not shown.</p> Full article ">Figure 7
<p>Illustration of the SMR-RAD configuration with eight ducted electric fans each with 1.89 m fan diameter. The turbo-generators have a fixed diameter of 1.0 m. It must be noted that the eight ducted fans are installed on the rear center body, and the two turbo-generators are installed under the inboard wing. The ducted fans are indicated by the green nacelles, and the turbo-generators are indicated by the blue nacelles.</p> Full article ">Figure 8
<p>Illustration of the turbo-electric propulsion system for SMR-RAD, with the main powertrain components incorporated as more or less elaborate component models.</p> Full article ">Figure 9
<p>Illustration of the electric architecture that has been defined in IMOTHEP for SMR-RAD, which is based on maximum redundancy and fault mitigation, derived from ONERA studies in IMOTHEP for the SMR-CON aircraft [<a href="#B22-aerospace-11-00477" class="html-bibr">22</a>].</p> Full article ">Figure 10
<p>Illustration of the gas turbine cycle model of the turboshaft engine, developed with DLR’s GTLab environment [<a href="#B23-aerospace-11-00477" class="html-bibr">23</a>]. (LPC is low pressure compressor; HPC is high pressure compressor).</p> Full article ">Figure 11
<p>Schematic side-view of a ducted fan model based on isentropic pressure duct equations. Dashed vertical lines indicate the various planes of freestream flow, inlet, duct and nozzle exit. Thick solid lines illustrate the values of total temperature and total pressure.</p> Full article ">Figure 12
<p>The resulting total pitch moment coefficient as function of the lift coefficient for SMR-RAD, both in powered (including thrust force from ducted fans) and unpowered (no thrust force included) states. Also, results are shown for the BWB configuration without any propulsors (indicated as BWB SMILE clean).</p> Full article ">Figure 13
<p>Depiction of the cruise altitude (<b>upper</b> graph) and climb time (<b>lower</b> graph) optimization for the typical 800 NM mission simulations with SMR-RAD. The line colors correspond to the colors on y-axes on the left- and right-sides of both graphs.</p> Full article ">Figure 13 Cont.
<p>Depiction of the cruise altitude (<b>upper</b> graph) and climb time (<b>lower</b> graph) optimization for the typical 800 NM mission simulations with SMR-RAD. The line colors correspond to the colors on y-axes on the left- and right-sides of both graphs.</p> Full article ">
15 pages, 4614 KiB  
Article
Wind Shear Response of Aircraft with C* and C*U Controller during Approach
by Yufei Yan and Lei Song
Aerospace 2024, 11(6), 476; https://doi.org/10.3390/aerospace11060476 - 17 Jun 2024
Viewed by 828
Abstract
This study investigates the impact of wind shear on the flight dynamics of commercial aircraft where C* and C*U control laws are employed during the approach phase. Given the high incidence of flight accidents during takeoff and landing attributed to wind shear, this [...] Read more.
This study investigates the impact of wind shear on the flight dynamics of commercial aircraft where C* and C*U control laws are employed during the approach phase. Given the high incidence of flight accidents during takeoff and landing attributed to wind shear, this research aims to enhance aviation safety by analyzing control law behavior under varying wind shear conditions. A nonlinear flight simulation model was developed, utilizing aerodynamic and engine data from a B737, to explore the aircraft’s response to different wind shear intensities. The simulation analysis was used to compare the response of the aircraft with C* and C*U controllers, respectively, under different wind shear, and to evaluate the effectiveness of its stability enhancement in wind shear. It was found that in most cases, the controller can achieve a good stabilization effect, but in some cases of wind fields, the aircraft suffered more significant oscillation. Full article
(This article belongs to the Special Issue Advanced Aircraft Technology)
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<p>Simulator architecture.</p> Full article ">Figure 2
<p>Static lift coefficient varies with angle of attack.</p> Full article ">Figure 3
<p>Static pitch moment coefficient varies with angle of attack.</p> Full article ">Figure 4
<p>The wind shear model.</p> Full article ">Figure 5
<p>Simplified C* controller structure.</p> Full article ">Figure 6
<p>Simplified C*U controller structure.</p> Full article ">Figure 7
<p>CAP flight quality level of short-period modal.</p> Full article ">Figure 8
<p>The response of the aircraft with different controllers in a 10-knot tailwind shear: (<b>a</b>) the angle of attack response; (<b>b</b>) the angle of pitch; (<b>c</b>) the climb angle response; (<b>d</b>) the airspeed response.</p> Full article ">Figure 9
<p>The response of the aircraft with different controllers in a 10-knot headwind shear: (<b>a</b>) the angle of attack response; (<b>b</b>) the angle of pitch; (<b>c</b>) the climb angle response; (<b>d</b>) the airspeed response.</p> Full article ">Figure A1
<p>The response of the aircraft with different controllers in a 15-knot tailwind shear: (<b>a</b>) the angle of attack response; (<b>b</b>) the angle of pitch; (<b>c</b>) the climb angle response; (<b>d</b>) the airspeed response.</p> Full article ">Figure A1 Cont.
<p>The response of the aircraft with different controllers in a 15-knot tailwind shear: (<b>a</b>) the angle of attack response; (<b>b</b>) the angle of pitch; (<b>c</b>) the climb angle response; (<b>d</b>) the airspeed response.</p> Full article ">Figure A2
<p>The response of the aircraft with different controllers in a 15-knot headwind shear: (<b>a</b>) the angle of attack response; (<b>b</b>) the angle of pitch; (<b>c</b>) the climb angle response; (<b>d</b>) the airspeed response.</p> Full article ">Figure A3
<p>The response of the aircraft with different controllers in a 25-knot headwind shear: (<b>a</b>) the angle of attack response; (<b>b</b>) the angle of pitch; (<b>c</b>) the climb angle response; (<b>d</b>) the airspeed response.</p> Full article ">
16 pages, 16714 KiB  
Article
Water Recuperation from Regolith at Martian, Lunar & Micro-Gravity during Parabolic Flight
by Dario Farina, Hatim Machrafi, Patrick Queeckers, Christophe Minetti and Carlo Saverio Iorio
Aerospace 2024, 11(6), 475; https://doi.org/10.3390/aerospace11060475 - 16 Jun 2024
Viewed by 1374
Abstract
Recent discoveries of potential ice particles and ice-cemented regolith on extraterrestrial bodies like the Moon and Mars have opened new opportunities for developing technologies to extract water, facilitating future space missions and activities on these extraterrestrial body surfaces. This study explores the potential [...] Read more.
Recent discoveries of potential ice particles and ice-cemented regolith on extraterrestrial bodies like the Moon and Mars have opened new opportunities for developing technologies to extract water, facilitating future space missions and activities on these extraterrestrial body surfaces. This study explores the potential for water extraction from regolith through an experiment designed to test water recuperation from regolith simulant under varying gravitational conditions. The resultant water vapor extracted from the regolith is re-condensed on a substrate surface and collected in liquid form. Three types of substrates, hydrophobic, hydrophilic, and grooved, are explored. The system’s functionality was assessed during a parabolic flight campaign simulating three distinct gravity levels: microgravity, lunar gravity, and Martian gravity. Our findings reveal that the hydrophobic surface demonstrates the highest efficiency due to drop-wise condensation, and lower gravity levels result in increased water condensation on the substrates. The experiments aimed to understand the performance of specific substrates under lunar, Martian, and microgravity conditions, providing an approach for in-situ water recovery, which is crucial for establishing economically sustainable water supplies for future missions. To enhance clarity and readability, in this paper, “H2O” will be referred to as “water”. Full article
(This article belongs to the Special Issue The (Near) Future of Space Resources)
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Figure 1

Figure 1
<p>Scheme of the Experiment.</p> Full article ">Figure 2
<p>View of the setup used for the parabolic flight campaign.</p> Full article ">Figure 3
<p>Gravity profile during the parabolic flight with respect to Earth Gravity.</p> Full article ">Figure 4
<p>Contact angle (CA) measured on the top of the groove, as well as for the hydrophobic and hydrophilic.</p> Full article ">Figure 5
<p>Generalization of the Droplet Volume Quantification Process.</p> Full article ">Figure 6
<p>Image of the condenser before and after applying the high-pass filter.</p> Full article ">Figure 7
<p>Image of the Condenser before and after Applying the High-Pass Filter and Thresholder by K-means.</p> Full article ">Figure 8
<p>Sequence of Clustering the Drop Parts Starting From the Original Grooves Surface Image.</p> Full article ">Figure 9
<p>Example of the volume quantification on grooves.</p> Full article ">Figure 10
<p>Example of the volume quantification on a hydrophobic surface.</p> Full article ">Figure 11
<p>Example of the volume quantification on a hydrophilic surface.</p> Full article ">Figure 12
<p>(<b>a</b>) The difference in temperature maintained during the experiment between the condenser plate and heat extraction system of the three days at a set temperature of 10 °C. (<b>b</b>) The difference in temperature maintained during the experiment between the condenser plate and heat extraction system of the three days at a set temperature of 15 °C.</p> Full article ">Figure 13
<p>Water condensed in ml for each parabola experienced for each substrate.</p> Full article ">
19 pages, 10263 KiB  
Article
Study on the Active Control of the Dynamic Stall of Rotor Airfoils Based on Plasma Excitation
by Weihong Kong, Keyi Guo and You Li
Aerospace 2024, 11(6), 474; https://doi.org/10.3390/aerospace11060474 - 15 Jun 2024
Viewed by 645
Abstract
This paper studies a rotor dynamic stall control method using an alternating current dielectric barrier discharge (AC DBD) plasma actuator through numerical simulation methods. The flow field evolution during a dynamic stalling process under the excitation of AC DBD plasma discharge is analyzed [...] Read more.
This paper studies a rotor dynamic stall control method using an alternating current dielectric barrier discharge (AC DBD) plasma actuator through numerical simulation methods. The flow field evolution during a dynamic stalling process under the excitation of AC DBD plasma discharge is analyzed using the two-dimensional Reynolds time-averaged (RANS) method. The impact of the AC DBD plasma discharge on the flow field is then simulated using the phenomenological method. The influence of the position and intensity of the plasma excitation on the static stall characteristics of the NACA0012 airfoil is also studied. Deformed mesh and dynamic mesh techniques are used to simulate an aerodynamic environment with variable incoming flow and variable angles of attraction on a rotor airfoil. The application of AC DBD plasma excitation for controlling mild and deep dynamic stalls of rotor blades is investigated. The obtained results show that the AC DBD plasma excitation accelerated the evolution and shedding of dynamic stall vortices and facilitated the reattachment of airflow. The application of plasma excitation allowed for significantly increasing the static stall angle of the airfoil and improving the lift coefficient. In addition, the intensity of the plasma excitation is a key factor affecting the control. Moreover, the application of AC DBD plasma excitation for rotor dynamic stalls allowed for reducing the size of the dynamic stall vortex, which helped mitigate the aerodynamic hysteresis effect caused by the dynamic stall and accelerated the recovery from aerodynamic forces. Full article
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<p>Volume force distribution of the AC DBD plasma discharge excitation.</p> Full article ">Figure 2
<p>Deforming mesh and dynamic mesh illustration.</p> Full article ">Figure 3
<p>Aerodynamic coefficients of the airfoil [<a href="#B9-aerospace-11-00474" class="html-bibr">9</a>].</p> Full article ">Figure 4
<p>Schematic of the computational domain for the validation of the plasma mathematical model.</p> Full article ">Figure 5
<p>Verification of the AC DBD plasma model computational results [<a href="#B22-aerospace-11-00474" class="html-bibr">22</a>].</p> Full article ">Figure 6
<p>Applying plasma excitation at the leading edge of the airfoil.</p> Full article ">Figure 7
<p>Aerodynamic coefficients of NACA0012 at different excitation intensities and positions.</p> Full article ">Figure 7 Cont.
<p>Aerodynamic coefficients of NACA0012 at different excitation intensities and positions.</p> Full article ">Figure 8
<p>Aerodynamic forces of the airfoil with plasma excitation applied at different freestream velocities.</p> Full article ">Figure 9
<p>Aerodynamic force coefficients of the airfoil under dynamic stall conditions for different excitation intensities.</p> Full article ">Figure 10
<p>The streamline near the stall angle of attack of the airfoil before and after plasma excitation.</p> Full article ">Figure 11
<p>Variations in airfoil angle of attack with azimuth angle.</p> Full article ">Figure 12
<p>Variation curves of angles of attack and freestream velocity with respect to azimuth angle for airfoil.</p> Full article ">Figure 13
<p>Aerodynamic forces of mild stall on the airfoil under plasma excitation.</p> Full article ">Figure 14
<p>The streamline of the airfoil near the azimuth angle of 270° under mild stall conditions.</p> Full article ">Figure 15
<p>Aerodynamic forces of deep stall on airfoil under plasma excitation.</p> Full article ">Figure 16
<p>The streamlines of the airfoil near the azimuth angle of 270° under deep stall conditions.</p> Full article ">
24 pages, 30756 KiB  
Article
Computational Investigations for the Feasibility of Passive Flow Control Devices for Enhanced Aerodynamics of Small-Scale UAVs
by Ali Arshad and Vadims Kovaļčuks
Aerospace 2024, 11(6), 473; https://doi.org/10.3390/aerospace11060473 - 13 Jun 2024
Viewed by 903
Abstract
The 4R-UAV project aims to develop aerodynamically efficient and environmentally friendly UAVs based on the 4R Circular Economy principle. In this study, the feasibility of the application of PFCDs (Passive Flow Control Devices) was investigated for the small-scale low-speed 4R-UAV wing. The application [...] Read more.
The 4R-UAV project aims to develop aerodynamically efficient and environmentally friendly UAVs based on the 4R Circular Economy principle. In this study, the feasibility of the application of PFCDs (Passive Flow Control Devices) was investigated for the small-scale low-speed 4R-UAV wing. The application of PFCDs for small-scale UAV wings is relatively unexplored. Two PFCD types, i.e., MVGs (Micro Vortex Generators) and winglets, were considered for the investigations. In the stepwise investigations, the aerodynamic performance of the MVGs and the winglets was analyzed for the short-span 4R-UAV wing, which was developed from the aerodynamically optimized airfoil SG6043mod. MVGs enhanced the wings near stall properties (especially maximum lift coefficient) and contributed to the delayed wing stall of up to 2°. MVGs manifested the main aerodynamic advantage, which was achieved at the higher angles of attack. Winglets, on the other hand, were found to be extremely effective in cruise conditions with improved pre-stall characteristics. Extensive investigations on winglets were carried out by designing six winglet configurations for the 4R-UAV wing. Blended-type winglets performed well and enhanced pre-stall properties by decreasing the drag (up to 10%) of the wing. The main performance improvement was found in the early angles of attack. In the final step, the combined effect of the integrated PFCDs was analyzed. The final wing (integrated MVGs and winglets) also exhibited enhanced performance with nearly 6% increased lift-to-drag ratio in cruise conditions. The limited aerodynamic advantage achieved from the PFCDs in small-scale UAV applications can be useful in specific (civil or military) missions. Further verifications are planned in the future by means of experimental and flight testing. Full article
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<p>UAV PFCD implementation workflow.</p> Full article ">Figure 2
<p>Comparison of drag polar of the original airfoil (SG6043) and optimized airfoil (SG6043mod).</p> Full article ">Figure 3
<p>Relation between lift coefficient and angle of attack of SG6043mod.</p> Full article ">Figure 4
<p>Lift-to-drag ratio of the baseline wing segment vs. AOA.</p> Full article ">Figure 5
<p>Baseline wing design.</p> Full article ">Figure 6
<p>Installation strategy of the trapezoidal MVGs illustrating 18° installation angle to the flow (top and side views).</p> Full article ">Figure 7
<p>Comparison of aerodynamic efficiency between baseline wing and MVGs.</p> Full article ">Figure 8
<p>Winglet configuration No. 1, front view, and side view.</p> Full article ">Figure 9
<p>Winglet configuration No. 2, front view, and side view.</p> Full article ">Figure 10
<p>Winglet configuration No. 3, front view, and side view.</p> Full article ">Figure 11
<p>Winglet configuration No. 4, front view, and side view.</p> Full article ">Figure 12
<p>Winglet configuration No. 5, front view, and side view.</p> Full article ">Figure 13
<p>Winglet configuration No. 6 (blended winglet of configuration No. 4 with installed “flow divider” on the tip).</p> Full article ">Figure 14
<p>Mesh elements’ density variation in the control volume.</p> Full article ">Figure 15
<p>Winglet surface meshing.</p> Full article ">Figure 16
<p>Domain sizes’ comparison of the adopted (white) and enlarged (black) control volume for domain independence study.</p> Full article ">Figure 17
<p>C<sub>D</sub> Vs. AOA graph of the baseline wing and different winglet configurations; (<b>a</b>) shows overall trends, while (<b>b</b>,<b>c</b>) show their respective AOA regions (4–12° and 12–17°) for better reader perception.</p> Full article ">Figure 17 Cont.
<p>C<sub>D</sub> Vs. AOA graph of the baseline wing and different winglet configurations; (<b>a</b>) shows overall trends, while (<b>b</b>,<b>c</b>) show their respective AOA regions (4–12° and 12–17°) for better reader perception.</p> Full article ">Figure 18
<p>Lift-to-drag ratio Vs. AOA graph of the baseline wing and different winglet configurations; (<b>a</b>) shows overall trends, while (<b>b</b>,<b>c</b>) show their respective AOA regions (1–9° and 9–17°) for better reader perception.</p> Full article ">Figure 18 Cont.
<p>Lift-to-drag ratio Vs. AOA graph of the baseline wing and different winglet configurations; (<b>a</b>) shows overall trends, while (<b>b</b>,<b>c</b>) show their respective AOA regions (1–9° and 9–17°) for better reader perception.</p> Full article ">Figure 19
<p>Lift-to-drag ratio enhancement vs. AOA.</p> Full article ">Figure 20
<p>Tip vortex flow mechanism at a 100 mm downstream station wing’s trailing edge: (<b>a</b>) baseline wing; (<b>b</b>) winglet.</p> Full article ">Figure 21
<p>3D streamlines of the vortex flow: (<b>a</b>) baseline wing (<b>b</b>) winglet.</p> Full article ">Figure 22
<p>Pressure distribution over suction side of the (<b>a</b>) baseline wing and (<b>b</b>) winglet.</p> Full article ">Figure 23
<p>Shear stress distribution over suction side of (<b>a</b>) baseline wing and (<b>b</b>) winglet.</p> Full article ">Figure 24
<p>UAV final wing design with integrated winglet and MVGs.</p> Full article ">Figure 25
<p>Wing surface mesh strategy.</p> Full article ">Figure 26
<p>Close-up view of the mesh scheme near the MVG region.</p> Full article ">Figure 27
<p>Mesh independence trend for the study.</p> Full article ">Figure 28
<p>Comparison of the baseline and final wing lift coefficient trends.</p> Full article ">Figure 29
<p>Comparison of the baseline and final wing drag coefficient trends.</p> Full article ">Figure 30
<p>Comparison of the baseline and final wing aerodynamic efficiency.</p> Full article ">Figure 31
<p>Lift-to-drag ratio enhancement of final wing.</p> Full article ">
14 pages, 2769 KiB  
Article
A Joint Surface Contact Stiffness Model Considering Micro-Asperity Interaction
by Tian Xia, Jie Qu and Yong Liu
Aerospace 2024, 11(6), 472; https://doi.org/10.3390/aerospace11060472 - 12 Jun 2024
Viewed by 699
Abstract
Mechanical joint interfaces are widely found in mechanical equipment, and their contact stiffness directly affects the overall performance of the mechanical system. Based on the fractal and elastoplastic contact mechanics theories, the K-E elastoplastic contact model is introduced to establish the contact stiffness [...] Read more.
Mechanical joint interfaces are widely found in mechanical equipment, and their contact stiffness directly affects the overall performance of the mechanical system. Based on the fractal and elastoplastic contact mechanics theories, the K-E elastoplastic contact model is introduced to establish the contact stiffness model for mechanical joint interfaces. This model considers the interaction effects between micro-asperities in the fully deformed state, including elasticity, first elastoplasticity, second elastoplasticity, and complete plastic deformation state. Based on this model, the effects of fractal parameters on normal contact stiffness and contact load are analyzed. It can be found that the larger fractal dimension D or smaller characteristic scale coefficient G will weaken the interaction between micro-asperities. The smoother processing surfaces lead to higher contact stiffness in mechanical joint interfaces. The applicability and effectiveness of the proposed model are verified by comparing it with the traditional contact model calculation results. Under the same load, the interaction between micro-rough surfaces leads to an increase in both overall deformation and contact stiffness. The accuracy of the predicted contact stiffness model is also validated by comparing it with experimental results. Full article
(This article belongs to the Special Issue State Monitoring and Health Management of Complex Equipment (2nd Edition))
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<p>Deformation diagram of the single asperity.</p> Full article ">Figure 2
<p>Schematic diagram of the rough surface contact.</p> Full article ">Figure 3
<p>The relationship between the contact load and the contact area of the largest asperity.</p> Full article ">Figure 4
<p>The influence of different fractal dimensions on contact stiffness.</p> Full article ">Figure 5
<p>The influence of different characteristic scale coefficients on contact stiffness.</p> Full article ">Figure 5 Cont.
<p>The influence of different characteristic scale coefficients on contact stiffness.</p> Full article ">Figure 6
<p>Comparison of contact stiffness calculated by different models.</p> Full article ">Figure 7
<p>Comparison of theoretical model and experimental data.</p> Full article ">Figure 8
<p>Comparison of theoretical model and existing models.</p> Full article ">
27 pages, 849 KiB  
Review
A Critical Review of Information Provision for U-Space Traffic Autonomous Guidance
by Ivan Panov and Asim Ul Haq
Aerospace 2024, 11(6), 471; https://doi.org/10.3390/aerospace11060471 - 12 Jun 2024
Cited by 1 | Viewed by 1096
Abstract
This paper identifies and classifies the essential constraints that must be addressed to allow U-space traffic autonomous guidance. Based on an extensive analysis of the state of the art in robotic guidance, physics of flight, flight safety, communication and navigation, uncrewed aircraft missions, [...] Read more.
This paper identifies and classifies the essential constraints that must be addressed to allow U-space traffic autonomous guidance. Based on an extensive analysis of the state of the art in robotic guidance, physics of flight, flight safety, communication and navigation, uncrewed aircraft missions, artificial intelligence (AI), social expectations in Europe on drones, etc., we analyzed the existing constraints and the information needs that are of essential importance to address the identified constraints. We compared the identified information needs with the last edition of the U-space Concept of Operations and identified critical gaps between the needs and proposed services. A high-level methodology to identify, measure, and close the gaps is proposed. Full article
(This article belongs to the Topic Civil and Public Domain Applications of Unmanned Aviation)
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<p>Four phases of U-space development. Redrawn based on [<a href="#B16-aerospace-11-00471" class="html-bibr">16</a>].</p> Full article ">Figure 2
<p>A methodology to identify, measure, and close the gaps.</p> Full article ">
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