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Aerospace, Volume 11, Issue 10 (October 2024) – 76 articles

Cover Story (view full-size image): In recent decades, the problem of pollution related to human activities has pushed research towards zero or net-zero carbon solutions for transportation. The main objective of this paper is to perform a preliminary performance assessment of the use of hydrogen in conventional turbine engines for aeronautical applications. A 0-D dynamic model of the Allison 250 C-18 turboshaft engine was designed in the MATLAB/Simulink environment. The model was dimensioned based on the parameters measured during the testing campaign and validated by the acquired data using Jet A-1 fuel. Further, the 0-D dynamic engine model was used to predict the performance of the engine, using hydrogen as the input of the theoretical combustion model. The simulation’s outputs running conventional Kerosene Jet A-1 and hydrogen and using different throttle profiles were compared. View this paper
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3 pages, 148 KiB  
Editorial
Guidance, Navigation, and Control for the Moon, Mars, and Beyond
by Marco Sagliano
Aerospace 2024, 11(10), 863; https://doi.org/10.3390/aerospace11100863 - 21 Oct 2024
Viewed by 1075
Abstract
The interdisciplinary field known as Guidance, Navigation, and Control (GNC) has been one of the key contributors to the tremendous advancements in space exploration since the inception of the Mercury and Apollo programs [...] Full article
(This article belongs to the Special Issue GNC for the Moon, Mars, and Beyond)
2 pages, 127 KiB  
Editorial
Human Behavior in Space Exploration Missions
by Vadim Gushin
Aerospace 2024, 11(10), 862; https://doi.org/10.3390/aerospace11100862 - 20 Oct 2024
Viewed by 821
Abstract
Fifty years of human space exploration have allowed space psychology as an applied area of science to take major steps forward [...] Full article
(This article belongs to the Special Issue Human Behaviors in Space Exploration Mission)
23 pages, 3282 KiB  
Article
Joint Optimization of Cost and Scheduling for Urban Air Mobility Operation Based on Safety Concerns and Time-Varying Demand
by Yantao Wang, Jiashuai Li, Yujie Yuan and Chun Sing Lai
Aerospace 2024, 11(10), 861; https://doi.org/10.3390/aerospace11100861 - 20 Oct 2024
Viewed by 1135
Abstract
As the value and importance of urban air mobility (UAM) are being recognized, there is growing attention towards UAM. To ensure that urban air traffic can serve passengers to the greatest extent while ensuring safety and generating revenue, there is an urgent need [...] Read more.
As the value and importance of urban air mobility (UAM) are being recognized, there is growing attention towards UAM. To ensure that urban air traffic can serve passengers to the greatest extent while ensuring safety and generating revenue, there is an urgent need for a transportation scheduling plan based on safety considerations. The region of Beijing–Tianjin–Hebei was selected as the case study in this research. A real-time demand transportation scheduling model for a single day was constructed, with the total service population and total cost as objective functions, and safety intervals, eVTOL performance, and passenger maximum waiting time as constraints. A Joint Optimization of Cost and Scheduling Particle Swarm Optimization (JOCS-PSO) algorithm was utilized to obtain the optimal solution. The optimal solution obtained in this study can serve 138,610,575 passengers during eVTOLs’ entire lifecycle (15 years) with a total cost of CNY 368.57 hundred million, with the cost of CNY 265.9 per passenger. Although it is higher than the driving cost, it saves 1–1.5 h and thus has high cost effectiveness during rush hours. Full article
(This article belongs to the Special Issue Future Airspace and Air Traffic Management Design)
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<p>Vertical takeoff and landing procedure schematic diagram.</p>
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<p>The relationship between Cost and Schedule optimization parts.</p>
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<p>Encoding diagram of the three characters in the scheduling algorithm.</p>
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<p>Vertiport locations in Beijing–Tianjin–Xiong’an (Hebei).</p>
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<p>(<b>a</b>) Route diagram considering detouring measure. (<b>b</b>) A general review of eVTOL’s whole flight process.</p>
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<p>The curve of passenger demand over time between location of vertiports.</p>
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<p>The Pareto frontier in this proposed algorithm.</p>
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<p>Parts of the schedule for different types of eVTOL. (<b>a</b>) The first Xpeng X2 (<b>b</b>) The 166th Xpeng X2 (<b>c</b>) The first Geely AE200 (<b>d</b>) The 171th Geely AE200.</p>
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39 pages, 35650 KiB  
Article
An Analysis of a Complete Aircraft Electrical Power System Simulation Based on a Constant Speed Constant Frequency Configuration
by Octavian Grigore-Müler
Aerospace 2024, 11(10), 860; https://doi.org/10.3390/aerospace11100860 - 18 Oct 2024
Viewed by 1687
Abstract
Recent developments in aircraft electrical technology, such as the design and production of more electric aircraft (MEA) and major steps in the development of all-electric aircraft (AEA), have had a significant impact on aircraft’s electrical power systems (EPSs). However, the EPSs of the [...] Read more.
Recent developments in aircraft electrical technology, such as the design and production of more electric aircraft (MEA) and major steps in the development of all-electric aircraft (AEA), have had a significant impact on aircraft’s electrical power systems (EPSs). However, the EPSs of the latest aircraft produced by the main players in the market, Airbus with the Neo series and Boeing with the NG and MAX series are still completely traditional and based on the constant speed constant frequency (CSCF) configuration. For alternating current ones, the EPS is composed of the following: prime movers, namely the aircraft turbofan engine (TE); the electrical power source, i.e., the integrated drive generator (IDG); the command and control system, the generator control unit (GCU); the transmission and the system distribution system; the protection system, i.e., the CBs (circuit breakers); and the electrical loads. This paper presents the analysis of this system using the Simscape package from Simulink v 8.7, a MATLAB v 9.0 program, which is actually the development of some systems designed in two previous personal papers. For the first time in the literature, a complete MATLAB modelled EPS system was presented, i.e., the aircraft turbofan engine model, driving the constant speed drive system (CSD) (model presented in the first reference as a standalone type and with different parameters), linked to the synchronous generator (SG) (model presented in second reference for lower power and rotational speed) in the so-called integrated drive generator (IDG) and electrical loads. Full article
(This article belongs to the Special Issue Electric Power Systems and Components for All-Electric Aircraft)
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<p>The typical EPS configurations [<a href="#B2-aerospace-11-00860" class="html-bibr">2</a>].</p>
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<p>The total number of commercial airplane MEA and non-MEA in service in 2024 (data from [<a href="#B15-aerospace-11-00860" class="html-bibr">15</a>]).</p>
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<p>The EPS architecture of a CSCF AC configuration [<a href="#B2-aerospace-11-00860" class="html-bibr">2</a>].</p>
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<p>Schematic representation of a gas turbine engine with a control volume around it to determine the thrust.</p>
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<p>The block diagram of IDGS and governor system [<a href="#B2-aerospace-11-00860" class="html-bibr">2</a>]: 1—centrifugal transducer; 2—measuring device (distributor)—two-way spool valve; 3—calibrating device (spring); 4—device for tuning centrifugal element; 5—hydraulic servomotor; 6—feedback mechanism.</p>
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<p>Sketch of a differential hydraulic CSD [<a href="#B1-aerospace-11-00860" class="html-bibr">1</a>]; P—pump; G—gear box; OT—oil tank; 1—hydraulic actuator; 2—centrifugal transducer; 3—piston; 4—fixed disc of FHU; 5—FHU rotor; 6—the mechanical differential planetary gear; 7—the suction washer of the suction and discharge channels (distribution element); 8—variable angle wobbler of VHU (swash plate); 9—VHU rotor; 10—the distributor.</p>
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<p>Sketch of the forces acting on CSD fixed disc [<a href="#B1-aerospace-11-00860" class="html-bibr">1</a>].</p>
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<p>Components of a modern BSG [<a href="#B2-aerospace-11-00860" class="html-bibr">2</a>]: <span class="html-italic">PE</span>—pilot exciter, a 3-ph <span class="html-italic">SG</span> with permanent magnet (<span class="html-italic">PMG</span>); <span class="html-italic">E</span>—exciter, a 3-ph <span class="html-italic">SG</span> of inverted construction; <span class="html-italic">RR</span>—rotary rectifier.</p>
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<p>SG phase-variable circuit model [<a href="#B2-aerospace-11-00860" class="html-bibr">2</a>]; <span class="html-italic">a</span>, <span class="html-italic">b</span>, <span class="html-italic">c</span>—stator phase windings; <span class="html-italic">fd</span>—field winding; <span class="html-italic">kd</span>—<span class="html-italic">d</span>-axis dumper circuit; <span class="html-italic">kq</span>—<span class="html-italic">q</span>-axis dumper circuit; <span class="html-italic">k</span> = 1, n, n—number pf dumper circuits; <span class="html-italic">θ</span>—angle by which <span class="html-italic">d</span>-axis leads the magnetic axis of phase <span class="html-italic">a</span> winding; <span class="html-italic">ω</span><sub>r</sub>—rotor angular speed.</p>
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<p>The RSR diagram [<a href="#B2-aerospace-11-00860" class="html-bibr">2</a>].</p>
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<p>Diagram of the driven generator model [<a href="#B2-aerospace-11-00860" class="html-bibr">2</a>].</p>
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<p>The control of a BSG [<a href="#B2-aerospace-11-00860" class="html-bibr">2</a>,<a href="#B48-aerospace-11-00860" class="html-bibr">48</a>].</p>
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<p>Type AC1A alternator rectifier excitation system with noncontrolled rectifiers and feedback from exciter field current [<a href="#B48-aerospace-11-00860" class="html-bibr">48</a>].</p>
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<p>Aircraft electrical power generation and distribution system Simulink model.</p>
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<p>Simulink model for aircraft turbofan engine.</p>
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<p>Variation of the aircraft turbofan engine characteristics.</p>
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<p>Simulink model for accessory gearbox.</p>
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<p>Variation of the accessory gearbox characteristics.</p>
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<p>Simulink model for IDGS.</p>
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<p>The Simulink model parameters.</p>
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<p>Simulink model for CSD.</p>
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<p>Variation of the CSD characteristics without AFR.</p>
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<p>Shape of the output characteristics of the CSD.</p>
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<p>Shape of the output characteristics of the CSD.</p>
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<p>Simulink model for BSG.</p>
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<p>Main characteristics of synchronous generator.</p>
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<p>Simulink model for GCU [<a href="#B2-aerospace-11-00860" class="html-bibr">2</a>].</p>
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<p>PID parameters.</p>
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<p>Variation of the CSD characteristics with AFR.</p>
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<p>Variation of the BSG frequency compared to the limits in the standard (data limits from [<a href="#B45-aerospace-11-00860" class="html-bibr">45</a>]).</p>
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<p>Variation of the BSG terminal parameters compared to the limits in the standard [<a href="#B45-aerospace-11-00860" class="html-bibr">45</a>].</p>
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<p>Variation of the BSG exciter output voltage.</p>
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<p>Variation of the accessory gearbox characteristics: (<b>a</b>) for GOL scenario; (<b>b</b>) for ICL scenario.</p>
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<p>Variation of the CSD characteristics: (<b>a</b>) for GOL scenario; (<b>b</b>) for ICL scenario.</p>
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<p>Variation of the BSG frequency compared to the limits in the standard (data limits from [<a href="#B45-aerospace-11-00860" class="html-bibr">45</a>]): (<b>a</b>) for GOL scenario; (<b>b</b>) for ICL scenario.</p>
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<p>Variation of the BSG characteristics compared to the limits in the standard (data limits from [<a href="#B45-aerospace-11-00860" class="html-bibr">45</a>]) for GOL scenario.</p>
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<p>Variation of the BSG exciter output voltage: (<b>a</b>) for GOL scenario; (<b>b</b>) for ICL scenario.</p>
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<p>Variation of the BSG characteristics compared to the limits in the standard (data limits from [<a href="#B45-aerospace-11-00860" class="html-bibr">45</a>]) for ICL scenario.</p>
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25 pages, 7196 KiB  
Article
Position Normalization of Propellant Grain Point Clouds
by Junchao Wang, Fengnian Tian, Renfu Li, Zhihui Li, Bin Zhang and Xuelong Si
Aerospace 2024, 11(10), 859; https://doi.org/10.3390/aerospace11100859 - 18 Oct 2024
Viewed by 697
Abstract
Point cloud data obtained from scanning propellant grains with 3D scanning equipment exhibit positional uncertainty in space, posing significant challenges for calculating the relevant parameters of the propellant grains. Therefore, it is essential to normalize the position of each propellant grain’s point cloud. [...] Read more.
Point cloud data obtained from scanning propellant grains with 3D scanning equipment exhibit positional uncertainty in space, posing significant challenges for calculating the relevant parameters of the propellant grains. Therefore, it is essential to normalize the position of each propellant grain’s point cloud. This paper proposes a normalization algorithm for propellant grain point clouds, consisting of two stages, coarse normalization and fine normalization, to achieve high-precision transformations of the point clouds. In the coarse normalization stage, a layer-by-layer feature points detection scheme based on k-dimensional trees (KD-tree) and k-means clustering (k-means) is designed to extract feature points from the propellant grain point cloud. In the fine normalization stage, a rotation angle compensation scheme is proposed to align the fitted symmetry axis of the propellant grain point cloud with the coordinate axes. Finally, comparative experiments with iterative closest point (ICP) and random sample consensus (RANSAC) validate the efficiency of the proposed normalization algorithm. Full article
(This article belongs to the Section Astronautics & Space Science)
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<p>The free assembly method and the wall pouring method.</p>
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<p>Virtual free assembly process.</p>
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<p>Propellant grain point cloud.</p>
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<p>Overall procedure of the method (the images have been specially processed for de-identification).</p>
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<p>The problems faced by layer-by-layer projection (the images have been specially processed for de-identification). The height for each layer is selected with certain limitations, considering the presence of certain tilt in the original point cloud and the abundance of convex points on the arc surface.</p>
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<p>KD-tree nearest neighbor search. After performing KD-tree search for the point <math display="inline"><semantics> <msubsup> <mi>p</mi> <mrow> <mi>i</mi> <mi>j</mi> </mrow> <mo>′</mo> </msubsup> </semantics></math> in <math display="inline"><semantics> <msubsup> <mi>P</mi> <mi>i</mi> <mo>′</mo> </msubsup> </semantics></math>, the nearest subset for this point is obtained. Using k-means, this subset is divided into two subsets, denoted as <math display="inline"><semantics> <msub> <mi>C</mi> <mn>1</mn> </msub> </semantics></math> and <math display="inline"><semantics> <msub> <mi>C</mi> <mn>2</mn> </msub> </semantics></math>. Line fitting is performed on both subsets, and based on the angle, it is determined whether to include this point in the candidate corner point set.</p>
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<p>Boundary of propellant grain point cloud. The boundary of the cross-sectional point cloud exhibits a periodic density distribution. The appropriate interval division is shown in figures (<b>a</b>,<b>b</b>). An excessively large interval is shown in figure (<b>c</b>), while an overly small interval is shown in figure (<b>d</b>).</p>
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<p>Symmetric axis fitting.</p>
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<p>The entire process for multiple symmetry axis fitting.</p>
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<p>FP-1, FP-2, FP-3, and FP-4.</p>
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<p>RMSE principle diagram.</p>
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<p>Transformation matrix error. When only <math display="inline"><semantics> <msub> <mi>θ</mi> <mi>y</mi> </msub> </semantics></math> is changed, the <math display="inline"><semantics> <msub> <mi>δ</mi> <mi>θ</mi> </msub> </semantics></math> of our method is smaller than that of RANSAC + ICP. When only <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi>x</mi> </mrow> </semantics></math> or <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi>z</mi> </mrow> </semantics></math> is changed, RANSAC + ICP shows higher accuracy. However, when only <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi>y</mi> </mrow> </semantics></math> is changed, the performance of RANSAC + ICP is unstable, while our method remains stable with a consistently low error.</p>
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<p><math display="inline"><semantics> <mi>RMSE</mi> </semantics></math> between <math display="inline"><semantics> <msup> <mrow> <msubsup> <mi>P</mi> <mi>T</mi> <mi>i</mi> </msubsup> </mrow> <mo>′</mo> </msup> </semantics></math> and <math display="inline"><semantics> <msubsup> <mi>P</mi> <mi>T</mi> <mi>i</mi> </msubsup> </semantics></math>. When only <math display="inline"><semantics> <msub> <mi>θ</mi> <mi>y</mi> </msub> </semantics></math> is changed, the two-stage <math display="inline"><semantics> <mi>RMSE</mi> </semantics></math> values of our method are lower. When only <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi>x</mi> </mrow> </semantics></math> or <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi>z</mi> </mrow> </semantics></math> is changed, the <math display="inline"><semantics> <mi>RMSE</mi> </semantics></math> values of RANSAC and ICP tend to be consistent and are lower than those of our method. However, when only <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi>y</mi> </mrow> </semantics></math> is changed, the <math display="inline"><semantics> <mi>RMSE</mi> </semantics></math> values of RANSAC and ICP show instability, while our method remains relatively stable and maintains a lower <math display="inline"><semantics> <mi>RMSE</mi> </semantics></math> value.</p>
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<p>Random position point cloud position normalization experiment.</p>
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<p>Position normalization results. It is evident that after position normalization using our method, most of the points align well with the target point cloud, with only a few points that are not completely aligned. In contrast, the results from RANSAC + ICP show that most points are not well aligned.</p>
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18 pages, 19399 KiB  
Article
An Online Data-Driven Method for Accurate Detection of Thermal Updrafts Using SINDy
by Yufeng Lu, Chenglou Liu, Haichao Hong, Yunwei Huang, Tingwei Ji and Fangfang Xie
Aerospace 2024, 11(10), 858; https://doi.org/10.3390/aerospace11100858 - 18 Oct 2024
Viewed by 857
Abstract
Utilizing thermal updrafts shows potential for enabling long-endurance cruising of fixed-wing unmanned aerial vehicles without energy consumption. This article presents a novel online method based on sparse identification of nonlinear dynamics (SINDy) approach to achievement identification of thermal sources in the atmosphere. Initially, [...] Read more.
Utilizing thermal updrafts shows potential for enabling long-endurance cruising of fixed-wing unmanned aerial vehicles without energy consumption. This article presents a novel online method based on sparse identification of nonlinear dynamics (SINDy) approach to achievement identification of thermal sources in the atmosphere. Initially, the algorithm is incorporated into the upper-level planning system, interacting with the lower-level controller. Then, experiments are conducted through software-in-the-loop simulations (SITL) to validate the implementation of the proposed algorithm. It is found that direct observation of thermal sources through measurements using SINDy is unfeasible during straight and circular flight modes. Nevertheless, simulation analysis of the proposed approach indicates that under unobservable conditions, a portion of the parameters can still be identified. By comparing results obtained using the particle filter algorithm, this method is shown to accurately estimate the parameters with negligible errors under observability conditions. The novelty of this approach lies in its significant improvement of the localization accuracy of the thermal source, without the need for parameter adjustments in the algorithm. Finally, the proposed methods are integrated into commonly used hardware platforms, and their online feasibility is verified through hardware-in-the-loop simulations. Full article
(This article belongs to the Section Aeronautics)
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<p>Block diagram of the proposed algorithm for detection of thermal updrafts. Note that the color of the numbers in the figure represents the coefficients of the corresponding candidate function.</p>
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<p>The illustration of the particle filter algorithm.</p>
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<p>Framework diagram of experiments integrating six subsystems.</p>
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<p>System components of Hardware-in-the-loop simulations.</p>
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<p>Top view of straight flight paths.</p>
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<p>Top view of circling flight paths.</p>
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<p>Top view of straight flight paths.</p>
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<p>Top view of circling flight paths.</p>
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<p>Estimated positions in circling flights.</p>
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<p>3-D flight trajectories in observable conditions.</p>
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<p>Estimated results of thermal positions obtained by SINDy. Note that the datum mark for estimated positions is the origin of the coordinate system shown in <a href="#aerospace-11-00858-f010" class="html-fig">Figure 10</a>.</p>
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<p>Estimation results of thermal radius obtained by SINDy.</p>
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21 pages, 12855 KiB  
Article
Noise Study Auralization of an Open-Rotor Engine
by Qing Zhang, Siyi Jiang, Xiaojun Yang, Yongjia Xu and Maosheng Zhu
Aerospace 2024, 11(10), 857; https://doi.org/10.3390/aerospace11100857 - 17 Oct 2024
Viewed by 1022
Abstract
Based on the performance and acoustic data files of reduced-size open-rotor engines in low-speed wind tunnels, the static sound pressure level was derived by converting the 1-foot lossless spectral density into sound-pressure-level data, the background noise was removed, and the results were corrected [...] Read more.
Based on the performance and acoustic data files of reduced-size open-rotor engines in low-speed wind tunnels, the static sound pressure level was derived by converting the 1-foot lossless spectral density into sound-pressure-level data, the background noise was removed, and the results were corrected according to the environmental parameters of the low-speed wind tunnels. In accordance with the requirements of Annex 16 of the Convention on International Civil Aviation Organization and Part 36 of the Civil Aviation Regulations of China on noise measurement procedures, the takeoff trajectory was physically modeled; the static noise source was mapped onto the takeoff trajectory to simulate the propagation process of the noise during takeoff; and the 24 one-third-octave center frequencies that corresponded to the SPL data were corrected for geometrical dispersion, atmospheric absorption, and Doppler effects, so that the takeoff noise could be corrected to represent a real environment. In addition, the audible processing of noise data with a 110° source pointing angle was achieved, which can be useful for enabling practical observers to analyze the noise characteristics. Full article
(This article belongs to the Section Aeronautics)
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<p>The new-generation open-rotor engine configuration.</p>
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<p>Aircraft-noise-monitoring points for noise airworthiness requirements.</p>
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<p>Full- and reduced-thrust takeoff trajectories.</p>
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<p>Example of reduced-thrust takeoff trajectory noise source localization.</p>
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<p>Attenuation curve of the geometric dispersion effect.</p>
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<p>Atmospheric absorption sound attenuation curve.</p>
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<p>Doppler effect curve (The intersection of the blue dotted line and the Doppler effect curve is the angle at which the sound pressure level attenuation is 0).</p>
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<p>Source synthesis, propagation path and receiver setting.</p>
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<p>Broadband and monophonic filtering results.</p>
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<p>Broadband synthesis.</p>
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<p>Flight path simulated in a 3D virtual environment.</p>
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<p>The 110° angle noise-data audio realization point (at the red dot).</p>
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<p>Wind tunnel environment simulation.</p>
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<p>Acoustic measurement position.</p>
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<p>Mixed-reality environment visualization.</p>
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<p>Labeling of the test.</p>
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<p>Dynamic bar graph of the mixed-reality environment.</p>
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18 pages, 7439 KiB  
Article
Effect of Temperature on Foreign Object Damage Characteristics and High Cycle Fatigue Performance of Nickel-Based Superalloy GH4169
by Li Sun, Xu Jia, Rong Jiang, Yingdong Song and Lei Zhu
Aerospace 2024, 11(10), 856; https://doi.org/10.3390/aerospace11100856 - 17 Oct 2024
Viewed by 847
Abstract
High-speed ballistic impact tests were conducted at room temperature and 500 °C on nickel-based superalloy GH4169 simulated blade specimens containing leading-edge features. The microscopic characteristics of the impact notch at room temperature versus 500 °C were observed by electron backscatter diffraction (EBSD), and [...] Read more.
High-speed ballistic impact tests were conducted at room temperature and 500 °C on nickel-based superalloy GH4169 simulated blade specimens containing leading-edge features. The microscopic characteristics of the impact notch at room temperature versus 500 °C were observed by electron backscatter diffraction (EBSD), and it was found that the grains on the notched subsurface were ruined, while in more distant regions, the impact energy was mainly absorbed by grain boundaries. Internal damage is more concentrated in the notched subsurface region at 500 °C compared to room temperature. The high cycle fatigue strength of the damaged specimens under different conditions was tested. The results showed that the high cycle fatigue strength of the damaged specimens increased with the increase in the notch depth, and the fatigue strength of the damaged specimens at 500 °C was higher than the fatigue strength at room temperature. Both the 48 h post-impact holding time at 500 °C and the preload during impact at 500 °C increased the fatigue strength of the damaged specimens. Full article
(This article belongs to the Section Aeronautics)
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<p>The microstructure of GH4169 nickel-based alloy.</p>
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<p>Hourglass specimen (unit: mm).</p>
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<p>The simulated blade specimen containing leading-edge features (unit: mm).</p>
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<p>The fragile plastic sabot with the projectile and the high-speed ballistic impact system.</p>
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<p>Notches that were cold mounted and grinded to the center cross-section.</p>
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<p>Macrograph of notch damage with different depths at room temperature.</p>
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<p>Macrograph of notch damage with different depths at 500 °C.</p>
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<p>The variations in FOD length with the incident depth and exit depth: (<b>a</b>) length with the incident depth at room temperature, (<b>b</b>) length with the exit depth at room temperature, (<b>c</b>) length with the incident depth at 500 °C, and (<b>d</b>) length with the exit depth at 500 °C.</p>
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<p>Macrograph of notch for microscopic observation.</p>
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<p>Metallograph of the notch center section (<b>left</b> side is the exit side, and <b>right</b> side is the incident side).</p>
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<p>IPFs and GND figures of the notch center section (<b>left</b> side is the exit side, and <b>right</b> side is the incident side).</p>
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<p>IPFs and GND figures of the notch center section (<b>left</b> side is the exit side, and <b>right</b> side is the incident side).</p>
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<p>The variation in HCF strength with damage depth of notch-type specimens at room temperature and 500 °C.</p>
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<p>The fractography of notch-type damage specimens at room temperature (<b>left</b> side is the exit side, and <b>right</b> side is the incident side).</p>
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<p>The fractography of notch-type damage specimens at 500 °C (<b>left</b> side is the exit side, and <b>right</b> side is the incident side).</p>
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<p>The variation in HCF strength with damage depth of notch-type specimens for the stress ratio R = 0.1 at room temperature, 500 °C, 500 °C with 48 h post-damage holding time, and 500 °C with preloading.</p>
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12 pages, 2926 KiB  
Article
The Maintenance of Orbital States in a Floating Partial Space Elevator Using the Reinforcement Learning Method
by Weili Xu, Xuerong Yang and Gefei Shi
Aerospace 2024, 11(10), 855; https://doi.org/10.3390/aerospace11100855 - 16 Oct 2024
Cited by 1 | Viewed by 764
Abstract
A partial space elevator (PSE) is a multi-body tethered space system in which the main satellite, typically an ultra-large spacecraft or a space station in a higher orbit, is connected to a transport spacecraft in a lower orbit via a tether, maintaining orbital [...] Read more.
A partial space elevator (PSE) is a multi-body tethered space system in which the main satellite, typically an ultra-large spacecraft or a space station in a higher orbit, is connected to a transport spacecraft in a lower orbit via a tether, maintaining orbital synchronization. One or more climbers can move along the tether driven by electric power, enabling cross-orbital payload transportation between the two spacecraft. The climbers’ motion significantly alters the main satellite’s orbital states, compromising its safe and stable operation. The dynamic coupling and nonlinearity of the PSE further exacerbate this challenge. This study aims to preliminarily address this issue by proposing a new mission planning strategy. This strategy utilizes reinforcement learning (RL) to select the waiting interval between two transfer missions, thereby maintaining the main satellite’s orbital motion in a stable state. Simulation results confirm the feasibility and effectiveness of the proposed mission-based method. Full article
(This article belongs to the Special Issue Application of Tether Technology in Space)
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<p>Diagram of a PSE.</p>
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<p>The expected goal to be achieved after the cargo transportation. (<b>a</b>) Next transportation starts without waiting (<b>b</b>) Next transportation starting time obtained by exhaustive method.</p>
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<p>Flowchart of the RL method using the DQN algorithm.</p>
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<p>Training episodes’ mean reward.</p>
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<p>Cumulative reward.</p>
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<p>Planning results: (<b>a</b>) libration angles; (<b>b</b>) orbital radius’ changing; (<b>c</b>) tensions; (<b>d</b>) main satellite’s orbital angular velocity; (<b>e</b>) tether length of <span class="html-italic">L</span><sub>1</sub>; (<b>f</b>) climber speed.</p>
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<p>Comparison of (<b>a</b>) without mission planning, (<b>b</b>) exhaustion, and (<b>c</b>) the RL method.</p>
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23 pages, 9625 KiB  
Article
Effects of Build Direction and Heat Treatment on the Defect Characterization and Fatigue Properties of Laser Powder Bed Fusion Ti6Al4V
by Wenbo Sun, Yu’e Ma, Peiyao Li, Ziad Moumni and Weihong Zhang
Aerospace 2024, 11(10), 854; https://doi.org/10.3390/aerospace11100854 - 16 Oct 2024
Cited by 1 | Viewed by 1026
Abstract
Laser powder bed fusion (LPBF) is one of the high-precision additive manufacturing techniques for producing complex 3D components. It is well known that defects appear in additive-manufactured parts, and they deeply affect the fatigue properties; even heat treatment is performed after printing. In [...] Read more.
Laser powder bed fusion (LPBF) is one of the high-precision additive manufacturing techniques for producing complex 3D components. It is well known that defects appear in additive-manufactured parts, and they deeply affect the fatigue properties; even heat treatment is performed after printing. In order to meet the safe-life design requirements of additive-manufactured aircraft structures, the effects of build direction and heat treatment on defects and fatigue properties need to be quantified. Hence, Ti6Al4V alloy samples with different build directions were designed and printed by LPBF. X-ray computed tomography was used to quantitatively analyze the defect size, the sphericity, and the defect orientation. And their effects on fatigue properties were studied. An extended effective defect size and a defect-based fatigue anisotropy evaluation process are proposed to qualify the effects of the defect size, sphericity, and defect orientation. It is shown that the build direction can affect the porosity distribution and maximum defect size, while the annealing treatment can cause the coalescence of small defects and higher porosity. The defect orientation exhibited a fluctuating trend of 0°–90°–0°–90°–0° as the volume increased. The elongated lack of fusion defects related to the build direction was the main crack source and could lead to fatigue anisotropy of LPBF Ti6Al4V. Full article
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<p>Set-up, (<b>a</b>) BLT-S310 machine, (<b>b</b>) the scanning strategies, (<b>c</b>) the sample design.</p>
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<p>The reconstructed 3D samples in different build directions and heat treatment conditions.</p>
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<p>Defect distribution of LPBF Ti6Al4V in different build directions and heat treatment conditions: (<b>a</b>) 0° as-built, (<b>b</b>) 45° as-built, (<b>c</b>) 90° as-built, (<b>d</b>) 0° heat-treated, (<b>e</b>) 45° heat-treated, (<b>f</b>) 90° heat-treated.</p>
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<p>Effect of build direction and heat treatment on porosity.</p>
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<p>Orthographic projection of defects along the build direction: (<b>a</b>) the 0° as-built sample, (<b>b</b>) the 45° as-built sample, (<b>c</b>) the 90° as-built sample, (<b>d</b>) the 0° heat-treated sample, (<b>e</b>) the 45° heat-treated sample, (<b>f</b>) the 90° heat-treated sample, (<b>g</b>) porosity curves in the 0° samples, (<b>h</b>) porosity curves in the 45° samples, (<b>i</b>) porosity curves in the 90° samples.</p>
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<p>Cross-section gradient and heat transfer.</p>
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<p>Defect along the radial direction and its porosity distribution: (<b>a</b>) the 0° as-built sample, (<b>b</b>) the 45° as-built sample, (<b>c</b>) the 90° as-built sample, (<b>d</b>) the 0° heat-treated sample, (<b>e</b>) the 45° heat-treated sample, (<b>f</b>) the 90° heat-treated sample.</p>
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<p>Defect size distribution in different build direction samples: (<b>a</b>) the 0° sample, (<b>b</b>) the 45° sample, (<b>c</b>) the 90° sample, (<b>d</b>) comparison of the as-built samples.</p>
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<p>Defect size distribution of the heat-treated samples.</p>
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<p>Sphericity distribution: (<b>a</b>) the 0° sample, (<b>b</b>) the 45° sample, (<b>c</b>) the 90° sample, (<b>d</b>) comparison of the fitted relative frequency curves.</p>
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<p>Defect orientation.</p>
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<p>Defect orientation in different build direction samples: (<b>a</b>) the 0° sample, (<b>b</b>) the 45° sample, (<b>c</b>) the 90°sample, (<b>d</b>) defect coalescence under annealing treatment.</p>
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<p>Volume–defect orientation–aspect ratio relationship: (<b>a</b>–<b>c</b>) the as-built sample in 0°, 45°, and 90° build directions, (<b>d</b>–<b>f</b>) the heat-treated sample in 0°, 45°, and 90° build directions.</p>
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<p><math display="inline"><semantics> <mrow> <msqrt> <mi>a</mi> <mi>r</mi> <mi>e</mi> <mi>a</mi> </msqrt> </mrow> </semantics></math> and its distance from the sample surface: (<b>a</b>) the 0° sample, (<b>b</b>) the 45° sample, (<b>c</b>) the 90° sample, (<b>d</b>) the relative frequency of large defects.</p>
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<p><math display="inline"><semantics> <mrow> <msqrt> <msub> <mrow> <mi>a</mi> <mi>r</mi> <mi>e</mi> <mi>a</mi> </mrow> <mrow> <mi>e</mi> <mi>f</mi> <mi>f</mi> </mrow> </msub> </msqrt> </mrow> </semantics></math> distribution under the heat-treated condition: (<b>a</b>) the 0° sample, (<b>b</b>) the 45° sample, (<b>c</b>) the 90° sample, (<b>d–f</b>) the heartland of the 0° sample, 45° sample, and 90° sample.</p>
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<p>Statistics of extremes of defects in heat-treated samples.</p>
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<p>Relationship between fatigue limit and <math display="inline"><semantics> <mrow> <msqrt> <mi>a</mi> <mi>r</mi> <mi>e</mi> <mi>a</mi> </msqrt> </mrow> </semantics></math> [<a href="#B60-aerospace-11-00854" class="html-bibr">60</a>].</p>
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<p>Effect of the defect characteristics on stress concentration: (<b>a</b>) defect with the same projected area, (<b>b</b>) position, (<b>c</b>) orientation, (<b>d</b>) aspect ratio.</p>
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<p>The <math display="inline"><semantics> <mrow> <msqrt> <mi>a</mi> <mi>e</mi> <mi>r</mi> <mi>a</mi> </msqrt> </mrow> </semantics></math> distribution of heat-treated samples: (<b>a</b>) the 0° sample, (<b>b</b>) the 45° sample, (<b>c</b>) the 90° sample, (<b>d</b>) stress distribution with real defects in the 90° sample.</p>
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17 pages, 2743 KiB  
Article
Experimental Study on Ice Shedding Behaviors for Aero-Engine Fan Blade Icing during Ground Idle
by Liping Wang, Kun Yang, Fang Yu and Fuxin Wang
Aerospace 2024, 11(10), 853; https://doi.org/10.3390/aerospace11100853 - 16 Oct 2024
Viewed by 1002
Abstract
Fan blade icing can affect efficiency and aerodynamic stability, and the shed ice may be sucked into the core of the engine, causing adverse effects or even damage to the compressor components. Ice accretion and shedding are among the key issues in engine [...] Read more.
Fan blade icing can affect efficiency and aerodynamic stability, and the shed ice may be sucked into the core of the engine, causing adverse effects or even damage to the compressor components. Ice accretion and shedding are among the key issues in engine design and tests. But they have not been clearly understood. In this work, ice shedding from rotating aero-engine fan blades during continuous icing is experimentally investigated under the relevant airworthiness requirements. The phenomena of icing and ice shedding under different ambient temperatures and engine speeds are recorded to obtain the ice-shedding time and the characteristic length of the residual ice. Force analysis is used to understand the corresponding behavior. The degree of ice-shedding balance Db is defined to explore the symmetry of ice shedding. The results show that the shedding time is significantly affected by the rotational speed, and the characteristic length will first shorten and then grow as the ambient temperature decreases. When the ice shedding is completed instantaneously, Db will show a violent shock. There is a critical ambient temperature, below which the ice accretion will worsen significantly as temperature decreases. For aero-engine fan blade icing tests during ground idle, the critical ambient temperature ranges from −5 C to −9 C. In order for the ice to shed faster, the engine speed has to reach a threshold. This study can shed light on the preliminary characteristics of ice shedding from rotating components and provide guidance and a data basis for the numerical simulation of fan blade icing and the design of an aero-engine. Full article
(This article belongs to the Section Aeronautics)
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<p>Schematic diagram of engine icing simulation system.</p>
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<p>Schematic diagram of the positions of each measuring instrument used to calibrate the spray system.</p>
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<p>(<b>a</b>) Icing state on the blade before ice shedding, (<b>b</b>) Residual ice on the blade after ice shedding and schematic diagram to calculate the characteristic length of residual ice.</p>
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<p>Sequence images of ice-shedding phenomena on the fan blades under different environmental temperature <span class="html-italic">T</span> at <span class="html-italic">n</span> = 2400 rpm: (<b>a</b>) <span class="html-italic">T</span> = −<math display="inline"><semantics> <msup> <mrow> <mn>2</mn> <mo> </mo> </mrow> <mo>∘</mo> </msup> </semantics></math>C, (<b>b</b>) <span class="html-italic">T</span> = −<math display="inline"><semantics> <msup> <mrow> <mn>3.5</mn> <mo> </mo> </mrow> <mo>∘</mo> </msup> </semantics></math>C, (<b>c</b>) <span class="html-italic">T</span> = −<math display="inline"><semantics> <msup> <mrow> <mn>5</mn> <mo> </mo> </mrow> <mo>∘</mo> </msup> </semantics></math>C, (<b>d</b>) <span class="html-italic">T</span> = −<math display="inline"><semantics> <msup> <mrow> <mn>7</mn> <mo> </mo> </mrow> <mo>∘</mo> </msup> </semantics></math>C, (<b>e</b>) <span class="html-italic">T</span> = −<math display="inline"><semantics> <msup> <mrow> <mn>9</mn> <mo> </mo> </mrow> <mo>∘</mo> </msup> </semantics></math>C. It needs to be explained here that, because the shooting is in a cloud environment and the blade is rotating at a high speed, the picture is captured by a high-speed camera at a low exposure time, so the clarity is not high; however, it is easy to identify the phenomenon of ice shedding by displaying continuous pictures.</p>
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<p>Ice shedding time <math display="inline"><semantics> <msub> <mi>t</mi> <mi>s</mi> </msub> </semantics></math> changing with environmental temperature <span class="html-italic">T</span> as <span class="html-italic">n</span> = 2400 rpm. Note that, when <span class="html-italic">T</span> = −<math display="inline"><semantics> <msup> <mrow> <mn>2</mn> <mo> </mo> </mrow> <mo>∘</mo> </msup> </semantics></math>C, the actual ice-shedding time is longer than the statistical ice-shedding time <math display="inline"><semantics> <msub> <mi>t</mi> <mi>s</mi> </msub> </semantics></math> because most of the fan blades do not show ice-shedding phenomenon during the record time.</p>
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<p>Characteristic length of residual ice after first shedding and the end of the test, <math display="inline"><semantics> <msub> <mi>l</mi> <mrow> <mi>c</mi> <mi>f</mi> </mrow> </msub> </semantics></math> and <math display="inline"><semantics> <msub> <mi>l</mi> <mrow> <mi>c</mi> <mi>e</mi> </mrow> </msub> </semantics></math>, at different environment temperatures <span class="html-italic">T</span> as <span class="html-italic">n</span> = 2400 rpm.</p>
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<p>Ice accretion on the fan blades after the test under different environmental temperatures <span class="html-italic">T</span> at <span class="html-italic">n</span> = 2400 rpm: (<b>a</b>) <span class="html-italic">T</span> = −<math display="inline"><semantics> <msup> <mrow> <mn>2</mn> <mo> </mo> </mrow> <mo>∘</mo> </msup> </semantics></math>C, (<b>b</b>) <span class="html-italic">T</span> = −<math display="inline"><semantics> <msup> <mrow> <mn>3.5</mn> <mo> </mo> </mrow> <mo>∘</mo> </msup> </semantics></math>C, (<b>c</b>) <span class="html-italic">T</span> = −<math display="inline"><semantics> <msup> <mrow> <mn>5</mn> <mo> </mo> </mrow> <mo>∘</mo> </msup> </semantics></math>C, (<b>d</b>) <span class="html-italic">T</span> = −<math display="inline"><semantics> <msup> <mrow> <mn>7</mn> <mo> </mo> </mrow> <mo>∘</mo> </msup> </semantics></math>C.</p>
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<p>Sequence images of ice-shedding phenomena on the fan blades under different engine speeds <span class="html-italic">n</span> at <span class="html-italic">T</span> = −<math display="inline"><semantics> <msup> <mrow> <mn>7</mn> <mo> </mo> </mrow> <mo>∘</mo> </msup> </semantics></math>C: (<b>a</b>) <span class="html-italic">n</span> = 1800 rpm, (<b>b</b>) <span class="html-italic">n</span> = 2400 rpm, (<b>c</b>) <span class="html-italic">n</span> = 2700 rpm, (<b>d</b>) <span class="html-italic">n</span> = 3000 rpm.</p>
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<p>Ice-shedding time <math display="inline"><semantics> <msub> <mi>t</mi> <mi>s</mi> </msub> </semantics></math> changing with engine speed <span class="html-italic">n</span> at <span class="html-italic">T</span> = −<math display="inline"><semantics> <msup> <mrow> <mn>7</mn> <mo> </mo> </mrow> <mo>∘</mo> </msup> </semantics></math>C.</p>
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<p>Characteristic length of residual ice after first shedding and the end of the test, <math display="inline"><semantics> <msub> <mi>l</mi> <mrow> <mi>c</mi> <mi>f</mi> </mrow> </msub> </semantics></math> and <math display="inline"><semantics> <msub> <mi>l</mi> <mrow> <mi>c</mi> <mi>e</mi> </mrow> </msub> </semantics></math>, under different engine speeds at <span class="html-italic">T</span> = −<math display="inline"><semantics> <msup> <mrow> <mn>7</mn> <mo> </mo> </mrow> <mo>∘</mo> </msup> </semantics></math>C.</p>
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<p>Ice accretion on the fan blades after the test under different rotational speeds <span class="html-italic">n</span> at <span class="html-italic">T</span> = −<math display="inline"><semantics> <msup> <mrow> <mn>7</mn> <mo> </mo> </mrow> <mo>∘</mo> </msup> </semantics></math>C: (<b>a</b>) <span class="html-italic">n</span> = 1800 rpm, (<b>b</b>) <span class="html-italic">n</span> = 2400 rpm, (<b>c</b>) <span class="html-italic">n</span> = 2700 rpm, (<b>d</b>) <span class="html-italic">n</span> = 3000 rpm.</p>
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<p>Ice accumulation on the high-pressure side of the blades after the test: (<b>a</b>) <span class="html-italic">T</span> = −<math display="inline"><semantics> <msup> <mrow> <mn>3.5</mn> <mo> </mo> </mrow> <mo>∘</mo> </msup> </semantics></math>C, <span class="html-italic">n</span> = 2400 rpm; (<b>b</b>) <span class="html-italic">T</span> = −<math display="inline"><semantics> <msup> <mrow> <mn>7</mn> <mo> </mo> </mrow> <mo>∘</mo> </msup> </semantics></math>C, <span class="html-italic">n</span> = 2400 rpm; (<b>c</b>) <span class="html-italic">T</span> = −<math display="inline"><semantics> <msup> <mrow> <mn>7</mn> <mo> </mo> </mrow> <mo>∘</mo> </msup> </semantics></math>C, <span class="html-italic">n</span> = 1800 rpm.</p>
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<p>Schematic diagram of a simplified force analysis of ice on the rotating component.</p>
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<p>Schematic diagram of symmetry interpretation on ice shedding with different blade numbers: (<b>a</b>) Two blades, (<b>b</b>) Three blades, (<b>c</b>) Four blades.</p>
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<p>Degree of ice-shedding balance <math display="inline"><semantics> <msub> <mi>D</mi> <mi>b</mi> </msub> </semantics></math> varies over time <span class="html-italic">t</span> under different cases.</p>
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24 pages, 10038 KiB  
Article
The Influence of Bleed Position on the Stability Expansion Effect of Self-Circulating Casing Treatment
by Haoguang Zhang, Jinhang Xiao, Xinyi Zhong, Yiming Feng and Wuli Chu
Aerospace 2024, 11(10), 852; https://doi.org/10.3390/aerospace11100852 - 16 Oct 2024
Viewed by 777
Abstract
The self-circulating casing treatment can effectively expand the stable working range of the compressor, with little impact on its efficiency. With a single-stage transonic axial flow compressor NASA (National Aeronautics and Space Administration) Stage 35 as the research object, a multi-channel unsteady numerical [...] Read more.
The self-circulating casing treatment can effectively expand the stable working range of the compressor, with little impact on its efficiency. With a single-stage transonic axial flow compressor NASA (National Aeronautics and Space Administration) Stage 35 as the research object, a multi-channel unsteady numerical calculation method was used here to design three types of self-circulating casing treatment structures: 20% Ca (axial chord length of the rotor blade tip), 60% Ca, and 178% Ca (at this time, the bleed position is at the stator channel casing) from the leading edge of the blade tip. The effects of these three bleed positions on the self-circulating stability expansion effect and compressor performance were studied separately. The calculation results indicate that the further the bleed position is from the leading edge of the blade tip, the weaker the expansion ability of the self-circulating casing treatment, and the greater the negative impact on the peak efficiency and design point efficiency of the compressor. This is because the air inlet of the self-circulating casing with an air intake position of 20% Ca is located directly above the core area of the rotor blade top blockage, which can more effectively extract low-energy fluid from the blockage area. Compared to the other two bleed positions, it has the greatest inhibitory effect on the leakage vortex in the rotor blade tip gap and has the strongest ability to improve the blockage at the rotor blade tip. Therefore, 20% Ca from the leading edge of the blade tip has the strongest stability expansion ability, achieving a stall margin improvement of 11.28%. Full article
(This article belongs to the Section Aeronautics)
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<p>Stage 35 meridian runner diagram.</p>
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<p>Stage 35 geometry model.</p>
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<p>Schematic of a single-channel grid.</p>
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<p>Total performance curves for different turbulence models.</p>
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<p>Performance of different turbulence model primitives.</p>
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<p>Total performance curves for the unsteady calculations [<a href="#B24-aerospace-11-00852" class="html-bibr">24</a>].</p>
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<p>Comparison of the numerical simulation and experimentally measured primitive performance.</p>
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<p>Schematic diagram of the self-circulating casing treatment structure.</p>
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<p>Schematic diagram of the meridian surface of the self-circulating casing treatment for different bleed positions.</p>
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<p>Three-dimensional schematic of self-circulating casing treatment.</p>
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<p>Single-stage compressor total performance curve (time-averaged results).</p>
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<p>Total pressure rotor performance curve (time-averaged results).</p>
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<p>Static total pressure recovery coefficient with the flow rate curve (time-averaged results).</p>
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<p>SMI, PEI, and DEI (time-averaged results) for self-circulating casing treatments with different bleed locations.</p>
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<p>Iso-surface plot of relative Mach number distribution at a 98% blade height.</p>
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<p>Distribution of the dimensionless dense flow along the blade height.</p>
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<p>Distribution of the static sub-inlet axial velocity and absolute airflow angle along the blade height.</p>
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<p>Relative Mach number iso-surface plot for different cross-sections of the static subchannel perpendicular to the axis.</p>
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<p>Distribution of the absolute airflow angle of the static sub-inlet along the circumferential direction.</p>
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<p>Rotor blade tip clearance leakage flow line diagram.</p>
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<p>Distribution of the radial velocity of CTB1.78 bleed flow.</p>
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<p>Variation curve of the dimensionless bleed volume with moments.</p>
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<p>Absolute Mach number and streamline distribution within self-circulating casing treatment.</p>
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<p>Rotor outlet relative total pressure loss coefficient distribution along blade height.</p>
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<p>Distribution of total pressure loss coefficients at static sub outlet along blade height.</p>
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19 pages, 9442 KiB  
Article
Optimal Selection of Active Jet Parameters for a Ducted Tail Wing Aimed at Improving Aerodynamic Performance
by Huayu Jia, Huilong Zheng, Hong Zhou and Shunbo Huo
Aerospace 2024, 11(10), 851; https://doi.org/10.3390/aerospace11100851 - 15 Oct 2024
Viewed by 795
Abstract
The foldable tail of the box-type launch vehicle poses a risk of mechanical jamming during the launch process, which is not conducive to the smooth completion of the flight mission. The integrated nonfolding ducted tail proposed in this article can solve the problem [...] Read more.
The foldable tail of the box-type launch vehicle poses a risk of mechanical jamming during the launch process, which is not conducive to the smooth completion of the flight mission. The integrated nonfolding ducted tail proposed in this article can solve the problem of storing the tail in the launch box. Moreover, traditional mechanical control surfaces have been eliminated, and active jet control has been adopted to control the pitch direction of the flight attitude, which can improve the structural reliability of the tail wing. By studying the effects of parameters such as momentum coefficient, jet hole position, jet hole height, and jet angle on improving the aerodynamic performance of ducted tail wing, relatively good jet parameters are selected. Research has found that compared with jet hole height and jet angle, momentum coefficient and jet hole position are more effective in improving the aerodynamic performance of ducted tail wings. Under a trailing edge jet, a relatively good jet condition occurs when the jet hole height is equal to0.25% of the aerodynamic chord length, and the jet angle is equal to 0°. At this time, with the increase of the jet momentum coefficient, the effect of increasing the lift of the ducted tail wing is the best. Finally, a comparative analysis is conducted on the lift and drag characteristics between the ducted tail wing and traditional tail wing, and it is found that the ducted tail wing can generate lift at a 0° attack angle and will not stall in the high attack angle range of 12°~22°, with broad application prospects. Full article
(This article belongs to the Special Issue Aerodynamic Numerical Optimization in UAV Design)
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<p>The advantages and parametric modeling of active jet ducted tail fins. (<b>a</b>) Compared with the traditional tail, the ducted tail in the launch box does not need to be folded; (<b>b</b>) during flight, the aircraft controls its flight attitude through an active jet ducted tail fin; (<b>c</b>) modeling jet parameters for ducted tail fins.</p>
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<p>Details of the grid. (<b>a</b>) Grid of ordinary tail wing; (<b>b</b>) grid of jet ducted tail wing.</p>
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<p>Verification of grid independence for the ordinary NACA0012 tail wing.</p>
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<p>Verification of calculation accuracy for the ordinary NACA0012 tail wing.</p>
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<p>The improvement effect of momentum coefficient on the aerodynamic performance under trailing edge jet conditions. (<b>a</b>) Lift coefficient; (<b>b</b>) growth value of lift coefficient; (<b>c</b>) drag coefficient; (<b>d</b>) growth value of drag coefficient.</p>
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<p>The improvement effect of momentum coefficient on the aerodynamic performance under 20% leading edge jet conditions. (<b>a</b>) Lift coefficient; (<b>b</b>) growth value of lift coefficient; (<b>c</b>) drag coefficient; (<b>d</b>) growth value of drag coefficient.</p>
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<p>The improvement effect of momentum coefficient on the aerodynamic performance under 20% leading edge jet conditions. (<b>a</b>) Lift coefficient; (<b>b</b>) growth value of lift coefficient; (<b>c</b>) drag coefficient; (<b>d</b>) growth value of drag coefficient.</p>
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<p>The improvement effect of jet hole height on aerodynamic performance in the state of momentum coefficient 0.02. (<b>a</b>) Lift coefficient; (<b>b</b>) growth value of lift coefficient; (<b>c</b>) drag coefficient; (<b>d</b>) growth value of drag coefficient.</p>
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<p>The improvement effect of jet hole height on aerodynamic performance in the state of momentum coefficient 0.04. (<b>a</b>) Lift coefficient; (<b>b</b>) growth value of lift coefficient; (<b>c</b>) drag coefficient; (<b>d</b>) growth value of drag coefficient.</p>
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<p>The improvement effect of jet hole height on aerodynamic performance in the state of momentum coefficient 0.04. (<b>a</b>) Lift coefficient; (<b>b</b>) growth value of lift coefficient; (<b>c</b>) drag coefficient; (<b>d</b>) growth value of drag coefficient.</p>
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<p>The improvement effect of jet hole position on aerodynamic performance at a jet angle of 0 ° and momentum coefficient of 0.04. (<b>a</b>) Lift coefficient; (<b>b</b>) growth value of lift coefficient; (<b>c</b>) drag coefficient; (<b>d</b>) growth value of drag coefficient.</p>
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<p>The improvement effect of jet hole position on aerodynamic performance at a jet angle of 10 ° and momentum coefficient of 0.04. (<b>a</b>) Lift coefficient; (<b>b</b>) growth value of lift coefficient; (<b>c</b>) drag coefficient; (<b>d</b>) growth value of drag coefficient.</p>
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<p>The improvement effect of jet angle on aerodynamic performance under the condition of leading edge 20% and momentum coefficient 0.04. (<b>a</b>) Lift coefficient; (<b>b</b>) growth value of lift coefficient; (<b>c</b>) drag coefficient; (<b>d</b>) growth value of drag coefficient.</p>
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<p>The improvement effect of jet angle on aerodynamic performance under the condition of leading edge 40% and momentum coefficient 0.04. (<b>a</b>) Lift coefficient; (<b>b</b>) growth value of lift coefficient; (<b>c</b>) drag coefficient; (<b>d</b>) growth value of drag coefficient.</p>
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<p>Comparison of lift and drag characteristics between the ducted tail and conventional tail. (<b>a</b>) Comparison of lift coefficients; (<b>b</b>) comparison of drag coefficients.</p>
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<p>Cloud maps of ducted tail wing. (<b>a</b>) <math display="inline"><semantics> <mrow> <msub> <mi>C</mi> <mi>μ</mi> </msub> <mo>=</mo> <mn>0.01</mn> <mo>,</mo> <mi>α</mi> <mo>=</mo> <mn>0</mn> <mo>°</mo> </mrow> </semantics></math> (<b>b</b>) <math display="inline"><semantics> <mrow> <msub> <mi>C</mi> <mi>μ</mi> </msub> <mo>=</mo> <mn>0.04</mn> <mo>,</mo> <mi>α</mi> <mo>=</mo> <mn>0</mn> <mo>°</mo> </mrow> </semantics></math>; (<b>c</b>) <math display="inline"><semantics> <mrow> <msub> <mi>C</mi> <mi>μ</mi> </msub> <mo>=</mo> <mn>0.01</mn> <mo>,</mo> <mi>α</mi> <mo>=</mo> <mn>20</mn> <mo>°</mo> </mrow> </semantics></math>; (<b>d</b>) <math display="inline"><semantics> <mrow> <msub> <mi>C</mi> <mi>μ</mi> </msub> <mo>=</mo> <mn>0.04</mn> <mo>,</mo> <mi>α</mi> <mo>=</mo> <mn>20</mn> <mo>°</mo> </mrow> </semantics></math>.</p>
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<p>Pressure cloud maps for ducted tail wing. (<b>a</b>) <math display="inline"><semantics> <mrow> <msub> <mi>C</mi> <mi>μ</mi> </msub> <mo>=</mo> <mn>0.01</mn> <mo>,</mo> <mi>α</mi> <mo>=</mo> <mn>0</mn> <mo>°</mo> </mrow> </semantics></math> (<b>b</b>) <math display="inline"><semantics> <mrow> <msub> <mi>C</mi> <mi>μ</mi> </msub> <mo>=</mo> <mn>0.04</mn> <mo>,</mo> <mi>α</mi> <mo>=</mo> <mn>0</mn> <mo>°</mo> </mrow> </semantics></math>; (<b>c</b>) <math display="inline"><semantics> <mrow> <msub> <mi>C</mi> <mi>μ</mi> </msub> <mo>=</mo> <mn>0.01</mn> <mo>,</mo> <mi>α</mi> <mo>=</mo> <mn>20</mn> <mo>°</mo> </mrow> </semantics></math>; (<b>d</b>) <math display="inline"><semantics> <mrow> <msub> <mi>C</mi> <mi>μ</mi> </msub> <mo>=</mo> <mn>0.04</mn> <mo>,</mo> <mi>α</mi> <mo>=</mo> <mn>20</mn> <mo>°</mo> </mrow> </semantics></math>.</p>
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<p>Comparison of pressure coefficients of ducted tail wing. (<b>a</b>) Comparison of pressure coefficients at <math display="inline"><semantics> <mrow> <mi>α</mi> <mo>=</mo> <mn>0</mn> <mo>°</mo> </mrow> </semantics></math> (<b>b</b>) comparison of pressure coefficients at <math display="inline"><semantics> <mrow> <mi>α</mi> <mo>=</mo> <mn>20</mn> <mo>°</mo> </mrow> </semantics></math>.</p>
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14 pages, 286 KiB  
Article
Interaction of a Dense Layer of Solid Particles with a Shock Wave Propagating in a Tube
by Konstantin Volkov
Aerospace 2024, 11(10), 850; https://doi.org/10.3390/aerospace11100850 - 15 Oct 2024
Viewed by 662
Abstract
A numerical simulation of an unsteady gas flow containing inert solid particles in a shock tube is carried out using the interpenetrating continuum model. The gas and dispersed phases are characterized by governing equations that express the concepts of mass, momentum, and energy [...] Read more.
A numerical simulation of an unsteady gas flow containing inert solid particles in a shock tube is carried out using the interpenetrating continuum model. The gas and dispersed phases are characterized by governing equations that express the concepts of mass, momentum, and energy conservation as well as an equation that shows the change of the volume fraction of the dispersed phase. Using a Godunov-type approach, the hyperbolic governing equations are solved numerically with an increased order of accuracy. The working section of the shock tube containing air and solid particles of various sizes is considered. The shock wave structure is discussed and computational results provide the spatial and temporal dependencies of the particle concentration and other flow quantities. The numerical simulation results are compared with available experimental and computational data. Full article
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<p>Experimental test rig used in [<a href="#B4-aerospace-11-00850" class="html-bibr">4</a>] (<b>a</b>), and computational domain with position of particle layer (<b>b</b>).</p>
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<p>Pressure distribution in pure gas at <math display="inline"><semantics> <mrow> <mi mathvariant="normal">M</mi> <mo>=</mo> <mn>1.3</mn> </mrow> </semantics></math>.</p>
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<p>Propagation of gas-dynamic discontinuities.</p>
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<p>Particle trajectories in comparison with data from [<a href="#B4-aerospace-11-00850" class="html-bibr">4</a>].</p>
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<p>Distributions of dispersed phase concentration at times <math display="inline"><semantics> <mrow> <mi>t</mi> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math> (1), 1.6 (2), and 4 ms (3). Line (4) corresponds to the calculated data [<a href="#B4-aerospace-11-00850" class="html-bibr">4</a>] for <math display="inline"><semantics> <mrow> <mi>t</mi> <mo>=</mo> <mn>4</mn> </mrow> </semantics></math> ms.</p>
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<p>Trajectories of lower and upper boundaries of particle layer in comparison with data from [<a href="#B4-aerospace-11-00850" class="html-bibr">4</a>] (symbols • and ∘).</p>
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<p>Pressure distributions (solid lines) in comparison with experimental data from [<a href="#B4-aerospace-11-00850" class="html-bibr">4</a>] (symbols • and symbols ∘).</p>
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18 pages, 8137 KiB  
Article
Precision Manufacturing in China of Replication Mandrels for Ni-Based Monolithic Wolter-I X-ray Mirror Mandrels
by Jiadai Xue, Bo Wang, Qiuyan Liao, Kaiji Wu, Yutao Liu, Yangong Wu, Wentao Chen, Zheng Qiao, Yuan Jin, Fei Ding, Dianlong Wang, Langping Wang, Guo Li, Yanji Yang and Yong Chen
Aerospace 2024, 11(10), 849; https://doi.org/10.3390/aerospace11100849 - 15 Oct 2024
Cited by 1 | Viewed by 845
Abstract
The X-ray satellite “Einstein Probe” of the Chinese Academy of Sciences (CAS) was successfully launched on 9 January 2024 at 15:03 Beijing Time from the Xichang Satellite Launch Center in China with a “Long March-2C” rocket. The Einstein Probe is equipped with two [...] Read more.
The X-ray satellite “Einstein Probe” of the Chinese Academy of Sciences (CAS) was successfully launched on 9 January 2024 at 15:03 Beijing Time from the Xichang Satellite Launch Center in China with a “Long March-2C” rocket. The Einstein Probe is equipped with two scientific X-ray telescopes. One is the Wide-field X-ray Telescope (WXT), which uses lobster-eye optics. The other is the Follow-up X-ray Telescope (FXT), a Wolter-I type telescope. These telescopes are designed to study the universe for high-energy X-rays associated with transient high-energy phenomena. The FXT consists of two modules based on 54 thin X-ray Wolter-I grazing incidence Ni-replicated mirrors produced by the Italian Media Lario company, as contributions from the European Space Agency and the Max Planck Institute for Extraterrestrial Physics (MPE), which also provided the focal-plane detectors. Meanwhile, the Institute of High Energy Physics (IHEP), together with the Harbin Institute of Technology and Xi’an Institute of Optics and Precision Mechanics, has also completed the development and production of the structural and thermal model (STM), qualification model (QM) and flight model (FM) of FXT mirrors for the Einstein Probe (EP) satellites for demonstration purposes. This paper introduces the precision manufacturing adopted in China of Wolter-I X-ray mirror mandrels similar to those used for the EP-FXT payload. Moreover, the adopted electroformed nickel replication process, based on a chemical nickel–phosphorus alloy, is reported. The final results show that the surface of the produced mandrels after demolding and the internal surface of the mirrors have been super polished to the roughness level better than 0.3 nm RMS and the surface accuracy is better than 0.2 μm, and the mirror angular resolution for single mirror shells may be as good as 17.3 arcsec HPD (Half Power Diameter), 198 arcsec W90 (90% Energy Width) @1.49 keV (Al-K line). These results demonstrate the reliability and advancement of the process. As the first efficient X-ray-focusing optics manufacturing chain established in China, we successfully developed the first focusing mirror prototype that could be used for future X-ray satellite payloads. Full article
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<p>Focus mirror of electroformed nickel replication process flow.</p>
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<p>The electroless nickel plating bath and mandrel #24.</p>
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<p>Schematic diagram of the DRL2000 with the EP-FXT #1 mandrel.</p>
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<p>The inversion method for the mandrel.</p>
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<p>Typical turning generatrix profile. (<b>a</b>) Profile error. (<b>b</b>) Slide error. (<b>c</b>) Separated profile accuracy of slide error of EP#18.</p>
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<p>A combined polishing chain process.</p>
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<p>The metrology of the mandrel and mirror (cutting samples) of surface roughness. (<b>a</b>) the metrology equipment of the mandrel, (<b>b</b>) The surface roughness of the mandrel #24, (<b>c</b>) the roughness level, (<b>d</b>) AFM test and results, (<b>e</b>) PSD calculation.</p>
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<p>Mandrel gold deposition.</p>
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<p>The electroformed nickel or NiCo process.</p>
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<p>Demolding device.</p>
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<p>Offline measurement device.</p>
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<p>Optical lever measurement result of mirror. (<b>a</b>) Repeatability error. (<b>b</b>) Paraboloid test. (<b>c</b>) Hyperboloid test.</p>
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<p>Diagram of the integration and calibration of the shells with visible light.</p>
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<p>Productions. (<b>a</b>) Mandrel after demolding. (<b>b</b>) Mirror after demolding. (<b>c</b>) Mirror assembly.</p>
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<p>The models made in PRC for EP-FXT mirrors. (<b>a</b>) STM. (<b>b</b>) QM. (<b>c</b>) FM.</p>
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<p>Results in a visible light test system with a parallel laser light source at 473 nm. (<b>a</b>) The intra-focal on-axis focal spot of 25 cm, (<b>b</b>) the focal plane, and the (<b>c</b>) encircled energy of the angular resolution for all the mirror shells of the FM.</p>
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<p>A test of the single shell #24 device and the results. (<b>a</b>) The model tested at IHEP/CAS. (<b>b</b>) The angular resolution of one mirror. (<b>c</b>) The defocus spot image.</p>
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15 pages, 8364 KiB  
Article
Research on the Design and Bidirectional Work Process of Metal Diaphragms in Small Double-Pulse Solid Rocket Motors
by Xueqin Du, Weihua Hui, Youwen Tan, Wen Feng and Yang Liu
Aerospace 2024, 11(10), 848; https://doi.org/10.3390/aerospace11100848 - 15 Oct 2024
Viewed by 2721
Abstract
According to the requirements of the small double-pulse solid rocket motor, a compartmentalized isolation device has been designed. This device consists of a metal diaphragm and a support frame. An experimental study and numerical simulation were used to verify the bidirectional working process [...] Read more.
According to the requirements of the small double-pulse solid rocket motor, a compartmentalized isolation device has been designed. This device consists of a metal diaphragm and a support frame. An experimental study and numerical simulation were used to verify the bidirectional working process of the metal diaphragm during operation of the double-pulse motor. The results show that the pressure-bearing capacity of the metal diaphragm meets the requirements under the working conditions of pulse I without affecting pulse II, because the metal diaphragm can provide insulation and flame retardancy. The metal diaphragm can be cracked in the direction of the preset V-groove in a relatively short time under the working conditions of pulse II, which allows the gas to flow to the first pulse combustion chamber normally. This indicates that the metal diaphragm can meet the requirements of bidirectional working process in dual-pulse motors. Full article
(This article belongs to the Section Astronautics & Space Science)
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<p>The longitudinal section of the double-pulse motor.</p>
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<p>(<b>a</b>) The structure of the cross-support frame; (<b>b</b>) the metal diaphragm plate (pulse I view); (<b>c</b>) the dimensions of the V-grooves.</p>
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<p>(<b>a</b>) The mesh model; (<b>b</b>) the boundary condition; (<b>c</b>) the working load of the PSD.</p>
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<p>Von Mises stress contour plot of the metal diaphragm: (<b>a</b>) pulse I view; (<b>b</b>) pulse II view; (<b>c</b>) lateral view.</p>
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<p>Von Mises stress contour plot of the support frame.</p>
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<p>(<b>a</b>) The metal diaphragm; (<b>b</b>) the cross-type support frame after the experiment.</p>
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<p>Metal diaphragm after the experiment: (<b>a</b>) pulse I view; (<b>b</b>) pulse II view; (<b>c</b>) lateral view.</p>
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<p>Pressure profile during the operation of the pulse I motor.</p>
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<p>The uniaxial tensile stress–strain curve of a typical metal specimen [<a href="#B27-aerospace-11-00848" class="html-bibr">27</a>].</p>
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<p>(<b>a</b>) The mesh model; (<b>b</b>) the boundary condition; (<b>c</b>) the working load of the diaphragm.</p>
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<p>Von Mises stress contour plot of the metal diaphragm at six moments.</p>
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<p>Components of the motor after the experiment: (<b>a</b>) pulse II combustion chamber and separation device; (<b>b</b>) ignition apparatus; (<b>c</b>) support frame; (<b>d</b>) metal diaphragm.</p>
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<p>Metal diaphragm after the experiment: (<b>a</b>) slotted side; (<b>b</b>) smooth side; (<b>c</b>) lateral side; (<b>d</b>) deformation of the metal diaphragm.</p>
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<p>Pressure profile of pulse I and pulse II in the test.</p>
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<p>Double-pulse motor before the experiment.</p>
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<p>Working process of the pulse I motor.</p>
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<p>Working process of the pulse II motor.</p>
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<p>Components of the motor after the experiment: (<b>a</b>) the pulse II motor section; (<b>b</b>) the support frame; (<b>c</b>) the metal diaphragm.</p>
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<p>Pressure profile of the pulse I motor during operation.</p>
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<p>Pressure profile of the pulse II motor during operation.</p>
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13 pages, 10265 KiB  
Article
Effects of Non-Uniform Center-Flow Distribution and Cavitation on Continuous-Type Pintle Injectors
by Dongwoo Choi, Seunghyeon Lee and Kyubok Ahn
Aerospace 2024, 11(10), 847; https://doi.org/10.3390/aerospace11100847 - 15 Oct 2024
Viewed by 758
Abstract
In this paper, the flow characteristics of a continuous-type liquid–liquid pintle injector are described, focusing on the differential impact of a non-uniform center-flow distribution on single- and bi-injection methodologies as well as the cavitation effect on the spray angle. Using cold-flow experiments, jet-type [...] Read more.
In this paper, the flow characteristics of a continuous-type liquid–liquid pintle injector are described, focusing on the differential impact of a non-uniform center-flow distribution on single- and bi-injection methodologies as well as the cavitation effect on the spray angle. Using cold-flow experiments, jet-type flows of the center propellant caused by a non-uniform flow distribution were observed during a single injection. This resulted in an augmented pressure drop, as opposed to the flow characteristics of uniform single-film injection. By contrast, bi-injection modalities exhibited a substantial reduction in the pressure drop of the center propellant, underscoring a more equitable flow distribution. Moreover, the occurrence of cavitation in the center propellant was found to markedly affect the spray angle. By considering the injection exit area reduction caused by cavitation, the spray-angle prediction accuracy increased. The findings of this study are expected to reveal the interplay between flow distribution and pressure drop as well as that between cavitation and the spray angle in pintle injectors. Through this understanding, this study provides crucial considerations for the development of more efficient propulsion systems. Full article
(This article belongs to the Section Aeronautics)
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<p>Schematic of a pintle injector depicting its geometric parameters.</p>
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<p>Schematic of the sectional view of the pintle injector.</p>
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<p>Experimental setup for high-speed backlit image acquisition and camcorder videography.</p>
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<p>Experimental setup for high-speed close-up image acquisition.</p>
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<p>Image processing steps for the spray -angle calculations.</p>
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<p>Criteria for the spray-angle calculations.</p>
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<p>Single-injection results for (<b>a</b>) <span class="html-italic">Δp</span> and (<b>b</b>) <span class="html-italic">C<sub>d</sub></span>.</p>
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<p>Camcorder video images of the flow pattern according to thrust level (indicated as % in the images) during center-propellant single injection under (<b>a</b>) upward- and (<b>b</b>) downward-flow conditions.</p>
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<p>High-speed close-up images of the flow pattern according to thrust level (indicated as %) during upward center-propellant single injection.</p>
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<p>Bi-injection results for (<b>a</b>) <span class="html-italic">Δp</span> and (<b>b</b>) <span class="html-italic">C<sub>d</sub></span>.</p>
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<p>Backlit spray images according to thrust level (indicated as %) during bi-injection.</p>
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<p>Flow inside the center-propellant slit (<b>a</b>) before and (<b>b</b>) after hydraulic flip occurrence.</p>
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<p>Selected high-speed close-up images of the flow pattern of the center propellant in response to the outer propellant flow rate.</p>
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22 pages, 4104 KiB  
Article
Modeling and Performance Analysis of Solid Oxide Fuel Cell Power Generation System for Hypersonic Vehicles
by Yiming Liu, Jianguo Tan, Dongdong Zhang and Zihan Kuai
Aerospace 2024, 11(10), 846; https://doi.org/10.3390/aerospace11100846 - 14 Oct 2024
Viewed by 1215
Abstract
Advanced airborne power generation technology represents one of the most effective solutions for meeting the electricity requirements of hypersonic vehicles during long-endurance flights. This paper proposes a power generation system that integrates a high-temperature fuel cell to tackle the challenges associated with power [...] Read more.
Advanced airborne power generation technology represents one of the most effective solutions for meeting the electricity requirements of hypersonic vehicles during long-endurance flights. This paper proposes a power generation system that integrates a high-temperature fuel cell to tackle the challenges associated with power generation in the hypersonic field, utilizing techniques such as inlet pressurization, autothermal reforming, and anode recirculation. Firstly, the power generation system is modeled modularly. Secondly, the influence of key parameters on the system’s performance is analyzed. Thirdly, the performance of the power generation system under the design conditions is simulated and evaluated. Finally, the weight distribution and exergy loss of the system’s components under the design conditions are calculated. The results indicate that the system’s electrical efficiency increases with fuel utilization, decreases with rising current density and steam-to-carbon ratio (SCR), and initially increases before declining with increasing fuel cell operating temperature. Under the design conditions, the system’s power output is 48.08 kW, with an electrical efficiency of 51.77%. The total weight of the power generation system is 77.09 kg, with the fuel cell comprising 69.60% of this weight, resulting in a power density of 0.62 kW/kg. The exergy efficiency of the system is 55.86%, with the solid oxide fuel cell (SOFC) exhibiting the highest exergy loss, while the mixer demonstrates the greatest exergy efficiency. This study supports the application of high-temperature fuel cells in the hypersonic field. Full article
(This article belongs to the Section Aeronautics)
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<p>The structure of the FCPS.</p>
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<p>The results of the SOFC model validation. (<b>a</b>) The different operating temperatures and (<b>b</b>) the different fuel concentrations. (Note: The “600 V” in the figure represents the SOFC operating voltage when the SOFC operating temperature is 600 °C).</p>
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<p>Diagram of computational logic.</p>
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<p>The effect of the fuel utilization on the FCPS performance.</p>
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<p>The effect of the current density on the FCPS performance.</p>
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<p>The effect of the SOFC operating temperature on the FCPS performance.</p>
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<p>The effect of the SCR on the FCPS performance.</p>
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<p>Effects of OCR on reformer performance. (<b>a</b>) Performance parameters; (<b>b</b>) outlet gas composition.</p>
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<p>Weight distribution of FCPS. (<b>a</b>) 0.9 kW/kg; (<b>b</b>) 1.2 kW/kg; (<b>c</b>) 2.5 kW/kg; (<b>d</b>) 4.6 kW/kg.</p>
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<p>Distribution of FCPS exergy loss.</p>
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<p>Exergy flow diagram of FCPS.</p>
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16 pages, 3196 KiB  
Article
A Cooperative Control Method for Wide-Range Maneuvering of Autonomous Aerial Refueling Controllable Drogue
by Jinxin Bai and Zhongjie Meng
Aerospace 2024, 11(10), 845; https://doi.org/10.3390/aerospace11100845 - 14 Oct 2024
Viewed by 784
Abstract
In the realm of autonomous aerial refueling missions for unmanned aerial vehicles (UAVs), the controllable drogue represents a novel approach that significantly enhances both the safety and efficiency of aerial refueling operations. This paper delves into the issue of wide-range maneuverability control for [...] Read more.
In the realm of autonomous aerial refueling missions for unmanned aerial vehicles (UAVs), the controllable drogue represents a novel approach that significantly enhances both the safety and efficiency of aerial refueling operations. This paper delves into the issue of wide-range maneuverability control for the controllable drogue. Initially, a dynamic model for the variable-length hose–drogue system is presented. Based on this, a cooperative control framework that synergistically utilizes both the hose and the drogue is designed to achieve wide-range maneuverability of the drogue. To address the delay in hose retrieval and release, an open-loop control strategy based on neural networks is proposed. Furthermore, a closed-loop control method utilizing fuzzy approximation and adaptive error estimation is designed to tackle the challenges posed by modeling inaccuracies and uncertainties in aerodynamic parameters. Comparative simulation results show that the proposed control strategy can make the drogue maneuvering range reach more than 6 m. And it can accurately track the time-varying trajectory under the influence of model uncertainty and wind disturbance with an error of less than 0.1 m throughout. This method provides an effective means for achieving wide-range maneuverability control of the controllable drogue in autonomous aerial refueling missions. Full article
(This article belongs to the Section Aeronautics)
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<p>Schematic diagram of the hose–drogue system in the AAR mission.</p>
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<p>Side view (<b>left</b>) and front view (<b>right</b>) of the controllable drogue.</p>
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<p>Cooperative control method for the controllable drogue.</p>
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<p>Structure of GRU neural network (<b>left</b>) and training process (<b>right</b>).</p>
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<p>Variation in wind disturbance (<b>left</b>) and the drogue’s aerodynamic parameters (<b>right</b>).</p>
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<p>Drogue positions (<b>left</b>) and hose length and strut angles (<b>right</b>) in simulation I.</p>
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<p>Drogue attitude (<b>left</b>) and upper bound of ε (<b>right</b>) in simulation I.</p>
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<p>Drogue positions (<b>left</b>) and hose length and strut angles (<b>right</b>) in simulation II.</p>
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30 pages, 35792 KiB  
Article
Research on the Structural Design of a Pressurized Cabin for Civil High-Speed Rotorcraft and the Multi-Dimensional Comprehensive Evaluation Method
by Yongjie Zhang, Tongxin Zhang, Jingpiao Zhou, Bo Cui and Fangyu Chen
Aerospace 2024, 11(10), 844; https://doi.org/10.3390/aerospace11100844 - 13 Oct 2024
Viewed by 847
Abstract
For civil high-speed rotorcraft designed to operate at specific cruising altitudes, this study proposes nine structural design schemes for pressurized cabins. These schemes integrate commonly used materials and processing technologies in the aviation industry with advanced PRSEUS (Pultruded Rod Stitched Efficient Unitized Structure) [...] Read more.
For civil high-speed rotorcraft designed to operate at specific cruising altitudes, this study proposes nine structural design schemes for pressurized cabins. These schemes integrate commonly used materials and processing technologies in the aviation industry with advanced PRSEUS (Pultruded Rod Stitched Efficient Unitized Structure) technology. An analysis of the structural composition reveals that frames constitute 8–19% of the total structural weight, while stringers and beams make up 15–50%, and skins account for 11–25%, with thicknesses ranging from 1.0 mm to 2.0 mm. The separating interface of the pressurized cabin contributes 4–29% of the total structural weight. The weight distribution of each component in the pressurized cabin structure varies significantly depending on the chosen materials and processing technologies. Utilizing the Analytic Hierarchy Process (AHP), along with Gray Relational Analysis (GRA) and Dempster–Shafer (D-S) evidence theory, this study compares the simulation results of the nine schemes across multiple dimensions. The findings indicate that the configuration combining 7075 aluminum alloy and T300 composite material has the greatest advantages in terms of the high structural reliability of the configuration, light weight, mature processing technology, and low production cost. This comprehensive evaluation method quantitatively analyzes the factors influencing the structural configuration design of the pressurized cabin for civil high-speed rotorcraft, offering a valuable reference for the design of similar structures in related fields. Full article
(This article belongs to the Section Aeronautics)
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<p>Civilian high-speed rotorcraft aerodynamic profile.</p>
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<p>Typical PRSEUS structural composition.</p>
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<p>Typical laminate structure diagram.</p>
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<p>Finite element model for Scheme 2.</p>
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<p>Finite element model for Scheme 5.</p>
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<p>Finite element model for Scheme 7.</p>
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<p>Frame and stringer size for Scheme 7 (unit: mm). (<b>a</b>) Frame size; (<b>b</b>) stringer size.</p>
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<p>Finite element model of Scheme 9.</p>
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<p>Structural load schematic diagram.</p>
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<p>Mesh convergence analysis.</p>
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<p>Scheme 1 finite element simulation results. (<b>a</b>) Structural von Mises stress; (<b>b</b>) structural shear stress; (<b>c</b>) structural displacement.</p>
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<p>Scheme 2 finite element simulation results. (a) Structural von Mises stress; (b) structural shear stress; (c) structural displacement.</p>
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<p>Scheme 3 finite element simulation results. (<b>a</b>) Structural von Mises stress; (<b>b</b>) structural shear stress; (<b>c</b>) structural displacement.</p>
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<p>Scheme 4 finite element simulation results. (<b>a</b>) Structural von Mises stress; (<b>b</b>) structural shear stress; (<b>c</b>) T300 composite strain; (<b>d</b>) structural displacement.</p>
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<p>Scheme 5 finite element simulation results. (<b>a</b>) Structural von Mises stress; (<b>b</b>) structural shear stress; (<b>c</b>) T300 composite strain; (<b>d</b>) structural displacement.</p>
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<p>Scheme 6 finite element simulation results. (<b>a</b>) AL7075 von Mises stress; (<b>b</b>) AL7075 shear stress; (<b>c</b>) T800 composite strain; (<b>d</b>) structural displacement.</p>
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<p>Scheme 7 finite element simulation results. (<b>a</b>) Structure strain; (<b>b</b>) T800 composite strain; (<b>c</b>) T300 composite strain; (<b>d</b>) structural displacement.</p>
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<p>Scheme 8 finite element simulation results. (<b>a</b>) Structure strain; (<b>b</b>) T1000 composite strain; (<b>c</b>) T800 composite strain; (<b>d</b>) structural displacement.</p>
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<p>Scheme 9 finite element simulation results. (<b>a</b>) Structure strain; (<b>b</b>) T800 composite strain; (<b>c</b>) T300 composite strain; (<b>d</b>) structural displacement.</p>
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<p>Weight variation of components in the pressurized cabin.</p>
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<p>Weight proportion of components in the structural configuration of the pressurized cabin.</p>
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<p>Evaluation implementation flowchart.</p>
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<p>Hierarchical relationship diagram of design indices for the pressurized cabin structural configuration.</p>
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20 pages, 9686 KiB  
Article
From Text to Safety: A Novel Framework for Mining Unsafe Aviation Events Using Advanced Neural Network and Feature Network
by Qiang Wang, Ruicong Xia, Jiayang Yu, Qiuhan Liu, Sirong Tong and Ziling Xu
Aerospace 2024, 11(10), 843; https://doi.org/10.3390/aerospace11100843 - 12 Oct 2024
Viewed by 845
Abstract
The rapid growth of the aviation industry highlights the need for strong safety management. Analyzing data on unsafe aviation events is crucial for preventing risks. This paper presents a new method that integrates the Transformer network model, clustering analysis, and feature network modeling [...] Read more.
The rapid growth of the aviation industry highlights the need for strong safety management. Analyzing data on unsafe aviation events is crucial for preventing risks. This paper presents a new method that integrates the Transformer network model, clustering analysis, and feature network modeling to analyze Chinese text data on unsafe aviation events. Initially, the Transformer model is used to generate summaries of event texts, and the performance of three pre-trained Chinese models is evaluated and compared. Next, the Jieba tool is applied to segment both summarized and original texts to extract key features of unsafe events and prove the effectiveness of the pre-trained Transformer model in simplifying lengthy and redundant original texts. Then, cluster analysis based on text similarity categorizes the extracted features. By solving the correlation matrix of these features, this paper constructs a feature network for unsafe aviation events. The network’s global and individual metrics are calculated and then used to identify key feature nodes, which alert aviation professionals to focus more on the decision-making process for safety management. Based on the established network and these metrics, a data-driven hidden danger warning strategy is proposed and illustrated. Overall, the proposed method can effectively analyze Chinese texts of unsafe aviation events and provide a basis for improving aviation safety management. Full article
(This article belongs to the Section Air Traffic and Transportation)
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<p>Technical route of this paper.</p>
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<p>The structure of a typical Transformer model.</p>
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<p>Word segmentation results of original and summarized event texts: (<b>a</b>) Word segmentation counts; (<b>b</b>) Word segmentation accuracy. The highlighted numbers in red are the average accuracy.</p>
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<p>Calculating the similarity of two features using Hash mapping.</p>
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<p>Workflow of the clustering process.</p>
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<p>Diagram of updating cluster centers using the Simhash algorithm.</p>
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<p>Feature network of unsafe aviation events.</p>
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<p>An example of a risk early warning strategy.</p>
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<p>Unsafe features connected with “Inspect” and “Engine”.</p>
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25 pages, 352 KiB  
Article
Assessing the Principle of Equitable Access versus Non-Appropriation in the Era of Mega-Constellations
by Kuan Yang and Sandra Amongin
Aerospace 2024, 11(10), 842; https://doi.org/10.3390/aerospace11100842 - 12 Oct 2024
Viewed by 1021
Abstract
In the 21st century, mega-constellations and interconnected satellite constellations deployed at various orbital altitudes, such as LEO, MEO, and GEO, with low Earth orbits (LEOs) being the most commonly used, have emerged as a trend, aiming to enhance the productivity and reduce the [...] Read more.
In the 21st century, mega-constellations and interconnected satellite constellations deployed at various orbital altitudes, such as LEO, MEO, and GEO, with low Earth orbits (LEOs) being the most commonly used, have emerged as a trend, aiming to enhance the productivity and reduce the costs in space service delivery. The UNOOSA has noted the uncertainty in the exact number of satellites but conducted simulations based on a substantial sample, projecting a significant increase from the 2075 satellites recorded in orbit in 2018. This surge in the launch of mega-constellations poses profound challenges to existing international space laws, originally formulated with limited consideration for private space actors, who are increasingly engaging in space activities, particularly with the cost-effective utilization of mega-constellations. This study critically analyzes the compatibility of mega-constellations with the current international space laws by examining the applicability of mega-constellations concerning equitable access and the non-appropriation principle, addressing their potential occupation of substantial orbital spaces during activities, and analyzing whether the acquisition of orbital slot licenses violates these two principles. Following an in-depth analysis, this study proposes recommendations to amend the existing laws, aiming to resolve ambiguities and address emerging challenges. Recognizing the time-consuming process of amending international space laws, this study suggests practical recommendations for supplementary rules of the road, prompting reflection on the potential obsolescence of the current international space laws in the face of evolving space activities. Full article
16 pages, 8740 KiB  
Article
Crack Growth Analytical Model Considering the Crack Growth Resistance Parameter Due to the Unloading Process
by Guo Li, Shuchun Huang, Zhenlei Li, Wanqiu Lu, Shuiting Ding, Rong Chen and Fan Cao
Aerospace 2024, 11(10), 841; https://doi.org/10.3390/aerospace11100841 - 12 Oct 2024
Viewed by 825
Abstract
Crack growth analysis is essential for probabilistic damage tolerance assessment of aeroengine life-limited parts. Traditional crack growth models directly establish the stress ratio–crack growth rate or crack opening stress relationship and focus less on changes in the crack tip stress field and its [...] Read more.
Crack growth analysis is essential for probabilistic damage tolerance assessment of aeroengine life-limited parts. Traditional crack growth models directly establish the stress ratio–crack growth rate or crack opening stress relationship and focus less on changes in the crack tip stress field and its influence, so the resolution and accuracy of maneuvering flight load spectral analysis are limited. To improve the accuracy and convenience of analysis, a parameter considering the effect of unloading amount on crack propagation resistance is proposed, and the corresponding analytical model is established. The corresponding process for acquiring the model parameters through the constant amplitude test data of a Ti-6AL-4V compact tension specimen is presented. Six kinds of flight load spectra with inserted load pairs with different stress ratios and repetition times are tested to verify the accuracy of the proposed model. All the deviations between the proposed model and test life results are less than 10%, which demonstrates the superiority of the proposed model over the crack closure and Walker-based models in addressing relevant loading spectra. The proposed analytical model provides new insights for the safety of aeroengine life-limited parts. Full article
(This article belongs to the Section Aeronautics)
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<p>Relationship between the present research and the previous research.</p>
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<p>The CT specimen and the corresponding finite element model. (<b>a</b>) Schematic of the size and shape of the CT specimen. (<b>b</b>) Finite element model.</p>
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<p>Stress distribution along the crack growth path. (<b>a</b>) Stress distribution at different moments. (<b>b</b>) Stress variation with respect to the maximum load.</p>
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<p>Schematic of the UCR model. (<b>a</b>) Schematic of the UCR model under constant loading. (<b>b</b>) Schematic of the UCR model under variable loading.</p>
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<p>“Unloading-reloading” segment.</p>
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<p>Stress contour and displacement at the crack tip at different moments.</p>
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<p>Test equipment. (<b>a</b>) Servo-hydraulic test machine and the DIC device. (<b>b</b>) COD gauge.</p>
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<p>Crack growth test results and data processing.</p>
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<p>Acquisition of the effective stress intensity factor curve.</p>
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<p>Schematic of the block loading.</p>
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<p>Acquisition of the control group data.</p>
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<p>Different combined load spectra and corresponding crack propagation results. (<b>a</b>) Load spectra (<math display="inline"><semantics> <mrow> <msub> <mi>R</mi> <mrow> <mi>insert</mi> </mrow> </msub> <mo>=</mo> <mn>0.3</mn> <mo>,</mo> <mo> </mo> <msub> <mi>N</mi> <mrow> <mi>insert</mi> </mrow> </msub> <mo>=</mo> <mn>11</mn> <mo stretchy="false">)</mo> </mrow> </semantics></math>. (<b>b</b>) Crack growth results (<math display="inline"><semantics> <mrow> <msub> <mi>R</mi> <mrow> <mi>insert</mi> </mrow> </msub> <mo>=</mo> <mn>0.3</mn> <mo>,</mo> <mo> </mo> <msub> <mi>N</mi> <mrow> <mi>insert</mi> </mrow> </msub> <mo>=</mo> <mn>11</mn> <mo stretchy="false">)</mo> </mrow> </semantics></math>. (<b>c</b>) Load spectra (<math display="inline"><semantics> <mrow> <msub> <mi>R</mi> <mrow> <mi>insert</mi> </mrow> </msub> <mo>=</mo> <mn>0.3</mn> <mo>,</mo> <mo> </mo> <msub> <mi>N</mi> <mrow> <mi>insert</mi> </mrow> </msub> <mo>=</mo> <mn>22</mn> </mrow> </semantics></math>). (<b>d</b>) Crack growth results (<math display="inline"><semantics> <mrow> <msub> <mi>R</mi> <mrow> <mi>insert</mi> </mrow> </msub> <mo>=</mo> <mn>0.3</mn> <mo>,</mo> <mo> </mo> <msub> <mi>N</mi> <mrow> <mi>insert</mi> </mrow> </msub> <mo>=</mo> <mn>22</mn> <mo stretchy="false">)</mo> </mrow> </semantics></math>. (<b>e</b>) Load spectra <math display="inline"><semantics> <mrow> <mfenced> <mrow> <msub> <mi>R</mi> <mrow> <mi>insert</mi> </mrow> </msub> <mo>=</mo> <mn>0.6</mn> <mo>,</mo> <mo> </mo> <msub> <mi>N</mi> <mrow> <mi>insert</mi> </mrow> </msub> <mo>=</mo> <mn>11</mn> </mrow> </mfenced> </mrow> </semantics></math>. (<b>f</b>) Crack growth results (<math display="inline"><semantics> <mrow> <msub> <mi>R</mi> <mrow> <mi>insert</mi> </mrow> </msub> <mo>=</mo> <mn>0.6</mn> <mo>,</mo> <mo> </mo> <msub> <mi>N</mi> <mrow> <mi>insert</mi> </mrow> </msub> <mo>=</mo> <mn>11</mn> <mo stretchy="false">)</mo> </mrow> </semantics></math>. (<b>g</b>) Load spectra <math display="inline"><semantics> <mrow> <mfenced> <mrow> <msub> <mi>R</mi> <mrow> <mi>insert</mi> </mrow> </msub> <mo>=</mo> <mn>0.6</mn> <mo>,</mo> <mo> </mo> <msub> <mi>N</mi> <mrow> <mi>insert</mi> </mrow> </msub> <mo>=</mo> <mn>22</mn> </mrow> </mfenced> </mrow> </semantics></math>. (<b>h</b>) Crack growth results (<math display="inline"><semantics> <mrow> <msub> <mi>R</mi> <mrow> <mi>insert</mi> </mrow> </msub> <mo>=</mo> <mn>0.6</mn> <mo>,</mo> <mo> </mo> <msub> <mi>N</mi> <mrow> <mi>insert</mi> </mrow> </msub> <mo>=</mo> <mn>22</mn> <mo stretchy="false">)</mo> </mrow> </semantics></math>. (<b>i</b>) Load spectra <math display="inline"><semantics> <mrow> <mfenced> <mrow> <msub> <mi>R</mi> <mrow> <mi>insert</mi> </mrow> </msub> <mo>=</mo> <mn>0.8</mn> <mo>,</mo> <mo> </mo> <msub> <mi>N</mi> <mrow> <mi>insert</mi> </mrow> </msub> <mo>=</mo> <mn>11</mn> </mrow> </mfenced> </mrow> </semantics></math>. (<b>j</b>) Crack growth results (<math display="inline"><semantics> <mrow> <msub> <mi>R</mi> <mrow> <mi>insert</mi> </mrow> </msub> <mo>=</mo> <mn>0.8</mn> <mo>,</mo> <mo> </mo> <msub> <mi>N</mi> <mrow> <mi>insert</mi> </mrow> </msub> <mo>=</mo> <mn>11</mn> <mo stretchy="false">)</mo> </mrow> </semantics></math>. (<b>k</b>) Load spectra <math display="inline"><semantics> <mrow> <mfenced> <mrow> <msub> <mi>R</mi> <mrow> <mi>insert</mi> </mrow> </msub> <mo>=</mo> <mn>0.8</mn> <mo>,</mo> <mo> </mo> <msub> <mi>N</mi> <mrow> <mi>insert</mi> </mrow> </msub> <mo>=</mo> <mn>22</mn> </mrow> </mfenced> </mrow> </semantics></math>. (<b>l</b>) Crack growth results (<math display="inline"><semantics> <mrow> <msub> <mi>R</mi> <mrow> <mi>insert</mi> </mrow> </msub> <mo>=</mo> <mn>0.8</mn> <mo>,</mo> <mo> </mo> <msub> <mi>N</mi> <mrow> <mi>insert</mi> </mrow> </msub> <mo>=</mo> <mn>22</mn> <mo stretchy="false">)</mo> </mrow> </semantics></math>.</p>
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<p>Different combined load spectra and corresponding crack propagation results. (<b>a</b>) Load spectra (<math display="inline"><semantics> <mrow> <msub> <mi>R</mi> <mrow> <mi>insert</mi> </mrow> </msub> <mo>=</mo> <mn>0.3</mn> <mo>,</mo> <mo> </mo> <msub> <mi>N</mi> <mrow> <mi>insert</mi> </mrow> </msub> <mo>=</mo> <mn>11</mn> <mo stretchy="false">)</mo> </mrow> </semantics></math>. (<b>b</b>) Crack growth results (<math display="inline"><semantics> <mrow> <msub> <mi>R</mi> <mrow> <mi>insert</mi> </mrow> </msub> <mo>=</mo> <mn>0.3</mn> <mo>,</mo> <mo> </mo> <msub> <mi>N</mi> <mrow> <mi>insert</mi> </mrow> </msub> <mo>=</mo> <mn>11</mn> <mo stretchy="false">)</mo> </mrow> </semantics></math>. (<b>c</b>) Load spectra (<math display="inline"><semantics> <mrow> <msub> <mi>R</mi> <mrow> <mi>insert</mi> </mrow> </msub> <mo>=</mo> <mn>0.3</mn> <mo>,</mo> <mo> </mo> <msub> <mi>N</mi> <mrow> <mi>insert</mi> </mrow> </msub> <mo>=</mo> <mn>22</mn> </mrow> </semantics></math>). (<b>d</b>) Crack growth results (<math display="inline"><semantics> <mrow> <msub> <mi>R</mi> <mrow> <mi>insert</mi> </mrow> </msub> <mo>=</mo> <mn>0.3</mn> <mo>,</mo> <mo> </mo> <msub> <mi>N</mi> <mrow> <mi>insert</mi> </mrow> </msub> <mo>=</mo> <mn>22</mn> <mo stretchy="false">)</mo> </mrow> </semantics></math>. (<b>e</b>) Load spectra <math display="inline"><semantics> <mrow> <mfenced> <mrow> <msub> <mi>R</mi> <mrow> <mi>insert</mi> </mrow> </msub> <mo>=</mo> <mn>0.6</mn> <mo>,</mo> <mo> </mo> <msub> <mi>N</mi> <mrow> <mi>insert</mi> </mrow> </msub> <mo>=</mo> <mn>11</mn> </mrow> </mfenced> </mrow> </semantics></math>. (<b>f</b>) Crack growth results (<math display="inline"><semantics> <mrow> <msub> <mi>R</mi> <mrow> <mi>insert</mi> </mrow> </msub> <mo>=</mo> <mn>0.6</mn> <mo>,</mo> <mo> </mo> <msub> <mi>N</mi> <mrow> <mi>insert</mi> </mrow> </msub> <mo>=</mo> <mn>11</mn> <mo stretchy="false">)</mo> </mrow> </semantics></math>. (<b>g</b>) Load spectra <math display="inline"><semantics> <mrow> <mfenced> <mrow> <msub> <mi>R</mi> <mrow> <mi>insert</mi> </mrow> </msub> <mo>=</mo> <mn>0.6</mn> <mo>,</mo> <mo> </mo> <msub> <mi>N</mi> <mrow> <mi>insert</mi> </mrow> </msub> <mo>=</mo> <mn>22</mn> </mrow> </mfenced> </mrow> </semantics></math>. (<b>h</b>) Crack growth results (<math display="inline"><semantics> <mrow> <msub> <mi>R</mi> <mrow> <mi>insert</mi> </mrow> </msub> <mo>=</mo> <mn>0.6</mn> <mo>,</mo> <mo> </mo> <msub> <mi>N</mi> <mrow> <mi>insert</mi> </mrow> </msub> <mo>=</mo> <mn>22</mn> <mo stretchy="false">)</mo> </mrow> </semantics></math>. (<b>i</b>) Load spectra <math display="inline"><semantics> <mrow> <mfenced> <mrow> <msub> <mi>R</mi> <mrow> <mi>insert</mi> </mrow> </msub> <mo>=</mo> <mn>0.8</mn> <mo>,</mo> <mo> </mo> <msub> <mi>N</mi> <mrow> <mi>insert</mi> </mrow> </msub> <mo>=</mo> <mn>11</mn> </mrow> </mfenced> </mrow> </semantics></math>. (<b>j</b>) Crack growth results (<math display="inline"><semantics> <mrow> <msub> <mi>R</mi> <mrow> <mi>insert</mi> </mrow> </msub> <mo>=</mo> <mn>0.8</mn> <mo>,</mo> <mo> </mo> <msub> <mi>N</mi> <mrow> <mi>insert</mi> </mrow> </msub> <mo>=</mo> <mn>11</mn> <mo stretchy="false">)</mo> </mrow> </semantics></math>. (<b>k</b>) Load spectra <math display="inline"><semantics> <mrow> <mfenced> <mrow> <msub> <mi>R</mi> <mrow> <mi>insert</mi> </mrow> </msub> <mo>=</mo> <mn>0.8</mn> <mo>,</mo> <mo> </mo> <msub> <mi>N</mi> <mrow> <mi>insert</mi> </mrow> </msub> <mo>=</mo> <mn>22</mn> </mrow> </mfenced> </mrow> </semantics></math>. (<b>l</b>) Crack growth results (<math display="inline"><semantics> <mrow> <msub> <mi>R</mi> <mrow> <mi>insert</mi> </mrow> </msub> <mo>=</mo> <mn>0.8</mn> <mo>,</mo> <mo> </mo> <msub> <mi>N</mi> <mrow> <mi>insert</mi> </mrow> </msub> <mo>=</mo> <mn>22</mn> <mo stretchy="false">)</mo> </mrow> </semantics></math>.</p>
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<p>Summary of the predicted life results.</p>
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15 pages, 4335 KiB  
Article
Rapid Aircraft Wake Vortex Identification Model Based on Optimized Image Object Recognition Networks
by Leilei Deng, Weijun Pan, Tian Luan, Chen Zhang and Yuanfei Leng
Aerospace 2024, 11(10), 840; https://doi.org/10.3390/aerospace11100840 - 11 Oct 2024
Viewed by 1026
Abstract
Wake vortices generated by aircraft during near-ground operations have a significant impact on airport safety during takeoffs and landings. Identifying wake vortices in complex airspaces assists air traffic controllers in making informed decisions, ensuring the safety of aircraft operations at airports, and enhancing [...] Read more.
Wake vortices generated by aircraft during near-ground operations have a significant impact on airport safety during takeoffs and landings. Identifying wake vortices in complex airspaces assists air traffic controllers in making informed decisions, ensuring the safety of aircraft operations at airports, and enhancing the intelligence level of air traffic control. Unlike traditional image recognition, identifying wake vortices using airborne LiDAR data demands a higher level of accuracy. This study proposes the IRSN-WAKE network by optimizing the Inception-ResNet-v2 network. To improve the model’s feature representation capability, we introduce the SE module into the Inception-ResNet-v2 network, which adaptively weights feature channels to enhance the network’s focus on key features. Additionally, we design and incorporate a noise suppression module to mitigate noise and enhance the robustness of feature extraction. Ablation experiments demonstrate that the introduction of the noise suppression module and the SE module significantly improves the performance of the IRSN-WAKE network in wake vortex identification tasks, achieving an accuracy rate of 98.60%. Comparative experimental results indicate that the IRSN-WAKE network has higher recognition accuracy and robustness compared to common recognition networks, achieving high-accuracy aircraft wake vortex identification and providing technical support for the safe operation of flights. Full article
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<p>Schematic diagram of aircraft wake vortex formation.</p>
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<p>LiDAR scanning wake vortex, RHI.</p>
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<p>(<b>a</b>) The flow field and velocity intensity, (<b>b</b>) The jet cloud image.</p>
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<p>Aircraft Wake Vortex Observation Site Selection and Observation Schematic Diagram (<b>a</b>) Location of LiDAR (<b>b</b>) Schematic diagram of LiDAR detection.</p>
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<p>Inception-ResNet-wake Network Architecture.</p>
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<p>Inception-ResNet-v2 Network Architecture.</p>
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<p>Squeeze-and-Excitation Module.</p>
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<p>Background wind field average wind change graph.</p>
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<p>Loss Value with the Number of Iterations.</p>
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<p>Confusion Matrix (<b>a</b>) ISRN-wake Model Confusion Matrix. (<b>b</b>) KNN Model Confusion Matrix. (<b>c</b>) RF Model Confusion Matrix. (<b>d</b>) SVM Model Confusion Matrix. (<b>e</b>) VGG16 Model Confusion Matrix.</p>
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22 pages, 9498 KiB  
Article
Pulsar Signal Adaptive Surrogate Modeling
by Tomáš Kašpárek and Peter Chudý
Aerospace 2024, 11(10), 839; https://doi.org/10.3390/aerospace11100839 - 11 Oct 2024
Viewed by 642
Abstract
As the number of spacecraft heading beyond Earth’s orbit increased in recent years, autonomous navigation solutions have become increasingly important. One such solution is pulsar-based navigation. The availability of pulsar signals for simulations and HIL testing is essential for the development of pulsar-based [...] Read more.
As the number of spacecraft heading beyond Earth’s orbit increased in recent years, autonomous navigation solutions have become increasingly important. One such solution is pulsar-based navigation. The availability of pulsar signals for simulations and HIL testing is essential for the development of pulsar-based navigation. This study proposes a method to develop a surrogate model of pulsar signals based on radio pulsar observations. The selection of suitable pulsars for the radio telescope is discussed, and a series of observations are conducted. The collected data are processed using the PRESTO software, and the pulsar parameters for the model are derived. Unlike current pulsar signal models, the proposed model anticipates pulsar signal parameters to change over time. It can provide dynamic input parameters for known synthetic pulsar signal generators, resulting in a more realistic signal. Full article
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<p>Pulsar signal time evolution—current models (<b>a</b>) and proposed model (<b>b</b>) considering ISM instabilities and reduced on-board sensor sensitivity.</p>
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<p>Pulsar navigation system architecture. Source: [<a href="#B34-aerospace-11-00839" class="html-bibr">34</a>].</p>
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<p>Highest flux pulsars and their estimation. The gray area on the map is never visible when using the RT2 radio telescope.</p>
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<p>The RT2 radio telescope in CAS Ondřejov observatory.</p>
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<p>Pulsar signal model with time evolution—current models (<b>a</b>) and proposed model (<b>b</b>).</p>
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<p>GPR-based pulsar signal surrogate model utilization in the whole signal generator pipeline.</p>
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<p>GPR-based pulsar radio signal model for PSR B0950+08.</p>
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<p>Input period shapes after normalization, minimum subtraction, and phase removal for PSR B0950+08.</p>
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<p>PSR B0950+08 pulse profile for selected time <math display="inline"><semantics> <msub> <mi>T</mi> <mi>g</mi> </msub> </semantics></math> with initial signal-phase <math display="inline"><semantics> <msub> <mi>ϕ</mi> <mi>g</mi> </msub> </semantics></math> (vertical black line). The orange part of the profile is a single period used for signal generation.</p>
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<p>Generated signals of PSR B0950+08 processed with PRESTO for different scales of <span class="html-italic">s</span> (SNR).</p>
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<p>Reduced <math display="inline"><semantics> <msup> <mi>χ</mi> <mn>2</mn> </msup> </semantics></math> statistic versus period and its first derivative for PSR B0818-13 (1239 ms).</p>
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<p>Reduced <math display="inline"><semantics> <msup> <mi>χ</mi> <mn>2</mn> </msup> </semantics></math> statistics for PSR B0950+08—radio vs. X-ray (<math display="inline"><semantics> <mrow> <mn>253.88</mn> </mrow> </semantics></math><math display="inline"><semantics> <mi mathvariant="normal">m</mi> </semantics></math><math display="inline"><semantics> <mi mathvariant="normal">s</mi> </semantics></math>).</p>
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<p>Reduced <math display="inline"><semantics> <msup> <mi>χ</mi> <mn>2</mn> </msup> </semantics></math> statistic for 600 s and 1200 s of generated PSR B0950+08 signal (253.88 ms).</p>
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<p>Evolution of reduced <math display="inline"><semantics> <msup> <mi>χ</mi> <mn>2</mn> </msup> </semantics></math> statistic shape for length of signal <span class="html-italic">l</span> decreasing left to right.</p>
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<p>Evolution of reduced <math display="inline"><semantics> <msup> <mi>χ</mi> <mn>2</mn> </msup> </semantics></math> statistic shape for scale <span class="html-italic">s</span> decreasing from left to right.</p>
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<p>Pulse profiles based on measured (blue) and generated data with high SNR (red) and low SNR (yellow) for PSR B0950+08.</p>
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<p>Coefficient of determination for the PSR B0950+08 changing scale <span class="html-italic">s</span>.</p>
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<p>Coefficient of determination for the PSR B0950+08 changing length <span class="html-italic">l</span>.</p>
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<p>Coefficient of determination for the PSR B0950+08 changing time <math display="inline"><semantics> <msub> <mi>T</mi> <mi>g</mi> </msub> </semantics></math> during 2024-04-10.</p>
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19 pages, 12747 KiB  
Article
State Analysis and Emergency Control of Planetary Rover with Faulty Drive Wheel
by Zhicheng Jia, Jingfu Jin, Xinju Dong, Yingchun Qi, Meng Zou and Qingyu Yu
Aerospace 2024, 11(10), 838; https://doi.org/10.3390/aerospace11100838 - 11 Oct 2024
Viewed by 821
Abstract
Wheel failure is one of the worst problems for a planetary rover working on Mars or the Moon, which may lead to the interruption of the exploration mission and even the loss of mobility. In this study, a driving test of a planetary [...] Read more.
Wheel failure is one of the worst problems for a planetary rover working on Mars or the Moon, which may lead to the interruption of the exploration mission and even the loss of mobility. In this study, a driving test of a planetary rover prototype with a faulty drive wheel was conducted, and state analysis and dynamics modeling were carried out. The drag motion relationship between the faulty drive wheel and the normal wheels on the same suspension was established based on the targeted single wheel test (faulty wheel-soil bin). In order to maintain the subsequent basic detection capability of the planetary rover, an emergency control system is proposed that integrates the path planning strategy with faulty wheel priority and the motion control method of correcting heading and coordinating allocation. The experimental results and emergency strategies of this study on simulating Martian soil and terrain can provide researchers with ideas to solve such problems. Full article
(This article belongs to the Section Astronautics & Space Science)
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<p>Driving scenes of the planetary rover wheel failure. (<b>a</b>) The track of NASA’s Mars Rover Spirit as it drove, dragging its inoperable right-front wheel. Image Credit: NASA/JPL-Caltech; (<b>b</b>) The mobility test of ‘Zhu Rong’ Mars rover prototype with faulty right-front wheel. Image Credit: China Academy of Space Technology.</p>
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<p>Experimental system. (<b>a</b>) Test system for vehicle movement; (<b>b</b>) planetary rover prototype with faulty drive wheel; (<b>c</b>) faulty wheel-soil bin test bench; (<b>d</b>) tested rover wheel.</p>
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<p>Fault planetary rover status during straight-line driving test. (<b>a</b>) Driving trajectory; (<b>b</b>) course changes.</p>
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<p>Fault planetary rover straight driving test process. (<b>a</b>) Normal planetary rover; (<b>b</b>) right front wheel drive failure; (<b>c</b>) right middle wheel drive failure; (<b>d</b>) right rear wheel drive failure.</p>
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<p>Motion state of the right rear wheel under different driving states. (<b>a</b>) Drive normal; (<b>b</b>) drive failure.</p>
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<p>Fault planetary rover in situ steering test process. (<b>a</b>) Normal planetary rover; (<b>b</b>) right front wheel drive failure; (<b>c</b>) right middle wheel drive failure; (<b>d</b>) right rear wheel drive failure.</p>
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<p>In situ steering state of planetary rover under different driving states of right front wheel. (<b>a</b>) Drive normal; (<b>b</b>) drive failure.</p>
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<p>Fault planetary rover status during in situ steering test. (<b>a</b>) Driving trajectory; (<b>b</b>) course changes.</p>
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<p>Right front wheel obstacle crossing test process. (<b>a</b>) Normal planetary rover, three stone slabs; (<b>b</b>) right front wheel drive failure, one stone slab.</p>
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<p>Rover current in the right front wheel obstacle crossing test process. (Right rear wheel drive normal and drive failure, three stone slabs).</p>
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<p>Pulling force of the tested wheel. (<b>a</b>) Drawbar pull of normal wheel; (<b>b</b>) resistance of faulty drive wheel towing motion.</p>
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<p>Torque of the tested wheel. (<b>a</b>) Driving torque of normal wheel; (<b>b</b>) stopping torque during faulty drive wheel towing motion.</p>
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<p>Motion scene and soil state of the tested wheel. (<b>a</b>) Normal wheel; (<b>b</b>) faulty drive wheel.</p>
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<p>Motion model of planetary rover with faulty drive wheel (left rear wheel).</p>
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<p>Flow form of soil when dragging the faulty drive wheel.</p>
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<p>Stress distribution when dragging the faulty drive wheel.</p>
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<p>Architecture diagram of emergency control system.</p>
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24 pages, 5059 KiB  
Article
Hazard Analysis for Massive Civil Aviation Safety Oversight Reports Using Text Classification and Topic Modeling
by Yaxi Xu, Zurui Gan, Rengang Guo, Xin Wang, Ke Shi and Pengfei Ma
Aerospace 2024, 11(10), 837; https://doi.org/10.3390/aerospace11100837 - 11 Oct 2024
Viewed by 799
Abstract
There are massive amounts of civil aviation safety oversight reports collected each year in the civil aviation of China. The narrative texts of these reports are typically short texts, recording the abnormal events detected during the safety oversight process. In the construction of [...] Read more.
There are massive amounts of civil aviation safety oversight reports collected each year in the civil aviation of China. The narrative texts of these reports are typically short texts, recording the abnormal events detected during the safety oversight process. In the construction of an intelligent civil aviation safety oversight system, the automatic classification of safety oversight texts is a key and fundamental task. However, all safety oversight reports are currently analyzed and classified into categories by manual work, which is time consuming and labor intensive. In recent years, pre-trained language models have been applied to various text mining tasks and have proven to be effective. The aim of this paper is to apply text classification to the mining of these narrative texts and to show that text classification technology can be a critical element of the aviation safety oversight report analysis. In this paper, we propose a novel method for the classification of narrative texts in safety oversight reports. Through extensive experiments, we validated the effectiveness of all the proposed components. The experimental results demonstrate that our method outperforms existing methods on the self-built civil aviation safety oversight dataset. This study undertakes a thorough examination of the precision and associated outcomes of the dataset, thereby establishing a solid basis for furnishing valuable insights to enhance data quality and optimize information. Full article
(This article belongs to the Special Issue Machine Learning for Aeronautics (2nd Edition))
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<p>A safety oversight organizational system with “two-level government and three-level supervision”.</p>
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<p>Model framework diagram.</p>
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<p>BERT structure.</p>
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<p>ChineseBERT structure.</p>
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<p>LSTM structure.</p>
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<p>Perplexity analysis.</p>
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<p>Coherence analysis.</p>
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<p>Semantic network of textual keywords for people-related issues.</p>
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<p>Semantic network of textual keywords for issues related to equipment and facilities.</p>
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<p>Semantic network of textual keywords for issues related to institutional procedures.</p>
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<p>Semantic network of textual keywords for questions related to duties.</p>
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23 pages, 9577 KiB  
Article
Evolution of Rotating Internal Channel for Heat Transfer Enhancement in a Gas Turbine Blade
by Xinxin Guo, Xueying Li and Jing Ren
Aerospace 2024, 11(10), 836; https://doi.org/10.3390/aerospace11100836 - 11 Oct 2024
Viewed by 1045
Abstract
To achieve higher thermal efficiency in a gas turbine, increasing the turbine inlet temperature is necessary. The rotor blade at the first stage tolerates the highest temperature, and the serpentine internal channel located in the middle chord of the rotor blade is vital [...] Read more.
To achieve higher thermal efficiency in a gas turbine, increasing the turbine inlet temperature is necessary. The rotor blade at the first stage tolerates the highest temperature, and the serpentine internal channel located in the middle chord of the rotor blade is vital in guaranteeing the blade’s service life. Therefore, it is essential to illustrate the evolution of the rotating internal channel in a gas turbine blade. In the paper, the influence of the Coriolis force, including its mechanisms, on the conventional rotating channel are reviewed and analyzed. A way to utilize the positive heat transfer effect of the Coriolis force is proposed. Recent investigations on corresponding novel rotating channels with a channel orientation angle of 90° (called bilaterally enhanced U-channels) are illustrated. Moreover, numerical investigations about the Re effects on bilaterally enhanced smooth U-channels were carried out in the study. The results indicated that bilaterally enhanced U-channels can utilize the Coriolis force positive heat transfer effect on the leading and the trailing walls at the same time. Re and Ro are vital non-dimensional numbers that influence the performance of bilaterally enhanced U-channels. Re and Ro have an independent influence on the heat transfer performance of the bilaterally enhanced U-channel. Ro is good for the heat transfer of the bilaterally enhanced U-channel on both the leading and the trailing walls. Therefore, the bilaterally enhanced U-channel is suitable for application in the middle chord region of a turbine blade, since it can utilize the rotation effect of the rotating blade to improve the heat transfer ability of the blade and thus reduced the blade temperature. At the same Ro, Re positively affects the Nu on the leading and the trailing walls of the Coriolis-utilization rotating smooth U-channel, but plays a negligible role on Nu/Nu0. Full article
(This article belongs to the Special Issue New Insights into Aerodynamics and Cooling in Gas Turbine Engines)
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<p>Schematic of internal channel in rotor blade [<a href="#B2-aerospace-11-00836" class="html-bibr">2</a>].</p>
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<p>Concept of inner channel structure of a rotor blade [<a href="#B33-aerospace-11-00836" class="html-bibr">33</a>].</p>
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<p>Direction of Coriolis force in a conventional rotating internal channel of a turbine blade.</p>
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<p>Straight rib-induced secondary flow in a stationary passage [<a href="#B75-aerospace-11-00836" class="html-bibr">75</a>].</p>
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<p>V-rib-induced secondary flow in a stationary channel [<a href="#B76-aerospace-11-00836" class="html-bibr">76</a>].</p>
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<p>V-rib-induced secondary flow in a rotating channel [<a href="#B76-aerospace-11-00836" class="html-bibr">76</a>].</p>
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<p>Coriolis force effect on heat transfer of conventional, ribbed rotating channel [<a href="#B77-aerospace-11-00836" class="html-bibr">77</a>].</p>
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<p>Flow mechanisms induced by Coriolis force inside a rotating smooth channel with channel orientation angle of 90°.</p>
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<p>Concept view of a rotating channel with orientation angle of 90° in a rotor blade [<a href="#B88-aerospace-11-00836" class="html-bibr">88</a>].</p>
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<p>Velocity distribution in a smooth rotating channel with orientation angle of 90° [<a href="#B4-aerospace-11-00836" class="html-bibr">4</a>] (upper: after optimization; lower: the original structure).</p>
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<p>Average <span class="html-italic">Nu/Nu</span><sub>0</sub> variations along <span class="html-italic">Ro</span> on the leading wall of one-pass channel with different cooling structures [<a href="#B69-aerospace-11-00836" class="html-bibr">69</a>,<a href="#B76-aerospace-11-00836" class="html-bibr">76</a>,<a href="#B90-aerospace-11-00836" class="html-bibr">90</a>,<a href="#B91-aerospace-11-00836" class="html-bibr">91</a>].</p>
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<p>Average <span class="html-italic">Nu/Nu</span><sub>0</sub> variations along <span class="html-italic">Ro</span> on the leading wall of two- or three-pass channels with bend region with different cooling structures [<a href="#B76-aerospace-11-00836" class="html-bibr">76</a>,<a href="#B92-aerospace-11-00836" class="html-bibr">92</a>,<a href="#B93-aerospace-11-00836" class="html-bibr">93</a>].</p>
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<p>Average <span class="html-italic">Nu/Nu</span><sub>0</sub> variations along <span class="html-italic">Ro</span> on the trailing wall of one-pass channel with different cooling structures [<a href="#B69-aerospace-11-00836" class="html-bibr">69</a>,<a href="#B76-aerospace-11-00836" class="html-bibr">76</a>,<a href="#B90-aerospace-11-00836" class="html-bibr">90</a>,<a href="#B91-aerospace-11-00836" class="html-bibr">91</a>].</p>
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<p>Average <span class="html-italic">Nu/Nu</span><sub>0</sub> variations along <span class="html-italic">Ro</span> on the trailing wall of two- or three-pass channels with bend region with different cooling structures [<a href="#B76-aerospace-11-00836" class="html-bibr">76</a>,<a href="#B92-aerospace-11-00836" class="html-bibr">92</a>,<a href="#B93-aerospace-11-00836" class="html-bibr">93</a>].</p>
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<p><span class="html-italic">Nu/Nu</span><sub>0</sub> contours of non-rotational U-channel, conventional rotating U-channel and the novel rotating U-channel [<a href="#B82-aerospace-11-00836" class="html-bibr">82</a>].</p>
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<p>Axial-average <span class="html-italic">Nu/Nu</span><sub>0</sub> variations along with main flow of the novel rotating channel under different <span class="html-italic">Ro</span> (Experiment results) [<a href="#B94-aerospace-11-00836" class="html-bibr">94</a>].</p>
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<p>Channel structures of conventional rotating U-channel and bilaterally enhanced U-channel in a blade [<a href="#B82-aerospace-11-00836" class="html-bibr">82</a>].</p>
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<p>Grid of the smooth, bilaterally enhanced U-channel.</p>
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<p>Validation result with existing experiment [<a href="#B96-aerospace-11-00836" class="html-bibr">96</a>].</p>
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<p>Average <span class="html-italic">Nu</span> variations under different <span class="html-italic">Re</span> along the flow direction on the trailing wall.</p>
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<p>Average <span class="html-italic">Nu</span> variations under different <span class="html-italic">Re</span> along the flow direction on the leading wall.</p>
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<p>Average <span class="html-italic">Nu/Nu</span><sub>0</sub> variations under different <span class="html-italic">Re</span> along the flow direction on the trailing wall at <span class="html-italic">Ro</span> = 0.025.</p>
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<p>Average <span class="html-italic">Nu/Nu</span><sub>0</sub> variations under different <span class="html-italic">Re</span> along the flow direction on the trailing wall at <span class="html-italic">Ro</span> = 0.</p>
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<p>Pressure loss variations of the bilaterally enhanced U-channel along different <span class="html-italic">Re</span>.</p>
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11 pages, 6060 KiB  
Article
Investigation of Asymmetric Flow of a Slender Body with Low-Aspect Ratio Fins Having Large Deflection Angles
by Yonghong Li, Lin Zhang, Chuan Gao, Jilong Zhu and Bin Dong
Aerospace 2024, 11(10), 835; https://doi.org/10.3390/aerospace11100835 - 10 Oct 2024
Viewed by 741
Abstract
To understand the asymmetric flow of a slender body with low-aspect ratio fins, a wind tunnel experiment was carried out, and the asymmetric flow was observed when the pair of fins had a symmetric deflection angle of 30° at a small angle of [...] Read more.
To understand the asymmetric flow of a slender body with low-aspect ratio fins, a wind tunnel experiment was carried out, and the asymmetric flow was observed when the pair of fins had a symmetric deflection angle of 30° at a small angle of attack and zero sideslip angle at transonic speeds. The unsteady characteristics of flow around the moving fins, especially for the evolution of the asymmetric flow, was carefully numerically investigated via the RANS method. To verify the numerical method, the experimental steady wind tunnel data of the NACA 0012 airfoil with sinusoidal pitching motion were adopted. A hysteresis loop exists as a function of the deflection angle during the upstroke and downstroke motions. The side force is periodic due to the asymmetric flow peaks at the downstroke and their peak value appeared at around δz = 25°, which was independent of the deflection frequency. As the deflection frequency increased, the asymmetric flow formed at a higher deflection angle during the upstroke motion, but decayed at a lower deflection angle during the downstroke motion, resulting in a more significant unsteady hysteresis effect. Full article
(This article belongs to the Section Aeronautics)
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<p>The sketch of the test model.</p>
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<p>The deflection of the pair of fins on the leeward and windward side with <span class="html-italic">δz</span> = 30°.</p>
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<p>The pitching moment and side force of the models with different pitching deflection angles against a range of angles of attack at <span class="html-italic">M</span> = 0.95, <span class="html-italic">β</span> = 0°. The uncertainties of C<sub>y</sub> and C<sub>m</sub> are 0.002 and 0.01, respectively.</p>
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<p>The side force coefficients under various Mach numbers at zero angle of attack, <span class="html-italic">β</span> = 0° (<span class="html-italic">δz</span> = 30°). The uncertainty of C<sub>y</sub> is 0.002.</p>
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<p>The original images of the PIV results at <span class="html-italic">β</span> = 0° (<span class="html-italic">δz</span> = 30°).</p>
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<p>The mesh topology of the fins and the rail body (2.4 million cells).</p>
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<p>Comparisons of the side forces between the experimental data and the CFD results of the model with <span class="html-italic">δz</span> = 30°.</p>
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<p>The stagnation pressure contours of type sections around the leeward and windward pair of fins.</p>
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<p>Mach number distributions of typical sections around the fins at <span class="html-italic">M</span> = 0.95.</p>
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<p>Comparisons of the experimental data with the present results.</p>
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<p>Evolution of the entropy flow field, <span class="html-italic">δz</span>(<span class="html-italic">t</span>) = 20° + 10° sin(2<span class="html-italic">πk∙t</span>), <span class="html-italic">k</span> = 0.044.</p>
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<p>Evolution of the side force coefficient, <span class="html-italic">δz</span>(<span class="html-italic">t</span>) = 20° + 10° sin(2<span class="html-italic">πk∙t</span>), <span class="html-italic">k</span> = 0.044.</p>
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<p>Time evolution of the side force coefficient of the fins with different deflection frequencies.</p>
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22 pages, 13437 KiB  
Article
A Novel Approach to Ripple Cancellation for Low-Speed Direct-Drive Servo in Aerospace Applications
by Xin Zhang, Ziting Wang, Chaoping Bai and Shuai Zhang
Aerospace 2024, 11(10), 834; https://doi.org/10.3390/aerospace11100834 - 10 Oct 2024
Viewed by 877
Abstract
Low-frequency harmonic interference is an important factor that affects the performance of low-speed direct-drive servo systems. In order to improve the low-speed smoothness of direct-drive servo, firstly, the causes of the first and second harmonics of electromagnetic torque and tooth harmonics are analyzed [...] Read more.
Low-frequency harmonic interference is an important factor that affects the performance of low-speed direct-drive servo systems. In order to improve the low-speed smoothness of direct-drive servo, firstly, the causes of the first and second harmonics of electromagnetic torque and tooth harmonics are analyzed based on the mathematical model of PMSM (permanent magnet synchronous motor) and the principle of vector control. Accordingly, the CC-EUMA (Electrical angle Update and Mechanical angle Assignment algorithm for Center Current) and SL-DQPR (Double Quasi-Proportional Resonant control algorithm for Speed Loop) algorithm are proposed. Second, to confirm the algorithm’s efficacy, the harmonic environment is simulated using Matlab/Simulink, and the built harmonic suppression module is simulated and analyzed. Then, a miniaturized, fully digital drive control system is built based on the architecture of the Zynq-7000 series chips. Finally, the proposed suppression algorithm is verified at the board level. According to the experimental results, the speed ripple decreases to roughly one-third of its initial value after the algorithm is included. This effectively delays the speed ripple’s low-speed deterioration and provides a new idea for the low-speed control of the space direct-drive servo system. Full article
(This article belongs to the Special Issue Aircraft Electric Power System: Design, Control, and Maintenance)
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<p>Double closed-loop servo system under the action of cogging torque.</p>
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<p>The algorithm of CC-EUMA.</p>
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<p>Amplitude–frequency characteristics of the PR controller: (<b>a</b>) <math display="inline"><semantics> <mrow> <msub> <mi>k</mi> <mi>p</mi> </msub> </mrow> </semantics></math> is a variable constant, <math display="inline"><semantics> <mrow> <msub> <mi>k</mi> <mi>r</mi> </msub> </mrow> </semantics></math> is an invariant constant; (<b>b</b>) <math display="inline"><semantics> <mrow> <msub> <mi>k</mi> <mi>p</mi> </msub> </mrow> </semantics></math> is an invariable constant, and <math display="inline"><semantics> <mrow> <msub> <mi>k</mi> <mi>r</mi> </msub> </mrow> </semantics></math> is a variant constant.</p>
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<p>Amplitude–frequency characteristics of the QPR controller. (<b>a</b>) <math display="inline"><semantics> <mrow> <msub> <mi>k</mi> <mi>p</mi> </msub> </mrow> </semantics></math> is a variable constant, <math display="inline"><semantics> <mrow> <msub> <mi>k</mi> <mi>r</mi> </msub> </mrow> </semantics></math> is an invariant constant; (<b>b</b>) <math display="inline"><semantics> <mrow> <msub> <mi>k</mi> <mi>p</mi> </msub> </mrow> </semantics></math> is an invariable constant, and <math display="inline"><semantics> <mrow> <msub> <mi>k</mi> <mi>r</mi> </msub> </mrow> </semantics></math> is a variant constant.</p>
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<p>Baud diagram of the system before and after parallel QPR control in a speed loop.</p>
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<p>DC bias module used to simulate torque current’s first harmonic.</p>
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<p>CC-EUMA algorithm module.</p>
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<p>Simulation results of speed before and after adding CC-EUMA: (<b>a</b>) before adding CC-EUMA; (<b>b</b>) after adding CC-EUMA.</p>
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<p>Simulation results of speed FFT before and after adding CC-EUMA: (<b>a</b>) before adding CC-EUMA; (<b>b</b>) after adding CC-EUMA.</p>
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<p>Module structure for torque current simulation of the second and tooth harmonics: (<b>a</b>) analog second harmonic; (<b>b</b>) analog tooth harmonic.</p>
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<p>SL-DQPR algorithm module.</p>
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<p>Simulation results of speed before and after adding SL-DQPR: (<b>a</b>) before adding SL-DQPR; (<b>b</b>) after adding SL-DQPR.</p>
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<p>Simulation results of speed FFT before and after adding SL-DQPR: (<b>a</b>) before adding SL-DQPR; (<b>b</b>) after adding SL-DQPR.</p>
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<p>Servo system architecture.</p>
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<p>Structure of the drive controller.</p>
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<p>Software architecture of PMSM low-speed direct-drive servo system.</p>
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<p>Operational interface of the master computer system.</p>
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<p>Experimental platform for the servo system.</p>
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<p>Internal hardware components of the drive controller.</p>
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<p>Timing control flow of the system.</p>
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<p>Speed and speed FFT before and after adding CC-EUMA at 50 rpm: (<b>a</b>) speed; (<b>b</b>) speed FFT.</p>
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<p>Speed and speed FFT before and after adding CC-EUMA at 40 rpm: (<b>a</b>) speed; (<b>b</b>) speed FFT.</p>
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<p>Speed and speed FFT before and after adding CC-EUMA at 30 rpm: (<b>a</b>) speed; (<b>b</b>) speed FFT.</p>
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<p>Speed and speed FFT before and after adding CC-EUMA at 20 rpm: (<b>a</b>) speed; (<b>b</b>) speed FFT.</p>
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<p>Speed and speed FFT before and after adding CC-EUMA at 10 rpm: (<b>a</b>) speed; (<b>b</b>) speed FFT.</p>
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<p>Speed and speed FFT before and after adding SL-DQPR at 50 rpm: (<b>a</b>) speed; (<b>b</b>) speed FFT.</p>
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<p>Speed and speed FFT before and after adding SL-DQPR at 40 rpm: (<b>a</b>) speed; (<b>b</b>) speed FFT.</p>
Full article ">Figure 28
<p>Speed and speed FFT before and after adding SL-DQPR at 30 rpm: (<b>a</b>) speed; (<b>b</b>) speed FFT.</p>
Full article ">Figure 29
<p>Speed and speed FFT before and after adding SL-DQPR at 20 rpm: (<b>a</b>) speed; (<b>b</b>) speed FFT.</p>
Full article ">Figure 30
<p>Speed and speed FFT before and after adding SL-DQPR at 10 rpm: (<b>a</b>) speed; (<b>b</b>) speed FFT.</p>
Full article ">Figure 31
<p>The speed ripples of adding CC-EUMA and SL-DQPR at the same time.</p>
Full article ">
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