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Aerospace
Volume 11
Issue 8
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Aerospace, Volume 11, Issue 8 (August 2024) – 92 articles

Cover Story (view full-size image): A self-aligned focusing schlieren (SAFS) system combines the field of view of a conventional schlieren system with the defocus blur of a focusing schlieren system away from the object plane. Its depth of field is sufficiently shallow to distinguish specific spanwise features in a supersonic flowfield within a 76.2 mm (3 in) wide test section and to blur the boundary layers on the windows. Laser spark velocimetry is performed in Mach 2 flow by tracking the blast wave of a laser spark using 500 kHz SAFS imaging with a 200 ns optical pulse width. The flow Mach number and stagnation temperature are measured by comparing the blast wave dynamics with an analytical solution. Additionally, schlieren image velocimetry is performed by analyzing natural flow perturbations by a self-correlation method. View this paper
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23 pages, 4137 KiB  
Article
Mars Exploration: Research on Goal-Driven Hierarchical DQN Autonomous Scene Exploration Algorithm
by Zhiguo Zhou, Ying Chen, Jiabao Yu, Bowen Zu, Qian Wang, Xuehua Zhou and Junwei Duan
Aerospace 2024, 11(8), 692; https://doi.org/10.3390/aerospace11080692 - 22 Aug 2024
Viewed by 571
Abstract
In the non-deterministic, large-scale navigation environment under the Mars exploration mission, there is a large space for action and many environmental states. Traditional reinforcement learning algorithms that can only obtain rewards at target points and obstacles will encounter the problems of reward sparsity [...] Read more.
In the non-deterministic, large-scale navigation environment under the Mars exploration mission, there is a large space for action and many environmental states. Traditional reinforcement learning algorithms that can only obtain rewards at target points and obstacles will encounter the problems of reward sparsity and dimension explosion, making the training speed too slow or even impossible. This work proposes a deep layered learning algorithm based on the goal-driven layered deep Q-network (GDH-DQN), which is more suitable for mobile robots to explore, navigate, and avoid obstacles without a map. The algorithm model is designed in two layers. The lower layer provides behavioral strategies to achieve short-term goals, and the upper layer provides selection strategies for multiple short-term goals. Use known position nodes as short-term goals to guide the mobile robot forward and achieve long-term obstacle avoidance goals. Hierarchical execution not only simplifies tasks but also effectively solves the problems of reward sparsity and dimensionality explosion. In addition, each layer of the algorithm integrates a Hindsight Experience Replay mechanism to improve performance, make full use of the goal-driven function of the node, and effectively avoid the possibility of misleading the agent by complex processes and reward function design blind spots. The agent adjusts the number of model layers according to the number of short-term goals, further improving the efficiency and adaptability of the algorithm. Experimental results show that, compared with the hierarchical DQN method, the navigation success rate of the GDH-DQN algorithm is significantly improved, and it is more suitable for unknown scenarios such as Mars exploration. Full article
(This article belongs to the Section Astronautics & Space Science)
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Figure 1
<p>Mars surface terrain from a large-scale perspective.</p> Full article ">Figure 2
<p>DQN training process.</p> Full article ">Figure 3
<p>Block diagram of the GDH-DQN algorithm.</p> Full article ">Figure 4
<p>Mars surface terrain, small-scale perspective.</p> Full article ">Figure 5
<p>High-level neural network structure.</p> Full article ">Figure 6
<p>Low-level neural network structure.</p> Full article ">Figure 7
<p>Grid map. (<b>a</b>) 10 × 10 grid map. (<b>b</b>) 20 × 20 grid. The red square marks the agent’s starting position, the yellow circle denotes the randomly generated target for each iteration, and the black grids are fixed obstacles throughout training.</p> Full article ">Figure 8
<p>Comparison of the navigation success rate of each algorithm. (<b>a</b>) 10 × 10 grid map. (<b>b</b>) 20 × 20 grid map.</p> Full article ">Figure 9
<p>Comparison of the average navigation steps of each algorithm. (<b>a</b>) 10 × 10 grid map. (<b>b</b>) 20 × 20 grid map.</p> Full article ">Figure 10
<p>20 × 30 grid map. The red square marks the agent’s starting position, the yellow circle denotes the randomly generated target for each iteration, and the black grids are fixed obstacles throughout training.</p> Full article ">Figure 11
<p>Comparison of navigation success rates of different algorithms. (<b>a</b>) 10 × 10 grid map. (<b>b</b>) 20 × 20 grid map. (<b>c</b>) 20 × 30 grid map.</p> Full article ">
22 pages, 5679 KiB  
Article
Mental Workload as a Predictor of ATCO’s Performance: Lessons Learnt from ATM Task-Related Experiments
by Enrique Muñoz-de-Escalona, Maria Chiara Leva and José Juan Cañas
Aerospace 2024, 11(8), 691; https://doi.org/10.3390/aerospace11080691 - 22 Aug 2024
Viewed by 667
Abstract
Air Traffic Controllers’ (ATCos) mental workload is likely to remain the specific greatest functional limitation on the capacity of the Air Traffic Management (ATM) system. Developing computational models to monitor mental workload and task complexity is essential for enabling ATCOs and ATM systems [...] Read more.
Air Traffic Controllers’ (ATCos) mental workload is likely to remain the specific greatest functional limitation on the capacity of the Air Traffic Management (ATM) system. Developing computational models to monitor mental workload and task complexity is essential for enabling ATCOs and ATM systems to adapt to varying task demands. Most methodologies have computed task complexity based on basic parameters such as air-traffic density; however, literature research has shown that it also depends on many other factors. In this paper, we present a study in which we explored the possibility of predicting task complexity and performance through mental workload measurements of participants performing an ATM task in an air-traffic control simulator. Our findings suggest that mental workload measurements better predict poor performance and high task complexity peaks than other established factors. This underscores their potential for research into how different ATM factors affect task complexity. Understanding the role and the weight of these factors in the overall task complexity confronted by ATCos constitutes one of the biggest challenges currently faced by the ATM sphere and would significantly contribute to the safety of our sky. Full article
(This article belongs to the Section Air Traffic and Transportation)
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Figure 1
<p><sup>ATC</sup>Lab-Advanced initial scenario screen during data collection stage. Outbound air traffic is displayed in green, while inbound air traffic in blue.</p> Full article ">Figure 2
<p>(<b>a</b>) ISA scale, 5 min intervals; (<b>b</b>) ISA scale, 2 min intervals.</p> Full article ">Figure 3
<p>(<b>a</b>) Air-traffic density through intervals for experimental condition 1; (<b>b</b>) air-traffic density through intervals for experimental condition 2.</p> Full article ">Figure 4
<p>Performance ratings (conflict rate), air-traffic density, left and right pupil size variation and subjective mental workload reports (ISA scale) during experimental scenario development for experimental condition 1. Vertical red dotted lines indicate the position of the local maxima in conflict rate and their alignment with the remaining measures. Orange line in pupil size variation indicate left pupil, while blue line indicate right pupil.</p> Full article ">Figure 5
<p>Cross-correlation chart for pupil size (right pupil as reference) and performance ratings during low-performance peaks for experimental condition 1. The blue and red dotted lines show where the cross-correlation is maximized (Lag 1) and minimized (Lag −1), respectively, while the green dotted line shows the position of Lag 0.</p> Full article ">Figure 6
<p>Cross-correlation chart for subjective reports and performance ratings during low-performance peaks for experimental condition 1. The blue and red dotted lines show where the cross-correlation is maximized (Lag 2) and minimized (Lag −2), respectively, while the green dotted line shows the position of Lag 0.</p> Full article ">Figure 7
<p>Performance ratings (conflict rate), air-traffic density, left and right pupil size variation and subjective mental workload reports (ISA Scale) during experimental scenario development for experimental condition 2. Vertical red dotted lines indicate the position of the local maxima in conflict rate and their alignment with the remaining measures. Orange line in pupil size variation indicate left pupil, while blue line indicate right pupil.</p> Full article ">Figure 8
<p>Cross-correlation chart for pupil size (right pupil as reference) and performance ratings during low-performance peaks for experimental condition 2. The blue and red dotted lines show where the cross-correlation is maximized (Lag 3) and minimized (Lag −2), respectively, while the green dotted line shows the position of Lag 0.</p> Full article ">Figure 9
<p>Cross-correlation chart for subjective reports and performance ratings during low-performance peaks for experimental condition 2. The blue and red dotted lines show where the cross-correlation is maximized (Lag 1) and minimized (Lag −4), respectively, while the green dotted line shows the position of Lag 0.</p> Full article ">Figure A1
<p>Histograms for subjective mental workload reports (ISA scale), performance ratings (conflict rate), air-traffic density and left and right pupil size for experimental condition 1.</p> Full article ">Figure A2
<p>Histograms for subjective mental workload reports (ISA scale), performance ratings (conflict rate), air-traffic density and left and right pupil size for experimental condition 2.</p> Full article ">
14 pages, 8922 KiB  
Article
Enabling High-Power Conditioning and High-Voltage Bus Integration Using Series-Connected DC Transformers in Spacecrafts
by Carlos Orts, Ausiàs Garrigós, David Marroquí, Antxon Arrizabalaga and Andreas Franke
Aerospace 2024, 11(8), 690; https://doi.org/10.3390/aerospace11080690 - 21 Aug 2024
Viewed by 575
Abstract
This article proposes a photovoltaic power processor for high-voltage and high-power distribution bus, between 300 V and 900 V, to be used in future space platforms like large space stations or lunar bases. Solar arrays with voltages higher than 100 V are not [...] Read more.
This article proposes a photovoltaic power processor for high-voltage and high-power distribution bus, between 300 V and 900 V, to be used in future space platforms like large space stations or lunar bases. Solar arrays with voltages higher than 100 V are not available for space application, being necessary to apply power conversion techniques. The idea behind this is to use series-connected zero-voltage and zero-current unregulated and isolated DC converters to achieve high bus voltage from the existing solar arrays. Bus regulation is then achieved through low-frequency hysteretic control. Topology description, semiconductor selection, design procedure, simulation and experimental validation, including tests in vacuum and partial pressures, are presented. Full article
(This article belongs to the Special Issue Advanced Spacecraft/Satellite Technologies)
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Figure 1
<p>S3DCX: Three power cells in independent input-parallel output configuration.</p> Full article ">Figure 2
<p>SEB threshold vs. LET. Commercial and experimental SiC diodes and space-qualified Si diodes [<a href="#B25-aerospace-11-00690" class="html-bibr">25</a>,<a href="#B27-aerospace-11-00690" class="html-bibr">27</a>,<a href="#B28-aerospace-11-00690" class="html-bibr">28</a>,<a href="#B29-aerospace-11-00690" class="html-bibr">29</a>,<a href="#B30-aerospace-11-00690" class="html-bibr">30</a>,<a href="#B31-aerospace-11-00690" class="html-bibr">31</a>,<a href="#B32-aerospace-11-00690" class="html-bibr">32</a>,<a href="#B33-aerospace-11-00690" class="html-bibr">33</a>].</p> Full article ">Figure 3
<p>Serialized power cells. (<b>a</b>) Average model. (<b>b</b>) Generalized IV curve from SAS. (<b>c</b>) Generalized PV curve from SAS.</p> Full article ">Figure 4
<p>S3DCX with two power cells serialized. Top figure: distribution bus voltage. Bottom figure: Solar Array Section voltage.</p> Full article ">Figure 5
<p>S3DCX with three power cells serialized. Top: distribution bus voltage. Bottom: Solar Array Section voltage.</p> Full article ">Figure 6
<p>S3DCX experimental setup. <b>Left figure</b>: pair of two DCX in series (2s2p). <b>Right figure</b>: single string of three DCX in series (3s1p). <b>Center figure</b>: Prototype implementation of S3DCX.</p> Full article ">Figure 7
<p>Experimental 2s2p S3DCX. <b>Top</b>: Distribution bus voltage (AC Coupled). <b>Bottom</b>: Solar Array Section voltage.</p> Full article ">Figure 8
<p>Experimental 3s1p S3DCX. <b>Top</b>: distribution bus voltage (AC mode). <b>Bottom</b>: Solar Array Section voltage.</p> Full article ">Figure 9
<p>High-voltage facilities with a vacuum chamber in ESTEC-ESA.</p> Full article ">Figure 10
<p>DCX transformer experimental setup for electrical isolation.</p> Full article ">Figure 11
<p>DCX transformer: Corona discharge at 1 mbar.</p> Full article ">Figure 12
<p>S3DCX experimental setup for validation and electrical isolation in vacuum and partial pressure conditions.</p> Full article ">Figure 13
<p>Drain source voltage (VDS) DCX MOSFETs at 10<sup>−4</sup> mbar.</p> Full article ">Figure 14
<p>DCX power cell: Corona discharge in rectifier diode at 3 mbar.</p> Full article ">
19 pages, 5553 KiB  
Article
Assembly Simulation and Optimization Method for Underconstrained Frame Structures of Aerospace Vehicles
by Jinyue Li, Gang Zhao, Jinhua Wei, Zhiyuan Hu, Wenqi Zhang and Pengfei Zhang
Aerospace 2024, 11(8), 689; https://doi.org/10.3390/aerospace11080689 - 21 Aug 2024
Viewed by 510
Abstract
Aerodynamic contour dimensional accuracy is very important for the stable and safe flight of aerospace vehicles. Nevertheless, due to the influence of various factors such as material properties, machining and manufacturing deviations, and assembly and installation deviations, key structural geometric dimensions are frequently [...] Read more.
Aerodynamic contour dimensional accuracy is very important for the stable and safe flight of aerospace vehicles. Nevertheless, due to the influence of various factors such as material properties, machining and manufacturing deviations, and assembly and installation deviations, key structural geometric dimensions are frequently exceeded. Therefore, this paper investigates a data-driven combined vector loop method (VLM)–Skin Model Shapes (SMS) method to realize aerospace vehicle structural geometric accuracy analysis; assembly optimization targeting contour deviation is also achieved. Tests are carried out on a typical aerospace vehicle’s underconstrained structural workpieces to validate the effectiveness of the proposed method. Full article
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<p>An example of vector loop and component vectors.</p> Full article ">Figure 2
<p>Difference between the nominal model, the skin model, and the physical part [<a href="#B20-aerospace-11-00689" class="html-bibr">20</a>].</p> Full article ">Figure 3
<p>Assembly-stage-oriented SMS modeling method.</p> Full article ">Figure 4
<p>Implementation steps of the SMS-VLM methodology.</p> Full article ">Figure 5
<p>Typical structure of aerospace vehicle.</p> Full article ">Figure 6
<p>Assembly mating surface deviation forms.</p> Full article ">Figure 7
<p>Mapping between assembly features and information models.</p> Full article ">Figure 8
<p>Relationships between assembly mating surface deviation and grinding amount.</p> Full article ">Figure 9
<p>Determined assembly sequence (From Part 1 to Part 9) of the structure.</p> Full article ">Figure 10
<p>Excess condition of the final mating surface.</p> Full article ">Figure 11
<p>Assembly test parts.</p> Full article ">Figure 12
<p>Vector loop of assembly test parts.</p> Full article ">Figure 13
<p>Components of vector IRs and ARps.</p> Full article ">Figure 14
<p>Parts’ SMSs built based on measured data.</p> Full article ">Figure 15
<p>Calculations based on measured data under constraints.</p> Full article ">Figure 16
<p>An assembly contour error before grinding. × indicates that the contour surface error is not qualified, and ∘ indicates that the contour surface error is qualified.</p> Full article ">Figure 17
<p>An assembly contour error after grinding. × indicates that the contour surface error is not qualified, and ∘ indicates that the contour surface error is qualified.</p> Full article ">
15 pages, 7281 KiB  
Article
Implementation of a 6U CubeSat Electrical Power System Digital Twin
by Pablo Casado, Cristian Torres, José M. Blanes, Ausiàs Garrigós and David Marroquí
Aerospace 2024, 11(8), 688; https://doi.org/10.3390/aerospace11080688 - 21 Aug 2024
Viewed by 641
Abstract
This paper presents the design of a digital twin for a 6U CubeSat electrical power system, including the solar arrays, solar array regulators, battery, power distribution unit, and load subsystems. The digital twin is validated by comparing its real-time outputs with those of [...] Read more.
This paper presents the design of a digital twin for a 6U CubeSat electrical power system, including the solar arrays, solar array regulators, battery, power distribution unit, and load subsystems. The digital twin is validated by comparing its real-time outputs with those of the physical system. Experimental tests confirm its feasibility, showing that the digital twin’s real-time outputs closely match those of the physical system. Additionally, the digital twin can be used for control-hardware-in-the-loop and power-hardware-in-the-loop tests, allowing the real-time integration of simulated subsystems with hardware. This capability facilitates testing of new subsystems and optimization during the project’s development phases. Additionally, to demonstrate the advanced capabilities of this model, the digital twin is used to simulate the CubeSat electrical power system behavior in real time throughout a complete orbital cycle in low Earth orbit conditions. This simulation provides valuable insights into the CubeSat operation by capturing the transient and steady-state responses of the EPS components under real orbital conditions. The results obtained indicate that the digital twin significantly enhances the testing and optimization process of new subsystems during the development phases of the project. Moreover, the capabilities of the digital twin can be further augmented by incorporating real-time telemetry data from the CubeSat, resulting in a highly accurate replication of the satellite’s in-orbit behavior. This approach is crucial for identifying and diagnosing failures or malfunctions in the electrical power system, ensuring the robust and reliable operation of the CubeSat. Full article
(This article belongs to the Special Issue From Satellite Systems Design, Verification and Testing to Spacecraft Operations)
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<p>6U CubeSat EPS block diagram.</p> Full article ">Figure 2
<p>Measured and digital twin solar array section I–V (blue) and P–V (red) curves.</p> Full article ">Figure 3
<p>Electrical diagram of the solar array regulator. The MPPT part of the circuit is marked in green and the error amplifier part is marked in red.</p> Full article ">Figure 4
<p>LT3845 IL experimental characterization as a function of <span class="html-italic">V<sub>C</sub></span> and duty cycle (<span class="html-italic">DC</span>).</p> Full article ">Figure 5
<p>Digital twin model diagram. It is composed of three blocks: Control circuit, power circuit, and SCADA panel.</p> Full article ">Figure 6
<p>Discharge curve points used for the battery model.</p> Full article ">Figure 7
<p>Measured and DT model of the 2S2P Samsung 18650 2600 mAh battery discharged at 520 mA (0.1 °C).</p> Full article ">Figure 8
<p>(<b>A</b>) Experimental setup; (<b>B</b>) physical EPS; (<b>C</b>) CubeSat 6U structure with two SASs.</p> Full article ">Figure 9
<p>DT validation. Upper graph: SAS voltage; middle graph: SAS current; lower graph: SAS power. The zoom shows the MPPT mode operation comparison.</p> Full article ">Figure 10
<p>CHIL diagram. The red arrows refer to the analog outputs of the real-time simulator, and the blue arrow to the analog input.</p> Full article ">Figure 11
<p>MPPT mode operation with a temperature variation using a CHIL simulation. Upper graph: SAS voltage; middle graph: SAS current; lower graph: SAS power.</p> Full article ">Figure 12
<p>PHIL diagram. The red arrows refer to the analog inputs of the real-time simulator, and the blue arrows to the analog output.</p> Full article ">Figure 13
<p>Digital P&amp;O MPPT operation using a PHIL simulation. Upper graph: SAS voltage; middle graph: SAS current; lower graph: SAS power.</p> Full article ">Figure 14
<p>Low Earth orbit simulation using the developed DT model.</p> Full article ">
22 pages, 43198 KiB  
Article
Modeling and Control of Reconfigurable Quadrotors Based on Model Reference Adaptive Control
by Zhiping Liu, Guoshao Chen and Shuping Xu
Aerospace 2024, 11(8), 687; https://doi.org/10.3390/aerospace11080687 - 21 Aug 2024
Viewed by 3364
Abstract
To expand the application prospects of quadrotors in challenging scenes such as those with dense obstacles and narrow corridors, task-driven reconfigurable quadrotors are highly desirable. Aiming to address hazard missions, in this paper, translational reconfigurable quadrotors and rotational reconfigurable quadrotors are proposed with [...] Read more.
To expand the application prospects of quadrotors in challenging scenes such as those with dense obstacles and narrow corridors, task-driven reconfigurable quadrotors are highly desirable. Aiming to address hazard missions, in this paper, translational reconfigurable quadrotors and rotational reconfigurable quadrotors are proposed with their assumptions and mathematical models. Related motion control laws were designed using model reference adaptive control (MRAC) theory based on Lyapunov stability theory, whose validity was demonstrated by sufficient numerical simulations. The simulation results verify the feasibility of the proposed control laws and reveal the important effect of time delay on the stability of the motion control system. Additionally, the dependence of motion control’s stability on the time constant of reference system was discussed. Full article
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Figure 1

Figure 1
<p>Traditional configuration.</p> Full article ">Figure 2
<p>Rotational configuration.</p> Full article ">Figure 3
<p>Translational configuration.</p> Full article ">Figure 4
<p>Combined configuration.</p> Full article ">Figure 5
<p>Body frame.</p> Full article ">Figure 6
<p>Adaptive control block.</p> Full article ">Figure 7
<p>Block diagram of motion control.</p> Full article ">Figure 8
<p>Simulation flow chart based on MRAC.</p> Full article ">Figure 9
<p><math display="inline"><semantics> <mrow> <msub> <mi>ϕ</mi> <mi>c</mi> </msub> <mo> </mo> <mi>and</mi> <mo> </mo> <mi>ϕ</mi> </mrow> </semantics></math>.</p> Full article ">Figure 10
<p><math display="inline"><semantics> <mrow> <msub> <mi>θ</mi> <mi>c</mi> </msub> <mo> </mo> <mi>and</mi> <mo> </mo> <mi>θ</mi> </mrow> </semantics></math>.</p> Full article ">Figure 11
<p><math display="inline"><semantics> <mrow> <msub> <mi>ψ</mi> <mi>c</mi> </msub> <mo> </mo> <mi>and</mi> <mo> </mo> <mi>ψ</mi> </mrow> </semantics></math>.</p> Full article ">Figure 12
<p><math display="inline"><semantics> <mrow> <msub> <mi>X</mi> <mi>c</mi> </msub> <mrow> <mo> </mo> <mi>and</mi> <mo> </mo> </mrow> <mi>X</mi> </mrow> </semantics></math>.</p> Full article ">Figure 13
<p><math display="inline"><semantics> <mrow> <msub> <mi>Y</mi> <mi>c</mi> </msub> <mrow> <mo> </mo> <mi>and</mi> <mo> </mo> </mrow> <mi>Y</mi> </mrow> </semantics></math>.</p> Full article ">Figure 14
<p><math display="inline"><semantics> <mrow> <msub> <mi>Z</mi> <mi>c</mi> </msub> <mrow> <mo> </mo> <mi>and</mi> <mo> </mo> </mrow> <mi>Z</mi> </mrow> </semantics></math>.</p> Full article ">Figure 15
<p>Body rate and lift.</p> Full article ">Figure 16
<p>Speed of rotor.</p> Full article ">Figure 17
<p>First column of <math display="inline"><semantics> <mrow> <msub> <mo>Θ</mo> <mi>R</mi> </msub> </mrow> </semantics></math>.</p> Full article ">Figure 18
<p>Second column of <math display="inline"><semantics> <mrow> <msub> <mo>Θ</mo> <mi>R</mi> </msub> </mrow> </semantics></math>.</p> Full article ">Figure 19
<p>Third column of <math display="inline"><semantics> <mrow> <msub> <mo>Θ</mo> <mi>R</mi> </msub> </mrow> </semantics></math>.</p> Full article ">Figure 20
<p>First column of <math display="inline"><semantics> <mrow> <msub> <mo>Θ</mo> <mi>X</mi> </msub> </mrow> </semantics></math>.</p> Full article ">Figure 21
<p>Second column of <math display="inline"><semantics> <mrow> <msub> <mo>Θ</mo> <mi>X</mi> </msub> </mrow> </semantics></math>.</p> Full article ">Figure 22
<p>Third column of <math display="inline"><semantics> <mrow> <msub> <mo>Θ</mo> <mi>X</mi> </msub> </mrow> </semantics></math>.</p> Full article ">
3 pages, 168 KiB  
Editorial
Shock-Dominated Flow
by He-Xia Huang
Aerospace 2024, 11(8), 686; https://doi.org/10.3390/aerospace11080686 - 21 Aug 2024
Viewed by 474
Abstract
This 2024 Special Issue of Aerospace, an open-access journal from MDPI, is entitled “Shock-Dominated Flow” and was guest-edited by Dr [...] Full article
(This article belongs to the Special Issue Shock-Dominated Flow)
16 pages, 4895 KiB  
Article
Optimal Design of High-Power Density Medium-Voltage Direct Current Bipolar Power Cables for Lunar Power Transmission
by Anoy Saha and Mona Ghassemi
Aerospace 2024, 11(8), 685; https://doi.org/10.3390/aerospace11080685 - 20 Aug 2024
Viewed by 467
Abstract
Power systems on the lunar surface require power lines of varying lengths and capacities to connect generation, storage, and load facilities. These lines must be designed to perform efficiently in the harsh lunar environment, considering factors such as weight, volume, safety, cost-effectiveness, and [...] Read more.
Power systems on the lunar surface require power lines of varying lengths and capacities to connect generation, storage, and load facilities. These lines must be designed to perform efficiently in the harsh lunar environment, considering factors such as weight, volume, safety, cost-effectiveness, and reliability. Traditional power transmission methods face challenges in this environment due to temperature fluctuations, micrometeoroid impacts, and ionizing radiation. Underground deployment, although generally safer, faces challenges due to low soil thermal conductivity. At a depth of 30 cm, the lunar temperature of −23.15 °C can be advantageous for managing waste heat. This study presents a novel approach, developed using COMSOL Multiphysics, for designing bipolar MVDC cables for lunar subsurface power transmission. Kapton® MT+ is chosen as the insulating material for its exceptional properties, including high thermal conductivity and superior dielectric strength. The cables are designed for voltages of ±10 kV and ±5 kV and capacities of 200 kW (low power), 1 MW (medium power), and 2 MW (high power). Our findings indicate that aluminum conductors offer superior performance compared to copper at medium and high power levels. Additionally, elevated voltage levels (±10 kV) enhance cable design and power transfer efficiency. These specially designed cables are well-suited for efficient operation in the challenging lunar environment. Full article
(This article belongs to the Section Astronautics & Space Science)
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<p>(<b>a</b>) Surface temperature of lunar soil over a lunar cycle; (<b>b</b>) mean temperature vs. depth produced by the Apollo 15 and 17 thermal property models [<a href="#B15-aerospace-11-00685" class="html-bibr">15</a>].</p> Full article ">Figure 2
<p>Geometry of the coaxial bipolar cables beneath the lunar surface.</p> Full article ">Figure 3
<p>Lunar surface temperature variation throughout the simulation period.</p> Full article ">Figure 4
<p>Close-up view of the mesh around the cable.</p> Full article ">Figure 5
<p>Cable configuration of the coaxial bipolar cable system.</p> Full article ">Figure 6
<p>Weight per unit length of the cables at three power levels.</p> Full article ">Figure 7
<p>Cross-sectional area of the cables at three power levels.</p> Full article ">Figure 8
<p>Parameter <math display="inline"><semantics> <mrow> <mi>J</mi> </mrow> </semantics></math> of the cables at three power levels.</p> Full article ">Figure 9
<p>Simulated temperature curve of the cable surface over time for 2 MW power transmission at ±10 kV voltage.</p> Full article ">Figure 10
<p>Electric field distribution of the coaxial bipolar cable system for 2 MW power transmission at ±10 kV voltage.</p> Full article ">Figure 11
<p>The geometry of the two cables under 30 cm beneath the lunar surface.</p> Full article ">
18 pages, 1220 KiB  
Article
Decoys Deployment for Missile Interception: A Multi-Agent Reinforcement Learning Approach
by Enver Bildik, Antonios Tsourdos, Adolfo Perrusquía and Gokhan Inalhan
Aerospace 2024, 11(8), 684; https://doi.org/10.3390/aerospace11080684 - 20 Aug 2024
Viewed by 851
Abstract
Recent advances in radar seeker technologies have considerably improved missile precision and efficacy during target interception. This is especially concerning in the arenas of protection and safety, where appropriate countermeasures against enemy missiles are required to ensure the protection of naval facilities. In [...] Read more.
Recent advances in radar seeker technologies have considerably improved missile precision and efficacy during target interception. This is especially concerning in the arenas of protection and safety, where appropriate countermeasures against enemy missiles are required to ensure the protection of naval facilities. In this study, we present a reinforcement-learning-based strategy for deploying decoys to enhance the survival probability of a target ship against a missile threat. Our approach involves the coordinated operation of three decoys, trained using the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) and Multi-Agent Twin Delayed Deep Deterministic Policy Gradient (MATD3) algorithms. The decoys operate in a leader–follower dynamic with a circular formation to ensure effective coordination. We evaluate the strategy across various parameters, including decoy deployment regions, missile launch directions, maximum decoy speeds, and missile speeds. The results indicate that, decoys trained with the MATD3 algorithm demonstrate superior performance compared to those trained with the MADDPG algorithm. Insights suggest that our decoy deployment strategy, particularly when utilizing MATD3-trained decoys, significantly enhances defensive measures against missile threats. Full article
(This article belongs to the Special Issue Artificial Intelligence in Drone Applications (2nd Edition))
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<p>High-Level Diagram of the Proposed Methodology.</p> Full article ">Figure 2
<p>Trajectories of the missile, target, and decoys.</p> Full article ">Figure 3
<p>Missile Target engagement.</p> Full article ">Figure 4
<p>Reinforcement Learning Environment.</p> Full article ">Figure 5
<p>Centralized training and decentralized execution multi-agent reinforcement learning framework.</p> Full article ">Figure 6
<p>Relative position of leader agent with goal point and follower agents.</p> Full article ">Figure 7
<p>Decoy deployment regions and angles.</p> Full article ">Figure 8
<p>Mission success rate based on the decoy deployment regions and missile launch direction.</p> Full article ">Figure 9
<p>Mission success rate irrespective of missile launch direction.</p> Full article ">Figure 10
<p>The impact of noise in the mission success rate.</p> Full article ">Figure 11
<p>Average reward curve (<b>left</b>) and mission success rate (<b>right</b>) for MADDPG and MATD3 algorithms.</p> Full article ">
17 pages, 4076 KiB  
Article
Adaptive Attitude Roll Control of Guided Projectile Based on a Novel Unidirectional Global Sliding Mode Algorithm
by Shouyi Guo, Liangming Wang and Jian Fu
Aerospace 2024, 11(8), 683; https://doi.org/10.3390/aerospace11080683 - 20 Aug 2024
Viewed by 507
Abstract
Aimed at addressing the strong nonlinearity and strong external disturbances that cause flight control issues in conventional guided projectiles, as well as the slow response and structural vibrations that often occur in sliding mode control systems, which have a detrimental impact on the [...] Read more.
Aimed at addressing the strong nonlinearity and strong external disturbances that cause flight control issues in conventional guided projectiles, as well as the slow response and structural vibrations that often occur in sliding mode control systems, which have a detrimental impact on the control effect and ultimate hit precision, a new type of fast and robust control algorithm with a unidirectional mode has been designed. The objective is to design an optimized aerodynamic shape for the projectile and to establish a dynamic model of the roll channel and a motion model of the entire trajectory. The dynamics of a new global terminal sliding mode are proposed, and an adaptive parameter term is realized by calculating the state of the critical sliding mode surface, which ensures that the tracking error converges within a finite time. Its combination with an adaptive approaching law is used to further speed up convergence while damping the structural vibration of the system. The bias error of the roll angle is constructed as the controller and simulation calculations are conducted on the basis of the aforementioned framework. The stability and time convergence of the control system are demonstrated through Lyapunov theory. The results indicate that, in comparison to the conventional terminal sliding mode controller, the designed controller exhibits a markedly rapid convergence rate and stronger robustness in tracking the command signal. Moreover, it also maintains a stable motion attitude of the projectile throughout the entire process. The superior control effect under different guidance schemes and the strong external disturbances also further reflect the anti-jamming capability and tracking performance of the system. Full article
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<p>Control flap location diagram of a different projectile.</p> Full article ">Figure 2
<p>A sketch of the configuration with the control surfaces.</p> Full article ">Figure 3
<p>The whole convergence process of the designed control algorithm.</p> Full article ">Figure 4
<p>A structure diagram of the designed autopilot.</p> Full article ">Figure 5
<p>The flow field simulation model.</p> Full article ">Figure 6
<p>Comparison of the control effect.</p> Full article ">Figure 7
<p>The change curves of the state variable.</p> Full article ">Figure 8
<p>The change curves of the motion attitude.</p> Full article ">Figure 9
<p>The anti-jamming performance of the system.</p> Full article ">Figure 10
<p>The control effect at different control torques.</p> Full article ">Figure 11
<p>The system tracking performance in actual condition.</p> Full article ">
12 pages, 3458 KiB  
Article
Conceptual Design of Compliant Structures for Morphing Wingtips Using Single-Row Corrugated Panels
by Ziyi He, Siyun Fan, Chen Wang, Songqi Li, Yan Zhao, Xing Shen and Jiaying Zhang
Aerospace 2024, 11(8), 682; https://doi.org/10.3390/aerospace11080682 - 19 Aug 2024
Viewed by 856
Abstract
Morphing wingtips have the potential to improve aircraft performance. By connecting the wingtips and the wings with a compliant structure, a continuous aerodynamic surface can be achieved for a better aerodynamic performance. However, how to maintain the shape-changing capability while keeping a high [...] Read more.
Morphing wingtips have the potential to improve aircraft performance. By connecting the wingtips and the wings with a compliant structure, a continuous aerodynamic surface can be achieved for a better aerodynamic performance. However, how to maintain the shape-changing capability while keeping a high stiffness to carry aerodynamic loads is a key problem. In this paper, based on asymmetric stiffness, a type of single-row corrugated panel is designed to satisfy the limited space around the wingtip. A finite element model of the single-row corrugated panels is established, and parameter analysis is performed to investigate the impact of the thickness characteristics of the corrugated panel on the folding angle. The corrugated panel is then optimised to find the maximum folding angle. Based on the optimisation results, corrugated panels with asymmetric and symmetric stiffness are fabricated and tested. The results demonstrate that the asymmetric stiffness corrugated panels have the capability to increase the wingtip folding angle. Full article
(This article belongs to the Special Issue Structures, Actuation and Control of Morphing Systems)
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<p>(<b>a</b>) Compliant morphing wing, (<b>b</b>) folding deformation diagram.</p> Full article ">Figure 2
<p>Asymmetric stiffness corrugated panel.</p> Full article ">Figure 3
<p>(<b>a</b>) FEM model, (<b>b</b>) meshes in corrugated panel, (<b>c</b>) folding deformation diagram.</p> Full article ">Figure 4
<p>The effect of stiffness tailoring on the folding angle. (<b>a</b>) The effect of the upper and lower thickness ratio N on the folding angle. (<b>b</b>) The effect of the corrugated unit thickness increment ratio K on the folding angle.</p> Full article ">Figure 5
<p>Effect of corrugated panel thickness on folding angle.</p> Full article ">Figure 6
<p>Optimisation flow chart.</p> Full article ">Figure 7
<p>Establishment of experiment platform. (<b>a</b>) Experiment platform, (<b>b</b>) motion capture device, (<b>c</b>) operation interface.</p> Full article ">Figure 8
<p>Folding angle diagram of corrugated panel. (<b>a</b>) Actuation displacement 0 mm. (<b>b</b>) Actuation displacement 10 mm. (<b>c</b>) Actuation displacement 20 mm. (<b>d</b>) Actuation displacement 30 mm.</p> Full article ">Figure 9
<p>Folding angle diagram of two kinds of corrugated panel actuation displacement.</p> Full article ">
26 pages, 16010 KiB  
Article
Conversion of a Coaxial Rotorcraft to a UAV—Lessons Learned
by Barzin Hosseini, Julian Rhein, Florian Holzapfel, Benedikt Grebing and Juergen Rauleder
Aerospace 2024, 11(8), 681; https://doi.org/10.3390/aerospace11080681 - 19 Aug 2024
Viewed by 674
Abstract
A coaxial helicopter with a maximum take-off weight of 600 kg was converted to an unmanned aerial vehicle. A minimally invasive robotic actuator system was developed, which can be retrofitted onto the copilot seat of the rotorcraft in a short period of time [...] Read more.
A coaxial helicopter with a maximum take-off weight of 600 kg was converted to an unmanned aerial vehicle. A minimally invasive robotic actuator system was developed, which can be retrofitted onto the copilot seat of the rotorcraft in a short period of time to enable automatic flight. The automatic flight control robot includes electromechanical actuators, which are connected to the cockpit inceptors and control the helicopter. Most of the sensors and avionic components were integrated into the modular robotic system for faster integration into the rotorcraft. The mechanical design of the control system, the development of the robot control software, and the control system architecture are described in this paper. Furthermore, the multi-body simulation of the robotic system and the estimation of the linear low-order actuator models from hover-frame flight test data are discussed. The developed technologies in this study are not specific to a coaxial helicopter and can be applied to the conversion of any crewed flight vehicle with mechanical controls to unmanned or fly-by-wire. This agile development of a full-size flying test-bed can accelerate the testing of advanced flight control laws, as well as advanced air mobility-related functions. Full article
(This article belongs to the Special Issue Recent Research on UAM/AAM Aircraft and Systems: Modeling, Advanced Control, and Emerging Technologies)
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<p>The actuators controlling the stick.</p> Full article ">Figure 2
<p>Actuators controlling the collective lever and the pedal.</p> Full article ">Figure 3
<p>Coupling of a servo model with a revolute joint.</p> Full article ">Figure 4
<p>Structure of the simulation framework for the robotic control system.</p> Full article ">Figure 5
<p><math display="inline"><semantics> <msub> <mi>Act</mi> <msub> <mi>Cyc</mi> <mi mathvariant="normal">C</mi> </msub> </msub> </semantics></math> (Actuator 1) inverse-kinematics grid.</p> Full article ">Figure 6
<p><math display="inline"><semantics> <msub> <mi>Act</mi> <msub> <mi>Cyc</mi> <mi mathvariant="normal">S</mi> </msub> </msub> </semantics></math> (Actuator 2) inverse-kinematics grid.</p> Full article ">Figure 7
<p>Collective lever actuator inverse-kinematics grid.</p> Full article ">Figure 8
<p>Pedal actuator inverse-kinematics grid.</p> Full article ">Figure 9
<p>APCU software structure.</p> Full article ">Figure 10
<p>Flight control system schematic overview.</p> Full article ">Figure 11
<p>Onboard flight control system electronic architecture.</p> Full article ">Figure 12
<p>Remote crew interfaces.</p> Full article ">Figure 13
<p>System setup for HIL tests.</p> Full article ">Figure 14
<p>HIL tests block diagram.</p> Full article ">Figure 15
<p>Flight tests in a hover frame.</p> Full article ">Figure 16
<p>Flight tests at Magdeburg–Cochstedt airport (EDBC).</p> Full article ">Figure 17
<p>The UAS operation volumes—each of the overlapping green fields represents one volume of operation for VLOS flights.</p> Full article ">Figure 18
<p>CoAX 600 UAS flight tests.</p> Full article ">Figure 19
<p>Rotor cyclic controls reference positions and swashplate instrumentation.</p> Full article ">Figure 20
<p>Actuator dynamics poles.</p> Full article ">Figure 21
<p>Actuator model fit in the frequency domain.</p> Full article ">Figure 22
<p>Comparison of the responses of the linear low-order systems of the rotorcraft in the hover frame (black) with the recorded data (red).</p> Full article ">Figure 23
<p>Poles (x) and zeros (o) of the low-order equivalent systems for roll (blue), pitch (red), and yaw (magenta) transfer functions.</p> Full article ">
32 pages, 4991 KiB  
Article
Finite-Time Convergence Guidance Law for Hypersonic Morphing Vehicle
by Dongdong Yao and Qunli Xia
Aerospace 2024, 11(8), 680; https://doi.org/10.3390/aerospace11080680 - 18 Aug 2024
Viewed by 450
Abstract
Aiming at the interception constraint posed by defensive aircrafts against hypersonic morphing vehicles (HMVs) during the terminal guidance phase, this paper designed a guidance law with the finite-time convergence theory and control allocation methods based on the event-triggered theory, achieving evasion of the [...] Read more.
Aiming at the interception constraint posed by defensive aircrafts against hypersonic morphing vehicles (HMVs) during the terminal guidance phase, this paper designed a guidance law with the finite-time convergence theory and control allocation methods based on the event-triggered theory, achieving evasion of the defensive aircraft and targeting objectives for a morphing vehicle in the terminal guidance phase. Firstly, this paper established the aircraft motion model; the relative motion relationships between HMV, defensive aircraft, and target; and the control equations for the guidance system. Secondly, a guidance law with finite-time convergence was designed, establishing a controller with the angle between the aircraft–target–defense aircraft triplet as the state variable and lift as the control variable. By ensuring the angle was non-zero, the aircraft maintained a certain relative distance from the defense aircraft, achieving evasion of interception. The delay characteristic of the aircraft’s flight controller was considered, analyzing its delay stability and applying control compensation. Thirdly, a multi-model switching control allocation method based on an event-triggered mechanism was designed. Optimal attack and bank angles were determined based on acceleration control variables, considering different sweep angles. Finally, simulations were conducted to validate the effectiveness and robustness of the designed guidance laws. Full article
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<p>Top view of the aircraft.</p> Full article ">Figure 2
<p>The relative relationship between the aircraft, target, and defense aircraft, where <span class="html-italic">r<sub>MT</sub></span> represents the distance between the aircraft and the target, <span class="html-italic">r<sub>MD</sub></span> is the distance between the aircraft and the defense aircraft, <span class="html-italic">r<sub>DT</sub></span> is the distance between the defense aircraft and the target, and N, E denote northward and eastward directions, respectively.</p> Full article ">Figure 3
<p>The longitudinal relative positional relationship between the aircraft, defense aircraft, and target.</p> Full article ">Figure 4
<p>Diagram illustrating configuration switching method.</p> Full article ">Figure 5
<p>The step response variation of the controller.</p> Full article ">Figure 6
<p>Curves of 3D trajectories.</p> Full article ">Figure 7
<p>Curves of speed.</p> Full article ">Figure 8
<p>Curves of flight path angle.</p> Full article ">Figure 9
<p>Curves of heading angle.</p> Full article ">Figure 10
<p>Curves of distance between the HMV and the target.</p> Full article ">Figure 11
<p>Curves of distance between the HMV and the DA.</p> Full article ">Figure 12
<p>Curves of angle of attack.</p> Full article ">Figure 13
<p>Curves of bank angle.</p> Full article ">Figure 14
<p>Curves of sweep angle.</p> Full article ">Figure 15
<p>Curves of tracking <span class="html-italic">q<sub>t</sub></span>.</p> Full article ">Figure 16
<p>Curves of normal overload of HMV.</p> Full article ">Figure 17
<p>Curves of lateral overload of HMV.</p> Full article ">Figure 18
<p>Curves of normal overload of DA.</p> Full article ">Figure 19
<p>Curves of lateral overload of DA.</p> Full article ">Figure 20
<p>Curves of event-trigger performance.</p> Full article ">Figure 21
<p>Monte Carlo three-dimensional trajectories.</p> Full article ">Figure 22
<p>Minimum distance distribution between HMV and DA.</p> Full article ">Figure 23
<p>Curves of 3D trajectories.</p> Full article ">Figure 24
<p>Curves of speed.</p> Full article ">Figure 25
<p>Curves of distance between the HMV and the target.</p> Full article ">Figure 26
<p>Curves of distance between the HMV and the DA.</p> Full article ">Figure 27
<p>Curves of normal overload of HMV.</p> Full article ">Figure 28
<p>Curves of 3D trajectories.</p> Full article ">Figure 29
<p>Curves of speed.</p> Full article ">Figure 30
<p>Curves of angle of attack.</p> Full article ">Figure 31
<p>Curves of bank angle.</p> Full article ">Figure 32
<p>Curves of sweep angle.</p> Full article ">Figure 33
<p>Curves of distance between the HMV and the target.</p> Full article ">Figure 34
<p>Curves of distance between the HMV and the DA.</p> Full article ">Figure 35
<p>Curves of normal overload of HMV.</p> Full article ">Figure 36
<p>Curves of lateral overload of HMV.</p> Full article ">Figure 37
<p>Curves of normal overload of DA.</p> Full article ">Figure 38
<p>Curves of lateral overload of DA.</p> Full article ">
24 pages, 9597 KiB  
Article
Missile Fault Detection and Localization Based on HBOS and Hierarchical Signed Directed Graph
by Hengsong Hu, Yuehua Cheng, Bin Jiang, Wenzhuo Li and Kun Guo
Aerospace 2024, 11(8), 679; https://doi.org/10.3390/aerospace11080679 - 17 Aug 2024
Viewed by 647
Abstract
The rudder surfaces and lifting surfaces of a missile are utilized to acquire aerodynamic forces and moments, adjust the missile’s attitude, and achieve precise strike missions. However, the harsh flying conditions of missiles make the rudder surfaces and lifting surfaces susceptible to faults. [...] Read more.
The rudder surfaces and lifting surfaces of a missile are utilized to acquire aerodynamic forces and moments, adjust the missile’s attitude, and achieve precise strike missions. However, the harsh flying conditions of missiles make the rudder surfaces and lifting surfaces susceptible to faults. In practical scenarios, there is often a scarcity of fault data, and sometimes, it is even difficult to obtain such data. Currently, data-driven fault detection and localization methods heavily rely on fault data, posing challenges for their applicability. To address this issue, this paper proposes an HBOS (Histogram-Based Outlier Score) online fault-detection method based on statistical distribution. This method generates a fault-detection model by fitting the probability distribution of normal data and incorporates an adaptive threshold to achieve real-time fault detection. Furthermore, this paper abstracts the interrelationships between the missile’s flight states and the propagation mechanism of faults into a hierarchical directed graph model. By utilizing bilateral adaptive thresholds, it captures the first fault features of each sub-node and determines the fault propagation effectiveness of each layer node based on the compatibility path principle, thus establishing a fault inference and localization model. The results of semi-physical simulation experiments demonstrate that the proposed algorithm is independent of fault data and exhibits high real-time performance. In multiple sets of simulated tests with randomly parameterized deviations, the fault-detection accuracy exceeds 98% with a false-alarm rate of no more than 0.31%. The fault-localization algorithm achieves an accuracy rate of no less than 97.91%. Full article
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<p>The overall algorithmic flow for fault detection and localization.</p> Full article ">Figure 2
<p>The process of feature extraction.</p> Full article ">Figure 3
<p>Chart of feature importance ranking.</p> Full article ">Figure 4
<p>Overall flow of the detection algorithm.</p> Full article ">Figure 5
<p>Missile semi-physical real-time simulation platform.</p> Full article ">Figure 6
<p>Histogram of missile states time-domain feature data.</p> Full article ">Figure 7
<p>Fault-detection results: HBOS value and adaptive thresholds. (<b>a</b>) is the detection effect when rudder is stuck. (<b>b</b>) is the detection effect when rudder is loose. (<b>c</b>) is the detection effect when rudder is damaged. (<b>d</b>) is the detection effect under lifting surface damage.</p> Full article ">Figure 8
<p>Comparison of AUC values of the four algorithms. k is the number of nearest neighbors, and in HBOS, the number of bins.</p> Full article ">Figure 9
<p>Comparison of average detection time of four algorithms.</p> Full article ">Figure 10
<p>The symbolic directed graph model of the aircraft.</p> Full article ">Figure 11
<p>The layering results of the directed graph.</p> Full article ">Figure 12
<p>Calculation of node status based on bilateral adaptive threshold.</p> Full article ">
22 pages, 7378 KiB  
Article
Study on the Mechanism of Cumulative Deformation and Method for Suppression in Aircraft Panel Riveting
by Yonggang Kang, Siren Song, Tianyu Wang, Guomao Li, Zihao Wang and Yonggang Chen
Aerospace 2024, 11(8), 678; https://doi.org/10.3390/aerospace11080678 - 16 Aug 2024
Viewed by 562
Abstract
In aircraft panel assembly, the interference fit unevenly distributed along the axial direction of the rivet holes leads to an uneven stress–strain field around the rivet holes. The uneven stress–strain fields of single rivets, when accumulated through multiple rivets, result in overall bending [...] Read more.
In aircraft panel assembly, the interference fit unevenly distributed along the axial direction of the rivet holes leads to an uneven stress–strain field around the rivet holes. The uneven stress–strain fields of single rivets, when accumulated through multiple rivets, result in overall bending and twisting deformation, severely impacting the assembly coordination quality of the panel. This study introduces a numerical model using a single row of multiple rivets to explore cumulative deformation during both sequential and changing order riveting. The results show that the deformation in sequential riveting is mainly bending-oriented towards the driven head side, with the maximum displacement exhibiting a fluctuating accumulation trend as the number of rivets increase. In contrast, a changing riveting order can lead to a reduction in deformation accumulation. To reveal the technological mechanism behind deformation accumulation during the riveting process, a model correlating to the residual stress field was established. It was indicated that the continuous increase in the maximum equivalent bending moment in the axial section is the primary factor leading to deformation accumulation. Based on this finding, a pre-bending suppression method aimed at reducing the local maximum equivalent bending moment was proposed. Numerical calculations and experimental results showed that the maximum displacement of the specimen was reduced by 73.27%, proving that this method can effectively suppress the cumulative increase in deformation. Full article
(This article belongs to the Section Aeronautics)
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<p>The workpieces and the FEM for the riveting process. (<b>a</b>) specimen with ten rivets (<b>b</b>) specimen with twenty (<b>c</b>) FEM model (<b>d</b>) The setup of boundary conditions.</p> Full article ">Figure 2
<p>Distribution of radial stress around the hole.</p> Full article ">Figure 3
<p>Z-direction displacement after riveting for each rivet (ten rivets).</p> Full article ">Figure 4
<p>Path 1 and the key measurement point A.</p> Full article ">Figure 5
<p>Riveting deformation accumulation. (<b>a</b>) Central axis displacement of the specimen before riveting 1 rivet. (<b>b</b>) The displacement of the central axis after riveting 1 rivet and 2 rivets. (<b>c</b>) Central axis displacement after riveting of rivets and 3 rivets. (<b>d</b>) Central axis displacement after riveting 3 rivets and 4 rivets. (<b>e</b>) Central axis displacement after riveting 4 rivets and 5 rivets. (<b>f</b>) Central axis displacement after riveting 5 rivets and 6 rivets. (<b>g</b>) Central axis displacement after riveting 6 rivets and 7 rivets. (<b>h</b>) Central axis displacement after riveting 7 rivets and 8 rivets. (<b>i</b>) Central axis displacement after riveting 8 rivets and 9 rivets. (<b>j</b>) Central axis displacement after riveting 9 rivets and 10 rivets.</p> Full article ">Figure 5 Cont.
<p>Riveting deformation accumulation. (<b>a</b>) Central axis displacement of the specimen before riveting 1 rivet. (<b>b</b>) The displacement of the central axis after riveting 1 rivet and 2 rivets. (<b>c</b>) Central axis displacement after riveting of rivets and 3 rivets. (<b>d</b>) Central axis displacement after riveting 3 rivets and 4 rivets. (<b>e</b>) Central axis displacement after riveting 4 rivets and 5 rivets. (<b>f</b>) Central axis displacement after riveting 5 rivets and 6 rivets. (<b>g</b>) Central axis displacement after riveting 6 rivets and 7 rivets. (<b>h</b>) Central axis displacement after riveting 7 rivets and 8 rivets. (<b>i</b>) Central axis displacement after riveting 8 rivets and 9 rivets. (<b>j</b>) Central axis displacement after riveting 9 rivets and 10 rivets.</p> Full article ">Figure 6
<p>Z displacement of the central axis nodes in sequential riveting of a ten-rivet workpiece.</p> Full article ">Figure 7
<p>The Z displacement of measuring point A.</p> Full article ">Figure 8
<p>Riveting sequence changing method.</p> Full article ">Figure 9
<p>Riveting deformation accumulation. (<b>a</b>) Central axis displacement of the specimen before riveting 1 rivet. (<b>b</b>) The displacement of the central axis after riveting 1 rivet and 2 rivets. (<b>c</b>) Central axis displacement after riveting 2 rivets and 3 rivets. (<b>d</b>) Central axis displacement after riveting 3 rivets and 4 rivets. (<b>e</b>) Central axis displacement after riveting 4 rivets and 5 rivets. (<b>f</b>) Central axis displacement after riveting 5 rivets and 6 rivets. (<b>g</b>) Central axis displacement after riveting 6 rivets and 7 rivets. (<b>h</b>) Central axis displacement after riveting 7 rivets and 8 rivets. (<b>i</b>) Central axis displacement after riveting 8 rivets and 9 rivets. (<b>j</b>) Central axis displacement after riveting 9 rivets and 10 rivets.</p> Full article ">Figure 10
<p>Z-direction displacement of central axis nodes in a ten-rivet specimen with varied sequencing riveting after aligning both ends.</p> Full article ">Figure 11
<p>Z-direction displacement of central axis nodes in a twenty-rivet workpiece.</p> Full article ">Figure 12
<p>Extraction location for radial residual stress.</p> Full article ">Figure 13
<p>Radial residual stress in path 2-2-2 and linear interpolation function.</p> Full article ">Figure 14
<p>Assumption of uniform distribution of radial residual stress along the Y-direction.</p> Full article ">Figure 15
<p>Bending moment distribution after riveting 7 rivets with 10 rivets.</p> Full article ">Figure 16
<p>Calculation results of equivalent bending moment.</p> Full article ">Figure 17
<p>Calculation results of deflection of a 10-rivet workpiece.</p> Full article ">Figure 18
<p>The variation in the maximum equivalent bending moment of the panel with the number of rivets.</p> Full article ">Figure 19
<p>Elastic pre-bending method. (<b>a</b>) The pre-bending process method. (<b>b</b>) Before applying pre-bending displacement. (<b>c</b>) After applying pre-bending displacement.</p> Full article ">Figure 20
<p>Single-nail pre-bending path 3 node Z-direction displacement.</p> Full article ">Figure 21
<p>Comparison chart of radial stress data for path 4 after applying pre-stress.</p> Full article ">Figure 22
<p>Comparison chart of radial stress data for path 4 after riveting.</p> Full article ">Figure 23
<p>Pressure riveting test environment.</p> Full article ">Figure 24
<p>Twenty-rivet workpieces.</p> Full article ">Figure 25
<p>Axis displacement of control group. (<b>a</b>) Displacement of the first 10 rivets specimen without pre-bending (<b>b</b>) Displacement of the last 10 rivets specimen without pre-bending (<b>c</b>) Displacement of the first 10 specimen with pre-bending (<b>d</b>) Displacement of the last 10 rivets specimen with pre-bending.</p> Full article ">
39 pages, 18913 KiB  
Article
Application of Deep Learning Models to Predict Panel Flutter in Aerospace Structures
by Yi-Ren Wang and Yu-Han Ma
Aerospace 2024, 11(8), 677; https://doi.org/10.3390/aerospace11080677 - 16 Aug 2024
Viewed by 620
Abstract
This study investigates the application of deep learning models—specifically Deep Neural Networks (DNN), Long Short-Term Memory (LSTM), and Long Short-Term Memory Neural Networks (LSTM-NN)—to predict panel flutter in aerospace structures. The goal is to improve the accuracy and efficiency of predicting aeroelastic behaviors [...] Read more.
This study investigates the application of deep learning models—specifically Deep Neural Networks (DNN), Long Short-Term Memory (LSTM), and Long Short-Term Memory Neural Networks (LSTM-NN)—to predict panel flutter in aerospace structures. The goal is to improve the accuracy and efficiency of predicting aeroelastic behaviors under various flight conditions. Utilizing a supersonic flat plate as the main structure, the research integrates various flight conditions into the aeroelastic equation. The resulting structural vibration data create a large-scale database for training the models. The dataset, divided into training, validation, and test sets, includes input features such as panel aspect ratio, Mach number, air density, and decay rate. The study highlights the importance of selecting appropriate hidden layers, epochs, and neurons to avoid overfitting. While DNN, LSTM, and LSTM-NN all showed improved training with more neurons and layers, excessive numbers beyond a certain point led to diminished accuracy and overfitting. Performance-wise, the LSTM-NN model achieved the highest accuracy in classification tasks, effectively capturing sequential features and enhancing classification precision. Conversely, LSTM excelled in regression tasks, adeptly handling long-term dependencies and complex non-linear relationships, making it ideal for predicting flutter Mach numbers. Despite LSTM’s higher accuracy, it required longer training times due to increased computational complexity, necessitating a balance between accuracy and training duration. The findings demonstrate that deep learning, particularly LSTM-NN, is highly effective in predicting panel flutter, showcasing its potential for broader aerospace engineering applications. By optimizing model architecture and training processes, deep learning models can achieve high accuracy in predicting critical aeroelastic phenomena, contributing to safer and more efficient aerospace designs. Full article
(This article belongs to the Special Issue Artificial Intelligence in Aeroacoustics for Aerospace Applications)
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<p>Illustration of panel flutter.</p> Full article ">Figure 2
<p>External flow field of a panel.</p> Full article ">Figure 3
<p>Deep learning flowchart.</p> Full article ">Figure 4
<p>Diagram of an artificial neuron.</p> Full article ">Figure 5
<p>Deep neural network architecture diagram.</p> Full article ">Figure 6
<p>RNN model.</p> Full article ">Figure 7
<p>LSTM model.</p> Full article ">Figure 8
<p>Epoch and total training time on CPU.</p> Full article ">Figure 9
<p>Epoch and total training time on RTX3060.</p> Full article ">Figure 10
<p>Epoch and total training time on RTX4070.</p> Full article ">Figure 11
<p>Epoch and total training time on RTX4080.</p> Full article ">Figure 12
<p>GPU memory error.</p> Full article ">Figure 13
<p>One-layer DNN training model architecture.</p> Full article ">Figure 14
<p>One-layer DNN model accuracy.</p> Full article ">Figure 15
<p>One-layer DNN model loss.</p> Full article ">Figure 16
<p>Prediction results for the 1-layer DNN model.</p> Full article ">Figure 17
<p>Weight values for different classes.</p> Full article ">Figure 18
<p>Predictions after weighting.</p> Full article ">Figure 19
<p>Accuracy of DNN with different hidden layers.</p> Full article ">Figure 20
<p>Accuracy of DNN with different neurons.</p> Full article ">Figure 21
<p>The best DNN model architecture in this study.</p> Full article ">Figure 22
<p>Training accuracy of the best DNN model in this study.</p> Full article ">Figure 23
<p>Training loss of the best DNN model in this study.</p> Full article ">Figure 24
<p>Prediction of the best DNN model in this study.</p> Full article ">Figure 25
<p>One-layer LSTM training model architecture.</p> Full article ">Figure 26
<p>One-layer LSTM model accuracy.</p> Full article ">Figure 27
<p>One-layer LSTM model loss.</p> Full article ">Figure 28
<p>One-layer LSTM prediction results.</p> Full article ">Figure 29
<p>Accuracy of different hidden layers of LSTM.</p> Full article ">Figure 30
<p>Accuracy of different neurons in LSTM.</p> Full article ">Figure 31
<p>The best LSTM model architecture in this study.</p> Full article ">Figure 32
<p>The best LSTM model training accuracy in this study.</p> Full article ">Figure 33
<p>The best LSTM model training loss in this study.</p> Full article ">Figure 34
<p>The best LSTM model prediction in this study.</p> Full article ">Figure 35
<p>LSTM-NN accuracy of different neurons.</p> Full article ">Figure 36
<p>The best LSTM-NN model architecture in this study.</p> Full article ">Figure 37
<p>The best LSTM-NN model training accuracy in this study.</p> Full article ">Figure 38
<p>The best LSTM-NN model training loss in this study.</p> Full article ">Figure 39
<p>Best LSTM-NN model prediction in this study.</p> Full article ">Figure 40
<p>Prediction and average residual value with regard to (<b>a</b>) DNN, (<b>b</b>) LSTM, and (<b>c</b>) LSTM-NN.</p> Full article ">Figure 41
<p>Prediction residual percentage regarding (<b>a</b>) DNN, (<b>b</b>) LSTM, and (<b>c</b>) LSTM-NN.</p> Full article ">Figure 42
<p>Relative error regarding (<b>a</b>) DNN, (<b>b</b>) LSTM, and (<b>c</b>) LSTM-NN.</p> Full article ">Figure 43
<p>Histogram of the aspect ratio (<span class="html-italic">a</span>/<span class="html-italic">b</span>) and occurrence of flutter.</p> Full article ">Figure 44
<p>Histogram of air density (altitude) and the occurrence of flutter.</p> Full article ">Figure 45
<p>Histogram of frequency and the occurrence of flutter.</p> Full article ">Figure 46
<p>Histogram of longitudinal force and the occurrence of flutter.</p> Full article ">Figure 47
<p>Histogram of total damping and the occurrence of flutter.</p> Full article ">Figure 48
<p>Histogram of the decay rate and occurrence of flutter.</p> Full article ">
19 pages, 6203 KiB  
Article
MobGSim-YOLO: Mobile Device Terminal-Based Crack Hole Detection Model for Aero-Engine Blades
by Xinyao Hou, Hao Zeng, Lu Jia, Jingbo Peng and Weixuan Wang
Aerospace 2024, 11(8), 676; https://doi.org/10.3390/aerospace11080676 - 16 Aug 2024
Viewed by 479
Abstract
Hole detection is an important means of crack detection for aero-engine blades, and the current technology still mainly relies on manual operation, which may cause safety hazards for visual reasons. To address this problem, this paper proposes a deep learning-based, aero-engine blade crack [...] Read more.
Hole detection is an important means of crack detection for aero-engine blades, and the current technology still mainly relies on manual operation, which may cause safety hazards for visual reasons. To address this problem, this paper proposes a deep learning-based, aero-engine blade crack detection model. First, the K-means++ algorithm is used to recalculate the anchor points, which reduces the influence of the anchor frame on the accuracy; second, the backbone network of YOLOv5s is replaced with Mobilenetv3 for a lightweight design; then, the slim-neck module is embedded into the neck part, and the activation function is replaced with Hard Sigmoid for redesign, which improves the accuracy and the convergence speed. Finally, in order to improve the learning ability for small targets, the SimAM attention mechanism is embedded in the head. A large number of ablation tests are conducted in real engine blade data, and the results show that the average precision of the improved model is 93.1%, which is 29.3% higher; the number of parameters of the model is 12.58 MB, which is 52.96% less, and the Frames Per Second (FPS) can be up to 95. The proposed algorithm meets the practical needs and is suitable for hole detection. Full article
(This article belongs to the Special Issue State Monitoring and Health Management of Complex Equipment (2nd Edition))
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<p>(<b>a</b>) Turbine blade corrosion, (<b>b</b>) Cracked compressor blade, (<b>c</b>) Torn compressor blade, (<b>d</b>) High-temperature ablation of the turbine blade.</p> Full article ">Figure 2
<p>MobGSim-YOLO Algorithm Structure Diagram.</p> Full article ">Figure 3
<p>Traditional Convolution Schematic.</p> Full article ">Figure 4
<p>Depth separable Convolution Schematic.</p> Full article ">Figure 5
<p>SE Attention Mechanism Schematic.</p> Full article ">Figure 6
<p>GSConv Schematic.</p> Full article ">Figure 7
<p>GS bottleneck.</p> Full article ">Figure 8
<p>VOVGSCSPC.</p> Full article ">Figure 9
<p>Sigmoid and derivative schematics.</p> Full article ">Figure 10
<p>Sigmoid and Hard Sigmoid.</p> Full article ">Figure 11
<p>SimAM Schematic.</p> Full article ">Figure 12
<p>The aero-engine blade cracks the intelligent detection process.</p> Full article ">Figure 13
<p>Image Expansion.</p> Full article ">Figure 14
<p>IOU Explanatory Chart.</p> Full article ">Figure 15
<p>MobGSim-YOLO algorithm training results.</p> Full article ">Figure 16
<p>Test results for the MobGSim-YOLO model.</p> Full article ">Figure 17
<p>Test results for the YOLOv5s model.</p> Full article ">
16 pages, 34662 KiB  
Article
Mechanistic Insights into Effects of Perforation Direction on Thermal Hydraulic Performance of Ribs in a Rectangular Cooling Channel
by Weijia Qian, Ruiyang Shuai, Qingkun Meng, Subhajit Roy, Songbai Yao and Ping Wang
Aerospace 2024, 11(8), 675; https://doi.org/10.3390/aerospace11080675 - 16 Aug 2024
Viewed by 457
Abstract
This study investigates the turbulent flow characteristics and heat transfer performance within a rectangular cooling channel with an aspect ratio of 5:3 and featuring perforated ribs, then explores the effects of the rib perforation directions on its thermal hydraulic performance. Through experimental tests [...] Read more.
This study investigates the turbulent flow characteristics and heat transfer performance within a rectangular cooling channel with an aspect ratio of 5:3 and featuring perforated ribs, then explores the effects of the rib perforation directions on its thermal hydraulic performance. Through experimental tests (transient thermographic liquid crystal technique) and numerical simulations, it is demonstrated that horizontal perforated ribs can effectively reduce pressure loss at a high Reynolds number while maintaining notable heat transfer enhancement. Additionally, changing the rib perforation directions results in diverse effects on flow field and heat transfer. Our results show that horizontal perforated ribs can compress the recirculation vortex behind ribs, enhancing heat transfer by flow scouring, whereas upward-tilted perforated ribs increase flow friction and weaken heat transfer due to coupling of the airflow with the separation vortices behind the ribs. Downward-tilted ribs enhance local heat transfer by directing airflow behind the rib, and can also cause detachment of vortices and reduced friction. Our results indicate that introducing horizontal perforated ribs into a rectangular internal cooling channel can decrease pressure loss without significantly compromising heat transfer performance. Full article
(This article belongs to the Section Aeronautics)
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<p>Schematic of the internal cooling channel test rig.</p> Full article ">Figure 2
<p>Schematic of rib configurations.</p> Full article ">Figure 3
<p>Cross-sectional views of different ribs.</p> Full article ">Figure 4
<p>Photograph of the test section during the experiment.</p> Full article ">Figure 5
<p>Comparison of <span class="html-italic">h</span> distributions along the centerline of the test surface and between the fourth and fifth rows at different grid resolutions and <math display="inline"><semantics> <mrow> <mi>Re</mi> <mo>=</mo> <mn>4000</mn> </mrow> </semantics></math>.</p> Full article ">Figure 6
<p>Heat transfer coefficient distributions on the test surface for different rib configurations at Re = 16,000.</p> Full article ">Figure 7
<p>Heat transfer coefficient distributions on the test surface for different rib configurations at Re = 4000.</p> Full article ">Figure 8
<p>Comparisons of the spanwise-averaged Nusselt number profiles of different cases at different Re: (<b>a</b>) <math display="inline"><semantics> <mi>Re</mi> </semantics></math> = 4000, (<b>b</b>) <math display="inline"><semantics> <mi>Re</mi> </semantics></math> = 8000, (<b>c</b>) <math display="inline"><semantics> <mi>Re</mi> </semantics></math> = 12,000, and (<b>d</b>) <math display="inline"><semantics> <mi>Re</mi> </semantics></math> = 16,000.</p> Full article ">Figure 9
<p>Area-average Nusselt number behind different rib configurations at <math display="inline"><semantics> <mi>Re</mi> </semantics></math> = 4000–16,000.</p> Full article ">Figure 10
<p>Axial velocity contours and streamlines on the vertical middle cross-section (<math display="inline"><semantics> <mrow> <mi>z</mi> <mo>=</mo> <mn>0.5</mn> <mi>W</mi> </mrow> </semantics></math>) at <math display="inline"><semantics> <mi>Re</mi> </semantics></math> = 4000 for different rib structures.</p> Full article ">Figure 11
<p>Axial velocity contours and streamlines on the vertical middle cross-section (<math display="inline"><semantics> <mrow> <mi>z</mi> <mo>=</mo> <mn>0.5</mn> <mi>W</mi> </mrow> </semantics></math>) at <math display="inline"><semantics> <mi>Re</mi> </semantics></math> = 16,000 for different rib structures.</p> Full article ">Figure 12
<p>Comparison of (<b>a</b>) (<math display="inline"><semantics> <mrow> <mover> <mi>Nu</mi> <mo>¯</mo> </mover> <mo>/</mo> <msub> <mi>Nu</mi> <mn>0</mn> </msub> </mrow> </semantics></math>), (<b>b</b>) (<math display="inline"><semantics> <mrow> <mi>f</mi> <mo>/</mo> <msub> <mi>f</mi> <mn>0</mn> </msub> </mrow> </semantics></math>), (<b>c</b>) <math display="inline"><semantics> <mrow> <mrow> <mo stretchy="false">(</mo> <mover> <mi>Nu</mi> <mo>¯</mo> </mover> <mo>/</mo> <msub> <mi>Nu</mi> <mn>0</mn> </msub> <mo stretchy="false">)</mo> </mrow> <mo>/</mo> <mrow> <mo stretchy="false">(</mo> <mi>f</mi> <mo>/</mo> <msub> <mi>f</mi> <mn>0</mn> </msub> <mo stretchy="false">)</mo> </mrow> </mrow> </semantics></math>, and (<b>d</b>) <math display="inline"><semantics> <mrow> <mrow> <mo stretchy="false">(</mo> <mover> <mi>Nu</mi> <mo>¯</mo> </mover> <mo>/</mo> <msub> <mi>Nu</mi> <mn>0</mn> </msub> <mo stretchy="false">)</mo> </mrow> <mo>/</mo> <msup> <mrow> <mo stretchy="false">(</mo> <mi>f</mi> <mo>/</mo> <msub> <mi>f</mi> <mn>0</mn> </msub> <mo stretchy="false">)</mo> </mrow> <mrow> <mn>1</mn> <mo>/</mo> <mn>3</mn> </mrow> </msup> </mrow> </semantics></math> between different cases.</p> Full article ">
16 pages, 7120 KiB  
Article
Analysis of Cable Shielding and Influencing Factors for Indirect Effects of Lightning on Aircraft
by Zhangang Yang, Yuhao Wei and Xudong Shi
Aerospace 2024, 11(8), 674; https://doi.org/10.3390/aerospace11080674 - 16 Aug 2024
Viewed by 513
Abstract
The widespread use of composite materials with low electrical conductivity in modern advanced aircraft has placed higher requirements on lightning protection for airborne equipment. To ensure the safe operation of aircraft under a lightning environment, the internal cables and cable tracks of composite [...] Read more.
The widespread use of composite materials with low electrical conductivity in modern advanced aircraft has placed higher requirements on lightning protection for airborne equipment. To ensure the safe operation of aircraft under a lightning environment, the internal cables and cable tracks of composite aircraft are modeled. The lightning protection performance of cables is calculated for different types and shielding parameters, and the effect of the cable layout inside a composite aircraft on the protection performance is analyzed. The role of the cable track in lightning protection is also verified. The calculation results show that the cable shield and track structure can provide good lightning protection for the cable in the electromagnetic exposure area, and the layout of the cable inside the aircraft has a greater impact on the protection performance. The analysis of cable shielding measures and their influencing factors can provide a reference for the performance improvement of cable screening measures for the lightning protection of composite aircraft. Full article
(This article belongs to the Special Issue Innovative Aircraft Electrical Power Systems)
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<p>Coupling mechanism between lightning and an aircraft.</p> Full article ">Figure 2
<p>The second step in the composite aircraft coupling process.</p> Full article ">Figure 3
<p>Aircraft after laying metal mesh on the composite aircraft fuselage.</p> Full article ">Figure 4
<p>Composite aircraft model diagram.</p> Full article ">Figure 5
<p>Waveform of the A component of the lightning current.</p> Full article ">Figure 6
<p>Aircraft internal cable segmentation.</p> Full article ">Figure 7
<p>Spatial electromagnetic field distribution after a lightning strike on a composite aircraft: (<b>a</b>) electric field distribution in space and (<b>b</b>) space magnetic field distribution.</p> Full article ">Figure 8
<p>Electromagnetic interference from portholes and hatches. (<b>a</b>) Airframe space electric field equivalent situation. (<b>b</b>) Airframe space magnetic field equivalent situation.</p> Full article ">Figure 9
<p>Different types of cables in aircraft: (<b>a</b>) a single wire; (<b>b</b>) a coaxial wire with a shielding layer; (<b>c</b>) a twisted pair; (<b>d</b>) a twisted pair with a shielding layer.</p> Full article ">Figure 10
<p>Simulation model for different types of cable shielding analyses.</p> Full article ">Figure 11
<p>Description of the cable metal braid shielding layer.</p> Full article ">Figure 12
<p>Grounding modes: (<b>a</b>) single-ended grounding; (<b>b</b>) double-ended balanced grounding; (<b>c</b>) double-ended unbalanced grounding; and (<b>d</b>) overhang.</p> Full article ">Figure 13
<p>Comparison of grounding methods for cable shields.</p> Full article ">Figure 14
<p>Cable layout inside the composite aircraft fuselage.</p> Full article ">Figure 15
<p>Induced current of cables at different positions: (<b>a</b>) P1 induction current; (<b>b</b>) P2–P5 induction current; (<b>c</b>) P5–P8 induction current; (<b>d</b>) P8–P11 induction current; and (<b>e</b>) P10–P14 induction current.</p> Full article ">Figure 16
<p>The U-shaped single-track groove.</p> Full article ">Figure 17
<p>Influence of the track on the induced current of the cable.</p> Full article ">Figure 18
<p>The cable position relative to the structural components: (<b>a</b>) plane; (<b>b</b>) angle; (<b>c</b>) groove; (<b>d</b>) enclosed structure.</p> Full article ">Figure 19
<p>Induced current of cables at different positions.</p> Full article ">
23 pages, 9009 KiB  
Article
Four-Dimensional Trajectory Optimization for CO2 Emission Benchmarking of Arrival Traffic Flow with Point Merge Topology
by Chao Wang, Chenyang Xu, Wenqing Li, Shanmei Li and Shilei Sun
Aerospace 2024, 11(8), 673; https://doi.org/10.3390/aerospace11080673 - 16 Aug 2024
Viewed by 611
Abstract
The benchmarking of CO2 emissions serves as the foundation for the accurate assessment of the environmental impact of air traffic. To calculate the environmental benchmarks of arrival traffic flows with Point Merge System (PMS) patterns, this study proposes a 4D trajectory optimization [...] Read more.
The benchmarking of CO2 emissions serves as the foundation for the accurate assessment of the environmental impact of air traffic. To calculate the environmental benchmarks of arrival traffic flows with Point Merge System (PMS) patterns, this study proposes a 4D trajectory optimization method that combines data-driven and optimal control models. First, the predominant arrival routes of traffic flows are identified using the trajectory spectral clustering method, which provides the horizontal reference for 4D trajectory optimization. Second, an optimal control model for vertical profiles with point merging topology is established, with the objective of minimizing the fuel–time cost. Finally, considering the complex structure of the PMS, a flexible and adaptable genetic algorithm-based vertical profile nonlinear optimization model is created. The experimental results demonstrate that the proposed method is adaptable to variations in aircraft type and cost index parameters, enabling the generation of different 4D trajectories. The results also indicate an environmental efficiency gap of approximately 10% between the actual CO2 emissions of the arrival traffic flow example and the obtained benchmark. With this benchmark trajectory generation methodology, the environmental performance of PMSs and associated arrival aircraft scheduling designs can be assessed on the basis of reliable data. Full article
(This article belongs to the Section Aeronautics)
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<p>The framework of the proposed method.</p> Full article ">Figure 2
<p>Spherical segment–path distance between trajectories: (<b>a</b>) the distance from point <span class="html-italic">O</span> to trajectory <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>T</mi> </mrow> <mrow> <mn>2</mn> </mrow> </msub> </mrow> </semantics></math>; (<b>b</b>) the spherical segment–path distance from <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>T</mi> </mrow> <mrow> <mn>1</mn> </mrow> </msub> </mrow> </semantics></math> to <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>T</mi> </mrow> <mrow> <mn>2</mn> </mrow> </msub> </mrow> </semantics></math>.</p> Full article ">Figure 3
<p>Route structure of point merge system.</p> Full article ">Figure 4
<p>Typical CDO process of point merge procedure.</p> Full article ">Figure 5
<p>Arrival CAS profile model, represented by seven decision variables.</p> Full article ">Figure 6
<p>PVG airspace configuration and arrival trajectories.</p> Full article ">Figure 7
<p>Five arrival traffic flows identified using spectral clustering.</p> Full article ">Figure 8
<p>Comparison of core trajectories generated by <span class="html-italic">k</span>-medoids and AP clustering.</p> Full article ">Figure 9
<p>The optimized 4D trajectory with <span class="html-italic">CI</span> = 30: (<b>a</b>) vertical profile; and (<b>b</b>) optimized trajectory.</p> Full article ">Figure 10
<p>Fuel flow comparison: (<b>a</b>) real trajectory; and (<b>b</b>) optimized trajectory.</p> Full article ">Figure 11
<p>Comparison of results between actual trajectory and optimized trajectories.</p> Full article ">Figure 12
<p>Comparison of glide path angles derived from GACDO, GPOPS, and the actual trajectory.</p> Full article ">Figure 13
<p>Trajectory optimization results for different <span class="html-italic">CI</span> values: (<b>a</b>) altitude profile; and (<b>b</b>) CAS profile.</p> Full article ">Figure 14
<p>Comparison of actual and optimized altitude profiles in arrival traffic flow <span class="html-italic">F</span><sub>4</sub>.</p> Full article ">
15 pages, 7344 KiB  
Article
The Effect of 0–8 MPa Environmental Pressure on the Ignition and Combustion Process of CL20/NEPE Solid Propellant
by Wenxiang Cai, Wei Li and Zhixiang Wang
Aerospace 2024, 11(8), 672; https://doi.org/10.3390/aerospace11080672 - 15 Aug 2024
Viewed by 563
Abstract
In order to study the effect of pressure on the ignition and combustion process of CL-20/NEPE solid propellant, the ignition delay, burning rate, and maximum combustion temperature of different solid propellant formulations with an ambient pressure of 0.1~8.0 MPa were measured experimentally by [...] Read more.
In order to study the effect of pressure on the ignition and combustion process of CL-20/NEPE solid propellant, the ignition delay, burning rate, and maximum combustion temperature of different solid propellant formulations with an ambient pressure of 0.1~8.0 MPa were measured experimentally by a solid propellant laser ignition experiment system, and the agglomeration process and the characteristics of condensed phase combustion products were analyzed. The experimental results show that, with the increase of pressure, the ignition-delay time decreases, and the burning rate and the maximum combustion temperature increase. With the increase of pressure, the influence on propellant ignition and combustion characteristics becomes smaller. In the experiment, the dynamic agglomeration phenomenon of aluminum particles in the propellant was recorded by a high-speed camera combined with a microscopic camera lens, and the dynamic agglomeration phenomenon of the combustion surface of the propellant and the dynamic agglomeration phenomenon, after the initial agglomeration was separated from the surface, were analyzed and expounded. Based on the experiment and combined with the agglomeration phenomenon, a mathematical model capable of predicting the particle size of aluminum aggregates was proposed. Full article
(This article belongs to the Special Issue Combustion of Solid Propellants)
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<p>Schematic diagram of the experimental system.</p> Full article ">Figure 2
<p>Flame-evolution process of ignition combustion. (<b>a</b>) Initial ignition stage. (<b>b</b>) Combustion-development stage. (<b>c</b>) Stable combustion stage. (<b>d</b>) Combustion-decay stage. (<b>e</b>) Flame-quenching stage.</p> Full article ">Figure 2 Cont.
<p>Flame-evolution process of ignition combustion. (<b>a</b>) Initial ignition stage. (<b>b</b>) Combustion-development stage. (<b>c</b>) Stable combustion stage. (<b>d</b>) Combustion-decay stage. (<b>e</b>) Flame-quenching stage.</p> Full article ">Figure 3
<p>Maximum combustion temperature field under different ambient atmospheres of 1.0 MPa. (<b>a</b>) Air. (<b>b</b>) N<sub>2</sub>.</p> Full article ">Figure 4
<p>Curve of the highest temperature of combustion flame changing with pressure.</p> Full article ">Figure 5
<p>Fitting of ignition-delay time curves under different pressures of the basic formula.</p> Full article ">Figure 6
<p>Effect of CL-20 particle size under different pressures on burning rate.</p> Full article ">Figure 7
<p>Effect of CL-20 particle size under different pressures on ignition-delay time.</p> Full article ">Figure 8
<p>Effect of pressure on burning rate of Al propellants with different particle sizes.</p> Full article ">Figure 9
<p>Fitting curves of ignition-delay time of A-1 and A-2 propellants with pressure variation.</p> Full article ">Figure 10
<p>Fit curve of burning rate for two propellants with different particle sizes of AP.</p> Full article ">Figure 11
<p>Agglomeration and escape of aluminum particles on the combustion surface under different pressures. (<b>a</b>) 0.1 MPa, (<b>b</b>) 1.0 MPa, (<b>c</b>) 2.0 MPa, (<b>d</b>) 3.0 MPa, (<b>e</b>) 5.0 MPa, (<b>f</b>) 7.0 MPa, and (<b>g</b>) 8.0 MPa.</p> Full article ">Figure 11 Cont.
<p>Agglomeration and escape of aluminum particles on the combustion surface under different pressures. (<b>a</b>) 0.1 MPa, (<b>b</b>) 1.0 MPa, (<b>c</b>) 2.0 MPa, (<b>d</b>) 3.0 MPa, (<b>e</b>) 5.0 MPa, (<b>f</b>) 7.0 MPa, and (<b>g</b>) 8.0 MPa.</p> Full article ">Figure 12
<p>SEM image of AP distribution on propellant surface.</p> Full article ">Figure 13
<p>Schematic diagram of pocket model.</p> Full article ">Figure 14
<p>Comparison between experimental results and agglomeration model.</p> Full article ">
23 pages, 3682 KiB  
Article
Adaptive Incremental Nonlinear Dynamic Inversion Control for Aerial Manipulators
by Chanhong Park, Alex Ramirez-Serrano and Mahdis Bisheban
Aerospace 2024, 11(8), 671; https://doi.org/10.3390/aerospace11080671 - 15 Aug 2024
Viewed by 663
Abstract
This paper proposes an adaptive incremental nonlinear dynamic inversion (INDI) controller for unmanned aerial manipulators (UAMs). A novel adaptive law is employed to enable aerial manipulators to manage the inertia parameter changes that occur when the manipulator moves or picks up unknown objects [...] Read more.
This paper proposes an adaptive incremental nonlinear dynamic inversion (INDI) controller for unmanned aerial manipulators (UAMs). A novel adaptive law is employed to enable aerial manipulators to manage the inertia parameter changes that occur when the manipulator moves or picks up unknown objects during any phase of the UAM’s flight maneuver. The adaptive law utilizes a Kalman filter to estimate a set of weighting factors employed to adjust the control gain matrix of a previously developed INDI control law formulated for the corresponding UAV (no manipulator included). The proposed adaptive control scheme uses acceleration and actuator input measurements of the UAV without necessitating any knowledge about the manipulator, its movements, or the objects being grasped, thus enabling the use of previously developed INDI UAV controllers for UAMs. The algorithm is validated through simulations demonstrating that the adaptive control gain matrix used in the UAV’s INDI controller is promptly updated based on the UAM maneuvers, resulting in effective UAV and robot arm control. Full article
(This article belongs to the Special Issue Challenges and Innovations in Aircraft Flight Control)
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<p>The Navig8-UAV and hypothetical Navig8-UAM: (<b>a</b>) The Navig8-UAV; (<b>b</b>) The hypothetical Navig8-UAM.</p> Full article ">Figure 2
<p>Schematic diagram of the Navig8-UAM.</p> Full article ">Figure 3
<p>Block diagram of the proposed adaptive INDI controller for UAMs.</p> Full article ">Figure 4
<p>Manipulator poses during the simulation.</p> Full article ">Figure 5
<p>Joint angles of the manipulator during the simulation.</p> Full article ">Figure 6
<p>Position and attitude control of the UAV during the simulation: (<b>a</b>) UAV position control in the east direction; (<b>b</b>) UAV position control in the north direction; (<b>c</b>) UAV position control in the upward direction; (<b>d</b>) UAV roll angle control; (<b>e</b>) UAV pitch angle control; (<b>f</b>) UAV yaw angle control.</p> Full article ">Figure 7
<p>Acceleration control of the UAV during the simulation: (<b>a</b>) UAV angular acceleration control in the x direction of the UAV frame; (<b>b</b>) UAV angular acceleration control in the y direction of the UAV frame; (<b>c</b>) UAV angular acceleration control in the z direction of the UAV frame; (<b>d</b>) UAV linear acceleration control in the x direction of the UAV frame; (<b>e</b>) UAV linear acceleration control in the y direction of the UAV frame; (<b>f</b>) UAV linear acceleration control in the z direction of the UAV frame.</p> Full article ">Figure 8
<p>Components of the inverse control effectiveness matrix during the simulation: (<b>a</b>) 1st column of the adapted <math display="inline"><semantics> <msup> <mi>G</mi> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </semantics></math>; (<b>b</b>) 1st column of the true <math display="inline"><semantics> <msup> <mi>G</mi> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </semantics></math>; (<b>c</b>) 2nd column of the adapted <math display="inline"><semantics> <msup> <mi>G</mi> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </semantics></math>; (<b>d</b>) 2nd column of the true <math display="inline"><semantics> <msup> <mi>G</mi> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </semantics></math>; (<b>e</b>) 3rd column of the adapted <math display="inline"><semantics> <msup> <mi>G</mi> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </semantics></math>; (<b>f</b>) 3rd column of the true <math display="inline"><semantics> <msup> <mi>G</mi> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </semantics></math>; (<b>g</b>) 4th column of the adapted <math display="inline"><semantics> <msup> <mi>G</mi> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </semantics></math>; (<b>h</b>) 4th column of the true <math display="inline"><semantics> <msup> <mi>G</mi> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </semantics></math>; (<b>i</b>) 5th column of the adapted <math display="inline"><semantics> <msup> <mi>G</mi> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </semantics></math>; (<b>j</b>) 5th column of the true <math display="inline"><semantics> <msup> <mi>G</mi> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </semantics></math>.</p> Full article ">Figure 9
<p>Side view of the helical trajectory achieved by the UAM.</p> Full article ">Figure 10
<p>Top view of the helical trajectory achieved by the UAM.</p> Full article ">Figure 11
<p>Attitude control errors during the helical trajectory tracking simulation: (<b>a</b>) roll angle control error; (<b>b</b>) pitch angle control error; (<b>c</b>) yaw angle control error.</p> Full article ">
11 pages, 455 KiB  
Article
Enhanced Computational Biased Proportional Navigation with Neural Networks for Impact Time Control
by Xue Zhang and Haichao Hong
Aerospace 2024, 11(8), 670; https://doi.org/10.3390/aerospace11080670 - 15 Aug 2024
Viewed by 442
Abstract
Advanced computational methods are being applied to address traditional guidance problems, yet research is still ongoing regarding how to utilize them effectively and scientifically. A numerical root-finding method was proposed to determine the bias in biased proportional navigation to achieve the impact time [...] Read more.
Advanced computational methods are being applied to address traditional guidance problems, yet research is still ongoing regarding how to utilize them effectively and scientifically. A numerical root-finding method was proposed to determine the bias in biased proportional navigation to achieve the impact time control without time-to-go estimation. However, the root-finding algorithm in the original method might experience efficiency and convergence issues. This paper introduces an enhanced method based on neural networks, where the bias is directly output by the neural networks, significantly improving computational efficiency and addressing convergence issues. The novelty of this method lies in the development of a reasonable structure that appropriately integrates off-the-shelf machine learning techniques to effectively enhance the original iteration-based methods. In addition to demonstrating its effectiveness and performance of its own, two comparative scenarios are presented: (a) Evaluate the time consumption when both the proposed and the original methods operate at the same update frequency. (b) Compare the achievable update frequencies of both methods under the condition of equal real-world time usage. Full article
(This article belongs to the Section Aeronautics)
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<p>Engagement geometry.</p> Full article ">Figure 2
<p>Trajectories of baseline and proposed methods.</p> Full article ">Figure 3
<p>Range histories of baseline and proposed methods.</p> Full article ">Figure 4
<p>Look angle histories of baseline and proposed methods.</p> Full article ">Figure 5
<p>Acceleration histories of baseline and proposed methods.</p> Full article ">Figure 6
<p>Time cost comparison at equal update frequencies.</p> Full article ">Figure 7
<p>Time cost at 1 Hz of baseline method.</p> Full article ">Figure 8
<p>Time cost of proposed method regarding increasing update frequency.</p> Full article ">
31 pages, 5634 KiB  
Article
Advanced UAV Design Optimization Through Deep Learning-Based Surrogate Models
by Hasan Karali, Gokhan Inalhan and Antonios Tsourdos
Aerospace 2024, 11(8), 669; https://doi.org/10.3390/aerospace11080669 - 14 Aug 2024
Viewed by 1169
Abstract
The conceptual design of unmanned aerial vehicles (UAVs) presents significant multidisciplinary challenges requiring the optimization of aerodynamic and structural performance, stealth, and propulsion efficiency. This work addresses these challenges by integrating deep neural networks with a multiobjective genetic algorithm to optimize UAV configurations. [...] Read more.
The conceptual design of unmanned aerial vehicles (UAVs) presents significant multidisciplinary challenges requiring the optimization of aerodynamic and structural performance, stealth, and propulsion efficiency. This work addresses these challenges by integrating deep neural networks with a multiobjective genetic algorithm to optimize UAV configurations. The proposed framework enables a comprehensive evaluation of design alternatives by estimating key performance metrics required for different operational requirements. The design process resulted in a significant improvement in computational time over traditional methods by more than three orders of magnitude. The findings illustrate the framework’s capability to optimize UAV designs for a variety of mission scenarios, including specialized tasks such as intelligence, surveillance, and reconnaissance (ISR), combat air patrol (CAP), and Suppression of Enemy Air Defenses (SEAD). This flexibility and adaptability was demonstrated through a case study, showcasing the method’s effectiveness in tailoring UAV configurations to meet specific operational requirements while balancing trade-offs between aerodynamic efficiency, stealth, and structural weight. Additionally, these results underscore the transformative impact of integrating AI into the early stages of the design process, facilitating rapid prototyping and innovation in aerospace engineering. Consequently, the current work demonstrates the potential of AI-driven optimization to revolutionize UAV design by providing a robust and effective tool for solving complex engineering problems. Full article
(This article belongs to the Special Issue Application of Multidisciplinary Optimization and Artificial Intelligence Techniques to Aerospace Engineering (Volume II))
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<p>Change in design phenomena in the design phases.</p> Full article ">Figure 2
<p>Next-generation high-performance UAV prototypes (with their first flight) and concepts in development [<a href="#B20-aerospace-11-00669" class="html-bibr">20</a>,<a href="#B21-aerospace-11-00669" class="html-bibr">21</a>,<a href="#B22-aerospace-11-00669" class="html-bibr">22</a>,<a href="#B23-aerospace-11-00669" class="html-bibr">23</a>,<a href="#B24-aerospace-11-00669" class="html-bibr">24</a>,<a href="#B25-aerospace-11-00669" class="html-bibr">25</a>,<a href="#B26-aerospace-11-00669" class="html-bibr">26</a>,<a href="#B27-aerospace-11-00669" class="html-bibr">27</a>].</p> Full article ">Figure 3
<p>Proposed approach to develop trustworthy autonomous systems.</p> Full article ">Figure 4
<p>General framework for UAV design process.</p> Full article ">Figure 5
<p>Typical mission profile of a UAV.</p> Full article ">Figure 6
<p>MQ-28 UAV representative fuselage sections: fore body, mid-body, and aft body.</p> Full article ">Figure 7
<p>Visualization of Latin hypercube sampling for wing component.</p> Full article ">Figure 8
<p>Scatter-plot matrix of target parameters and geometry/flow features.</p> Full article ">Figure 9
<p>Flowchart of the Nondominated Sorting Genetic Algorithm (NSGA-II) process: initialization, evaluation, selection, crossover, mutation, and ranking.</p> Full article ">Figure 10
<p>Performance visualization of neural networks models.</p> Full article ">Figure 11
<p>Pareto front visualization of UAV design optimization.</p> Full article ">Figure 12
<p>Comparative analysis of UAV configurations.</p> Full article ">
17 pages, 5200 KiB  
Article
Optimisation Design of Thermal Test System for Metal Fibre Surface Combustion Structure
by Bin Qi, Rong A, Dongsheng Yang, Ri Wang, Sujun Dong and Yinjia Zhou
Aerospace 2024, 11(8), 668; https://doi.org/10.3390/aerospace11080668 - 14 Aug 2024
Viewed by 565
Abstract
The metal fibre surface combustion structure has the characteristics of strong thermal matching ability, short response time, and strong shape adaptability. It has more advantages in the thermal test of complex hypersonic vehicle surface inlet, leading edge, etc. In this paper, a method [...] Read more.
The metal fibre surface combustion structure has the characteristics of strong thermal matching ability, short response time, and strong shape adaptability. It has more advantages in the thermal test of complex hypersonic vehicle surface inlet, leading edge, etc. In this paper, a method of aerodynamic thermal simulation test based on metal fibre surface combustion is proposed. The aim of the study was to create a uniform target heat flow on the inner wall surface of a cylindrical specimen by matching the gas jet flow rate and the geometry of the combustion surface. The research adopted the optimisation design method based on the surrogate model to establish the numerical calculation model of a metal fibre combustion jet heating cylinder specimen. One hundred sample points were obtained through Latin hypercube sampling, and a database of design parameters and heat flux was established through numerical simulation. The kriging surrogate model and the non-dominated sequencing genetic optimisation algorithm with elite strategy were adopted. A bi-objective optimisation design was carried out with the optimisation objective of the coincidence between the predicted and the target heat flux on the inner wall of the specimen. The results showed that the average relative errors of heat flow density on the specimen surface were 8.8% and 6% through the leave-one-out cross-validation strategy and the validation of six test sample points, respectively. The relative error values in most regions were within 5%, which indicates that the established kriging surrogate model has high prediction accuracy. Under the optimal solution conditions, the numerical calculation results of the heat flow on the inner wall of the specimen were in good agreement with the target heat flow values, with an average relative error of less than 5% and a maximum value of less than 8%. These results show that the optimisation design method based on the kriging surrogate model can effectively match the thermal test parameters of metal fibre combustion structures. Full article
(This article belongs to the Special Issue Aerospace Human–Machine and Environmental Control Engineering)
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<p>Schematic of the thermal test device for the metal fibre surface burning structure.</p> Full article ">Figure 2
<p>Schematic diagram of the gas radial jet for a cylindrical specimen.</p> Full article ">Figure 3
<p>Profile drawing of the simplified cylinder specimen of the air inlet.</p> Full article ">Figure 4
<p>Mesh structure of the computational model.</p> Full article ">Figure 5
<p>Heat flux at the inner wall surface of the specimen with different mesh volumes.</p> Full article ">Figure 6
<p>The temperature distribution of combustion gas on the symmetry plane.</p> Full article ">Figure 7
<p>The heat flux distribution on the inner surface of the cylindrical specimen.</p> Full article ">Figure 8
<p>Radial jet heating of cylindrical specimens with conical combustion surface.</p> Full article ">Figure 9
<p>Flow chart of the NSGA-II algorithm.</p> Full article ">Figure 10
<p>Flow chart of parameter optimisation based on the surrogate model.</p> Full article ">Figure 11
<p>Relative error of the specimen surface by the leave-one-out cross-validation strategy.</p> Full article ">Figure 12
<p>Relative error of surface heat flux of the No.1 test sample.</p> Full article ">Figure 13
<p>Objective space of a feasible solution.</p> Full article ">Figure 14
<p>Heat flux distribution on the cylindrical specimen for the optimal solution.</p> Full article ">Figure 15
<p>Relative error of surface heat flux at optimum condition.</p> Full article ">Figure 16
<p>Comparison between surface heat flux and target value at optimum condition.</p> Full article ">
24 pages, 7845 KiB  
Article
Optimization of Plasma-Propelled Drone Performance Parameters
by Zewei Xia, Yulong Ying, Heli Li, Tong Lin, Yuxuan Yao, Naiming Qi and Mingying Huo
Aerospace 2024, 11(8), 667; https://doi.org/10.3390/aerospace11080667 - 14 Aug 2024
Viewed by 597
Abstract
Recently, the world’s first plasma-propelled drone was successfully flown, demonstrating that plasma propulsion technology is suitable for drone flight. The research on plasma propulsion drones has sparked a surge of interest. This study utilized a proxy model and the NSGA-II multi-objective genetic algorithm [...] Read more.
Recently, the world’s first plasma-propelled drone was successfully flown, demonstrating that plasma propulsion technology is suitable for drone flight. The research on plasma propulsion drones has sparked a surge of interest. This study utilized a proxy model and the NSGA-II multi-objective genetic algorithm to optimize the geometric parameters based on staggered thrusters that affect the performance of electroaerodynamics (EAD) thrusters used for solid-state plasma aircraft. This can help address key issues, such as the thrust density and the thrust-to-power ratio of solid-state plasma aircraft, promoting the widespread application of plasma propulsion drones. An appropriate sample set was established using Latin hypercube sampling, and the thrust and current data were collected using a customized experimental setup. The proxy model employed a genetically optimized Bayesian regularization backpropagation neural network, which was trained to predict the effects of variations in the geometric parameters of the electrode assembly on the performance parameters of the plasma aircraft. Based on this information, the maximum achievable value for a given performance parameter and its corresponding geometric parameters were determined, showing a significant increase compared to the sample data. Finally, the optimal parameter combination was determined by using the NSGA-II multi-objective genetic algorithm and the Analytic Hierarchy Process. These findings can serve as a basis for future researchers in the design of EAD thrusters, helping them produce plasma propulsion drones that better meet specific requirements. Full article
(This article belongs to the Section Aeronautics)
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<p>Time-lapse image of the EAD aircraft in flight.</p> Full article ">Figure 2
<p>Photograph of the actual EAD aircraft tested by MIT, including its high-voltage power converter and EAD thruster.</p> Full article ">Figure 3
<p>Two main electrode distribution types.</p> Full article ">Figure 4
<p>Direction of ionized wind and the aerodynamic components in the staggered distribution of thruster units.</p> Full article ">Figure 5
<p>Performance space of the selected parameters.</p> Full article ">Figure 6
<p>Customized experimental setup.</p> Full article ">Figure 7
<p>Comparison between the predictions of the two proxy models and the actual values. (<b>a</b>) comparison of thrust. (<b>b</b>) comparison of thrust to power.</p> Full article ">Figure 8
<p>Prediction of the impact of the thruster unit spacing <span class="html-italic">s</span> on the performance parameters. (<b>a</b>) performance of thrust. (<b>b</b>) performance of thrust density. (<b>c</b>) performance of thrust-to-power.</p> Full article ">Figure 9
<p>Prediction of the impact of the electrode gap <span class="html-italic">d</span> on the performance parameters. (<b>a</b>) performance of thrust. (<b>b</b>) performance of thrust density. (<b>c</b>) performance of thrust-to-power.</p> Full article ">Figure 10
<p>Prediction of the impact of the thruster stage spacing <span class="html-italic">θ</span> on the performance parameters. (<b>a</b>) performance of thrust. (<b>b</b>) performance of thrust density. (<b>c</b>) performance of thrust-to-power.</p> Full article ">Figure 11
<p>Prediction of the impact of the collector airfoil thickness <span class="html-italic">t</span> on the performance parameters. (<b>a</b>) performance of thrust. (<b>b</b>) performance of thrust density. (<b>c</b>) performance of thrust-to-power.</p> Full article ">Figure 12
<p>The Pareto solution set optimized with the NSGA-II multi-objective genetic algorithm.</p> Full article ">Figure 13
<p>The normalized performance distribution of the optimized solution sets.</p> Full article ">
24 pages, 7273 KiB  
Review
A Brief Review of the Actuation Systems of the Morphing Systems in Wind Tunnel Models and a Case Study
by Guogang Pan, Xiaoyu Cui, Pengfei Sun and Biling Wang
Aerospace 2024, 11(8), 666; https://doi.org/10.3390/aerospace11080666 - 13 Aug 2024
Viewed by 666
Abstract
Typical wind tunnel testing involves a series of configuration changes (to the angles of control surfaces) to simulate the lift and resistance characteristics of control surfaces in different flight conditions. It is very time-consuming and labor-intensive to manually change the angles of control [...] Read more.
Typical wind tunnel testing involves a series of configuration changes (to the angles of control surfaces) to simulate the lift and resistance characteristics of control surfaces in different flight conditions. It is very time-consuming and labor-intensive to manually change the angles of control surfaces, especially in the large continuous reflux wind tunnel. Thus, there is a demand for a morphing system design within the wind tunnel model that can deflect the control surfaces remotely and automatically. The basic design flow and characteristics of different actuator techniques for the morphing systems were summarized in this paper, including electromechanical actuator, pneumatic actuator, shape memory material actuator and piezoelectric actuator. In the case study, the accuracy of the control surface angle and aerodynamic performance of the ultrasonic-driven automatic control surface system reached the level of traditional fixed control surface systems, while its efficiency was much higher than that of the traditional fixed control surface systems. Full article
(This article belongs to the Special Issue Structures, Actuation and Control of Morphing Systems)
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<p>Manual adjustments to the control surface.</p> Full article ">Figure 2
<p>Typical morphing wing wind tunnel test model system.</p> Full article ">Figure 3
<p>The CleanSky Camber Morph. Flap (CMF) [<a href="#B8-aerospace-11-00666" class="html-bibr">8</a>]: (<b>a</b>) the layout of CMF and (<b>b</b>) actuation transmission stages.</p> Full article ">Figure 4
<p>The operating principle of PAM actuation and flap/actuator system design [<a href="#B13-aerospace-11-00666" class="html-bibr">13</a>]. (<b>a</b>) The geometric structure of two antagonistic pneumatic actuators. (<b>b</b>) Pneumatically actuated flap deflection device.</p> Full article ">Figure 5
<p>A 2D airfoil with an RCA spoiler including the planform using SMA elements and sensors [<a href="#B25-aerospace-11-00666" class="html-bibr">25</a>]. (<b>a</b>) A conceptual model of flap deflection based on SMA torque tube. (<b>b</b>) A solid internal structural model of the SMA torque tube actuator.</p> Full article ">Figure 6
<p>Trailing edge segment design incorporating two eccentuators [<a href="#B30-aerospace-11-00666" class="html-bibr">30</a>].</p> Full article ">Figure 7
<p>Design flow of wind tunnel test models for morphing aircraft.</p> Full article ">Figure 8
<p>The basic composition of an ultrasonic motor and its principle of operation [<a href="#B38-aerospace-11-00666" class="html-bibr">38</a>,<a href="#B43-aerospace-11-00666" class="html-bibr">43</a>]. (<b>a</b>) A schematic diagram of the USM structure. (<b>b</b>) The operating principle diagram of the USM.</p> Full article ">Figure 9
<p>An ultrasonic motor based on the inverse piezoelectric effect and a control surface with flange. (<b>a</b>) Ultrasonic motor model. (<b>b</b>) Control surface model.</p> Full article ">Figure 10
<p>Wind tunnel test model.</p> Full article ">Figure 11
<p>Installation module.</p> Full article ">Figure 12
<p>The stress distribution of USM under specific loads.</p> Full article ">Figure 13
<p>The displacement distribution of USM under specific loads.</p> Full article ">Figure 14
<p>A schematic diagram of the measurement and control system.</p> Full article ">Figure 15
<p>Results of the no-load deflection test. (<b>a</b>) The +20° rotation test. (<b>b</b>) The −20° rotation test.</p> Full article ">Figure 16
<p>The load schematic model.</p> Full article ">Figure 17
<p>The results of the load deflection test. (<b>a</b>) The +20° rotation test. (<b>b</b>) The −20° rotation test.</p> Full article ">Figure 18
<p>The three-coordinate measuring system.</p> Full article ">Figure 19
<p>A schematic representation of measurement point markings.</p> Full article ">Figure 20
<p>Coordinate errors along the three-axis directions when the rudder was deflected from 15° to 0°.</p> Full article ">Figure 21
<p>Coordinate errors along the three-axis directions when the rudder was deflected from 0° to 15°.</p> Full article ">Figure 22
<p>Coordinate errors along the three-axis directions when the rudder was deflected from 15° to −30°.</p> Full article ">Figure 23
<p>The wind tunnel model for the control surface deflection experiment.</p> Full article ">Figure 24
<p>A comparison of lift coefficients between automatic and fixed control surface tests at Ma = 0.4.</p> Full article ">
11 pages, 1112 KiB  
Article
Fuel Efficiency Evaluation of A380 Aircraft through Comparative Analysis of Actual Flight Data of the A380–800 and A350–900
by Sungwoo Jang, Seongjoo Yoon and Jae Leame Yoo
Aerospace 2024, 11(8), 665; https://doi.org/10.3390/aerospace11080665 - 13 Aug 2024
Viewed by 1335
Abstract
The Airbus A380 was initially expected to replace existing aircraft due to its remarkable fuel efficiency on long-haul routes when operating with a full passenger load. However, recent changes in the commercial aviation environment have resulted in a decrease in demand for four-engine [...] Read more.
The Airbus A380 was initially expected to replace existing aircraft due to its remarkable fuel efficiency on long-haul routes when operating with a full passenger load. However, recent changes in the commercial aviation environment have resulted in a decrease in demand for four-engine aircraft. Rising fuel prices have pushed airlines to focus on more efficient operations, while manufacturers prioritize producing advanced twin-engine aircraft. The debate over the long-term economic viability of A380 operations remains ongoing. This study compares and evaluates the fuel efficiency of the Airbus A380 and the Airbus A350 using actual flight data. The analysis employs a fuel efficiency prediction model to compare scenarios based on identical payload and load factor. Results indicate that the A350 is approximately twice as fuel efficient as the A380 under the same payload and about 1.34 times more efficient under the same load factor. The A380’s economic viability is analyzed by considering the balance between revenue per available ton-kilometer (RASK) and cost per available ton-kilometer (CASK). If the A380’s RASK is significantly higher than 1.34 times the A350’s or exceeds its own CASK, it can sustain operations. Achieving a balance between RASK and CASK is essential for the economic sustainability of A380 operations. Full article
(This article belongs to the Collection Air Transportation—Operations and Management)
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<p>Fuel efficiency prediction model comparison (A380 vs. A350).</p> Full article ">Figure 2
<p>Fuel efficiency index comparison—payload of 30 tons (A380 vs. A350).</p> Full article ">Figure 3
<p>Fuel efficiency index comparison—100% load factor (A380 vs. A350).</p> Full article ">
19 pages, 9961 KiB  
Article
Propeller Effects and Elasticity in Aerodynamic Analysis of Small Propeller-Driven Aircraft and UAVs
by Mohsen Rostami
Aerospace 2024, 11(8), 664; https://doi.org/10.3390/aerospace11080664 - 13 Aug 2024
Viewed by 785
Abstract
The importance of propeller effects and power contribution to the aerodynamics of small aircraft and unmanned aerial vehicles (UAVs) is indispensable. The aerodynamic analysis of wings in flight varies from rigid wing analysis due to wing deflection caused by transferred aerodynamic loads. This [...] Read more.
The importance of propeller effects and power contribution to the aerodynamics of small aircraft and unmanned aerial vehicles (UAVs) is indispensable. The aerodynamic analysis of wings in flight varies from rigid wing analysis due to wing deflection caused by transferred aerodynamic loads. This paper investigates the intertwined influence of propeller effects and elasticity on the aerodynamics of small propeller-driven aircraft and UAVs. Through a detailed methodology, a twin-engine propeller-driven aircraft is analyzed as a case study, providing insights into the proposed approach. Two critical analyses are presented: an examination of propeller effects in rigid aircraft and the incorporation of elastic wing properties. The former establishes a foundational understanding of aerodynamic behavior, while the latter explores the impact of wing elasticity on performance. Validation is achieved through comparative analysis with wind tunnel test results from a similar rigid structure aircraft. Utilizing NASTRAN software V2010.1, aerodynamic analysis of the elastic aircraft is conducted, complemented by semi-empirical insights. The results highlight the importance of these factors across different angles of attack. Furthermore, deviations from the rigid aircraft configuration emphasize the considerable influence of static aeroelasticity analysis, notably increasing longitudinal characteristics by approximately 20%, while showing a lower impact of 5% in lateral-directional characteristics. This study contributes to enhanced design and operational considerations for small propeller-driven aircraft, with implications for future research and innovation, particularly for the purpose of efficient concepts in advanced air mobility. Full article
(This article belongs to the Special Issue Aerodynamic Numerical Optimization in UAV Design)
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<p>The twin-engine propeller-driven small airplane under investigation is referenced from NASA’s report [<a href="#B40-aerospace-11-00664" class="html-bibr">40</a>,<a href="#B41-aerospace-11-00664" class="html-bibr">41</a>], denoted as (<b>a</b>) for the reported aircraft and (<b>b</b>) for the modeled aircraft utilizing MAPLA.</p> Full article ">Figure 2
<p>Comparison of longitudinal aerodynamic characteristics between the twin-engine small airplane using MAPLA and wind tunnel test results [<a href="#B40-aerospace-11-00664" class="html-bibr">40</a>,<a href="#B41-aerospace-11-00664" class="html-bibr">41</a>] considering (<b>a</b>) lift coefficient, (<b>b</b>) drag coefficient, and (<b>c</b>) pitching moment coefficient across various flight conditions, with an empty weight at CG = 10%.</p> Full article ">Figure 3
<p>Comparison of lateral-directional aerodynamic characteristics between the investigated twin-engine small airplane using MAPLA and wind tunnel test results [<a href="#B40-aerospace-11-00664" class="html-bibr">40</a>,<a href="#B41-aerospace-11-00664" class="html-bibr">41</a>], focusing on (<b>a</b>) weathercock stability and (<b>b</b>) effective dihedral coefficient across various flight conditions, with an empty weight at CG = 10%.</p> Full article ">Figure 4
<p>The investigated twin-engine propeller-driven small airplane.</p> Full article ">Figure 5
<p>Results of investigated twin-engine propeller-driven small aircraft model in various flight conditions, where CT = 0, CT = 0.028, CT = 0.1, and CT = 0.3, versus angle of attack for (<b>a</b>) lift coefficient, (<b>b</b>) drag coefficient, and (<b>c</b>) pitching moment coefficient.</p> Full article ">Figure 6
<p>Results of investigated twin-engine propeller-driven small aircraft model in 1/rad in various flight conditions, where CT = 0, CT = 0.028, CT = 0.1, and CT = 0.3, versus angle of attack for (<b>a</b>) weathercock stability coefficient, and (<b>b</b>) effective dihedral coefficient.</p> Full article ">Figure 7
<p>Results of investigated twin-engine propeller-driven small aircraft model in 1/rad various flight conditions, where CT = 0, CT = 0.028, CT = 0.1, and CT = 0.3, versus angle of attack for (<b>a</b>) lift coefficient due to pitch rate, (<b>b</b>) lift coefficient due to vertical acceleration, (<b>c</b>) pitching moment coefficient due to pitch rate, and (<b>d</b>) pitching moment coefficient due to vertical acceleration.</p> Full article ">Figure 8
<p>Results of investigated twin-engine propeller-driven small aircraft model in 1/rad in various flight conditions, where CT = 0, CT = 0.028, CT = 0.1, and CT = 0.3, versus angle of attack for (<b>a</b>) lift coefficient due to pitch rate, (<b>b</b>) lift coefficient due to vertical acceleration, (<b>c</b>) pitching moment coefficient due to pitch rate, and (<b>d</b>) pitching moment coefficient due to vertical acceleration.</p> Full article ">Figure 9
<p>(<b>a</b>). Schematic of the modeled wing in Patran considering four airfoil sections and the connection boxes (<b>b</b>). Schematic of the mesh model in Patran.</p> Full article ">Figure 10
<p>The Flat Plate aero modeling definition for Aero-Structure Coupling analysis.</p> Full article ">Figure 11
<p>The coupled aerodynamic model and structural model (Aero-Structural Coupling model) for aeroelastic analysis in Nastran.</p> Full article ">Figure 12
<p>The pressure distribution on the wing in Newton per square meter considering (<b>a</b>) rigid components in the model and (<b>b</b>) elastic components in the model.</p> Full article ">Figure 13
<p>The moment distribution on the wing in Newton–meters considering (<b>a</b>) rigid components in the model and (<b>b</b>) elastic components in the model.</p> Full article ">Figure 14
<p>The force distribution on the wing in Newtons considering (<b>a</b>) rigid components in the model and (<b>b</b>) elastic components in the model.</p> Full article ">
25 pages, 4887 KiB  
Article
High-Resolution CAD-Based Shape Parametrisation of a U-Bend Channel
by Rejish Jesudasan and Jens-Dominik Müeller
Aerospace 2024, 11(8), 663; https://doi.org/10.3390/aerospace11080663 - 13 Aug 2024
Viewed by 565
Abstract
The parametrisation of the geometry in shape optimisation has an important influence on the quality of the optimum and the rate of convergence of the optimiser. Refinement studies for the parametrisation are not shown in the literature, as most methods use non-orthogonal parametrisations, [...] Read more.
The parametrisation of the geometry in shape optimisation has an important influence on the quality of the optimum and the rate of convergence of the optimiser. Refinement studies for the parametrisation are not shown in the literature, as most methods use non-orthogonal parametrisations, which cause issues with convergence when the parametrisation is refined. The NURBS-based parametrisation with complex constraints (NSPCC) is the only CAD-based parametrisation method that guarantees orthogonal shape modes by constructing an optimal basis. We conduct a parametrisation refinement study for the benchmark turbomachinery cooling bend (U-bend) geometry, an intially symmetric geometry. Using an adjoint RANS solver, we optimise for mininmal total pressure drop. The results show significant effects of the control net density on the final shape, with the finest control net producing an asymmetric optimal shape resembling strakes that induces swirl ahead of the bend. These asymmetric modes have not been reported in the literature so far. We also demonstrate that the convergence rate of the optimiser is not significantly affected by the refinement of the parametrisation. The effectiveness of these shape features obtained with single-point optimisation is evaluated for a range of Reynolds numbers. It is shown that total pressure drop reduction is not sensitive to Reynolds number. Full article
(This article belongs to the Section Aeronautics)
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<p>Baseline U-bend geometry with design patches highlighted in green and geometric constraints imposed on different edges.</p> Full article ">Figure 2
<p>Shape deformation of a NURBS patch with its control net: (<b>a</b>) original NURBS and (<b>b</b>) perturbed NURBS.</p> Full article ">Figure 3
<p>Test points along a common edge and corresponding control net of adjacent NURBS patches.</p> Full article ">Figure 4
<p>Effect of <math display="inline"><semantics> <msub> <mi>G</mi> <mn>1</mn> </msub> </semantics></math> constraint recovery: (<b>a</b>) inner U-bend without constraint recovery, (<b>b</b>) inner U-bend with constraint recovery, (<b>c</b>) U-bend duct without constraint recovery and (<b>d</b>) U-bend duct with constraint recovery.</p> Full article ">Figure 5
<p>The inlet and outlet surfaces of the meshes: (<b>a</b>) coarse, (<b>b</b>) medium, (<b>c</b>) fine and (<b>d</b>) extra fine.</p> Full article ">Figure 6
<p>Locations used for velocity profiles.</p> Full article ">Figure 7
<p>Streamwise velocity profiles along the vertical lines (along <span class="html-italic">z</span> axis) at (<b>a</b>) <span class="html-italic">A</span>, (<b>b</b>) <span class="html-italic">B</span> and (<b>c</b>) <span class="html-italic">C</span>.</p> Full article ">Figure 8
<p>Radial velocity profiles along the horizontal lines (along <span class="html-italic">y</span> axis) at (<b>a</b>) <span class="html-italic">A</span>, (<b>b</b>) <span class="html-italic">B</span> and (<b>c</b>) <span class="html-italic">C</span>.</p> Full article ">Figure 9
<p>(<b>a</b>) Grid convergence study and (<b>b</b>) convergence history of flow and adjoint solver using fine (F) mesh.</p> Full article ">Figure 10
<p>Comparison of normalised streamwise velocity profile taken at the inlet leg between experiment and mgopt.</p> Full article ">Figure 11
<p>Comparison of normalised velocity field <math display="inline"><semantics> <mrow> <mo>(</mo> <msup> <mi>U</mi> <mo>*</mo> </msup> <mo>)</mo> </mrow> </semantics></math> along streamwise direction between experiment and simulation taken at mid-plane. (<b>a</b>) Experiment [<a href="#B6-aerospace-11-00663" class="html-bibr">6</a>], (<b>b</b>) LES simulation [<a href="#B9-aerospace-11-00663" class="html-bibr">9</a>], (<b>c</b>) RANS simulation [<a href="#B9-aerospace-11-00663" class="html-bibr">9</a>], (<b>d</b>) RANS-mgopt (current work).</p> Full article ">Figure 12
<p>Comparison of normalised velocity field at <math display="inline"><semantics> <msup> <mn>90</mn> <mo>∘</mo> </msup> </semantics></math> turn region. (<b>a</b>) LES [<a href="#B9-aerospace-11-00663" class="html-bibr">9</a>], (<b>b</b>) mgopt.</p> Full article ">Figure 13
<p>Comparison of normalised velocity field at outlet leg. (<b>a</b>) LES [<a href="#B9-aerospace-11-00663" class="html-bibr">9</a>], (<b>b</b>) RANS [<a href="#B9-aerospace-11-00663" class="html-bibr">9</a>], (<b>c</b>) mgopt.</p> Full article ">Figure 14
<p>Convergence of relative error in the surface sensitivity estimates by complex step, forward and central difference using algorithmic differentiation result as the reference. Error: <math display="inline"><semantics> <mrow> <mi>ϵ</mi> <mo>=</mo> <mstyle scriptlevel="0" displaystyle="true"> <mfrac> <mrow> <mo>∣</mo> <mi>F</mi> <mi>D</mi> <mo>−</mo> <mi>A</mi> <mi>D</mi> <mo>∣</mo> </mrow> <mrow> <mo>∣</mo> <mi>A</mi> <mi>D</mi> <mo>∣</mo> </mrow> </mfrac> </mstyle> </mrow> </semantics></math>.</p> Full article ">Figure 15
<p>Convergence of relative error in the surface sensitivity for seven surface mesh points using complex step derivative (CSD) method. Each curve corresponds to a distinct mesh point on the surface. Error: <math display="inline"><semantics> <mrow> <mi>ϵ</mi> <mo>=</mo> <mstyle scriptlevel="0" displaystyle="true"> <mfrac> <mrow> <mo>∣</mo> <mi>C</mi> <mi>S</mi> <mi>D</mi> <mo>−</mo> <mi>A</mi> <mi>D</mi> <mo>∣</mo> </mrow> <mrow> <mo>∣</mo> <mi>A</mi> <mi>D</mi> <mo>∣</mo> </mrow> </mfrac> </mstyle> </mrow> </semantics></math>.</p> Full article ">Figure 16
<p>Comparison of sensitivity of the objective function with respect to control points <math display="inline"><semantics> <mfenced open="(" close=")"> <mstyle scriptlevel="0" displaystyle="true"> <mfrac> <mrow> <mo mathvariant="sans-serif">∂</mo> <mi>J</mi> </mrow> <mrow> <mo mathvariant="sans-serif">∂</mo> <mi mathvariant="bold">P</mi> </mrow> </mfrac> </mstyle> </mfenced> </semantics></math> with shape sensitivities computed using reverse mode and forward mode differentiation of NSPCC CAD kernel.</p> Full article ">Figure 17
<p>Levels of parametrisation: (<b>a</b>) coarse: level-1, (<b>b</b>) medium: level-2, (<b>c</b>) fine: level-3.</p> Full article ">Figure 18
<p>Shape sensitivity of a control point on the outer U-bend patch <math display="inline"><semantics> <mfenced open="(" close=")"> <mstyle scriptlevel="0" displaystyle="true"> <mfrac> <mrow> <mo mathvariant="sans-serif">∂</mo> <msub> <mi>X</mi> <mi>S</mi> </msub> </mrow> <mrow> <mo mathvariant="sans-serif">∂</mo> <mi mathvariant="bold">P</mi> </mrow> </mfrac> </mstyle> </mfenced> </semantics></math>: (<b>a</b>) coarse: level-1, (<b>b</b>) medium: level-2, (<b>c</b>) fine: level-3.</p> Full article ">Figure 19
<p>Normalised velocity field <math display="inline"><semantics> <mrow> <mo>(</mo> <msup> <mi>U</mi> <mo>*</mo> </msup> <mo>)</mo> </mrow> </semantics></math> of the baseline geometry at <math display="inline"><semantics> <mrow> <mi>R</mi> <mi>e</mi> <mo>=</mo> <mn>4.3</mn> <mo>×</mo> <msup> <mn>10</mn> <mn>4</mn> </msup> </mrow> </semantics></math>. CS view taken at various locations ordered from upstream to downstream.</p> Full article ">Figure 20
<p>Comparison of normalised velocity field <math display="inline"><semantics> <mrow> <mo>(</mo> <msup> <mi>U</mi> <mo>*</mo> </msup> <mo>)</mo> </mrow> </semantics></math> along streamwise direction between baseline and optimised geometries at <math display="inline"><semantics> <mrow> <mi>R</mi> <mi>e</mi> <mo>=</mo> <mn>4.3</mn> <mo>×</mo> <msup> <mn>10</mn> <mn>4</mn> </msup> </mrow> </semantics></math> taken at mid-plane. (<b>a</b>) Baseline, (<b>b</b>) opt-L1, (<b>c</b>) opt-L2, (<b>d</b>) opt-L3.</p> Full article ">Figure 21
<p>Shape optimisation workflow using extended design structure matrix (XDSM).</p> Full article ">Figure 22
<p>Comparison of objective function convergence using Steepest Descent between three different parametrisation levels. L1, L2 and L3 have 288, 576 and 1152 BSpline control points. L3 was restarted after 64 design iterations using BFGS.</p> Full article ">Figure 23
<p>Comparison of inner bend radius between baseline and optimised geometries.</p> Full article ">Figure 24
<p>Comparison of design patches of the optimised U-bend region obtained using three levels of parametrisation. (<b>a</b>) Baseline, (<b>b</b>) Opt-L1, (<b>c</b>) Opt-L2, (<b>d</b>) Opt-L3.</p> Full article ">Figure 25
<p>Comparison of normalised velocity field <math display="inline"><semantics> <mrow> <mo>(</mo> <msup> <mi>U</mi> <mo>*</mo> </msup> <mo>)</mo> </mrow> </semantics></math> between optimised geometries at <math display="inline"><semantics> <mrow> <mi>R</mi> <mi>e</mi> <mo>=</mo> <mn>4.3</mn> <mo>×</mo> <msup> <mn>10</mn> <mn>4</mn> </msup> </mrow> </semantics></math>. Cross-sectional (CS) views taken at various locations ordered from upstream to downstream. (<b>a</b>) Baseline, (<b>b</b>) Opt-L1, (<b>c</b>) Opt-L2, (<b>d</b>) Opt-L3.</p> Full article ">Figure 26
<p>Influence of Reynolds number on the objective function value.</p> Full article ">
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