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Aerospace, Volume 8, Issue 7 (July 2021) – 27 articles

Cover Story (view full-size image): The low-pressure turbine drives the fan, which produces most of the thrust in modern high-bypass ratio turbojet engines. Modern front-loaded high-lift airfoils promise overall weight savings but suffer from increased endwall losses. Large-eddy simulations of a 50% reaction stage and a linear cascade with two different bar wake generators were carried out to obtain insight into the mean flow topology and unsteady flow physics. By comparing the results from the simulations, the effect of the three-dimensional wake components on the downstream flow field is ascertained. For the chosen chord Reynolds number of 50,000, the stator laminar separation is substantial. Nevertheless, the passing wakes almost entirely suppress the laminar separation from the rotor blades. View this paper
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14 pages, 7316 KiB  
Article
A Simplified Finite Element Model of Riveted Joints for Structural Analyses with Consideration of Nonlinear Load-Transfer Characteristics
by Atsushi Kondo, Toshiyuki Kasahara and Atsushi Kanda
Aerospace 2021, 8(7), 196; https://doi.org/10.3390/aerospace8070196 - 19 Jul 2021
Cited by 6 | Viewed by 3515
Abstract
A simplified finite element model of riveted joints for structural analyses which effectively incorporates nonlinear response of riveted joints is proposed. Load-transfer characteristics of riveted joints were experimentally and numerically studied. First, a detailed finite element analysis for the process of a tensile [...] Read more.
A simplified finite element model of riveted joints for structural analyses which effectively incorporates nonlinear response of riveted joints is proposed. Load-transfer characteristics of riveted joints were experimentally and numerically studied. First, a detailed finite element analysis for the process of a tensile test of a single-row joint which consists of squeezing of the rivet and tensile loading to the joint was conducted to confirm the validity of a conventional method of analysis. The load–relative displacement behaviors of single-row joints observed in the detailed finite element analysis and previously conducted experiments agreed well. Then, a simplified method of the analysis was developed based on the detailed analysis and the experiments and was applied to analyses of multiple-row joints. A nonlinear relationship between load and relative displacement in the simplified analyses had good agreement with the detailed one. Distributed loads to the multiple rivets in the simplified analysis coincided with those of the detailed analysis under the maximum load. Memory and CPU time required to run the simplified analyses were reduced to about 1/4 and 1/6 compared to those of the detailed analysis, respectively. Full article
(This article belongs to the Section Aeronautics)
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<p>Practical finite element analyses for the design of an aircraft structure.</p>
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<p>Definition of loads carried through rivet joints.</p>
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<p>Experimental setup for tensile tests of a single-row joint.</p>
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<p>Relationship between applied load and relative displacement where <span class="html-italic">Fsq</span> = 13 kN.</p>
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<p>Detailed finite element model of a single-row joint.</p>
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<p>Processes analyzed in the finite element analyses.</p>
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<p>Distribution of displacement along the center line of the specimen where <span class="html-italic">F<sub>0</sub></span> = 15 kN.</p>
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<p>Load–relative displacement curves of single-row joints with various squeezing forces.</p>
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<p>Ratio of frictional force to total transferred load of the single-row joint.</p>
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<p>Bearing load–relative displacement curve of the single-row joints.</p>
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<p>Change in cross-sectional area of rivet shank after squeezing where <span class="html-italic">Fsq</span> = 19.5 kN.</p>
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<p>Difference in cross-sectional area of rivet shank with respect to squeezing force.</p>
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<p>Regularization of the curves of bearing load by the cross-sectional area of rivet shank.</p>
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<p>Detailed model of triple-row joint for the finite element analyses.</p>
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<p>Simplified model of triple-row joint for the finite element analyses.</p>
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<p>Derivation of stiffness of nonlinear springs in the simplified model.</p>
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<p>Load–relative displacement curves obtained from the detailed and simplified analysis of triple-row joints.</p>
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<p>Ratio of distributed loads on each rivet to total applied load to the triple-row joints.</p>
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31 pages, 2792 KiB  
Article
PSO-Based Soft Lunar Landing with Hazard Avoidance: Analysis and Experimentation
by Andrea D’Ambrosio, Andrea Carbone, Dario Spiller and Fabio Curti
Aerospace 2021, 8(7), 195; https://doi.org/10.3390/aerospace8070195 - 19 Jul 2021
Cited by 10 | Viewed by 2572
Abstract
The problem of real-time optimal guidance is extremely important for successful autonomous missions. In this paper, the last phases of autonomous lunar landing trajectories are addressed. The proposed guidance is based on the Particle Swarm Optimization, and the differential flatness approach, which is [...] Read more.
The problem of real-time optimal guidance is extremely important for successful autonomous missions. In this paper, the last phases of autonomous lunar landing trajectories are addressed. The proposed guidance is based on the Particle Swarm Optimization, and the differential flatness approach, which is a subclass of the inverse dynamics technique. The trajectory is approximated by polynomials and the control policy is obtained in an analytical closed form solution, where boundary and dynamical constraints are a priori satisfied. Although this procedure leads to sub-optimal solutions, it results in beng fast and thus potentially suitable to be used for real-time purposes. Moreover, the presence of craters on the lunar terrain is considered; therefore, hazard detection and avoidance are also carried out. The proposed guidance is tested by Monte Carlo simulations to evaluate its performances and a robust procedure, made up of safe additional maneuvers, is introduced to counteract optimization failures and achieve soft landing. Finally, the whole procedure is tested through an experimental facility, consisting of a robotic manipulator, equipped with a camera, and a simulated lunar terrain. The results show the efficiency and reliability of the proposed guidance and its possible use for real-time sub-optimal trajectory generation within laboratory applications. Full article
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<p>Experimental facility.</p>
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<p>Geometry associated with the camera.</p>
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<p>Simulated lunar landing terrain and example of a possible reference frame.</p>
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<p>Ascension and declination of the thrust vector.</p>
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<p>Guidance loop.</p>
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<p>PSO flow chart.</p>
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<p>PWM representation.</p>
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<p>Continuous to pulsed control representation.</p>
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<p>Circle detection algorithm to identify craters’ shape.</p>
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<p>Image acquisition, Canny and circles detection algorithms, and the avoidance approach performed on the lunar terrain of the facility.</p>
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<p>Terminal descent flow chart.</p>
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<p>Results of Monte Carlo simulations.</p>
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<p>Statistics for the final velocity error in the terminal descent phase (uncontrolled on the (<b>left</b>) and controlled on the (<b>right</b>)).</p>
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<p>Time-history of position with 10 successive optimizations.</p>
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<p>Time-history of velocity with 10 successive optimizations.</p>
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<p>Time-history of commanded acceleration and direction angles with 10 successive optimizations.</p>
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<p>Trajectory and control with GPOPS-II.</p>
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<p>Position and velocity components obtained with GPOPS-II.</p>
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<p>High level blocks of the implemented model.</p>
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<p>Low level blocks of the GNC module.</p>
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<p>Example of graphic interface for the initialization of the hardware simulation.</p>
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<p>Trajectory with detection for the hardware simulation.</p>
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<p>Time-history of position for the hardware simulation.</p>
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<p>Time-history of velocity for the hardware simulation.</p>
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<p>Time-history of commanded acceleration and direction angles for the hardware simulation.</p>
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<p>Hazard detection and avoidance procedure for the hardware simulation (images taken at t = 0 s, t = 20 s, and t = 40 s).</p>
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22 pages, 3371 KiB  
Article
Sensitivity Analysis of Maximum Circulation of Wake Vortex Encountered by En-Route Aircraft
by Jose I. Rojas, Marc Melgosa and Xavier Prats
Aerospace 2021, 8(7), 194; https://doi.org/10.3390/aerospace8070194 - 16 Jul 2021
Cited by 4 | Viewed by 3011
Abstract
Wake vortex encounters (WVE) can pose significant hazard for en-route aircraft. We studied the sensitivity of wake vortex (WV) circulation and decay to aircraft mass, altitude, velocity, density, time of catastrophic wake demise event, eddy dissipation rate, wing span, span-wise load factor, and [...] Read more.
Wake vortex encounters (WVE) can pose significant hazard for en-route aircraft. We studied the sensitivity of wake vortex (WV) circulation and decay to aircraft mass, altitude, velocity, density, time of catastrophic wake demise event, eddy dissipation rate, wing span, span-wise load factor, and WV core radius. Then, a tool was developed to compute circulations of WV generated/encountered by aircraft en-route, while disregarding unrealistic operational conditions. A comprehensive study is presented for most aircraft in the Base of Aircraft Data version 4.1 for different masses, altitudes, speeds, and separation values between generator and follower aircraft. The maximum WV circulation corresponds to A380-861 as generator: 864 and 840 m2/s at horizontal separation of 3 and 5 NM, respectively. In cruise environment, these WV may descend 1000 ft in 2.6 min and 2000 ft in 6.2 min, while retaining 74% and 49% of their initial strength, respectively. The maximum circulation of WV encountered by aircraft at horizontal separation of 3 NM from an A380-861 is 593, 726, and 745 m2/s, at FL200, FL300, and FL395, respectively. At 5 NM, the circulations decrease down to 578, 708, and 726 m2/s. Our results allow reducing WVE simulations only to critical scenarios, and thus perform more efficient test programs for computing aircraft upsets en-route. Full article
(This article belongs to the Special Issue Aircraft Operations and CNS/ATM)
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Figure 1
<p>Parameter <math display="inline"><semantics> <mi>A</mi> </semantics></math> vs. generator aircraft mass <math display="inline"><semantics> <mi>m</mi> </semantics></math>, assuming linearity between wing lay-out area <math display="inline"><semantics> <mi>S</mi> </semantics></math> and <math display="inline"><semantics> <mi>m</mi> </semantics></math> as in [<a href="#B39-aerospace-08-00194" class="html-bibr">39</a>].</p>
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<p>Parameter <math display="inline"><semantics> <mi>B</mi> </semantics></math> vs. flight altitude <math display="inline"><semantics> <mi>h</mi> </semantics></math> of the generator aircraft.</p>
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<p>Parameter <math display="inline"><semantics> <mrow> <mi>C</mi> <mo>=</mo> <mn>0.55</mn> <mo> </mo> <mrow> <mo>(</mo> <mrow> <mi>t</mi> <mo>/</mo> <msub> <mi>t</mi> <mi>c</mi> </msub> </mrow> <mo>)</mo> </mrow> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">Γ</mi> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>=</mo> <msub> <mi mathvariant="sans-serif">Γ</mi> <mn>0</mn> </msub> <mo> </mo> <mi>exp</mi> <mrow> <mo>(</mo> <mrow> <mo>−</mo> <mn>0.55</mn> <mo> </mo> <mrow> <mo>(</mo> <mrow> <mi>t</mi> <mo>/</mo> <msub> <mi>t</mi> <mi>c</mi> </msub> </mrow> <mo>)</mo> </mrow> </mrow> <mo>)</mo> </mrow> </mrow> </semantics></math> vs. time of the catastrophic wake demise event <math display="inline"><semantics> <mrow> <msub> <mi>t</mi> <mi>c</mi> </msub> </mrow> </semantics></math>, for <math display="inline"><semantics> <mrow> <mi>t</mi> <mo>=</mo> <mn>100</mn> </mrow> </semantics></math> s and <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="sans-serif">Γ</mi> <mn>0</mn> </msub> <mo>=</mo> <mn>100</mn> </mrow> </semantics></math> m<sup>2</sup>/s.</p>
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<p>Parameter <math display="inline"><semantics> <mi>D</mi> </semantics></math> vs. eddy dissipation rate (EDR), for <math display="inline"><semantics> <mrow> <mi>t</mi> <mo>=</mo> <mn>100</mn> </mrow> </semantics></math> s, wing span <math display="inline"><semantics> <mrow> <mi>b</mi> <mo>=</mo> <mn>79.75</mn> </mrow> </semantics></math> m, and <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="sans-serif">Γ</mi> <mn>0</mn> </msub> <mo>=</mo> <mn>500</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mn>800</mn> </mrow> </semantics></math> m<sup>2</sup>/s, from (<b>left</b>) Equation (27) and (<b>right</b>) Equation (28).</p>
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<p>Parameter <math display="inline"><semantics> <mi>E</mi> </semantics></math> vs. span-wise load factor <math display="inline"><semantics> <mi>s</mi> </semantics></math>, for WV core radius <math display="inline"><semantics> <mrow> <msub> <mi>r</mi> <mi>c</mi> </msub> <mo>=</mo> <mn>5.2</mn> <mo>%</mo> <mo> </mo> <msub> <mi>b</mi> <mn>0</mn> </msub> </mrow> </semantics></math>, as in [<a href="#B43-aerospace-08-00194" class="html-bibr">43</a>].</p>
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<p>Wake vortex circulation <math display="inline"><semantics> <mi mathvariant="sans-serif">Γ</mi> </semantics></math> vs. time <math display="inline"><semantics> <mi>t</mi> </semantics></math> from Sarpkaya’s [<a href="#B26-aerospace-08-00194" class="html-bibr">26</a>] and P2P/D2P [<a href="#B31-aerospace-08-00194" class="html-bibr">31</a>] decay models, and data for <math display="inline"><semantics> <mrow> <msup> <mi>ε</mi> <mo>∗</mo> </msup> <mo>=</mo> <mn>0.01</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msup> <mi>N</mi> <mo>∗</mo> </msup> <mo>=</mo> <mn>0.00</mn> </mrow> </semantics></math>, taken from [<a href="#B24-aerospace-08-00194" class="html-bibr">24</a>], for A380-861 aircraft.</p>
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<p>Maximum initial wake vortex circulation <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="sans-serif">Γ</mi> <mn>0</mn> </msub> </mrow> </semantics></math> vs. flight level (1 FL = 100 ft) and aircraft mass (in tons), for aircraft (<b>a</b>) A320-212, (<b>b</b>) A330-301, (<b>c</b>) B772LR, and (<b>d</b>) A380-861.</p>
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<p>Wake vortex descent in altitude <math display="inline"><semantics> <mrow> <msub> <mi>h</mi> <mrow> <mi>W</mi> <mi>V</mi> </mrow> </msub> </mrow> </semantics></math> vs. time <math display="inline"><semantics> <mi>t</mi> </semantics></math> from the Burnham-Hallock model [<a href="#B34-aerospace-08-00194" class="html-bibr">34</a>], and data for <math display="inline"><semantics> <mrow> <msup> <mi>ε</mi> <mo>∗</mo> </msup> <mo>=</mo> <mn>0.01</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msup> <mi>N</mi> <mo>∗</mo> </msup> <mo>=</mo> <mn>0.00</mn> </mrow> </semantics></math> [<a href="#B24-aerospace-08-00194" class="html-bibr">24</a>], for the A380-861 aircraft.</p>
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29 pages, 16167 KiB  
Article
SU2-NEMO: An Open-Source Framework for High-Mach Nonequilibrium Multi-Species Flows
by Walter T. Maier, Jacob T. Needels, Catarina Garbacz, Fábio Morgado, Juan J. Alonso and Marco Fossati
Aerospace 2021, 8(7), 193; https://doi.org/10.3390/aerospace8070193 - 16 Jul 2021
Cited by 31 | Viewed by 9407
Abstract
SU2-NEMO, a recent extension of the open-source SU2 multiphysics suite’s set of physical models and code architecture, is presented with the aim of introducing its enhanced capabilities in addressing high-enthalpy and high-Mach number flows. This paper discusses the thermal nonequilibrium and finite-rate chemistry [...] Read more.
SU2-NEMO, a recent extension of the open-source SU2 multiphysics suite’s set of physical models and code architecture, is presented with the aim of introducing its enhanced capabilities in addressing high-enthalpy and high-Mach number flows. This paper discusses the thermal nonequilibrium and finite-rate chemistry models adopted, including a link to the Mutation++ physio-chemical library. Further, the paper discusses how the software architecture has been designed to ensure modularity, incorporating the ability to introduce additional models in an efficient manner. A review of the numerical formulation and the discretization schemes utilized for the convective fluxes is also presented. Several test cases in two- and three-dimensions are examined for validation purposes and to illustrate the performance of the solver in addressing complex nonequilibrium flows. Full article
(This article belongs to the Special Issue Computational Fluid Dynamics on High-Speed and Non-Equilibrium Flows)
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Figure 1
<p>SU2 class architecture with parent–child and class instantiation relationships.</p>
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<p>Diagrams of extensions of SU2 classes implemented in SU2-NEMO for simulating nonequilibrium flows.</p>
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<p>SU2 CFluidModel with CNEMOGas, with native and Mutation++ thermochemical libraries as child classes.</p>
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<p>N2 thermal bath thermochemical nonequilibrium time-evolution.</p>
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<p>Air-5 thermal bath thermochemical nonequilibrium time-evolution.</p>
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<p>Temperature contours for the HEG cylinder test case.</p>
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<p>HEG cylinder surface plots with comparison ro experimental [<a href="#B48-aerospace-08-00193" class="html-bibr">48</a>] results.</p>
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<p>Flow contours around RAM-C vehicle forebody.</p>
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<p>RAM-C species concentrations. Comparison with experimental [<a href="#B50-aerospace-08-00193" class="html-bibr">50</a>] and numerical [<a href="#B45-aerospace-08-00193" class="html-bibr">45</a>, <a href="#B51-aerospace-08-00193" class="html-bibr">51</a>, <a href="#B52-aerospace-08-00193" class="html-bibr">52</a>] results.</p>
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<p>Diagram of experimental [<a href="#B53-aerospace-08-00193" class="html-bibr">53</a>] configuration for axisymmetric shock-wave boundary-layer interaction.</p>
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<p>ASWBLI Mach contours over the compression corner.</p>
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<p>Boundary layer profile 6 cm before the compression corner. Comparison with experimental [<a href="#B53-aerospace-08-00193" class="html-bibr">53</a>] and numerical [<a href="#B54-aerospace-08-00193" class="html-bibr">54</a>] results.</p>
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<p>Normalized surface quantities across the compression corner. Comparison with experimental [<a href="#B53-aerospace-08-00193" class="html-bibr">53</a>] and numerical [<a href="#B54-aerospace-08-00193" class="html-bibr">54</a>] results.</p>
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<p>Temperature contours of an N<sub>2</sub> hypersonic gas flow over a cylinder.</p>
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<p>Skin friction coefficient.</p>
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<p>Heat flux coefficient.</p>
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<p>Pressure coefficient.</p>
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<p>X43a volume and surface meshes.</p>
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<p>X43a Mach contours along symmetry plane.</p>
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<p>X43a Mach number contours at 95% of fuselage.</p>
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21 pages, 6337 KiB  
Article
Experimental CanSat Platform for Functional Verification of Burn Wire Triggering-Based Holding and Release Mechanisms
by Shankar Bhattarai, Ji-Seong Go and Hyun-Ung Oh
Aerospace 2021, 8(7), 192; https://doi.org/10.3390/aerospace8070192 - 16 Jul 2021
Cited by 4 | Viewed by 7953
Abstract
In this study, we present the Diverse Holding and Release Mechanism Can Satellite (DHRM CanSat) platform developed by the Space Technology Synthesis Laboratory (STSL) at Chosun University, South Korea. This platform focuses on several types of holding and release mechanisms (HRMs) for application [...] Read more.
In this study, we present the Diverse Holding and Release Mechanism Can Satellite (DHRM CanSat) platform developed by the Space Technology Synthesis Laboratory (STSL) at Chosun University, South Korea. This platform focuses on several types of holding and release mechanisms (HRMs) for application in deployable appendages of nanosatellites. The objectives of the DHRM CanSat mission are to demonstrate the design effectiveness and functionality of the three newly proposed HRMs based on the burn wire triggering method, i.e., the pogo pin-type HRM, separation nut-type HRM, and Velcro tape-type HRM, which were implemented on deployable dummy solar panels of the CanSat. The proposed mechanisms have many advantages, including a high holding capability, simultaneous constraints in multi-plane directions, and simplicity of handling. Additionally, each mechanism has distinctive features, such as spring-loaded pins to initiate deployment, a plate with a thread as a nut for a high holding capability, and a hook and loop fastener for easy access to subsystems of the satellite without releasing the holding constraint. The design effectiveness and functional performance of the proposed mechanisms were demonstrated through an actual flight test of the DHRM CanSat launched by a model rocket. Full article
(This article belongs to the Special Issue Vibration Control for Space Application)
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<p>System architecture and operation concept for the DHRM CanSat mission.</p>
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<p>Block diagram of the electrical system of the DHRM CanSat.</p>
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<p>Mechanical configurations of the DHRM CanSat illustrating the solar panels in the (<b>a</b>) stowed and (<b>b</b>) deployed states.</p>
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<p>Close-up views of the pogo pin-type HRM: (<b>a</b>) fully stowed solar panel, and (<b>b</b>) partially deployed solar panel.</p>
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<p>Close-up views of the separation nut-type HRM for the (<b>a</b>) fully stowed and (<b>b</b>) partially deployed solar panels.</p>
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<p>Close-up views of the Velcro tape-type HRM showing the (<b>a</b>) fully stowed and (<b>b</b>) partially deployed solar panels.</p>
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<p>Free vibration test setup configuration of the solar panel employing the Velcro tape-type HRM.</p>
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<p>Free vibration test results of the solar panels with and without application of the Velcro tape.</p>
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<p>Captured monitor view of the software developed for the DHRM CanSat ground station.</p>
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<p>Flight model of the DHRM CanSat showing the (<b>a</b>) stowed and (<b>b</b>) deployed solar panels.</p>
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<p>On-ground release function test setup of the mechanisms implemented in the DHRM CanSat flight model (<b>a</b>) before and (<b>b</b>) after deployment of the solar panels.</p>
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<p>Images captured by the onboard cameras of the CanSat (<b>a</b>) before and (<b>b</b>) after deployment of the solar panels.</p>
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<p>Time histories of the input voltage, separation signal, and acceleration response of the solar panels with the (<b>a</b>) separation nut-type HRM, (<b>b</b>) pogo pin-type HRM, (<b>c</b>) Velcro tape-type HRM (1st), and (<b>d</b>) Velcro tape-type HRM (2nd).</p>
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<p>Launch status data obtained from the GPS, IMU, and OBC during the flight of the DHRM CanSat.</p>
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25 pages, 2211 KiB  
Article
A Comparative Analysis of Multi-Epoch Double-Differenced Pseudorange Observation and Other Dual-Satellite Lunar Global Navigation Systems
by Toshiki Tanaka, Takuji Ebinuma, Shinichi Nakasuka and Heidar Malki
Aerospace 2021, 8(7), 191; https://doi.org/10.3390/aerospace8070191 - 15 Jul 2021
Cited by 4 | Viewed by 2686
Abstract
In this study, dual-satellite lunar global navigation systems that consist of a constellation of two navigation satellites providing geo-spatial positioning on the lunar surface were compared. In our previous work, we proposed a new dual-satellite relative-positioning navigation method called multi-epoch double-differenced pseudorange observation [...] Read more.
In this study, dual-satellite lunar global navigation systems that consist of a constellation of two navigation satellites providing geo-spatial positioning on the lunar surface were compared. In our previous work, we proposed a new dual-satellite relative-positioning navigation method called multi-epoch double-differenced pseudorange observation (MDPO). While the mathematical model of the MDPO and its behavior under specific conditions were studied, we did not compare its performance with other dual-satellite relative-positioning navigation systems. In this paper, we performed a comparative analysis between the MDPO and other two dual-satellite navigation methods. Based on the difference in their mathematical models, as well as numerical simulation results, we developed useful insights on the system design of dual-satellite lunar global navigation systems. Full article
(This article belongs to the Special Issue New Space: Advances in Space Science and Engineering)
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<p>Overview of the multi-epoch double-differenced pseudorange observation (MDPO) method.</p>
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<p>Overview of the double-differenced TOA–FOA method.</p>
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<p>Two-way ranging.</p>
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<p>Overview of the single-differenced two-way ranging method.</p>
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<p>Simulation overview.</p>
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<p>The simulated rover trajectories and user position errors. (<b>a</b>,<b>b</b>) correspond to the MDPO, (<b>c</b>,<b>d</b>) correspond to the double-differenced TOA–FOA, (<b>e</b>,<b>f</b>) correspond to the single-differenced two-way ranging.</p>
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19 pages, 2599 KiB  
Review
Challenges of Ablatively Cooled Hybrid Rockets for Satellites or Upper Stages
by Francesco Barato
Aerospace 2021, 8(7), 190; https://doi.org/10.3390/aerospace8070190 - 14 Jul 2021
Cited by 13 | Viewed by 4095
Abstract
Ablative-cooled hybrid rockets could potentially combine a similar versatility of a liquid propulsion system with a much simplified architecture. These characteristics make this kind of propulsion attractive, among others, for applications such as satellites and upper stages. In this paper, the use of [...] Read more.
Ablative-cooled hybrid rockets could potentially combine a similar versatility of a liquid propulsion system with a much simplified architecture. These characteristics make this kind of propulsion attractive, among others, for applications such as satellites and upper stages. In this paper, the use of hybrid rockets for those situations is reviewed. It is shown that, for a competitive implementation, several challenges need to be addressed, which are not the general ones often discussed in the hybrid literature. In particular, the optimal thrust to burning time ratio, which is often relatively low in liquid engines, has a deep impact on the grain geometry, that, in turn, must comply some constrains. The regression rate sometime needs to be tailored in order to avoid unreasonable grain shapes, with the consequence that the dimensional trends start to follow some sort of counter-intuitive behavior. The length to diameter ratio of the hybrid combustion chamber imposes some packaging issues in order to compact the whole propulsion system. Finally, the heat soak-back during long off phases between multiple burns could compromise the integrity of the case and of the solid fuel. Therefore, if the advantages of hybrid propulsion are to be exploited, the aspects mentioned in this paper shall be carefully considered and properly faced. Full article
(This article belongs to the Special Issue Hybrid Rocket(Volume II))
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<p>Examples of hybrid rocket design limits for satellites: (<b>a</b>) Propellant mass vs. thrust; (<b>b</b>) Velocity increment vs. thrust for different maximum allowable accelerations. Reprinted with permission from ref. [<a href="#B30-aerospace-08-00190" class="html-bibr">30</a>]. Copyright 2020 American Institute of Aeronautics and Astronautics.</p>
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<p>Examples of liquid propelled space tugs: (<b>a</b>) NPO Lavochkin Fregat (Soyuz/Zenit); (<b>b</b>) GKNPTs Khrunichev Briz-M (Proton). Note the extremely compact length to diameter ratio and the propellant tank arrangement. Copyright: publicly available pictures on the internet.</p>
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<p>Examples of upper stages for small launch vehicles: (<b>a</b>) Liquid propelled SpaceX Falcon 1; (<b>b</b>) ORPHEE Hybrid Upper Stage proposal. Note the relatively high length to diameter ratio of the serial hybrid and the compact packaging of the common bulkhead liquid propellant design. (<b>a</b>) Reprinted with permission from ref. [<a href="#B36-aerospace-08-00190" class="html-bibr">36</a>]. Copyright 2008 International Astronautical Federation. (<b>b</b>) Reprinted with permission from ref. [<a href="#B6-aerospace-08-00190" class="html-bibr">6</a>]. Copyright 2010 American Institute of Aeronautics and Astronautics.</p>
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<p>Examples of solid rocket upper stages: (<b>a</b>) Avio Zefiro 9 (Vega); (<b>b</b>) Northrop Grumman Star 48 (Minotaur IV). Note the very compact length to diameter ratio. (<b>a</b>) Reprinted with permission from <a href="https://www.avio.com/" target="_blank">https://www.avio.com/</a> (accessed on 9 July 2021). Copyright 2021 Avio. (<b>b</b>) Reprinted with permission from ref. [<a href="#B34-aerospace-08-00190" class="html-bibr">34</a>]. Copyright 2016 Northrop Grumman.</p>
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<p>Examples of hybrid upper stage arrangements: (<b>a</b>) Serial; (<b>b</b>) Toroidal; (<b>c</b>) Multiple tanks. Reprinted with permission from ref. [<a href="#B9-aerospace-08-00190" class="html-bibr">9</a>]. Copyright 2011 American Institute of Aeronautics and Astronautics.</p>
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<p>Sampling of Scorpius<sup>®</sup> [USA] Composite Tanks (1.7 MPa MEOP (250 psi)): Volume vs. Tank mass factor. Reprinted with permission from ref. [<a href="#B39-aerospace-08-00190" class="html-bibr">39</a>]. Copyright 2010 American Institute of Aeronautics and Astronautics.</p>
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24 pages, 5430 KiB  
Article
Ion Source—Thermal and Thermomechanical Simulation
by Victoria V. Svotina, Andrey I. Mogulkin and Alexandra Y. Kupreeva
Aerospace 2021, 8(7), 189; https://doi.org/10.3390/aerospace8070189 - 14 Jul 2021
Cited by 3 | Viewed by 2524
Abstract
The main purpose of this work is to conduct ground development testing of the ion source intended for use the space debris contactless transportation system. In order to substantiate the operating capability of the developed ion source, its thermal and thermomechanical simulation was [...] Read more.
The main purpose of this work is to conduct ground development testing of the ion source intended for use the space debris contactless transportation system. In order to substantiate the operating capability of the developed ion source, its thermal and thermomechanical simulation was carried out. The ion source thermal model should verify the ion source operating capability under thermal loading conditions, and demonstrate the conditions for ion source interfacing with the systems of the service spacecraft with the ion source installed as a payload. The mechanical and mathematical simulation for deformation of the ion source ion-extraction system profiled electrodes under thermal loading in conjunction with the prediction of the strained state based on the numerical simulation of the ion source ion-extraction system units, making it possible to ensure the stability of the ion source performance. Good agreement between the thermal and thermo-mechanical ion source simulation results and experimental data has been demonstrated. It is shown that the developed ion source will be functional in outer space and can be used as an element of the space debris contactless transportation system into graveyard orbits. Full article
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<p>Forces arising in the “SSC-SD” virtual assembly (<span class="html-italic">F<sub>IS</sub></span> is the force acting on the SSC (3) from the IS; <span class="html-italic">F<sub>EP</sub></span> is the force acting on the SSC from the compensating propulsion system (1,2); <span class="html-italic">F<sub>d</sub></span> is the force acting on space debris).</p>
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<p>IS discrete elements.</p>
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<p>Predicted temperature patterns in the IS from the Slit-Type IES end.</p>
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<p>Predicted temperature patterns in the IS from the GDC end (sectional view).</p>
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<p>Predicted temperature patterns in the IS for the screen electrode.</p>
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<p>IS GDC thermogram.</p>
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<p>IS acceleration electrode thermogram.</p>
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<p>Plate element.</p>
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<p>Deflection diagram.</p>
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<p>Internal forces and moments.</p>
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<p>IS screen electrode additional deflection (∆w(0)) as a function of initially designed deflection (w<sub>0</sub>(0)) at different levels of temperature drop along SE radius.</p>
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<p>IS acceleration electrode additional deflection (∆w(0)) as a function of initially designed deflection (w<sub>0</sub>(0)) at different levels of temperature drop along AE radius.</p>
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<p>Additional deflection (∆w(0)) for the SE made of titanium alloy with 160 mm diameter as a function of its initially designed deflection (w<sub>0</sub>(0)).</p>
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<p>Additional deflection (∆w(0)) for the AE made of titanium alloy with 160 mm diameter as a function of its initially designed deflection (w<sub>0</sub>(0)).</p>
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<p>Numerical simulations of electrode geometry in the ANSYS software.</p>
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<p>Element-by-element partition of electrodes in the ANSYS software—mesh model development.</p>
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<p>Thermal patterns of electrodes in the ANSYS software.</p>
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<p>Electrode deflection w(0) as a function of temperature differential along the electrode radius calculated by simplified algorithm and in the ANSYS software.</p>
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<p>IS thermal fields with 350 W power supplied to the inductor.</p>
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25 pages, 4184 KiB  
Review
A Review on the Current Status of Icing Physics and Mitigation in Aviation
by Masafumi Yamazaki, Aleksandar Jemcov and Hirotaka Sakaue
Aerospace 2021, 8(7), 188; https://doi.org/10.3390/aerospace8070188 - 14 Jul 2021
Cited by 54 | Viewed by 7493
Abstract
Icing on an aircraft is the cause of numerous adverse effects on aerodynamic performance. Although the issue was recognized in the 1920s, the icing problem is still an area of ongoing research due to the complexity of the icing phenomena. This review article [...] Read more.
Icing on an aircraft is the cause of numerous adverse effects on aerodynamic performance. Although the issue was recognized in the 1920s, the icing problem is still an area of ongoing research due to the complexity of the icing phenomena. This review article aims to summarize current research on aircraft icing in two fundamental topics: icing physics and icing mitigation techniques. The icing physics focuses on fixed wings, rotors, and engines severely impacted by icing. The study of engine icing has recently become focused on ice-crystal icing. Icing mitigation techniques reviewed are based on active, passive, and hybrid methods. The active mitigation techniques include those based on thermal and mechanical methods, which are currently in use on aircraft. The passive mitigation techniques discussed are based on current ongoing studies in chemical coatings. The hybrid mitigation technique is reviewed as a combination of the thermal method (active) and chemical coating (passive) to lower energy consumption. Full article
(This article belongs to the Special Issue Deicing and Anti-Icing of Aircraft (Volume II))
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<p>Schematic of parameters for the mass conservation in Messinger model.</p>
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<p>Main splashing parameters related to the numerical models.</p>
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<p>Overview of AERTS (<b>left</b>) and test stand (<b>right</b>) (Reproduced with permission from Palacios, J.L. [<a href="#B68-aerospace-08-00188" class="html-bibr">68</a>]).</p>
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<p>Ice shedding observed in [<a href="#B75-aerospace-08-00188" class="html-bibr">75</a>] (Reproduced with permission from Brouwers, E).</p>
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<p>Structure of the engine and potential ice accretion area (reproduced from [<a href="#B7-aerospace-08-00188" class="html-bibr">7</a>]).</p>
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<p>Cloud condition calibration test setup using the isokinetic probe (obtained from [<a href="#B85-aerospace-08-00188" class="html-bibr">85</a>]).</p>
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<p>Schematic of parameters contributing to the mass conservation (obtained from [<a href="#B96-aerospace-08-00188" class="html-bibr">96</a>]).</p>
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<p>Relation between the melting ratio and sticking efficiency (obtained from [<a href="#B96-aerospace-08-00188" class="html-bibr">96</a>]).</p>
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<p>Examples of pneumatic deicing boot.</p>
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<p>Deicing mechanism of EESS (Reproduced with permission from Goraj, Z. [<a href="#B125-aerospace-08-00188" class="html-bibr">125</a>]).</p>
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<p>Schematic of EIDI (obtained from [<a href="#B122-aerospace-08-00188" class="html-bibr">122</a>]).</p>
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34 pages, 3847 KiB  
Article
A Modular Framework for the Life Cycle Based Evaluation of Aircraft Technologies, Maintenance Strategies, and Operational Decision Making Using Discrete Event Simulation
by Ahmad Ali Pohya, Jennifer Wehrspohn, Robert Meissner and Kai Wicke
Aerospace 2021, 8(7), 187; https://doi.org/10.3390/aerospace8070187 - 14 Jul 2021
Cited by 14 | Viewed by 4463
Abstract
Current practices for investment and technology decision making in aeronautics largely rely on regression-based cost estimation methods. Although quick to implement and easy to use, they suffer from a variety of limitations, both in temporal space and scope of applicability. While recent research [...] Read more.
Current practices for investment and technology decision making in aeronautics largely rely on regression-based cost estimation methods. Although quick to implement and easy to use, they suffer from a variety of limitations, both in temporal space and scope of applicability. While recent research and development in this area addresses these to a certain extent, aerospace engineering still lacks a flexible and customizable valuation framework. To this end, a generic environment for economic and operational assessment of aircraft and related products named LYFE is presented. This tool employs a discrete event simulation which models the product life cycle from its order through decades of operation and maintenance until disposal. This paper introduces its key characteristics and default methods alongside its modular program architecture. The capabilities are demonstrated with a case study of on-wing engine cleaning procedures which are triggered by a customized decision making module. Thereby, the impact on engine health, fuel efficiency and overall economic viability is quantified. On the whole, the framework introduced in this paper can be used to analyze not only physical products but also operational procedures and maintenance strategies as well as specified decision making algorithms in terms of their impact on an aircraft’s or system’s life cycle. Full article
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<p>Classification of product cost estimation based on Niazi et al. [<a href="#B11-aerospace-08-00187" class="html-bibr">11</a>].</p>
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<p>Typical Airline TOC breakdown, based on Clark [<a href="#B13-aerospace-08-00187" class="html-bibr">13</a>].</p>
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<p>Modular program structure of AirLYFE with individual modules in green, their initialization and main functions in blue, databases in orange, and optional (i.e., user specified) procedures in dashed boxes.</p>
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<p>Excerpt of available classes and their attributes and methods in (Air)LYFE.</p>
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<p>Excerpt of an event calendar of a Helsinki (HEL) based short range aircraft including flight events (blue), maintenance events (red), monthly recurring payments (green), and the takeoff and landing curfews from the Munich (MUC) airport (hatched).</p>
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<p>LYFE’s default weight dependent estimation of the aircraft list price (in 2018 USD).</p>
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<p>The waiting mechanism during curfews.</p>
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<p>AirLYFE’s default distance dependent ticket price model.</p>
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<p>Implemented fuel price scenarios based on US Gulf Coast kerosene-type jet fuel spot price, taken from the US Energy Information Administration [<a href="#B57-aerospace-08-00187" class="html-bibr">57</a>].</p>
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<p>The monitoring and triggering process shown for the A-check and C-check.</p>
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<p>Aircraft performance response surfaces with a payload-range variation (<b>left</b>) and the impact of the three technology factors drag, Operating EmptyWeight (OEW), and Specific Fuel Consumption (SFC) (<b>right</b>).</p>
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<p>Available airports in LYFE with representative average daytime temperatures in July.</p>
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<p>Route network of an Airbus A321-211 from Finnair (MSN 1185, Tailsign OH-LZC. Time period from 1 January 2019 to 31 December 2019 extracted from <a href="http://flightradar24.com" target="_blank">flightradar24.com</a> (accessed on 1 September 2020)).</p>
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<p>Exhaust Gas Temperature Increase (EGTI) of the CFM56-5B3 including an engine wash.</p>
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<p>Decision making algorithm for triggering fuel cost optimized EW events.</p>
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<p>Graphical visualization of overall economics (<b>top left</b>), ownership cost (<b>bottom left</b>), flyaway cost (<b>top right</b>), and maintenance cost (<b>bottom right</b>).</p>
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<p>Development of Exhaust Gas Temperature (EGT) (<b>top</b>) and fuel efficiency (<b>bottom</b>) of the reference case, i.e., without Engine Wash.</p>
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<p>Effects of EW on the EGT (<b>top</b>), fuel efficiency (<b>center</b>), and accumulated fuel burn (<b>bottom</b>).</p>
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<p>Accumulated fuel cost changes (compared to the reference, (<b>top</b>)) and accumulated engine wash cost (<b>bottom</b>).</p>
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<p>Excerpt of LYFE’s Excel-based input mask for discrete flight operations.</p>
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<p>Excerpt of LYFE’s Excel-based input mask for discrete scheduled maintenance.</p>
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<p>Excerpt of AirLYFE’s Excel-based report with summary sheet (<b>top</b>) and annual cost breakdown sheet (<b>bottom</b>).</p>
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14 pages, 3256 KiB  
Systematic Review
Water Recognition on the Moon by Using THz Heterodyne-Spectrometer for Identifying the Appropriate Locations to Extract Water for Providing Oxygen for Breathing and Fuel for Spaceships’ Propulsion on the Moon with CubeSat
by Vahid Rastinasab, Weidong Hu and Mohammad Kazem Tahmasebi
Aerospace 2021, 8(7), 186; https://doi.org/10.3390/aerospace8070186 - 12 Jul 2021
Cited by 6 | Viewed by 3394
Abstract
Asteroid mining offers vital sources for improving human lives and provides opportunities for interplanetary missions and space travel. There are many professional commercial space companies that are only investing billions of dollars on asteroids mining, but prior to that, one condition for asteroid [...] Read more.
Asteroid mining offers vital sources for improving human lives and provides opportunities for interplanetary missions and space travel. There are many professional commercial space companies that are only investing billions of dollars on asteroids mining, but prior to that, one condition for asteroid mining could be planetary stations to refuel the pioneers’ spacecraft or human colonies on alien planets; hence, one of the vital sources for these purposes is water. Water can be harvested to split oxygen for breathing and hydrogen for refueling spaceships’ propulsions, and Earth-to-space water payload transporting is extremely expensive; therefore, discovering extraterrestrial water in outer space is economically beneficial. This paper presents a Lunar CubeSat Injector to deliver four 3U CubeSats into Low Lunar Orbit to make a constellation to identify locations of water sources on the Moon by using a THz heterodyne-spectrometer. In sum, this project can help scientists to recognize more water resources for those who will colonize the Moon and for those planning to go beyond it. Full article
(This article belongs to the Special Issue Small Satellite Technologies and Mission Concepts)
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<p>LOCI concepts of operation.</p>
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<p>Mission Scenario.</p>
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<p>THz Heterodyne-Spectrometer block diagram.</p>
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<p>Multichannel THz heterodyne-spectrometer first sketch.</p>
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<p>LOCI Low Lunar Orbit insertion.</p>
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<p>LOCI Low Lunar Orbit insertion.</p>
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<p>CubeSat cold gas thruster.</p>
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<p>CubeSat characteristics.</p>
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<p>LOCI Microsatellite characteristics.</p>
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21 pages, 7207 KiB  
Article
Tuning of NASA Standard Breakup Model for Fragmentation Events Modelling
by Nicola Cimmino, Giorgio Isoletta, Roberto Opromolla, Giancarmine Fasano, Aniello Basile, Antonio Romano, Moreno Peroni, Alessandro Panico and Andrea Cecchini
Aerospace 2021, 8(7), 185; https://doi.org/10.3390/aerospace8070185 - 12 Jul 2021
Cited by 12 | Viewed by 4722
Abstract
The continuous growth of space debris motivates the development and the improvement of tools that support the monitoring of a more and more congested space environment. Satellite breakup models play a key role to predict and analyze orbital debris evolution, and the NASA [...] Read more.
The continuous growth of space debris motivates the development and the improvement of tools that support the monitoring of a more and more congested space environment. Satellite breakup models play a key role to predict and analyze orbital debris evolution, and the NASA Standard Breakup Model represents a widely used reference, with current activities relevant to its evolution and improvements especially towards fragmentation of small mass spacecraft. From an operational perspective, an important point for fragmentation modelling concerns the tuning of the breakup model to achieve consistency with orbital data of observed fragments. In this framework, this paper proposes an iterative approach to estimate the model inputs, and in particular, the parents’ masses involved in a collision event. The iterative logic exploits the knowledge of Two Line Elements (TLE) of the fragments at some time after the event to adjust the input parameters of the breakup model with the objective of obtaining the same number of real fragments within a certain tolerance. Atmospheric re-entry is accounted for. As a result, the breakup model outputs a set of fragments whose statistical distribution, in terms of number and size, is consistent with the catalogued ones. The iterative approach is demonstrated for two different scenarios (i.e., catastrophic collision and non-catastrophic collision) using numerical simulations. Then, it is also applied to a real collision event. Full article
(This article belongs to the Special Issue New Space: Advances in Space Science and Engineering)
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<p>Mean (<b>a</b>) and standard deviation (<b>b</b>) of velocity distribution of sample fragments for different values of target mass for an impact velocity of 1 km/s. Mass of projectile is assumed to be 100 kg.</p>
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<p>Mean (<b>a</b>) and standard deviation (<b>b</b>) of velocity distribution of sample fragments for different values of target mass for an impact velocity of 5 km/s. Mass of projectile is assumed to be 100 kg.</p>
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<p>Mean (<b>a</b>) and standard deviation (<b>b</b>) of velocity distribution of sample fragments for different values of target mass for an impact velocity of 10 km/s. Mass of projectile is assumed to be 100 kg.</p>
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<p>Median of target (<b>a</b>) and projectile (<b>b</b>) fragments sample velocities for different values of impact velocity and target mass. Mass of projectile is assumed to be 100 kg.</p>
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<p>Scheme of SBM implementation. Each fragment is characterised by physical characteristics (i.e., size, mass, <span class="html-italic">A/M</span>) and a state vector (i.e., position and velocity).</p>
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<p>Scheme of iterative logic.</p>
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<p>Distribution of reference fragments in plane <span class="html-italic">i</span>sin(<span class="html-italic">Ω</span>)-<span class="html-italic">i</span>cos(<span class="html-italic">Ω</span>) for first test case.</p>
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<p>Histograms of reference (<b>a</b>) and estimated (<b>b</b>) fragments in range [75°, 105°] of inclination for first test case.</p>
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<p>Histograms of reference (<b>a</b>) and estimated (<b>b</b>) fragments in range [6900, 12,000] km of semimajor axis for first test case.</p>
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<p>Gabbard diagrams for the target (<b>a</b>) and the projectile (<b>b</b>), obtained with input masses equal to m<sub>1</sub> = 850 kg and m<sub>2</sub> = 490 kg.</p>
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<p>Gabbard diagrams for the target (<b>a</b>) and the projectile (<b>b</b>), obtained with fragmented masses estimated by iterative approach for first test case.</p>
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<p>(<b>a</b>) Mean and standard deviation errors for number of fragments greater than 10 cm, both for target and projectile. (<b>b</b>) Mean and standard deviation errors for estimated fragmented mass, both for target and projectile.</p>
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<p>Distribution of reference fragments in plane <span class="html-italic">i</span>sin(<span class="html-italic">Ω</span>)-<span class="html-italic">i</span>cos(<span class="html-italic">Ω</span>) for second test case.</p>
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<p>Histograms of reference (<b>a</b>) and estimated (<b>b</b>) fragments in range [80°, 100°] of inclination for second test case.</p>
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<p>Histograms of reference (<b>a</b>) and estimated (<b>b</b>) fragments in range [6900, 12,000] km of semimajor axis for second test case.</p>
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<p>Gabbard diagrams for the target (<b>a</b>) and the projectile (<b>b</b>), obtained with input masses equal to m<sub>1</sub> = 30 kg and m<sub>2</sub> = 30 kg.</p>
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<p>Gabbard diagrams for the target (<b>a</b>) and the projectile (<b>b</b>), obtained with fragmented masses estimated by iterative approach for second test case.</p>
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<p>Number of iterations against tolerance on number of catalogued fragments for different values of <span class="html-italic">N<sub>cat</sub></span>, both for target (solid line) and projectile (dashed line).</p>
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28 pages, 38156 KiB  
Article
Large-Eddy Simulation of Low-Pressure Turbine Cascade with Unsteady Wakes
by Zachary Robison and Andreas Gross
Aerospace 2021, 8(7), 184; https://doi.org/10.3390/aerospace8070184 - 8 Jul 2021
Cited by 8 | Viewed by 3235
Abstract
To better understand the wake effects at low Reynolds numbers, large-eddy simulations of a 50% reaction low-pressure turbine stage and a linear cascade with two different bar wake generators were carried out for a chord Reynolds number of 50,000. For the chosen front-loaded [...] Read more.
To better understand the wake effects at low Reynolds numbers, large-eddy simulations of a 50% reaction low-pressure turbine stage and a linear cascade with two different bar wake generators were carried out for a chord Reynolds number of 50,000. For the chosen front-loaded high-lift airfoil, the endwall structures are stronger than for more traditional mid-loaded moderate-lift airfoils. By comparing the 50% reaction stage results with the bar wake generator results, insight is gained into the effect of the three-dimensional wake components on the downstream flow field.For the cases with bar wake generator, the endwall boundary layer is growing faster because of the relative motion of the endwall with respect to the freestream. The half-width of the wake is approximately matched for the larger one of the two considered bar wake generators. To improve the quality of the phase-averaged flow fields, the proper orthogonal decomposition was employed as a filter to remove the low-energy unsteady flow field content. Both the mean flow and filtered phase-averaged flow fields were analyzed in detail. Visualizations of the phase-averaged flow field reveal a periodic suppression of the laminar suction side separation from the downstream airfoil even for the smaller bar wake generator. The passage vortex is entirely suppressed for the 50% reaction stage and for the larger bar wake generator. Furthermore, the phase-averaged data for the 50% reaction stage reveal a new longitudinal flow structure that is traced back to near-wall wake vorticity. This flow structure is missing for the bar wake generator cases. Full article
(This article belongs to the Special Issue Large Eddy Simulation in Aerospace Engineering)
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<p>RANS profiles.</p>
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<p>Computational grids for the 50% reaction stage (<b>a</b>) and cascade with bar wake generator with small diameter (<b>b</b>) and large diameter (<b>c</b>). (Reprinted from [<a href="#B42-aerospace-08-00184" class="html-bibr">42</a>].)</p>
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<p>Near−wall grid resolutions in wall units.</p>
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<p>Number of cells within boundary layer (<math display="inline"><semantics> <mrow> <mi>z</mi> <mo>≤</mo> <mo>δ</mo> </mrow> </semantics></math>) and viscous sublayer (<math display="inline"><semantics> <mrow> <msup> <mi>z</mi> <mo>+</mo> </msup> <mo>≤</mo> <mn>10</mn> </mrow> </semantics></math>).</p>
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<p>Stage velocities for the 50% reaction stage. (Reprinted from [<a href="#B42-aerospace-08-00184" class="html-bibr">42</a>].)</p>
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<p>Instantaneous iso−surfaces of <math display="inline"><semantics> <mrow> <mi>Q</mi> <mo>=</mo> <mn>5</mn> </mrow> </semantics></math> colored by streamwise velocity for the 50% reaction stage (<b>top</b>), small bar wake generator (<b>bottom left</b>) and large bar wake generator (<b>bottom right</b>). Red circle highlights streaky structures. (Reprinted from [<a href="#B42-aerospace-08-00184" class="html-bibr">42</a>].)</p>
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<p>Time−averaged velocity profiles <math display="inline"><semantics> <mrow> <mn>1</mn> <mo>/</mo> <mn>2</mn> <msub> <mi>C</mi> <mi>x</mi> </msub> </mrow> </semantics></math> upstream of stator vane (<b>left</b>) and bar wake generators (<b>right</b>). (Reprinted from [<a href="#B42-aerospace-08-00184" class="html-bibr">42</a>].)</p>
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<p>Iso−surfaces of <math display="inline"><semantics> <mrow> <mi>Q</mi> <mo>=</mo> <mn>25</mn> </mrow> </semantics></math> flooded by wall distance (<math display="inline"><semantics> <mrow> <mi>z</mi> <mo>&gt;</mo> <mn>0.02</mn> </mrow> </semantics></math> is red) for the 50% reaction stage (phase−averaged flow). (Reprinted from [<a href="#B42-aerospace-08-00184" class="html-bibr">42</a>].)</p>
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<p>Iso−surfaces of <math display="inline"><semantics> <mrow> <mi>Q</mi> <mo>=</mo> <mn>25</mn> </mrow> </semantics></math> flooded by wall distance (<math display="inline"><semantics> <mrow> <mi>z</mi> <mo>&gt;</mo> <mn>0.02</mn> </mrow> </semantics></math> is red) for small bar wake generator (phase−averaged flow). (Reprinted from [<a href="#B42-aerospace-08-00184" class="html-bibr">42</a>].)</p>
Full article ">Figure 10
<p>Iso−surfaces of <math display="inline"><semantics> <mrow> <mi>Q</mi> <mo>=</mo> <mn>25</mn> </mrow> </semantics></math> flooded by wall distance (<math display="inline"><semantics> <mrow> <mi>z</mi> <mo>&gt;</mo> <mn>0.02</mn> </mrow> </semantics></math> is red) for large bar wake generator (phase−averaged flow). The wakes are considerably wider than for the smaller wake generator (<a href="#aerospace-08-00184-f009" class="html-fig">Figure 9</a>). (Reprinted from [<a href="#B42-aerospace-08-00184" class="html-bibr">42</a>].)</p>
Full article ">Figure 11
<p>Instantaneous iso−surfaces of <math display="inline"><semantics> <mrow> <mi>Q</mi> <mo>=</mo> <mn>25</mn> </mrow> </semantics></math> colored by wall−normal vorticity for the 50% reaction stage (<b>a</b>), small bar wake generator (<b>b</b>), and large bar wake generator (<b>c</b>). The wakes for the bar wake generators exhibit a von Kármán vortex street. (Reprinted from [<a href="#B42-aerospace-08-00184" class="html-bibr">42</a>].)</p>
Full article ">Figure 12
<p>Iso−contours of streamwise velocity, <math display="inline"><semantics> <mrow> <mn>0</mn> <mo>&lt;</mo> <msub> <mi>u</mi> <mrow> <mi>i</mi> <mi>n</mi> </mrow> </msub> <mo>&lt;</mo> <mn>1</mn> </mrow> </semantics></math>, (<b>top</b>) and streamwise vorticity, <math display="inline"><semantics> <mrow> <mo>−</mo> <mn>20</mn> <mo>&lt;</mo> <msub> <mi>ω</mi> <mrow> <mi>i</mi> <mi>n</mi> </mrow> </msub> <mo>&lt;</mo> <mn>20</mn> </mrow> </semantics></math>, (<b>bottom</b>) for the 50% reaction stage (<b>a</b>), small bar wake generator (<b>b</b>), and large bar wake generator (<b>c</b>) (time-averaged flow). The wake width for the large bar wake generator matches the wake width for the L2F. A strong endwall structure is present for the L2F but not for the bar wake generators. (Reprinted from [<a href="#B42-aerospace-08-00184" class="html-bibr">42</a>].)</p>
Full article ">Figure 13
<p>Wake profiles of <math display="inline"><semantics> <msub> <mi>u</mi> <mrow> <mi>i</mi> <mi>n</mi> </mrow> </msub> </semantics></math> for <math display="inline"><semantics> <mrow> <mi>x</mi> <mo>=</mo> <mo>−</mo> <mn>0.149</mn> </mrow> </semantics></math> averaged over <math display="inline"><semantics> <mrow> <mn>0.198</mn> <mo>&lt;</mo> <mi>z</mi> <mo>&lt;</mo> <mn>0.791</mn> </mrow> </semantics></math>. (Reprinted from [<a href="#B42-aerospace-08-00184" class="html-bibr">42</a>].)</p>
Full article ">Figure 14
<p>Iso−surfaces of <math display="inline"><semantics> <mrow> <mi>Q</mi> <mo>=</mo> <mn>25</mn> </mrow> </semantics></math> flooded by <math display="inline"><semantics> <mrow> <mo>−</mo> <mn>5</mn> <mo>&lt;</mo> <msub> <mi>w</mi> <mi>x</mi> </msub> <mo>&lt;</mo> <mn>5</mn> </mrow> </semantics></math> (<b>left</b>) and and iso−contours of <math display="inline"><semantics> <mrow> <mo>−</mo> <mn>0.01</mn> <mo>&lt;</mo> <msub> <mi>c</mi> <mrow> <mi>f</mi> <mo>,</mo> <mi>x</mi> </mrow> </msub> <mo>&lt;</mo> <mn>0.01</mn> </mrow> </semantics></math> (<b>right</b>) for upstream stator vane of 50% reaction stage (time−averaged flow).</p>
Full article ">Figure 15
<p>Iso−surfaces of <math display="inline"><semantics> <mrow> <mi>Q</mi> <mo>=</mo> <mn>25</mn> </mrow> </semantics></math> flooded by <math display="inline"><semantics> <mrow> <mo>−</mo> <mn>5</mn> <mo>&lt;</mo> <msub> <mi>w</mi> <mi>x</mi> </msub> <mo>&lt;</mo> <mn>5</mn> </mrow> </semantics></math> (<b>left</b>) and skin−friction lines and iso−contours of <math display="inline"><semantics> <mrow> <mo>−</mo> <mn>0.01</mn> <mo>&lt;</mo> <msub> <mi>c</mi> <mrow> <mi>f</mi> <mo>,</mo> <mi>x</mi> </mrow> </msub> <mo>&lt;</mo> <mn>0.01</mn> </mrow> </semantics></math> (<b>right</b>) for downstream rotor blade of 50% reaction stage (time−averaged flow). Circle marks longitudinal near-wall structures. (Reprinted from [<a href="#B42-aerospace-08-00184" class="html-bibr">42</a>].)</p>
Full article ">Figure 16
<p>Iso−surfaces of <math display="inline"><semantics> <mrow> <mi>Q</mi> <mo>=</mo> <mn>25</mn> </mrow> </semantics></math> flooded by <math display="inline"><semantics> <mrow> <mo>−</mo> <mn>5</mn> <mo>&lt;</mo> <msub> <mi>w</mi> <mi>x</mi> </msub> <mo>&lt;</mo> <mn>5</mn> </mrow> </semantics></math> (<b>left</b>) and skin−friction lines (<b>right</b>) for the 50% reaction stage. Endwall marked by red line (time−averaged flow). (Reprinted from [<a href="#B42-aerospace-08-00184" class="html-bibr">42</a>].)</p>
Full article ">Figure 17
<p>Iso−contours of <math display="inline"><semantics> <mrow> <mo>−</mo> <mn>0.01</mn> <mo>&lt;</mo> <msub> <mi>c</mi> <mrow> <mi>f</mi> <mo>,</mo> <mi>x</mi> </mrow> </msub> <mo>&lt;</mo> <mn>0.01</mn> </mrow> </semantics></math> for suction surface of downstream blade averaged over <math display="inline"><semantics> <mrow> <mn>0.60</mn> <mo>&lt;</mo> <mi>z</mi> <mo>&lt;</mo> <mn>1</mn> </mrow> </semantics></math> for instantaneous flow for the 50% reaction stage. (Reprinted from [<a href="#B42-aerospace-08-00184" class="html-bibr">42</a>].)</p>
Full article ">Figure 18
<p>Skin−friction lines on pressure surfaces for the 50% reaction stage for upstream stator vane (<b>a</b>) and downstream rotor blade (<b>b</b>). Endwall marked by red line (time−averaged flow). (Reprinted from [<a href="#B42-aerospace-08-00184" class="html-bibr">42</a>].)</p>
Full article ">Figure 19
<p>Iso−contours of flow angle at <math display="inline"><semantics> <mrow> <mi>z</mi> <mo>=</mo> <mn>1</mn> </mrow> </semantics></math> for the 50% reaction stage (phase−averaged flow). Arrow marks pressure side flow separation. (Reprinted from [<a href="#B42-aerospace-08-00184" class="html-bibr">42</a>].)</p>
Full article ">Figure 20
<p>Iso−surfaces of <math display="inline"><semantics> <mrow> <mi>Q</mi> <mo>=</mo> <mn>25</mn> </mrow> </semantics></math> flooded by <math display="inline"><semantics> <mrow> <mo>−</mo> <mn>5</mn> <mo>&lt;</mo> <msub> <mi>w</mi> <mi>x</mi> </msub> <mo>&lt;</mo> <mn>5</mn> </mrow> </semantics></math> (left) and skin−friction lines and iso−contours of <math display="inline"><semantics> <mrow> <mo>−</mo> <mn>0.01</mn> <mo>&lt;</mo> <msub> <mi>c</mi> <mrow> <mi>f</mi> <mo>,</mo> <mi>x</mi> </mrow> </msub> <mo>&lt;</mo> <mn>0.01</mn> </mrow> </semantics></math> (<b>right</b>) for small bar wake generator (<b>top</b>) and large bar wake generator (<b>bottom</b>) (time−averaged flow). (Reprinted from [<a href="#B42-aerospace-08-00184" class="html-bibr">42</a>].)</p>
Full article ">Figure 21
<p>Iso−surfaces of <math display="inline"><semantics> <mrow> <mi>Q</mi> <mo>=</mo> <mn>25</mn> </mrow> </semantics></math> flooded by <math display="inline"><semantics> <mrow> <mo>−</mo> <mn>5</mn> <mo>&lt;</mo> <msub> <mi>w</mi> <mi>x</mi> </msub> <mo>&lt;</mo> <mn>5</mn> </mrow> </semantics></math> (<b>left</b>) and skin−friction lines for <math display="inline"><semantics> <mrow> <mi>D</mi> <mo>=</mo> <mn>0.04</mn> </mrow> </semantics></math> (<b>top</b>) and <math display="inline"><semantics> <mrow> <mi>D</mi> <mo>=</mo> <mn>0.12</mn> </mrow> </semantics></math> (<b>bottom</b>) bar wake generators. Endwall marked by red line (time−averaged flow). The bar wake generator wakes suppress the suction surface laminar separation. For the larger bar wake generator, the suction side corner separation is weakened. (Reprinted from [<a href="#B42-aerospace-08-00184" class="html-bibr">42</a>].)</p>
Full article ">Figure 22
<p>Iso−contours of <math display="inline"><semantics> <mrow> <mo>−</mo> <mn>0.01</mn> <mo>&lt;</mo> <msub> <mi>c</mi> <mrow> <mi>f</mi> <mo>,</mo> <mi>x</mi> </mrow> </msub> <mo>&lt;</mo> <mn>0.01</mn> </mrow> </semantics></math> for suction surface of downstream blade averaged over <math display="inline"><semantics> <mrow> <mn>0.60</mn> <mo>&lt;</mo> <mi>z</mi> <mo>&lt;</mo> <mn>1</mn> </mrow> </semantics></math> (phase−averages). For the larger bar wake generator, flow separation is suppressed over the entire period. (Reprinted from [<a href="#B42-aerospace-08-00184" class="html-bibr">42</a>].)</p>
Full article ">Figure 23
<p>Iso−contours of flow angle at <math display="inline"><semantics> <mrow> <mi>z</mi> <mo>=</mo> <mn>1</mn> </mrow> </semantics></math> for large bar wake generator (phase−averaged flow). Different from the 50% reaction stage, the bar wake generators do not induce flow separation from the pressure side. (Reprinted from [<a href="#B42-aerospace-08-00184" class="html-bibr">42</a>].)</p>
Full article ">Figure 24
<p>POD eigenvalues and time-coefficients for the 50% reaction stage. (Reprinted from [<a href="#B42-aerospace-08-00184" class="html-bibr">42</a>].)</p>
Full article ">Figure 25
<p>Reconstructions of phase−averaged flow at <math display="inline"><semantics> <mrow> <mi>t</mi> <mo>/</mo> <mi>T</mi> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math> from 5 (<b>left</b>), 10 (<b>center</b>), and 15 (<b>right</b>) POD modes for the 50% reaction stage. Iso−surfaces of <math display="inline"><semantics> <mrow> <mi>Q</mi> <mo>=</mo> <mn>25</mn> </mrow> </semantics></math> flooded by wall distance (<math display="inline"><semantics> <mrow> <mi>z</mi> <mo>&gt;</mo> <mn>0.02</mn> </mrow> </semantics></math> is red). By including additional modes in the reconstruction, the endwall flow structures are revealed more clearly. (Reprinted from [<a href="#B42-aerospace-08-00184" class="html-bibr">42</a>].)</p>
Full article ">Figure 26
<p>Iso−surfaces of <math display="inline"><semantics> <mrow> <mi>Q</mi> <mo>=</mo> <mn>25</mn> </mrow> </semantics></math> flooded by wall distance (<math display="inline"><semantics> <mrow> <mi>z</mi> <mo>&gt;</mo> <mn>0.02</mn> </mrow> </semantics></math> is red) for the 50% reaction stage (phase−averaged flow). POD reconstructions from 15 modes. Longitudinal structure is marked by letter “A”. (Reprinted from [<a href="#B42-aerospace-08-00184" class="html-bibr">42</a>].)</p>
Full article ">Figure 27
<p>Iso−surfaces of <math display="inline"><semantics> <mrow> <mi>Q</mi> <mo>=</mo> <mn>20</mn> </mrow> </semantics></math> flooded by <math display="inline"><semantics> <mrow> <mo>−</mo> <mn>5</mn> <mo>&lt;</mo> <msub> <mi>w</mi> <mi>x</mi> </msub> <mo>&lt;</mo> <mn>5</mn> </mrow> </semantics></math> for the 50% reaction stage (phase−averaged flow). POD reconstructions from 15 modes. (Reprinted from [<a href="#B42-aerospace-08-00184" class="html-bibr">42</a>].)</p>
Full article ">Figure 27 Cont.
<p>Iso−surfaces of <math display="inline"><semantics> <mrow> <mi>Q</mi> <mo>=</mo> <mn>20</mn> </mrow> </semantics></math> flooded by <math display="inline"><semantics> <mrow> <mo>−</mo> <mn>5</mn> <mo>&lt;</mo> <msub> <mi>w</mi> <mi>x</mi> </msub> <mo>&lt;</mo> <mn>5</mn> </mrow> </semantics></math> for the 50% reaction stage (phase−averaged flow). POD reconstructions from 15 modes. (Reprinted from [<a href="#B42-aerospace-08-00184" class="html-bibr">42</a>].)</p>
Full article ">Figure 28
<p>Iso−surfaces of <math display="inline"><semantics> <mrow> <mi>Q</mi> <mo>=</mo> <mn>10</mn> </mrow> </semantics></math> flooded by <math display="inline"><semantics> <mrow> <mo>−</mo> <mn>20</mn> <mo>&lt;</mo> <msub> <mi>w</mi> <mi>x</mi> </msub> <mo>&lt;</mo> <mn>20</mn> </mrow> </semantics></math> for the 50% reaction stage (phase−averaged flow). POD reconstructions from 15 modes. (Reprinted from [<a href="#B42-aerospace-08-00184" class="html-bibr">42</a>].)</p>
Full article ">Figure 29
<p>POD eigenvalues and time−coefficients for bar wake generator with <math display="inline"><semantics> <mrow> <mi>D</mi> <mo>=</mo> <mn>0.04</mn> </mrow> </semantics></math> (<b>top</b>) and <math display="inline"><semantics> <mrow> <mi>D</mi> <mo>=</mo> <mn>0.12</mn> </mrow> </semantics></math> (<b>bottom</b>). (Reprinted from [<a href="#B42-aerospace-08-00184" class="html-bibr">42</a>].)</p>
Full article ">Figure 30
<p>Reconstructions of phase-averaged flow for small bar wake generator. Iso−surfaces of <math display="inline"><semantics> <mrow> <mi>Q</mi> <mo>=</mo> <mn>25</mn> </mrow> </semantics></math> flooded by wall distance (<math display="inline"><semantics> <mrow> <mi>z</mi> <mo>&gt;</mo> <mn>0.02</mn> </mrow> </semantics></math> is red). POD reconstructions from 15 modes. (Reprinted from [<a href="#B42-aerospace-08-00184" class="html-bibr">42</a>].)</p>
Full article ">Figure 31
<p>Reconstructions of phase−averaged flow for large bar wake generator. Iso−surfaces of <math display="inline"><semantics> <mrow> <mi>Q</mi> <mo>=</mo> <mn>25</mn> </mrow> </semantics></math> flooded by wall distance (<math display="inline"><semantics> <mrow> <mi>z</mi> <mo>&gt;</mo> <mn>0.02</mn> </mrow> </semantics></math> is red). POD reconstructions from 15 modes. (Reprinted from [<a href="#B42-aerospace-08-00184" class="html-bibr">42</a>].)</p>
Full article ">Figure 32
<p>Iso−surfaces of <math display="inline"><semantics> <mrow> <mi>Q</mi> <mo>=</mo> <mn>25</mn> </mrow> </semantics></math> flooded by wall distance, <math display="inline"><semantics> <mrow> <mo>−</mo> <mn>5</mn> <mo>&lt;</mo> <msub> <mi>w</mi> <mi>x</mi> </msub> <mo>&lt;</mo> <mn>5</mn> </mrow> </semantics></math>, for large bar wake generator (phase−averaged flow). POD reconstructions from 15 modes. (Reprinted from [<a href="#B42-aerospace-08-00184" class="html-bibr">42</a>].)</p>
Full article ">
15 pages, 4111 KiB  
Article
Some Special Types of Orbits around Jupiter
by Yongjie Liu, Yu Jiang, Hengnian Li and Hui Zhang
Aerospace 2021, 8(7), 183; https://doi.org/10.3390/aerospace8070183 - 8 Jul 2021
Cited by 4 | Viewed by 2673
Abstract
This paper intends to show some special types of orbits around Jupiter based on the mean element theory, including stationary orbits, sun-synchronous orbits, orbits at the critical inclination, and repeating ground track orbits. A gravity model concerning only the perturbations of J2 [...] Read more.
This paper intends to show some special types of orbits around Jupiter based on the mean element theory, including stationary orbits, sun-synchronous orbits, orbits at the critical inclination, and repeating ground track orbits. A gravity model concerning only the perturbations of J2 and J4 terms is used here. Compared with special orbits around the Earth, the orbit dynamics differ greatly: (1) There do not exist longitude drifts on stationary orbits due to non-spherical gravity since only J2 and J4 terms are taken into account in the gravity model. All points on stationary orbits are degenerate equilibrium points. Moreover, the satellite will oscillate in the radial and North-South directions after a sufficiently small perturbation of stationary orbits. (2) The inclinations of sun-synchronous orbits are always bigger than 90 degrees, but smaller than those for satellites around the Earth. (3) The critical inclinations are no-longer independent of the semi-major axis and eccentricity of the orbits. The results show that if the eccentricity is small, the critical inclinations will decrease as the altitudes of orbits increase; if the eccentricity is larger, the critical inclinations will increase as the altitudes of orbits increase. (4) The inclinations of repeating ground track orbits are monotonically increasing rapidly with respect to the altitudes of orbits. Full article
(This article belongs to the Special Issue Spacecraft Dynamics and Control)
Show Figures

Figure 1

Figure 1
<p>Variations of <math display="inline"><semantics> <mrow> <msub> <mi>f</mi> <mn>0</mn> </msub> <mo stretchy="false">(</mo> <mi>r</mi> <mo stretchy="false">)</mo> <mo>,</mo> <mo> </mo> <msub> <mi>f</mi> <mn>1</mn> </msub> <mo stretchy="false">(</mo> <mi>r</mi> <mo stretchy="false">)</mo> </mrow> </semantics></math> as <math display="inline"><semantics> <mi>r</mi> </semantics></math> increases from <math display="inline"><semantics> <mrow> <mn>2.2</mn> <mi>R</mi> </mrow> </semantics></math> to <math display="inline"><semantics> <mrow> <mn>2.3</mn> <mi>R</mi> </mrow> </semantics></math>.</p>
Full article ">Figure 2
<p>Orbits around Jupiter: green—stationary orbits; red—<math display="inline"><semantics> <mrow> <mi>r</mi> <mo>=</mo> <mn>0.99</mn> <msub> <mi>r</mi> <mn>0</mn> </msub> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>ϕ</mi> <mo>=</mo> <mo>−</mo> <mn>0.1</mn> </mrow> </semantics></math> deg; blue—<math display="inline"><semantics> <mrow> <mi>r</mi> <mo>=</mo> <mn>0.99</mn> <msub> <mi>r</mi> <mn>0</mn> </msub> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>ϕ</mi> <mo>=</mo> <mn>0.1</mn> </mrow> </semantics></math> deg; cyan—<math display="inline"><semantics> <mrow> <mi>r</mi> <mo>=</mo> <mn>1.01</mn> <msub> <mi>r</mi> <mn>0</mn> </msub> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>ϕ</mi> <mo>=</mo> <mo>−</mo> <mn>0.1</mn> </mrow> </semantics></math> deg; black—<math display="inline"><semantics> <mrow> <mi>r</mi> <mo>=</mo> <mn>1.01</mn> <msub> <mi>r</mi> <mn>0</mn> </msub> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>ϕ</mi> <mo>=</mo> <mn>0.1</mn> </mrow> </semantics></math> deg.</p>
Full article ">Figure 3
<p>(<b>a</b>,<b>b</b>) Variations of orbital radius (<math display="inline"><semantics> <mi>r</mi> </semantics></math>), (<b>c</b>,<b>d</b>) drift of longitude (<math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">Δ</mi> <mi>λ</mi> </mrow> </semantics></math>), (<b>e</b>,<b>f</b>) evolution of latitude (<math display="inline"><semantics> <mi>ϕ</mi> </semantics></math>): green—stationary orbits; red, <math display="inline"><semantics> <mrow> <mi>r</mi> <mo>=</mo> <mn>0.99</mn> <msub> <mi>r</mi> <mn>0</mn> </msub> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>ϕ</mi> <mo>=</mo> <mo>−</mo> <mn>0.1</mn> </mrow> </semantics></math> deg; blue—<math display="inline"><semantics> <mrow> <mi>r</mi> <mo>=</mo> <mn>0.99</mn> <msub> <mi>r</mi> <mn>0</mn> </msub> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>ϕ</mi> <mo>=</mo> <mn>0.1</mn> </mrow> </semantics></math> deg; cyan—<math display="inline"><semantics> <mrow> <mi>r</mi> <mo>=</mo> <mn>1.01</mn> <msub> <mi>r</mi> <mn>0</mn> </msub> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>ϕ</mi> <mo>=</mo> <mo>−</mo> <mn>0.1</mn> </mrow> </semantics></math> deg; black—<math display="inline"><semantics> <mrow> <mi>r</mi> <mo>=</mo> <mn>1.01</mn> <msub> <mi>r</mi> <mn>0</mn> </msub> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>ϕ</mi> <mo>=</mo> <mn>0.1</mn> </mrow> </semantics></math> deg, and <math display="inline"><semantics> <mi>T</mi> </semantics></math> is a Jovian day.</p>
Full article ">Figure 3 Cont.
<p>(<b>a</b>,<b>b</b>) Variations of orbital radius (<math display="inline"><semantics> <mi>r</mi> </semantics></math>), (<b>c</b>,<b>d</b>) drift of longitude (<math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">Δ</mi> <mi>λ</mi> </mrow> </semantics></math>), (<b>e</b>,<b>f</b>) evolution of latitude (<math display="inline"><semantics> <mi>ϕ</mi> </semantics></math>): green—stationary orbits; red, <math display="inline"><semantics> <mrow> <mi>r</mi> <mo>=</mo> <mn>0.99</mn> <msub> <mi>r</mi> <mn>0</mn> </msub> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>ϕ</mi> <mo>=</mo> <mo>−</mo> <mn>0.1</mn> </mrow> </semantics></math> deg; blue—<math display="inline"><semantics> <mrow> <mi>r</mi> <mo>=</mo> <mn>0.99</mn> <msub> <mi>r</mi> <mn>0</mn> </msub> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>ϕ</mi> <mo>=</mo> <mn>0.1</mn> </mrow> </semantics></math> deg; cyan—<math display="inline"><semantics> <mrow> <mi>r</mi> <mo>=</mo> <mn>1.01</mn> <msub> <mi>r</mi> <mn>0</mn> </msub> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>ϕ</mi> <mo>=</mo> <mo>−</mo> <mn>0.1</mn> </mrow> </semantics></math> deg; black—<math display="inline"><semantics> <mrow> <mi>r</mi> <mo>=</mo> <mn>1.01</mn> <msub> <mi>r</mi> <mn>0</mn> </msub> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>ϕ</mi> <mo>=</mo> <mn>0.1</mn> </mrow> </semantics></math> deg, and <math display="inline"><semantics> <mi>T</mi> </semantics></math> is a Jovian day.</p>
Full article ">Figure 4
<p>(<b>a</b>) Variation of the discriminant with respect to <math display="inline"><semantics> <mi>a</mi> </semantics></math> and <math display="inline"><semantics> <mi>e</mi> </semantics></math>; (<b>b</b>) variations of the inclination <math display="inline"><semantics> <mi>i</mi> </semantics></math> as the semi-major axis a increases from <math display="inline"><semantics> <mi>R</mi> </semantics></math> to <math display="inline"><semantics> <mrow> <mn>2</mn> <mi>R</mi> </mrow> </semantics></math>.</p>
Full article ">Figure 5
<p>Evolution of <math display="inline"><semantics> <mi mathvariant="sans-serif">Ω</mi> </semantics></math> and difference between <math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">Δ</mi> <mi mathvariant="sans-serif">Ω</mi> <mo>=</mo> <mi mathvariant="sans-serif">Ω</mi> <mo>−</mo> <msub> <mi mathvariant="sans-serif">Ω</mi> <mn>0</mn> </msub> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mi>n</mi> <mi>s</mi> </msub> <mi>t</mi> </mrow> </semantics></math> over <math display="inline"><semantics> <mrow> <mn>25</mn> <mi>T</mi> </mrow> </semantics></math>, where <math display="inline"><semantics> <mi>T</mi> </semantics></math> is a Jovian day, <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="sans-serif">Ω</mi> <mn>0</mn> </msub> <mo>=</mo> <mn>60</mn> </mrow> </semantics></math> deg is the initial condition (the blue curve was indeed obtained by numerical integration of osculating elements). (<b>a</b>) Evolution of <math display="inline"><semantics> <mi mathvariant="sans-serif">Ω</mi> </semantics></math> for <math display="inline"><semantics> <mrow> <mi>a</mi> <mo>=</mo> <mn>102</mn> <mtext>,</mtext> <mn>755.451</mn> </mrow> </semantics></math> km, <math display="inline"><semantics> <mrow> <mi>i</mi> <mo>=</mo> <mn>90.183</mn> </mrow> </semantics></math> deg. (<b>b</b>) Difference between <math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">Δ</mi> <mi mathvariant="sans-serif">Ω</mi> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mi>n</mi> <mi>s</mi> </msub> <mi>t</mi> </mrow> </semantics></math> for <math display="inline"><semantics> <mrow> <mi>a</mi> <mo>=</mo> <mn>102</mn> <mtext>,</mtext> <mn>755.451</mn> </mrow> </semantics></math> km, <math display="inline"><semantics> <mrow> <mi>e</mi> <mo>=</mo> <mn>0.4</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>i</mi> <mo>=</mo> <mn>90.183</mn> </mrow> </semantics></math> deg. (<b>c</b>) Evolution of for <math display="inline"><semantics> <mrow> <mi>a</mi> <mo>=</mo> <mn>109</mn> <mtext>,</mtext> <mn>439.953</mn> </mrow> </semantics></math> km, <math display="inline"><semantics> <mrow> <mi>e</mi> <mo>=</mo> <mn>0.1</mn> <mo>,</mo> </mrow> </semantics></math> <math display="inline"><semantics> <mrow> <mi>i</mi> <mo>=</mo> <mn>90.321</mn> </mrow> </semantics></math> deg. (<b>d</b>) Difference between <math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">Δ</mi> <mi mathvariant="sans-serif">Ω</mi> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mi>n</mi> <mi>s</mi> </msub> <mi>t</mi> </mrow> </semantics></math> for <math display="inline"><semantics> <mrow> <mi>a</mi> <mo>=</mo> <mn>109</mn> <mtext>,</mtext> <mn>439.953</mn> </mrow> </semantics></math> km, <math display="inline"><semantics> <mrow> <mi>e</mi> <mo>=</mo> <mn>0.1</mn> <mo>,</mo> </mrow> </semantics></math> <math display="inline"><semantics> <mrow> <mi>i</mi> <mo>=</mo> <mn>90.321</mn> </mrow> </semantics></math> deg.</p>
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<p>Evolution of <math display="inline"><semantics> <mi mathvariant="sans-serif">Ω</mi> </semantics></math> and difference between <math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">Δ</mi> <mi mathvariant="sans-serif">Ω</mi> <mo>=</mo> <mi mathvariant="sans-serif">Ω</mi> <mo>−</mo> <msub> <mi mathvariant="sans-serif">Ω</mi> <mn>0</mn> </msub> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mi>n</mi> <mi>s</mi> </msub> <mi>t</mi> </mrow> </semantics></math> over <math display="inline"><semantics> <mrow> <mn>25</mn> <mi>T</mi> </mrow> </semantics></math>, where <math display="inline"><semantics> <mi>T</mi> </semantics></math> is a Jovian day, <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="sans-serif">Ω</mi> <mn>0</mn> </msub> <mo>=</mo> <mn>60</mn> </mrow> </semantics></math> deg is the initial condition (the blue curve was indeed obtained by numerical integration of osculating elements). (<b>a</b>) Evolution of <math display="inline"><semantics> <mi mathvariant="sans-serif">Ω</mi> </semantics></math> for <math display="inline"><semantics> <mrow> <mi>a</mi> <mo>=</mo> <mn>102</mn> <mtext>,</mtext> <mn>755.451</mn> </mrow> </semantics></math> km, <math display="inline"><semantics> <mrow> <mi>i</mi> <mo>=</mo> <mn>90.183</mn> </mrow> </semantics></math> deg. (<b>b</b>) Difference between <math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">Δ</mi> <mi mathvariant="sans-serif">Ω</mi> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mi>n</mi> <mi>s</mi> </msub> <mi>t</mi> </mrow> </semantics></math> for <math display="inline"><semantics> <mrow> <mi>a</mi> <mo>=</mo> <mn>102</mn> <mtext>,</mtext> <mn>755.451</mn> </mrow> </semantics></math> km, <math display="inline"><semantics> <mrow> <mi>e</mi> <mo>=</mo> <mn>0.4</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>i</mi> <mo>=</mo> <mn>90.183</mn> </mrow> </semantics></math> deg. (<b>c</b>) Evolution of for <math display="inline"><semantics> <mrow> <mi>a</mi> <mo>=</mo> <mn>109</mn> <mtext>,</mtext> <mn>439.953</mn> </mrow> </semantics></math> km, <math display="inline"><semantics> <mrow> <mi>e</mi> <mo>=</mo> <mn>0.1</mn> <mo>,</mo> </mrow> </semantics></math> <math display="inline"><semantics> <mrow> <mi>i</mi> <mo>=</mo> <mn>90.321</mn> </mrow> </semantics></math> deg. (<b>d</b>) Difference between <math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">Δ</mi> <mi mathvariant="sans-serif">Ω</mi> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mi>n</mi> <mi>s</mi> </msub> <mi>t</mi> </mrow> </semantics></math> for <math display="inline"><semantics> <mrow> <mi>a</mi> <mo>=</mo> <mn>109</mn> <mtext>,</mtext> <mn>439.953</mn> </mrow> </semantics></math> km, <math display="inline"><semantics> <mrow> <mi>e</mi> <mo>=</mo> <mn>0.1</mn> <mo>,</mo> </mrow> </semantics></math> <math display="inline"><semantics> <mrow> <mi>i</mi> <mo>=</mo> <mn>90.321</mn> </mrow> </semantics></math> deg.</p>
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<p>(<b>a</b>) Variation of <math display="inline"><semantics> <mrow> <mi>h</mi> <mo stretchy="false">(</mo> <mn>1</mn> <mo stretchy="false">)</mo> </mrow> </semantics></math> as a function of the semi-major axis <math display="inline"><semantics> <mi>a</mi> </semantics></math> and the eccentricity <math display="inline"><semantics> <mi mathvariant="normal">e</mi> </semantics></math>. (<b>b</b>) Variation of the critical inclination <math display="inline"><semantics> <mi mathvariant="normal">i</mi> </semantics></math> as a function of the semi-major axis for different values of the eccentricity.</p>
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<p>Evolution of <math display="inline"><semantics> <mi>ω</mi> </semantics></math> and <math display="inline"><semantics> <mi>e</mi> </semantics></math> over <math display="inline"><semantics> <mrow> <mn>25</mn> <mi>T</mi> </mrow> </semantics></math>, (<b>a1</b>,<b>a2</b>) <math display="inline"><semantics> <mrow> <mi>a</mi> <mo>=</mo> <mn>1.6832</mn> <mi>R</mi> <mo>=</mo> <mn>120</mn> <mtext>,</mtext> <mn>335.334</mn> </mrow> </semantics></math> km, <math display="inline"><semantics> <mrow> <mi>i</mi> <mo>=</mo> <mn>63.497</mn> </mrow> </semantics></math> deg; (<b>b1</b>,<b>b2</b>) <math display="inline"><semantics> <mrow> <mi>a</mi> <mo>=</mo> <mn>2.1488</mn> <mi>R</mi> <mo>=</mo> <mn>153</mn> <mtext>,</mtext> <mn>622.009</mn> </mrow> </semantics></math> km, <math display="inline"><semantics> <mrow> <mi>i</mi> <mo>=</mo> <mn>63.431</mn> </mrow> </semantics></math> deg, where <math display="inline"><semantics> <mi>T</mi> </semantics></math> is a Jovian day.</p>
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<p><math display="inline"><semantics> <mrow> <mi>e</mi> <mo>~</mo> <mi>ω</mi> </mrow> </semantics></math> evolution over <math display="inline"><semantics> <mrow> <mn>25</mn> <mi>T</mi> </mrow> </semantics></math>, red points correspond to <math display="inline"><semantics> <mrow> <mi>a</mi> <mo>=</mo> <mn>1.6832</mn> <mi>R</mi> <mo>=</mo> <mn>120</mn> <mtext>,</mtext> <mn>335.334</mn> </mrow> </semantics></math> km and <math display="inline"><semantics> <mrow> <mi>i</mi> <mo>=</mo> <mn>63.497</mn> </mrow> </semantics></math> deg; green points correspond to <math display="inline"><semantics> <mrow> <mi>a</mi> <mo>=</mo> <mn>2.1488</mn> <mi>R</mi> <mo>=</mo> <mn>153</mn> <mtext>,</mtext> <mn>622.009</mn> </mrow> </semantics></math> km and <math display="inline"><semantics> <mrow> <mi>i</mi> <mo>=</mo> <mn>63.431</mn> </mrow> </semantics></math> deg.</p>
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<p>Inclinations of sun-synchronous repeating ground track orbits as a function of the semi-major axis, for different values of the repetition parameter <span class="html-italic">Q</span>.</p>
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9 pages, 658 KiB  
Article
The Lunar Radiation Environment: Comparisons between PHITS, HETC-HEDS, and the CRaTER Instrument
by Fahad A. Zaman, Lawrence W. Townsend, Wouter C. de Wet and Naser T. Burahmah
Aerospace 2021, 8(7), 182; https://doi.org/10.3390/aerospace8070182 - 8 Jul 2021
Cited by 2 | Viewed by 2419
Abstract
Understanding the radiation environment near the lunar surface is a key step towards planning for future missions to the Moon. However, the complex variety of energies and particle types constituting the space radiation environment makes the process of replicating such environment very difficult [...] Read more.
Understanding the radiation environment near the lunar surface is a key step towards planning for future missions to the Moon. However, the complex variety of energies and particle types constituting the space radiation environment makes the process of replicating such environment very difficult in Earth-based laboratories. Radiation transport codes provide a practical alternative covering a wider range of particle energy, angle, and type than can be experimentally attainable. Comparing actual measurements with simulation results help in validating particle flux input models, and input collision models and databases involving nuclear and electromagnetic interactions. Thus, in this work, we compare the LET spectra simulated using the Monte Carlo transport code PHITS with measurements made by the CRaTER instrument that is currently orbiting the Moon studying its radiation environment. In addition, we utilize a feature in PHITS that allows the user to run the simulations without Vavilov energy straggling to test whether it is the root cause of erroneous phenomena exhibited in similar studies in literature. The results herein show good agreement between the LET spectra of PHITS and the CRaTER instrument. They also confirm that using a Vavilov distribution correction would ultimately provide a better agreement between CRaTER measurements and the previous LET spectra from the transport codes HETC-HEDS and HZETRN. Full article
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<p>The relative abundance of each incident GCR ion simulated in this work obtained from [<a href="#B17-aerospace-08-00182" class="html-bibr">17</a>] for solar modulation of 400 MV.</p>
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<p>The energy spectra of the source term used in this work obtained from [<a href="#B17-aerospace-08-00182" class="html-bibr">17</a>] for solar modulation of 400 MV. All ions were run collectively in PHITS, while each ion was run separately in HETC-HEDS. The division of GCR ions into three categories in this plot is for demonstration purposes.</p>
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<p>A comparison between the LET spectra in D6 obtained from CRaTER measurements and PHITS simulations (with Vavilov energy straggling) during a period of solar minimum.</p>
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<p>A comparison between the LET spectra in D6 obtained from HETC-HEDS and PHITS with and without Vavilov energy straggling.</p>
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14 pages, 372 KiB  
Article
Optimal Design of Electrically Fed Hybrid Mars Ascent Vehicle
by Lorenzo Casalino, Filippo Masseni and Dario Pastrone
Aerospace 2021, 8(7), 181; https://doi.org/10.3390/aerospace8070181 - 6 Jul 2021
Cited by 15 | Viewed by 2856
Abstract
The optimal design of the propulsion system for a potential Mars Ascent Vehicle is analyzed, in the context of the Mars Sample Return Mission. The Mars Ascent Vehicle has to perform an initial ascent phase from the surface and then circularize into a [...] Read more.
The optimal design of the propulsion system for a potential Mars Ascent Vehicle is analyzed, in the context of the Mars Sample Return Mission. The Mars Ascent Vehicle has to perform an initial ascent phase from the surface and then circularize into a 170 km orbit. A two-stage launcher is taken into account: the same hybrid rocket engine is considered for both stages in order to limit the development costs. A cluster of two, three or four engines is employed in the first stage, whereas a single engine is always used in the second stage. Concerning the feeding system, three alternatives are taken into consideration, namely a blow down, a regulated and an electric turbo-pump feed system. The latter employs an electric motor to drive the oxidizer turbopump, whereas the power is supplied to the motor by lithium batteries. All the design options resulted in viable Mars Ascent Vehicle configurations (payloads are in the range of 70–100 kg), making the hybrid alternative worth considering for the sample return mission. The use of an electric turbo-pump feed system determines the highest vehicle performance with an estimated 10–25% payload gain with respect to gas-pressure feed systems. Full article
(This article belongs to the Special Issue Hybrid Rocket(Volume II))
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<p>Drag coefficient.</p>
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<p>Specific impulse <math display="inline"><semantics> <msub> <mi>I</mi> <mrow> <mi>S</mi> <mi>P</mi> </mrow> </msub> </semantics></math> and mixture ratio <math display="inline"><semantics> <mi>α</mi> </semantics></math> histories for the best solution of each feed system. The BD solution was obtained with <math display="inline"><semantics> <mrow> <msub> <mi>N</mi> <mn>1</mn> </msub> <mo>=</mo> <mn>2</mn> </mrow> </semantics></math> and R and TP solutions with <math display="inline"><semantics> <mrow> <msub> <mi>N</mi> <mn>1</mn> </msub> <mo>=</mo> <mn>3</mn> </mrow> </semantics></math>.</p>
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<p>Thrust <span class="html-italic">F</span> and longitudinal acceleration <math display="inline"><semantics> <mrow> <mi>F</mi> <mo>/</mo> <mi>m</mi> </mrow> </semantics></math> histories for the best solution of each feed system. The BD solution was obtained with <math display="inline"><semantics> <mrow> <msub> <mi>N</mi> <mn>1</mn> </msub> <mo>=</mo> <mn>2</mn> </mrow> </semantics></math> and R and TP solutions with <math display="inline"><semantics> <mrow> <msub> <mi>N</mi> <mn>1</mn> </msub> <mo>=</mo> <mn>3</mn> </mrow> </semantics></math>.</p>
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<p>Trajectory for the best solution of each feed system. The BD solution was been obtained with <math display="inline"><semantics> <mrow> <msub> <mi>N</mi> <mn>1</mn> </msub> <mo>=</mo> <mn>2</mn> </mrow> </semantics></math> and R and TP solutions with <math display="inline"><semantics> <mrow> <msub> <mi>N</mi> <mn>1</mn> </msub> <mo>=</mo> <mn>3</mn> </mrow> </semantics></math>. The dots and crosses represent the stages ignition and burnout, respectively.</p>
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18 pages, 11008 KiB  
Article
FMI-Based Multi-Domain Simulation for an Aero-Engine Control System
by Juan Fang, Maochun Luo, Jiqiang Wang and Zhongzhi Hu
Aerospace 2021, 8(7), 180; https://doi.org/10.3390/aerospace8070180 - 2 Jul 2021
Cited by 5 | Viewed by 4439
Abstract
The simulation of an aero-engine control system involves numerous disciplines due to its complex functions and architecture, which generally consist of mechanical, hydraulic and electrical, and electronic systems. For each discipline, the modeling and simulation are usually dependent on different commercial software and [...] Read more.
The simulation of an aero-engine control system involves numerous disciplines due to its complex functions and architecture, which generally consist of mechanical, hydraulic and electrical, and electronic systems. For each discipline, the modeling and simulation are usually dependent on different commercial software and tools, which makes the simulation, integration, and verification of system-level models very difficult. To meet this challenge, a multi-domain co-simulation method based on the Functional Mock-up Interface (FMI) standard is proposed to integrate models developed by different software or tools. The simulation and testing results demonstrate that multi-disciplinary model integration and cross-platform simulation based on the FMI standard can be realized for an aero-engine control system, which lays a foundation for high-fidelity control system design, simulation, integration, and testing. Full article
(This article belongs to the Special Issue Progress in Jet Engine Technology II)
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<p>Control system diagram for a typical turbofan engine.</p>
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<p>Closed-loop speed control diagram.</p>
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<p>Magnetoelectric speed sensor diagram.</p>
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<p>Function diagram for the rotor speed sensor model.</p>
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<p>Rotor speed sensor model in Simulink.</p>
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<p>Rotor speed sensor conditioning circuit.</p>
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<p>Rotor speed sensor conditioning circuit model in Modelica.</p>
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<p>Aero-engine controller diagram.</p>
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<p>Speed closed-loop control model in Simulink.</p>
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<p>Fuel metering diagram.</p>
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<p>Fuel metering model diagram.</p>
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<p>Fuel Metering Model in AMESim.</p>
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<p>JT9D engine diagram.</p>
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<p>JT9D engine model diagram.</p>
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<p>(<b>a</b>) JT9D engine model using C/Simulink. (<b>b</b>) JT9D compressor model using C/Simulink.</p>
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<p>FMI CS mode simulation mechanism.</p>
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<p>FMU structure and variables [<a href="#B28-aerospace-08-00180" class="html-bibr">28</a>].</p>
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<p>The connections between different tools.</p>
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<p>Full-Digital co-simulation (Simulink Master).</p>
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<p>Full-digital simulation of rotor speed control.</p>
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<p>Relative error between the actual rotor speed and measured rotor speed.</p>
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<p>Full-digital simulation LVDT displacement closed loop control.</p>
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<p>HIL platform.</p>
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<p>Model and control logic design, analysis, integration and testing process.</p>
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<p>Schematic Diagram of FMU Integrated on HIL.</p>
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<p>FMU simulation process.</p>
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<p>Rotor speed control comparison between HIL and digital simulation.</p>
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<p>Relative rotor speed error between HIL and the digital simulation.</p>
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18 pages, 24794 KiB  
Article
The Effect of Real-World Interference on CNN Feature Extraction and Machine Learning Classification of Unmanned Aerial Systems
by Carolyn J. Swinney and John C. Woods
Aerospace 2021, 8(7), 179; https://doi.org/10.3390/aerospace8070179 - 1 Jul 2021
Cited by 14 | Viewed by 4191
Abstract
Small unmanned aerial systems (UASs) present many potential solutions and enhancements to industry today but equally pose a significant security challenge. We only need to look at the levels of disruption caused by UASs at airports in recent years. The accuracy of UAS [...] Read more.
Small unmanned aerial systems (UASs) present many potential solutions and enhancements to industry today but equally pose a significant security challenge. We only need to look at the levels of disruption caused by UASs at airports in recent years. The accuracy of UAS detection and classification systems based on radio frequency (RF) signals can be hindered by other interfering signals present in the same frequency band, such as Bluetooth and Wi-Fi devices. In this paper, we evaluate the effect of real-world interference from Bluetooth and Wi-Fi signals concurrently on convolutional neural network (CNN) feature extraction and machine learning classification of UASs. We assess multiple UASs that operate using different transmission systems: Wi-Fi, Lightbridge 2.0, OcuSync 1.0, OcuSync 2.0 and the recently released OcuSync 3.0. We consider 7 popular UASs, evaluating 2 class UAS detection, 8 class UAS type classification and 21 class UAS flight mode classification. Our results show that the process of CNN feature extraction using transfer learning and machine learning classification is fairly robust in the presence of real-world interference. We also show that UASs that are operating using the same transmission system can be distinguished. In the presence of interference from both Bluetooth and Wi-Fi signals, our results show 100% accuracy for UAV detection (2 classes), 98.1% (+/−0.4%) for UAV type classification (8 classes) and 95.4% (+/−0.3%) for UAV flight mode classification (21 classes). Full article
(This article belongs to the Special Issue AI/Machine Learning in Aerospace Autonomy)
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<p>Experimental Setup.</p>
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<p>Spectrogram: No UAS present.</p>
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<p>PSD: No UAS present.</p>
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<p>Spectrogram: Phantom 4 switched on.</p>
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<p>PSD: Phantom 4 switched on.</p>
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<p>Spectrogram: Phantom 4 hovering.</p>
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<p>PSD: Phantom 4 hovering.</p>
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<p>Spectrogram: Phantom 4 flying.</p>
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<p>PSD: Phantom 4 flying.</p>
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<p>Spectrogram: Disco switched on.</p>
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<p>PSD: Disco switched on.</p>
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<p>Spectrogram: Disco flying.</p>
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<p>PSD: Disco flying.</p>
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<p>UAS type classification confusion matrix.</p>
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<p>UAS flight mode classification confusion matrix.</p>
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20 pages, 12789 KiB  
Article
Development of Detailed FE Numerical Models for Assessing the Replacement of Metal with Composite Materials Applied to an Executive Aircraft Wing
by Valerio Acanfora, Roberto Petillo, Salvatore Incognito, Gerardo Mario Mirra and Aniello Riccio
Aerospace 2021, 8(7), 178; https://doi.org/10.3390/aerospace8070178 - 1 Jul 2021
Cited by 9 | Viewed by 3501
Abstract
This work provides a feasibility and effectiveness analysis, through numerical investigation, of metal replacement of primary components with composite material for an executive aircraft wing. In particular, benefits and disadvantages of replacing metal, usually adopted to manufacture this structural component, with composite material [...] Read more.
This work provides a feasibility and effectiveness analysis, through numerical investigation, of metal replacement of primary components with composite material for an executive aircraft wing. In particular, benefits and disadvantages of replacing metal, usually adopted to manufacture this structural component, with composite material are explored. To accomplish this task, a detailed FEM numerical model of the composite aircraft wing was deployed by taking into account process constraints related to Liquid Resin Infusion, which was selected as the preferred manufacturing technique to fabricate the wing. We obtained a geometric and material layup definition for the CFRP components of the wing, which demonstrated that the replacement of the metal elements with composite materials did not affect the structural performance and can guarantee a substantial advantage for the structure in terms of weight reduction when compared to the equivalent metallic configuration, even for existing executive wing configurations. Full article
(This article belongs to the Special Issue Aircraft Modeling for Design, Simulation and Control)
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<p>Single-lap and double-lap joints shear stresses.</p>
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<p>Wing without upper skin-rib BL0 and rib ST225 positioning in the wing.</p>
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<p>Joints details: (<b>a</b>) Rib BL0 zone; (<b>b</b>) Rib ST 225 connections.</p>
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<p>Example of fastener application via CBUSH elements for the Rib BL0 connection.</p>
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<p>MPCs used for load applications (transfer of loads from one master node to a set of predefined nodes).</p>
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<p>Spar (red)–upper skin (blue) MPCs connection.</p>
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<p>Tensile (<b>a</b>), Shear (<b>b</b>), and Bearing (<b>c</b>) failure.</p>
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<p>Bearing Distribution Factor. (<b>a</b>) Analytical evolution; (<b>b</b>) geometrical parameters.</p>
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<p>Half-Wing: (<b>a</b>) whole wing CAD model; (<b>b</b>) composite (red) and metal (blue) components.</p>
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<p>Geometrical information. (<b>a</b>) Half-wing dimensions and stacking sequence view of the upper skin highlighted by different colours; (<b>b</b>) stacking sequence details of the composite upper half wing; (<b>c</b>) thickness trend of the aluminium upper half wing.</p>
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<p>Geometrical information. (<b>a</b>) Stacking sequence view of the lower skin highlighted by different colours; (<b>b</b>) stacking sequence details of the composite lower half wing; (<b>c</b>) thickness trend of the aluminium lower half wing.</p>
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<p>(<b>a</b>) Loading conditions; (<b>b</b>) detailed View.</p>
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<p>Boundary Conditions (wing–fuselage connection by MPCs).</p>
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<p>Maximum tensile strain. (<b>a</b>) Laminae direction 0°; (<b>b</b>) laminae direction 90°; (<b>c</b>) laminae direction ±45°.</p>
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<p>Maximum tensile strain. (<b>a</b>) Laminae direction 0°; (<b>b</b>) laminae direction 90°; (<b>c</b>) laminae direction ±45°.</p>
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<p>Maximum compressive strain. (<b>a</b>) Laminae direction 0°; (<b>b</b>) laminae direction 90°; (<b>c</b>) laminae direction ±45°.</p>
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<p>Maximum compressive strain. (<b>a</b>) Laminae direction 0°; (<b>b</b>) laminae direction 90°; (<b>c</b>) laminae direction ±45°.</p>
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<p>Maximum stress—Von Mises.</p>
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<p>Buckling analysis output. (<b>a</b>) Bending up; (<b>b</b>) bending down; (<b>c</b>) flap only; (<b>d</b>) take off; (<b>e</b>) landing.</p>
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<p>Buckling analysis output. (<b>a</b>) Bending up; (<b>b</b>) bending down; (<b>c</b>) flap only; (<b>d</b>) take off; (<b>e</b>) landing.</p>
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19 pages, 8370 KiB  
Article
Investigation of Reynolds Number Effects on Aerodynamic Characteristics of a Transport Aircraft
by Yuanjing Wang, Dawei Liu, Xin Xu and Guoshuai Li
Aerospace 2021, 8(7), 177; https://doi.org/10.3390/aerospace8070177 - 1 Jul 2021
Cited by 8 | Viewed by 5029
Abstract
The scale difference between the real flight vehicle and the experimental model results in the Reynolds number effect, which makes it unreliable to predict the aerodynamic characteristics of flight vehicles by wind tunnel testing. To understand the mechanism of Reynolds number effects on [...] Read more.
The scale difference between the real flight vehicle and the experimental model results in the Reynolds number effect, which makes it unreliable to predict the aerodynamic characteristics of flight vehicles by wind tunnel testing. To understand the mechanism of Reynolds number effects on the aerodynamic characteristics of the supercritical wing that is commonly used in transport aircraft in more detail, surface pressure wind tunnel tests of a transport aircraft reference model with a wing-body configuration were conducted in the European Transonic Windtunnel (ETW) at different Reynolds numbers. There are 495 pressure taps in total equipped on the surface of the test model with the Mach numbers ranging from 0.6 to 0.86 and Reynolds number varying from 3.3 × 106 to 35 × 106. In addition, an in-house developed CFD tool that has been validated by extensive experimental data was used to correct the wing deformation effect of the test model and achieve detailed flow structures. The results show that the Reynolds number has a significant impact on the boundary layer displacement thickness, surface pressure distribution, shock wave position, and overall aerodynamic force coefficients of the transport aircraft in the presence of shock wave and the induced boundary layer separation. The wind tunnel data combined with flow fields achieved from CFD show that the essence of the Reynolds number effect on the aerodynamic characteristics of transport aircraft is the difference of boundary layer development, shock wave/boundary layer interaction, and induced flow separation at different Reynolds numbers. Full article
(This article belongs to the Special Issue Advances in Aerospace Sciences and Technology II)
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<p>Aerodynamic circuit sketch of the ETW facility.</p>
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<p>Model configuration and distribution of orifice taps.</p>
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<p>SPT markers on the supercritical wing.</p>
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<p>Longitudinal aerodynamic characteristics under different dynamic pressures (M = 0.76, Re = 15 × 10<sup>6</sup>): (<b>a</b>) lift coefficient; (<b>b</b>) pitching moment coefficient.</p>
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<p>Increments of the lift and pitching moment coefficients caused by dynamic pressure variation (M = 0.76, Re = 15 × 10<sup>6</sup>): (<b>a</b>) increments of the lift coefficient; (<b>b</b>) increments of the pitching moment coefficient.</p>
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<p>Pressure distributions under different dynamic pressures (α = 0°, M = 0.76, Re = 15 × 10<sup>6</sup>): (<b>a</b>) η = 54%; (<b>b</b>) η = 96.61%.</p>
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<p>Pressure distributions under different dynamic pressures (α = 2°, M = 0.76, Re = 15 × 10<sup>6</sup>): (<b>a</b>) η = 54%; (<b>b</b>) η = 96.61%.</p>
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<p>Numerical flow patterns near the wing tip with and without deformation (M = 0.76, Re = 15 × 10<sup>6</sup>, Q = 94 kPa, α = 4°): (<b>a</b>) rigid model; (<b>b</b>) deformed model.</p>
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<p>Comparison of surface pressure distributions from CFD and ETW wind tunnel tests (M = 0.76, Re = 15 × 10<sup>6</sup>, α = 2°): (<b>a</b>) η = 72.3%; (<b>b</b>) η = 86.1%.</p>
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<p>Pressure distributions with and without transition band under different Reynolds numbers (M = 0.76, α = 4°, η = 72.36%): (<b>a</b>) Re = 3.3 × 10<sup>6</sup>; (<b>b</b>) Re = 6.6 × 10<sup>6</sup>; (<b>c</b>) Re = 15 × 10<sup>6</sup>.</p>
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<p>Pressure distributions under different Reynolds numbers (M = 0.76, α = 0°): (<b>a</b>) η = 54%; (<b>b</b>) η = 72.36%.</p>
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<p>Pressure distributions at different angles of attack and Reynolds numbers (M = 0.76, η = 54%): (<b>a</b>) α = 2°; (<b>b</b>) α = 4°.</p>
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<p>Numerical flow structures at different Reynolds numbers (M = 0.76, α = 4°): (<b>a</b>) Re = 3.3 × 10<sup>6</sup>; (<b>b</b>) Re = 35 × 10<sup>6</sup>.</p>
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<p>Numerical flow structures at different Reynolds numbers (M = 0.76, α = 6°): (<b>a</b>) Re = 3.3 × 10<sup>6</sup>; (<b>b</b>) Re = 35 × 10<sup>6</sup>.</p>
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<p>Reynolds number effect on shock wave position: (<b>a</b>) M = 0.74, α = 3°, η = 43.72%; (<b>b</b>) M = 0.74, α = 4°, η = 54%; (<b>c</b>) M = 0.76, α = 3°, η = 72.36%.</p>
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<p>Reynolds number effect on trailing edge pressure recovery (M = 0.74): (<b>a</b>) α = 3°, η = 43.72%; (<b>b</b>) α = 4°, η = 54%.</p>
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<p>Numerical boundary layer displacement thicknesses of the upper wing surface at different Reynolds numbers (M = 0.76, η = 0.35): (<b>a</b>) α = 0°; (<b>b</b>) α = 4°.</p>
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<p>Typical lift coefficient curves at different Reynolds numbers: (<b>a</b>) M = 0.76; (<b>b</b>) M = 0.79.</p>
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<p>Increments of lift coefficient versus Reynolds number (M = 0.76): (<b>a</b>) α = 2°; (<b>b</b>) α = 4°.</p>
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<p>Pitch moment coefficient and polar curves at different Reynolds number: (<b>a</b>) polar curves; (<b>b</b>) pitch moment coefficient.</p>
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<p>Numerical flow structures at different Reynolds numbers (M = 0.76, η = 63%, α = 6°): (<b>a</b>) Re = 3.3 × 10<sup>6</sup>; (<b>b</b>) Re = 35 × 10<sup>6</sup>.</p>
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20 pages, 993 KiB  
Article
\({\mathcal{L}_1}\) Adaptive Loss Fault Tolerance Control of Unmanned Hypersonic Aircraft with Elasticity
by Zhaoying Li and Shuai Shi
Aerospace 2021, 8(7), 176; https://doi.org/10.3390/aerospace8070176 - 29 Jun 2021
Cited by 5 | Viewed by 2241
Abstract
This paper investigates the fault tolerance control of hypersonic aircrafts with L1 adaptive control method in the presence of loss of actuator effectiveness fault. The hypersonic model considers the uncertainties caused by the features of nonlinearities and couplings. Elasticity is taken into [...] Read more.
This paper investigates the fault tolerance control of hypersonic aircrafts with L1 adaptive control method in the presence of loss of actuator effectiveness fault. The hypersonic model considers the uncertainties caused by the features of nonlinearities and couplings. Elasticity is taken into account in hypersonic vehicle modeling which makes the model more accurate. A velocity L1 adaptive controller and an altitude L1 adaptive controller are designed to control flexible hypersonic vehicle model with actuator loss fault. A PID controller is designed as well for comparison. Finally, the simulation results are used to analyze the effectiveness of the controller. Compared to the results of PID controller, L1 controllers have better performance. Full article
(This article belongs to the Special Issue Spacecraft Dynamics and Control)
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<p><math display="inline"><semantics> <msub> <mi mathvariant="script">L</mi> <mn>1</mn> </msub> </semantics></math> adaptive controller.</p>
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<p>Fault tolerance of hypersonic aircraft by <math display="inline"><semantics> <msub> <mi mathvariant="script">L</mi> <mn>1</mn> </msub> </semantics></math> adaptive control.</p>
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<p>Velocity error and altitude error proposed by PID controller in horizontal flight and fault free state.</p>
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<p>Control value proposed by PID controller in horizontal flight and fault free state.</p>
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<p>Comparison of velocity and velocity error, altitude and altitude error proposed by PID and <math display="inline"><semantics> <msub> <mi mathvariant="script">L</mi> <mn>1</mn> </msub> </semantics></math> controller in horizontal flight and fault state.</p>
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<p>Comparison of control value proposed by PID and <math display="inline"><semantics> <msub> <mi mathvariant="script">L</mi> <mn>1</mn> </msub> </semantics></math> controller in horizontal flight and fault state.</p>
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<p>Track angle proposed by PID and <math display="inline"><semantics> <msub> <mi mathvariant="script">L</mi> <mn>1</mn> </msub> </semantics></math> controller in horizontal flight and fault state.</p>
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<p>Velocity error and altitude error proposed by PID controller in pitching maneuver and fault free state.</p>
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<p>Control value proposed by PID controller in pitching maneuver and fault free state.</p>
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<p>Track angle proposed by PID controller in pitching maneuver and fault free state.</p>
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<p>Comparison of velocity and velocity error, altitude and altitude error proposed by PID and <math display="inline"><semantics> <msub> <mi mathvariant="script">L</mi> <mn>1</mn> </msub> </semantics></math> controller in pitching maneuver and fault state.</p>
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<p>Comparison of control value proposed by PID and <math display="inline"><semantics> <msub> <mi mathvariant="script">L</mi> <mn>1</mn> </msub> </semantics></math> controller in pitching maneuver and fault state.</p>
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<p>Comparison of track angle proposed by PID and <math display="inline"><semantics> <msub> <mi mathvariant="script">L</mi> <mn>1</mn> </msub> </semantics></math> controller in pitching maneuver and fault state.</p>
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22 pages, 551 KiB  
Article
Design Process and Environmental Impact of Unconventional Tail Airliners
by Alejandro Sanchez-Carmona and Cristina Cuerno-Rejado
Aerospace 2021, 8(7), 175; https://doi.org/10.3390/aerospace8070175 - 28 Jun 2021
Cited by 4 | Viewed by 3422
Abstract
The future of aviation depends on reducing the environmental impact of the aircraft. Unconventional configurations can be the change the industry needs to achieve that goal. Therefore, the development of a tool that allows analyzing these configurations will contribute to their being considered [...] Read more.
The future of aviation depends on reducing the environmental impact of the aircraft. Unconventional configurations can be the change the industry needs to achieve that goal. Therefore, the development of a tool that allows analyzing these configurations will contribute to their being considered more easily in future designs. This design procedure is based on an aerodynamic model and a weight methodology validated for unconventional tail designs. The load cases selected to size the structure were extracted from the certification regulations in force. In order to validate the methodology, the V-tail configuration was selected as a case study. The fuel savings reached with this tail configurations are around 0.7%, and the reduction in NOx emissions are even greater. Thus, the methodology has been validated and it can be easily adapted to other unconventional tail configurations. Full article
(This article belongs to the Special Issue Smart Wing Aircraft)
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<p>Airplane drag polar of CSR-01 in clean configuration for different Mach numbers (<b>left</b>); and in take-off, climb, approach and final approach with landing gear extended conditions (<b>right</b>).</p>
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<p>Torsion box drawing, where height is <math display="inline"><semantics> <msub> <mi>h</mi> <mi>t</mi> </msub> </semantics></math>, length is <math display="inline"><semantics> <msub> <mi>b</mi> <mi>t</mi> </msub> </semantics></math>, spar web thickness is <math display="inline"><semantics> <msub> <mi>t</mi> <mi>L</mi> </msub> </semantics></math> and pitch between stringers is <span class="html-italic">b</span>.</p>
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<p>Compression load per unit length and shear flux distributions for symmetric loads in horizontal tail surface (<b>left</b>) and for asymmetric loads in vertical tail surface (<b>right</b>).</p>
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<p>Feasible design space defined by the constraints imposed by static derivatives at cruising conditions, both longitudinal (<math display="inline"><semantics> <msub> <mi>c</mi> <mrow> <mi>m</mi> <mi>α</mi> </mrow> </msub> </semantics></math>) and lateral (<math display="inline"><semantics> <msub> <mi>c</mi> <mrow> <mi>n</mi> <mi>β</mi> </mrow> </msub> </semantics></math>), and by the maximum deflection of the lateral control for crosswind landing conditions, establishing a maximum value for the dihedral angle (CwL MAX).</p>
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<p>Weight estimation of V-tail for the nine conditions studied, for variation in span and for the parameters <math display="inline"><semantics> <mo>Γ</mo> </semantics></math> = 30°, <math display="inline"><semantics> <msub> <mi>c</mi> <mi>r</mi> </msub> </semantics></math> = 3.7 m and <math display="inline"><semantics> <mi>λ</mi> </semantics></math> = 0.32.</p>
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<p>Design variables combination resulting of intersecting the constraints associated with the static stability conditions, both longitudinal and lateral.</p>
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<p>Friction drag coefficient as a function of the taper ratio and root chord along the new feasible design space determined intersecting the active constraints.</p>
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<p>Fuel consumption variation with respect to the reference aircraft for the design route as a function of the taper ratio and root chord along the new feasible design space determined by intersecting the active constraints.</p>
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<p>Emissions variation with respect to the reference aircraft for the design route as a function of the taper ratio and root chord along the new feasible design space determined by intersecting the active constraints.</p>
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11 pages, 1849 KiB  
Article
Vibro-Acoustical Sensitivities of Stiffened Aircraft Structures Due to Attached Mass-Spring-Dampers with Uncertain Parameters
by Johannes Seidel, Stephan Lippert and Otto von Estorff
Aerospace 2021, 8(7), 174; https://doi.org/10.3390/aerospace8070174 - 28 Jun 2021
Viewed by 1890
Abstract
The slightest manufacturing tolerances and variances of material properties can indeed have a significant impact on structural modes. An unintentional shift of eigenfrequencies towards dominant excitation frequencies may lead to increased vibration amplitudes of the structure resulting in radiated noise, e.g., reducing passenger [...] Read more.
The slightest manufacturing tolerances and variances of material properties can indeed have a significant impact on structural modes. An unintentional shift of eigenfrequencies towards dominant excitation frequencies may lead to increased vibration amplitudes of the structure resulting in radiated noise, e.g., reducing passenger comfort inside an aircraft’s cabin. This paper focuses on so-called non-structural masses of an aircraft, also known as the secondary structure that are attached to the primary structure via clips, brackets, and shock mounts and constitute a significant part of the overall mass of an aircraft’s structure. Using the example of a simplified fuselage panel, the vibro-acoustical consequences of parameter uncertainties in linking elements are studied. Here, the fuzzy arithmetic provides a suitable framework to describe uncertainties, create combination matrices, and evaluate the simulation results regarding target quantities and the impact of each parameter on the overall system response. To assess the vibrations of the fuzzy structure and by taking into account the excitation spectra of engine noise, modal and frequency response analyses are conducted. Full article
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<p>Fuzzy number representations.</p>
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<p>Decomposition pattern adapted from the general transformation method [<a href="#B11-aerospace-08-00174" class="html-bibr">11</a>].</p>
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<p>Acoustic Flight-LAB Demonstrator and isolated test setup.</p>
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<p>Uncertain parameters.</p>
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<p>Fuzzy modes #1–#64.</p>
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<p>A detailed look at modes #1–#10.</p>
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<p>Modeshapes #13 to #15.</p>
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<p>Fuzzy frequency response in the range from 10 Hz to 500 Hz.</p>
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<p>A detailed look at the fuzzy frequency response in the range from 40 Hz to 120 Hz.</p>
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<p>Modal gain factors for modes #1–#64.</p>
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<p>Frequency response gain factors in the range from 10 Hz to 500 Hz.</p>
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20 pages, 1851 KiB  
Article
Exploring the Habitability of Venus: Conceptual Design of a Small Atmospheric Probe
by Pol Ribes-Pleguezuelo, Bruno Delacourt, Mika K. G. Holmberg, Elisabetta Iorfida, Philipp Reiss, Guillermo Salinas and Agnieszka Suliga
Aerospace 2021, 8(7), 173; https://doi.org/10.3390/aerospace8070173 - 25 Jun 2021
Viewed by 3060
Abstract
The possible presence of life in the atmosphere of Venus has been debated frequently over the last 60 years. The discussion was recently reignited by the possible detection of phosphine (PH3), but several other chemicals potentially relevant for life processes are [...] Read more.
The possible presence of life in the atmosphere of Venus has been debated frequently over the last 60 years. The discussion was recently reignited by the possible detection of phosphine (PH3), but several other chemicals potentially relevant for life processes are also found in the middle atmosphere. Moreover, the reasons for the heterogeneous ultraviolet (UV) absorption between 320 and 400 nm in the altitude range ∼40–70 km are still not well understood. These aspects could be further studied in-situ by UV Raman and fluorescence instruments. Here, the conceptual design of a small balloon probe (<20 kg) is presented, including a science payload comprising a UV laser, spectrometer, and a telescope. The goal of the proposed mission is to analyse the absorption of UV light in Venus’ atmosphere, to study the atmospheric composition, and to verify the possible presence of biomarkers. Current state-of-the-art technologies would allow a more cost-efficient and easy to develop mission, as compared to previous Venus probes. This article is focused on the scientific instrumentation, as well as on the mass and power budgets required to realise the proposed mission. Full article
(This article belongs to the Special Issue Small Satellite Technologies and Mission Concepts)
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<p>Raman shift range versus laser excitation. Raman shift or excitation of bounced light into the target material depends on the excitation source/laser. For a certain wavelength input, a different output shifted wavelength is obtained with different peaks. This shift can be used to identify the analysed molecules. Reprinted from [<a href="#B24-aerospace-08-00173" class="html-bibr">24</a>].</p>
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<p>Raman and fluorescence emission ranges for a 250 nm excitation. Reprinted from [<a href="#B24-aerospace-08-00173" class="html-bibr">24</a>].</p>
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<p>Atmospheric density (red line) and temperature (black line) versus altitude and pressure, based on the VIRA model. Figure adapted from Figure 1 in [<a href="#B33-aerospace-08-00173" class="html-bibr">33</a>].</p>
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<p>Space and ground observation results of zonal wind latitudinal profiles at Venus’ cloud-top (70 km). Reprinted from [<a href="#B36-aerospace-08-00173" class="html-bibr">36</a>].</p>
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<p>Conceptual design of the proposed Venus probe in stowed (left, during cruise and entry) and deployed (right, during operations in the atmosphere) configurations.</p>
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<p>Detailed views of the electronics and instrument compartment of the probe, showing dummy volumes for the subsystems (housing not shown on right image).</p>
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<p>Schematic of the instrument baseline. The laser is obliquely placed near the receiving telescope.</p>
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<p>Transient heating of the subsystems with baseline duty cycle of laser, spectrometer, telescope and camera. The initial temperature equals the steady state temperatures with the aforementioned components switched off.</p>
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18 pages, 74708 KiB  
Article
Effect of the Reynolds Number and Clearance Flow on the Aerodynamic Characteristics of a New Variable Inlet Guide Vane
by Hengtao Shi
Aerospace 2021, 8(7), 172; https://doi.org/10.3390/aerospace8070172 - 25 Jun 2021
Viewed by 2481
Abstract
Recently, a new type of low-loss variable inlet guide vane (VIGV) was proposed for improving a compressor’s performance under off-design conditions. To provide more information for applications, this work investigated the effect of the Reynolds number and clearance flow on the aerodynamic characteristics [...] Read more.
Recently, a new type of low-loss variable inlet guide vane (VIGV) was proposed for improving a compressor’s performance under off-design conditions. To provide more information for applications, this work investigated the effect of the Reynolds number and clearance flow on the aerodynamic characteristics of this new type of VIGV. The performance and flow field of two representative airfoils with different chord Reynolds numbers were studied with the widely used commercial software ANSYS CFX after validation was completed. Calculations indicate that, with the decrease in the Reynolds number Rec, the airfoil loss coefficient ω and deviation δ first increase slightly and then entered a high growth rate in a low range of Rec. Afterwards, a detailed boundary-layer analysis was conducted to reveal the flow mechanism for the airfoil performance degradation with a low Reynolds number. For the design point, it is the appearance and extension of the separation region on the rear portion; for the maximum incidence point, it is the increase in the length and height of the separation region on the former portion. The three-dimensional VIGV research confirms the Reynolds number effect on airfoils. Furthermore, the clearance leakage flow forms a strong stream-wise vortex by injection into the mainflow, resulting in a high total-pressure loss and under-turning in the endwall region, which shows the potential benefits of seal treatment. Full article
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<p>VIGV airfoil definitions (<b>a</b>); Researched VIGV airfoil geometry and surface slope at the design condition (<b>b</b>,<b>c</b>); VIGV airfoil scaling for varying the chord Reynolds number <math display="inline"><semantics> <mrow> <mi>R</mi> <msub> <mi>e</mi> <mi mathvariant="normal">c</mi> </msub> </mrow> </semantics></math> (<b>d</b>).</p>
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<p>Operation schedule for airfoil performance evaluation (<b>a</b>); flow plot at the design and maximum incidence points (<b>b</b>).</p>
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<p>Mesh topology and details at the leading edge and trailing edge.</p>
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<p>Grid independence study: (<b>a</b>,<b>b</b>) for Case 1; (<b>c</b>,<b>d</b>) for Case 2.</p>
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<p>Simulated (Grid 4) and experimental results [<a href="#B14-aerospace-08-00172" class="html-bibr">14</a>] for the effect of chord Reynolds number <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>Re</mi> </mrow> <mi mathvariant="normal">c</mi> </msub> </mrow> </semantics></math> on the symmetric airfoil. (<b>a</b>,<b>b</b>) for the design (<math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">γ</mi> <mo>=</mo> <mn>0</mn> <mo>°</mo> </mrow> </semantics></math>) condition and (<b>c</b>,<b>d</b>) for the high incidence condition (<math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">γ</mi> <mo>=</mo> <mn>20</mn> <mo>°</mo> </mrow> </semantics></math>).</p>
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<p>Influence of chord Reynolds number on the VIGV airfoil loss coefficient (<b>a</b>) and the deviation (<b>b</b>); Case 1 (<math display="inline"><semantics> <mrow> <mi>M</mi> <msub> <mi>a</mi> <mn>1</mn> </msub> <mo>=</mo> <mn>0.45</mn> <mo>)</mo> </mrow> </semantics></math>.</p>
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<p>Influence of chord Reynolds number on the VIGV airfoil loss coefficient (<b>a</b>) and the deviation (<b>b</b>); Case 2 (<math display="inline"><semantics> <mrow> <mi>M</mi> <msub> <mi>a</mi> <mn>1</mn> </msub> <mo>=</mo> <mn>0.60</mn> </mrow> </semantics></math>).</p>
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<p>Suction surface boundary layer at the design point (<math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">Δ</mi> <msub> <mi>β</mi> <mn>2</mn> </msub> <mo>=</mo> <mn>0</mn> <mo>°</mo> </mrow> </semantics></math>): from transition to separation; (<b>a</b>) for Case 1 (<math display="inline"><semantics> <mrow> <mi>M</mi> <msub> <mi>a</mi> <mn>1</mn> </msub> <mo>=</mo> <mn>0.45</mn> </mrow> </semantics></math> ); (<b>b</b>) for Case 2 (<math display="inline"><semantics> <mrow> <mi>M</mi> <msub> <mi>a</mi> <mn>1</mn> </msub> <mo>=</mo> <mn>0.60</mn> </mrow> </semantics></math> ).</p>
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<p>Influence of the Reynolds number <math display="inline"><semantics> <mrow> <mi>R</mi> <msub> <mi>e</mi> <mi mathvariant="normal">c</mi> </msub> </mrow> </semantics></math> on (<b>a</b>) the boundary-layer shape factor <math display="inline"><semantics> <mrow> <msub> <mi>H</mi> <mi mathvariant="normal">b</mi> </msub> </mrow> </semantics></math>, (<b>b</b>) the skin friction coefficient <math display="inline"><semantics> <mrow> <msub> <mi>C</mi> <mi mathvariant="normal">f</mi> </msub> </mrow> </semantics></math>, and (<b>c</b>) the momentum thickness <math display="inline"><semantics> <mrow> <msub> <mi>δ</mi> <mn>2</mn> </msub> <mo>/</mo> <mi>c</mi> </mrow> </semantics></math> for Case 1 (<math display="inline"><semantics> <mrow> <mi>M</mi> <msub> <mi>a</mi> <mn>1</mn> </msub> <mo>=</mo> <mn>0.45</mn> </mrow> </semantics></math>).</p>
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<p>Separation pattern at maximum incidence (<math display="inline"><semantics> <mrow> <msub> <mrow> <mrow> <mi mathvariant="sans-serif">Δ</mi> <mi mathvariant="sans-serif">β</mi> </mrow> </mrow> <mn>2</mn> </msub> <mo>=</mo> <mn>30</mn> <mo>°</mo> </mrow> </semantics></math>): inception and re-attachment point and LE peak Mach number: (<b>a</b>,<b>b</b>) for Case 1 (<math display="inline"><semantics> <mrow> <mi>M</mi> <msub> <mi>a</mi> <mn>1</mn> </msub> <mo>=</mo> <mn>0.45</mn> </mrow> </semantics></math>); (<b>c</b>,<b>d</b>) for Case 2 (<math display="inline"><semantics> <mrow> <mi>M</mi> <msub> <mi>a</mi> <mn>1</mn> </msub> <mo>=</mo> <mn>0.60</mn> </mrow> </semantics></math>).</p>
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<p>Reynolds number effect on (<b>a</b>) the boundary-layer shape factor <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="normal">H</mi> <mi mathvariant="normal">b</mi> </msub> </mrow> </semantics></math>, (<b>b</b>) the skin friction coefficient <math display="inline"><semantics> <mrow> <msub> <mi>C</mi> <mi mathvariant="normal">f</mi> </msub> </mrow> </semantics></math>, and (<b>c</b>) the momentum thickness <math display="inline"><semantics> <mrow> <msub> <mi>δ</mi> <mn>2</mn> </msub> <mo>/</mo> <mi>c</mi> </mrow> </semantics></math> at maximum incidence (<math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">Δ</mi> <msub> <mi>β</mi> <mn>2</mn> </msub> <mo>=</mo> <mn>30</mn> <mo>°</mo> </mrow> </semantics></math> ) for Case 1 (<math display="inline"><semantics> <mrow> <mi>M</mi> <msub> <mi>a</mi> <mn>1</mn> </msub> <mo>=</mo> <mn>0.45</mn> </mrow> </semantics></math> ).</p>
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<p>Front portion Mach number contour for SP1 to SP4 (<b>a</b>), a comparison of boundary-layer velocity profiles where suction surface <math display="inline"><semantics> <mrow> <mi>x</mi> <mo>/</mo> <msub> <mi>C</mi> <mrow> <mi>ax</mi> </mrow> </msub> <mo>=</mo> <mn>10</mn> <mo>%</mo> <mo>,</mo> <mo> </mo> <mn>15</mn> <mo>%</mo> <mo>,</mo> <mn>20</mn> <mo>%</mo> <mo>,</mo> <mrow> <mo> </mo> <mi>and</mi> <mo> </mo> </mrow> <mn>25</mn> <mo>%</mo> </mrow> </semantics></math> (<b>b</b>); Case 1 (<math display="inline"><semantics> <mrow> <mi>M</mi> <msub> <mi>a</mi> <mrow> <mn>1</mn> <mi mathvariant="normal">D</mi> </mrow> </msub> <mo>=</mo> <mn>0.45</mn> </mrow> </semantics></math> ).</p>
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<p>The schematic for a meridian view of the normal-chord version (<b>a</b>) and the small-chord version (<b>b</b>), with tip clearance (<b>c</b>).</p>
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<p>Mesh topology with tip LE and TE details.</p>
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<p>Operation schedule (<b>a</b>) and Reynolds number at mid-span (<b>b</b>), radial distribution of the inlet Mach number <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>Ma</mi> </mrow> <mn>1</mn> </msub> </mrow> </semantics></math> (<b>c</b>), and the outflow angle <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="sans-serif">β</mi> <mn>2</mn> </msub> </mrow> </semantics></math> (<b>d</b>) for normal- and small-chord VIGVs.</p>
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<p>Radial distribution of the pitchwise-averaged total-pressure loss coefficient for normal-chord and small-chord VIGVs (<b>a</b>–<b>e</b>); corresponding blade passage-averaged total pressure drop (<b>f</b>).</p>
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<p>Impact of hub and tip clearance on the endwall region total-pressure drop (<math display="inline"><semantics> <mrow> <mn>1</mn> <mo>−</mo> <mi mathvariant="sans-serif">σ</mi> </mrow> </semantics></math>), the blade and hub static pressure p at the high stagger angle point (P5, <math display="inline"><semantics> <mrow> <mrow> <mi mathvariant="sans-serif">Δ</mi> <mi mathvariant="sans-serif">γ</mi> </mrow> <mo>=</mo> <mn>35</mn> <mo>°</mo> </mrow> </semantics></math>). (<b>a</b>) VIGV with clearance, (<b>b</b>) VIGV without clearance, and (<b>c</b>) loss coefficient decomposition.</p>
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<p>Flow details of the tip region for the VIGVs with clearance (<b>a</b>–<b>c</b>) and without clearance (<b>d</b>); P5 (<math display="inline"><semantics> <mrow> <mrow> <mi mathvariant="sans-serif">Δ</mi> <mi mathvariant="sans-serif">γ</mi> </mrow> <mo>=</mo> <mn>35</mn> <mo>°</mo> </mrow> </semantics></math>).</p>
Full article ">Figure 19
<p>Impact of endwall clearance on the radial distribution of tangential, axial, and radial velocity at the outflow measurement plane (<b>a</b>) and the surface Mach number distribution near the endwall (<b>b</b>), at the operation points P3 (<math display="inline"><semantics> <mrow> <mrow> <mi mathvariant="sans-serif">Δ</mi> <mi mathvariant="sans-serif">γ</mi> </mrow> <mo>=</mo> <mn>25</mn> <mo>°</mo> </mrow> </semantics></math>) and P5 (<math display="inline"><semantics> <mrow> <mrow> <mi mathvariant="sans-serif">Δ</mi> <mi mathvariant="sans-serif">γ</mi> </mrow> <mo>=</mo> <mn>35</mn> <mo>°</mo> </mrow> </semantics></math>).</p>
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17 pages, 1803 KiB  
Article
Propulsion Sizing Correlations for Electrical and Fuel Powered Unmanned Aerial Vehicles
by Victor Alulema, Esteban Valencia, Edgar Cando, Victor Hidalgo and Dario Rodriguez
Aerospace 2021, 8(7), 171; https://doi.org/10.3390/aerospace8070171 - 24 Jun 2021
Cited by 16 | Viewed by 4644
Abstract
Despite the increasing demand of Unmanned Aerial Vehicles (UAVs) for a wide range of civil applications, there are few methodologies for their initial sizing. Nowadays, classical methods, mainly developed for transport aircraft, have been adapted to UAVs. However, these tools are not always [...] Read more.
Despite the increasing demand of Unmanned Aerial Vehicles (UAVs) for a wide range of civil applications, there are few methodologies for their initial sizing. Nowadays, classical methods, mainly developed for transport aircraft, have been adapted to UAVs. However, these tools are not always suitable because they do not fully adapt to the plethora of geometrical and propulsive configurations that the UAV sector represents. Therefore, this work provides series of correlations based on off-the-shelf components for the preliminary sizing of propulsion systems for UAVs. This study encompassed electric and fuel-powered propulsion systems, considering that they are the most used in the UAV industry and are the basis of novel architectures such as hybrid propulsion. For these systems, weight correlations were derived, and, depending on data availability, correlations regarding their geometry and energy consumption are also provided. Furthermore, a flowchart for the implementation of the correlations in the UAV design procedure and two practical examples are provided to highlight their usability. To summarize, the main contribution of this work is to provide parametric tools to size rapidly the propulsion system components, which can be embedded in a UAV design and optimization framework. This research complements other correlation studies for UAVs, where the initial sizing of the vehicle is discussed. The present correlations suit multiple UAV categories ranging from micro to Medium-Altitude-Long-Endurance (MALE) UAVs. Full article
(This article belongs to the Collection Unmanned Aerial Systems)
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Figure 1

Figure 1
<p>Propulsion systems for UAVs addressed in this work.</p>
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<p>Schematic flowchart for implementation of empirical correlations.</p>
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<p>Weight of unitary cells (<math display="inline"><semantics> <msub> <mi>W</mi> <mrow> <mi>c</mi> <mi>e</mi> <mi>l</mi> <mi>l</mi> </mrow> </msub> </semantics></math>) as function of the cell capacity (<math display="inline"><semantics> <msub> <mi>C</mi> <mrow> <mi>c</mi> <mi>e</mi> <mi>l</mi> <mi>l</mi> </mrow> </msub> </semantics></math>) [<a href="#B31-aerospace-08-00171" class="html-bibr">31</a>].</p>
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<p>Weight of LiPo batteries (<math display="inline"><semantics> <msub> <mi>W</mi> <mrow> <mi mathvariant="italic">Li</mi> <mo>−</mo> <mi mathvariant="italic">Po</mi> </mrow> </msub> </semantics></math>) as function of the battery capacity (<math display="inline"><semantics> <msub> <mi>C</mi> <mi mathvariant="italic">battery</mi> </msub> </semantics></math>) [<a href="#B31-aerospace-08-00171" class="html-bibr">31</a>].</p>
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<p>Sizing correlations for EDFs from 2 to 250 [N] of maximum static thrust. <math display="inline"><semantics> <mrow> <mi>n</mi> <mspace width="0.166667em"/> <mo>=</mo> <mspace width="0.166667em"/> <mn>270</mn> </mrow> </semantics></math> [<a href="#B31-aerospace-08-00171" class="html-bibr">31</a>].</p>
Full article ">Figure 6
<p>Motor constant <math display="inline"><semantics> <mrow> <mi>K</mi> <msub> <mi>V</mi> <mi mathvariant="italic">motor</mi> </msub> </mrow> </semantics></math> and motor weight <math display="inline"><semantics> <msub> <mi>W</mi> <mi mathvariant="italic">motor</mi> </msub> </semantics></math> characteristics of electric brushless motors.</p>
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<p>Sizing correlations for turbojet engines from 20 to 10,000 [N] of maximum thrust.</p>
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<p>Weight of turboprop engines <math display="inline"><semantics> <mrow> <mo>(</mo> <msub> <mi>W</mi> <mi mathvariant="italic">tprop</mi> </msub> <mo>)</mo> </mrow> </semantics></math> as function of the cruise power output <math display="inline"><semantics> <mrow> <mo>(</mo> <msub> <mi>P</mi> <mi mathvariant="italic">out</mi> </msub> <mo>)</mo> </mrow> </semantics></math> from 2 to 200 [kW]. <math display="inline"><semantics> <mrow> <mi>n</mi> <mspace width="0.166667em"/> <mo>=</mo> <mspace width="0.166667em"/> <mn>20</mn> </mrow> </semantics></math>.</p>
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<p>Sizing correlations for ICE engines from 200 to 100,000 W of cruise power.</p>
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<p>Effect of battery capacity on the UAV endurance using two formulations of the endurance equation [<a href="#B49-aerospace-08-00171" class="html-bibr">49</a>].</p>
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16 pages, 3130 KiB  
Article
Visual Search and Conflict Mitigation Strategies Used by Expert en Route Air Traffic Controllers
by Ricardo Palma Fraga, Ziho Kang, Jerry M. Crutchfield and Saptarshi Mandal
Aerospace 2021, 8(7), 170; https://doi.org/10.3390/aerospace8070170 - 23 Jun 2021
Cited by 9 | Viewed by 3090
Abstract
The role of the en route air traffic control specialist (ATCS) is vital to maintaining safety and efficiency within the National Airspace System (NAS). ATCSs must vigilantly scan the airspace under their control and adjacent airspaces using an En Route Automation Modernization (ERAM) [...] Read more.
The role of the en route air traffic control specialist (ATCS) is vital to maintaining safety and efficiency within the National Airspace System (NAS). ATCSs must vigilantly scan the airspace under their control and adjacent airspaces using an En Route Automation Modernization (ERAM) radar display. The intent of this research is to provide an understanding of the expert controller visual search and aircraft conflict mitigation strategies that could be used as scaffolding methods during ATCS training. Interviews and experiments were conducted to elicit visual scanning and conflict mitigation strategies from the retired controllers who were employed as air traffic control instructors. The interview results were characterized and classified using various heuristics. In particular, representative visual scanpaths were identified, which accord with the interview results of the visual search strategies. The highlights of our findings include: (1) participants used systematic search patterns, such as circular, spiral, linear or quadrant-based, to extract operation-relevant information; (2) participants applied an information hierarchy when aircraft information was cognitively processed (altitude -> direction -> speed); (3) altitude or direction changes were generally preferred over speed changes when imminent potential conflicts were mitigated. Potential applications exist in the implementation of the findings into the training curriculum of candidates. Full article
(This article belongs to the Special Issue Aircraft Operations and CNS/ATM)
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Figure 1
<p>Representative example of a scanpath.</p>
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<p>Example scenarios: (<b>a</b>) Layout of aircraft during an experiment using one of the scenarios; (<b>b</b>) Example of aircraft conflicts highlighted in red and yellow (left side shows the aircraft layout at a certain point in time, and the right side shows the future conflict locations.</p>
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<p>More detailed examples of aircraft conflicts presented to participants: Aircraft that will have a conflict in the near future are highlighted with yellow boxes and the conflict locations in the near future (approximately a few minutes for these examples) are highlighted with red circles. The subfigures (<b>a</b>–<b>c</b>), correspond to examples of an overtaking, converging, and head-on conflicts.</p>
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<p>Representative examples of various kinds of scanpath in a visual search task. (<b>a</b>) Spiral; (<b>b</b>) Circular; (<b>c</b>) Linear; (<b>d</b>) Quadrants.</p>
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<p>Representative examples of visual search strategies. (<b>a</b>) Spiral; (<b>b</b>) Circular; (<b>c</b>) Linear; (<b>d</b>) Quadrant, delineated by dashed yellow squares; (<b>e</b>) Mixed.</p>
Full article ">
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