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16 pages, 4856 KiB  
Article
Multistep Prediction Analysis of Pure Pursuit Method for Automated Guided Vehicles in Aircraft Industry
by Biling Wang, Gaojian Fan, Xinming Zhang, Liangjie Gao, Xiaobo Wang and Weijie Fu
Actuators 2024, 13(12), 518; https://doi.org/10.3390/act13120518 - 15 Dec 2024
Viewed by 387
Abstract
The pure pursuit (PP) method has been widely employed in automated guided vehicles (AGVs) to address path tracking challenges. However, the traditional pure pursuit method exhibits certain limitations in tracking performance. For instance, selecting a look-ahead point that is too close can lead [...] Read more.
The pure pursuit (PP) method has been widely employed in automated guided vehicles (AGVs) to address path tracking challenges. However, the traditional pure pursuit method exhibits certain limitations in tracking performance. For instance, selecting a look-ahead point that is too close can lead to oscillations during tracking, while selecting one that is too far away can result in slow tracking and corner-cutting issues. To address these challenges, this paper proposes a multistep prediction pure pursuit method. First, the look-ahead distance calculation equation is adjusted by incorporating path curvature, allowing it to adaptively adjust according to road conditions. Next, to avoid oscillations caused by constant changes in the look-ahead distance, this paper adopts the prediction concept of model predictive control (MPC) to make multistep predictions for the pure pursuit method. The final input is derived from a linear weighted combination of the multistep prediction results. Simulation analyses and experiments demonstrate that the multistep predictive pure pursuit method significantly enhances the tracking performance of the traditional pure pursuit method. Full article
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Figure 1
<p>Bicycle model.</p>
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<p>Schematic diagram of the pure pursuit method.</p>
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<p>Problems associated with the pure pursuit method: (<b>a</b>) long look-ahead distance, (<b>b</b>) short look-ahead distance.</p>
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<p>The relationship between the look-ahead distances of tracking errors.</p>
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<p>The influence of look-ahead distance on tracking performance.</p>
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<p>The relationship between <span class="html-italic">ld</span> and maximum error when curvature is fixed.</p>
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<p>The change rule of function <span class="html-italic">f</span>(<span class="html-italic">x</span>) with respect to <span class="html-italic">a</span>.</p>
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<p>Linear two degrees of freedom model.</p>
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<p>Multistep prediction principle.</p>
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<p>Experimental facility.</p>
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<p>The simulation results of sin-path: (<b>a</b>) tracking effect when <span class="html-italic">k</span> = 1; (<b>b</b>) tracking effect when <span class="html-italic">k</span> = 5; (<b>c</b>) lateral tracking error when <span class="html-italic">k</span> = 1; (<b>d</b>) lateral tracking error when <span class="html-italic">k</span> = 5; (<b>e</b>) heading tracking error when <span class="html-italic">k</span> = 1; (<b>f</b>) heading tracking error when <span class="html-italic">k</span> = 5.</p>
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<p>The simulation results of <span class="html-italic">k</span>1: (<b>a</b>) tracking effect when <span class="html-italic">Ld</span> = 0.2; (<b>b</b>) tracking effect when <span class="html-italic">Ld</span> = 5; (<b>c</b>) lateral tracking error when <span class="html-italic">Ld</span> = 0.2; (<b>d</b>) lateral tracking error when <span class="html-italic">Ld</span> = 5; (<b>e</b>) heading tracking error when <span class="html-italic">Ld</span> = 0.2; (<b>f</b>) heading tracking error when <span class="html-italic">Ld</span> = 5.</p>
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<p>The simulation results of <span class="html-italic">Nc</span>: (<b>a</b>) tracking effect when <span class="html-italic">Ld</span> = 0.2; (<b>b</b>) tracking effect when <span class="html-italic">Ld</span> = 5; (<b>c</b>) lateral tracking error when <span class="html-italic">Ld</span> = 0.2; (<b>d</b>) lateral tracking error when <span class="html-italic">Ld</span> = 5; (<b>e</b>) heading tracking error when <span class="html-italic">Ld</span> = 0.2; (<b>f</b>) heading tracking error when <span class="html-italic">Ld</span> = 5.</p>
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<p>The experiment results of U-path: (<b>a</b>) tracking effect; (<b>b</b>) lateral tracking error; (<b>c</b>) heading tracking error.</p>
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15 pages, 1709 KiB  
Article
An Aircraft-Manipulator System for Virtual Flight Testing of Longitudinal Flight Dynamics
by Ademayowa A. Ishola, James F. Whidborne and Gilbert Tang
Robotics 2024, 13(12), 179; https://doi.org/10.3390/robotics13120179 - 15 Dec 2024
Viewed by 274
Abstract
A virtual flight test is the process of flying an aircraft model inside a wind tunnel in a manner that replicates free-flight. In this paper, a 3-DOF aircraft-manipulator system is proposed that can be used for longitudinal dynamics virtual flight tests. The system [...] Read more.
A virtual flight test is the process of flying an aircraft model inside a wind tunnel in a manner that replicates free-flight. In this paper, a 3-DOF aircraft-manipulator system is proposed that can be used for longitudinal dynamics virtual flight tests. The system consists of a two rotational degrees-of-freedom manipulator arm with an aircraft wind tunnel model attached to the third joint. This aircraft-manipulator system is constrained to operate for only the longitudinal motion of the aircraft. Thus, the manipulator controls the surge and heave of the aircraft whilst the pitch is free to rotate and can be actively controlled by means of an all-moving tailplane of the aircraft if required. In this initial study, a flight dynamics model of the aircraft is used to obtain dynamic response trajectories of the aircraft in free-flight. A model of the coupled aircraft-manipulator system developed using the Euler method is presented, and PID controllers are used to control the manipulator so that the aircraft follows the free-flight trajectory (with respect to the air). The inverse kinematics are used to produce the reference joint angles for the manipulator. The system is simulated in MATLAB/Simulink and a virtual flight test trajectory is compared with a free-flight test trajectory, demonstrating the potential of the proposed system for virtual flight tests. Full article
(This article belongs to the Special Issue Adaptive and Nonlinear Control of Robotics)
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Figure 1
<p>Schematic of the aircraft-manipulator system sited in the working section of an open-section wind tunnel (not to scale). The wind tunnel airspeed is shown as <math display="inline"><semantics> <msub> <mi>V</mi> <mrow> <mi>W</mi> <mi>T</mi> </mrow> </msub> </semantics></math>.</p>
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<p>A 1/12th scaled BAe Hawk aircraft.</p>
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<p>Configuration for 3-DOF aircraft longitudinal dynamics.</p>
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<p>Manipulator aircraft system configuration.</p>
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<p>Air velocity as the sum of wind tunnel velocity and aircraft velocity.</p>
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<p>Block diagram showing system control architecture for the <span class="html-italic">i</span>th loop. The block denoted as IK represents the inverse kinematics’ reference source.</p>
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<p>Short period mode pitch response of the free-flying Hawk model and the aircraft-manipulator system (AMS) model.</p>
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<p>Phugoid mode pitch response of the free-flying Hawk model and the aircraft-manipulator system (AMS) model.</p>
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<p>Short period mode altitude response of the free-flying Hawk model and the aircraft-manipulator system (AMS) model.</p>
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<p>Phugoid mode height and lateral surge position response of the free-flying Hawk model (resolved into the wind tunnel axes) and the aircraft-manipulator system (AMS) model.</p>
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<p>Phugoid mode position response of the aircraft-manipulator system (AMS) model in the wind tunnel.</p>
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16 pages, 3077 KiB  
Article
Comparison Between Numerical and Experimental Methodologies for Total Enthalpy Determination in Scirocco PWT
by Antonio Smoraldi and Luigi Cutrone
Aerospace 2024, 11(12), 1023; https://doi.org/10.3390/aerospace11121023 - 14 Dec 2024
Viewed by 397
Abstract
Arc-jet facility tests are critical for replicating the extreme thermal conditions encountered during high-speed planetary entry, where the precise determination of flow enthalpy is essential. Despite its importance, a systematic comparison of methods for determining enthalpy in the Scirocco Plasma Wind Tunnel had [...] Read more.
Arc-jet facility tests are critical for replicating the extreme thermal conditions encountered during high-speed planetary entry, where the precise determination of flow enthalpy is essential. Despite its importance, a systematic comparison of methods for determining enthalpy in the Scirocco Plasma Wind Tunnel had not yet been conducted. This study evaluates three experimental techniques—the sonic throat method, the heat balance method, and the heat transfer method—under various operating conditions in the Scirocco facility, employing a nozzle C configuration (10° half-angle conical nozzle with a 90 cm exit diameter). These methods are compared with computational fluid dynamics (CFDs) simulations to address discrepancies between experimental and predicted enthalpy and heat flux values. Significant deviations between measured and simulated results prompted a reassessment of the numerical and experimental models. Initially, the Navier–Stokes model, which assumes chemically reacting, non-equilibrium flows and fully catalytic copper walls, underestimated the heat flux. By incorporating partial catalytic behavior for the copper probe surface, the CFD results showed better agreement with the experimental data, providing a more accurate representation of heat flux and flow enthalpy within the test environment. Full article
(This article belongs to the Special Issue Thermal Protection System Design of Space Vehicles)
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Figure 1
<p>Simplified scheme of the heat balance methodology for the determination of the mass-averaged total enthalpy, for the CIRA Scirocco facility.</p>
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<p>Simplified scheme of the measurement setup for the stagnation heat flux and pressure. CIRA Scirocco facility.</p>
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<p>Tip of standard 100 mm diameter hemispherical copper-cooled probe. Gardon gauge and pressure ports are highlighted.</p>
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<p>Computational domain and mesh for Scirocco rebuilding test cases.</p>
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<p>Final comparison of enthalpy determination methods: enthalpy values, computed using various methods, are plotted against the Zoby-derived enthalpy calculated from heat flux measurements. Linear interpolations of these data and error bars are also shown for each method.</p>
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<p>Results of CFDs calculations at nozzle throat for Scirocco: (<b>a</b>) enthalpy and temperature and (<b>b</b>) velocity and density. Mean radiation distribution from the free-stream plasma flow at the nozzle exit (Test 13, <a href="#aerospace-11-01023-t001" class="html-table">Table 1</a>).</p>
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<p>Example of radial profile of (<b>a</b>) stagnation heat flux (<math display="inline"><semantics> <mrow> <msub> <mrow> <mi>q</mi> </mrow> <mrow> <mi>s</mi> </mrow> </msub> </mrow> </semantics></math>) and (<b>b</b>) pressure (<math display="inline"><semantics> <mrow> <msub> <mrow> <mi>p</mi> </mrow> <mrow> <mi>s</mi> </mrow> </msub> </mrow> </semantics></math>) of the free-stream plasma flow of the SICROCCO facility (Test 26, <a href="#aerospace-11-01023-t001" class="html-table">Table 1</a>).</p>
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<p>Mean radiation distribution from the free-stream plasma flow (<b>a</b>); transversal profiles extracted from the mean radiation distribution of the free-stream plasma flow for <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>I</mi> </mrow> <mrow> <mi>t</mi> <mi>o</mi> <mi>t</mi> </mrow> </msub> </mrow> </semantics></math> = 3681 A and <math display="inline"><semantics> <mrow> <msub> <mrow> <mover accent="true"> <mrow> <mi>m</mi> </mrow> <mo>˙</mo> </mover> </mrow> <mrow> <mi>AIR</mi> </mrow> </msub> </mrow> </semantics></math> = 0.74 kg/s (Test 4, <a href="#aerospace-11-01023-t001" class="html-table">Table 1</a>) (<b>b</b>).</p>
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<p>Dependence of deduced centerline enthalpy <math display="inline"><semantics> <mrow> <msubsup> <mrow> <mi>H</mi> </mrow> <mrow> <mi>c</mi> </mrow> <mrow> <mn>0</mn> </mrow> </msubsup> </mrow> </semantics></math> on assumed catalytic efficiency <math display="inline"><semantics> <mrow> <mi>γ</mi> </mrow> </semantics></math> for Scirocco (test 26–28, <a href="#aerospace-11-01023-t001" class="html-table">Table 1</a>).</p>
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19 pages, 9204 KiB  
Article
Study of Aerodynamic Characteristics of Asymmetrical Blades and a Wind-Driven Power Plant with a Vertical Axis of Rotation
by Muhtar Isataev, Rustem Manatbayev, Zhanibek Seydulla, Birzhan Bektibai and Nurdaulet Kalassov
Appl. Sci. 2024, 14(24), 11654; https://doi.org/10.3390/app142411654 - 13 Dec 2024
Viewed by 372
Abstract
This paper presents the results of wind tunnel experiments, where lift and drag coefficients were studied at various angles of attack and flow speeds, alongside numerical simulations conducted in ANSYS. The main objectives of this study are to investigate the aerodynamic characteristics and [...] Read more.
This paper presents the results of wind tunnel experiments, where lift and drag coefficients were studied at various angles of attack and flow speeds, alongside numerical simulations conducted in ANSYS. The main objectives of this study are to investigate the aerodynamic characteristics and self-starting capabilities of three-bladed Darrieus rotors with asymmetrical blades and assess their efficiency. This study presents results on pressure distribution, velocity contours, and the impact of the angle of attack on pressure and aerodynamic characteristics. The results show that blades with asymmetric shapes achieve maximum values of lift and drag coefficients at angles of attack between 180° and 210°, with peak coefficients of Cx = 1.38 and Cy = 2.84, respectively. These findings indicate high effectiveness of the blades at low wind speeds, making them promising for use in WEIs where good starting characteristics and high power output are especially important. A good correlation was found between experimental data and numerical simulation results. This study contributes to the development of recommendations for optimizing the design and operating parameters of wind-driven powerplants, which in turn can improve their reliability and economic efficiency. Thus, the paper aims to expand the knowledge in the field of wind power engineering and to develop technologies to facilitate a wider adoption of wind-driven powerplants in the energy infrastructure of different regions. Full article
(This article belongs to the Section Energy Science and Technology)
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Figure 1
<p>General view of the T–1–M wind tunnel.</p>
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<p>Diagram of a closed-flow wind tunnel. 1—working part, 2—ring, 3—diffuser, 4—fan, 5—transition channel, 6,7,9,10—turning blades, 8—return channel, 11–13—equalizing meshes, 12—pre chamber, 14—collector (nozzle), 15—the object of the study.</p>
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<p>Cross-sectional blade diagram.</p>
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<p>Asymmetrical blade on the working part of the wind tunnel.</p>
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<p>Model of a wind power installation with asymmetrically shaped blades.</p>
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<p>Dependence of the drag coefficient of asymmetrically shaped blades on the angle of attack at different flow speeds.</p>
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<p>Dependence of the lift coefficient of asymmetrically shaped blades on the angle of attack at different flow speeds.</p>
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<p>Dependence of the lift and drag coefficients at different angles of attack for the blades of an asymmetrical shape at a wind speed of 15 m/s.</p>
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<p>Aerodynamic characteristics of the NACA 0015 configuration.</p>
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<p>Dependence of the thrust resistance on wind speed of the wind-driven plant mock-up.</p>
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<p>Influence of wind speed on wind wheel RPM values.</p>
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<p>Schedule of the moment of starting.</p>
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<p>Computational mesh of the investigated mathematical model.</p>
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<p>Mesh model of the asymmetric airfoil.</p>
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<p>Grid independence study (for the maximum number of cells).</p>
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<p>Contours of pressure distribution on the asymmetric airfoil at different angles of attack.</p>
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<p>Contours of pressure distribution on the asymmetric airfoil at different angles of attack.</p>
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<p>Velocity vector distributions of the asymmetric airfoil at different angles of attack.</p>
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<p>Comparative analysis of numerical modeling and experimental investigation of the drag coefficient of asymmetrical blades as a function of the angle of attack at various flow velocities. Numerical modeling: 1—3 <span class="html-small-caps">m</span>/c, 2—6 <span class="html-small-caps">m</span>/c, 3—9 <span class="html-small-caps">m</span>/c, 4—12 <span class="html-small-caps">m</span>/c, 5—15 <span class="html-small-caps">m</span>/c. Experimental investigation: 6—3 <span class="html-small-caps">m</span>/c, 7—6 <span class="html-small-caps">m</span>/c, 8—9 <span class="html-small-caps">m</span>/c, 9—12 <span class="html-small-caps">m</span>/c, 10—15 <span class="html-small-caps">m</span>/c.</p>
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<p>Comparative analysis of numerical modeling and experimental investigation of the lift coefficient of asymmetrical blades as a function of the angle of attack at various flow velocities. Numerical modeling: 1—3 <span class="html-small-caps">m</span>/c, 2—6 <span class="html-small-caps">m</span>/c, 3—9 <span class="html-small-caps">m</span>/c, 4—12 <span class="html-small-caps">m</span>/c, 5—15 <span class="html-small-caps">m</span>/c. Experimental investigation: 6—3 <span class="html-small-caps">m</span>/c, 7—6 <span class="html-small-caps">m</span>/c, 8—9 <span class="html-small-caps">m</span>/c, 9—12 <span class="html-small-caps">m</span>/c, 10—15 <span class="html-small-caps">m</span>/c.</p>
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25 pages, 8761 KiB  
Article
A Refined Approach for Angle of Attack Estimation and Dynamic Force Hysteresis in H-Type Darrieus Wind Turbines
by Jan Michna and Krzysztof Rogowski
Energies 2024, 17(24), 6264; https://doi.org/10.3390/en17246264 - 12 Dec 2024
Viewed by 292
Abstract
This study investigates the aerodynamic performance and flow dynamics of an H-type Darrieus vertical axis wind turbine (VAWT) using combined numerical and experimental methods. The analysis examines the effects of operational parameters, such as rotor solidity and pitch angle, on aerodynamic loads and [...] Read more.
This study investigates the aerodynamic performance and flow dynamics of an H-type Darrieus vertical axis wind turbine (VAWT) using combined numerical and experimental methods. The analysis examines the effects of operational parameters, such as rotor solidity and pitch angle, on aerodynamic loads and flow characteristics, using a 2-D URANS simulation with the Transition SST model to capture transient effects. Validation was conducted in a low-turbulence wind tunnel to observe the impact of variable flow conditions. The LineAverage method for determining the angle of attack demonstrated strong correlations between rotor configuration and load variations, particularly highlighting the influence of blade number and pitch angle on aerodynamic efficiency. This research supports optimization strategies for Darrieus VAWTs in urban environments, where turbulent, low-speed conditions challenge conventional wind turbine designs. Full article
(This article belongs to the Special Issue Wind Turbine and Wind Farm Flows)
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Figure 1
<p>Diagram of an H-type vertical axis wind turbine structure with dimensions.</p>
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<p>Domain schema with dimensions (<b>upper</b>) and mesh layout with boundary conditions for the CFD simulation (<b>bottom</b>). The diagram shows the defined boundary conditions: velocity inlet on the left, pressure outlet on the right, symmetry on the top and bottom, wall on the airfoil surface, and interface region connecting different mesh zones. Enlarged sections illustrate the mesh refinement around the airfoil and interface areas.</p>
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<p>Variation of the mesh sensitivity test results: The plot shows the normal force coefficient C<sub>N</sub> (red) and the tangential force coefficient C<sub>T</sub> (blue) as a function of the number of mesh cells. C<sub>N</sub> is plotted on the left axis and C<sub>T</sub> on the right axis. The results indicate that both coefficients stabilize as the mesh density increases from 180,000 to 340,000 cells.</p>
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<p>Variation of the tangential force coefficient C<sub>T</sub> as a function of the number of rotor revolutions. Black dots represent values averaged over individual rotor revolutions, while the red dashed line shows the average C<sub>T</sub> over the last ten rotor revolutions. The figure illustrates the consistency of the aerodynamic loads over the examined revolutions, with minimal variation observed between individual revolutions and the overall average.</p>
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<p>Comparison of the tangential force coefficient C<sub>T</sub> as a function of azimuth angle <span class="html-italic">θ</span>. The red solid line represents the tangential load component calculated for the last rotor revolution, while the blue dashed line shows the same component, averaged over ten revolutions, both plotted as a function of the azimuth angle.</p>
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<p>Comparison of the V<sub>x</sub>/V<sub>0</sub> velocity component in the rotor wake for various downstream positions x/D, calculated using the SAS approach and validated against experimental data [<a href="#B46-energies-17-06264" class="html-bibr">46</a>].</p>
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<p>Comparison of the <span class="html-italic">V<sub>y</sub>/V<sub>0</sub></span> velocity component in the rotor wake for various downstream positions x/D, calculated using the SAS approach and validated against experimental data [<a href="#B46-energies-17-06264" class="html-bibr">46</a>].</p>
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<p>Comparison of simulated and experimental force coefficients [<a href="#B53-energies-17-06264" class="html-bibr">53</a>] as a function of azimuthal angle, <span class="html-italic">θ</span>. (<b>a</b>) Normal force coefficient C<sub>N</sub>, shows a general agreement between the SAS simulation and experimental data. (<b>b</b>) Tangential force coefficient, C.</p>
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<p>Illustration of the angle of attack (<span class="html-italic">α</span>) and relative velocity (<span class="html-italic">V<sub>rel</sub></span>) for a blade in a vertical-axis wind turbine. The pitch angle (<span class="html-italic">β</span> = 10°) and the azimuthal position angle (<span class="html-italic">θ</span> = 48°) are indicated.</p>
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<p>Velocity distribution around the airfoil at an azimuthal angle θ = 48°. The circular sampling line with evenly distributed points surrounds the airfoil, with arrows representing the local flow velocities at each point.</p>
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<p>Sensitivity analysis of the angle of attack (<math display="inline"><semantics> <mrow> <mi>α</mi> <mo>−</mo> <mover accent="true"> <mrow> <msup> <mrow> <mi>α</mi> </mrow> <mrow> <mfenced separators="|"> <mrow> <mn>2000</mn> </mrow> </mfenced> </mrow> </msup> </mrow> <mo>¯</mo> </mover> </mrow> </semantics></math>) based on the number of sampling points along the circular line surrounding the airfoil as a function of the azimuth angle θ. Standard deviation is chosen as an error measure.</p>
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<p>The mean relative velocity is represented by lines. Standard deviation is chosen as an error measure.</p>
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<p>Validation of angle of attack (<span class="html-italic">α</span>) and relative velocity (<span class="html-italic">V<sub>rel</sub></span>) results obtained using the SAS approach, compared with literature data from Melani et al. [<a href="#B45-energies-17-06264" class="html-bibr">45</a>] and Cacciali et al. [<a href="#B8-energies-17-06264" class="html-bibr">8</a>].</p>
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<p>The upper subfigures illustrate the drag coefficient C<sub>D</sub> (<b>a</b>) and lift coefficient C<sub>L</sub> (<b>b</b>) as functions of the azimuthal angle <span class="html-italic">θ</span>. The lower subfigures depict the same coefficients—drag (<b>c</b>) and lift (<b>d</b>)—analyzed for varying angles of attack α. The presented data include results obtained using the SAS approach, static results reported by Rogowski et al. [<a href="#B22-energies-17-06264" class="html-bibr">22</a>], and reference data from Melani et al. [<a href="#B45-energies-17-06264" class="html-bibr">45</a>].</p>
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<p>Effect of the number of blades on the normal and tangential force coefficients, as well as on the local angle of attack and relative velocity, as a function of azimuth <span class="html-italic">θ</span> at a tip-speed ratio of 4.5.</p>
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<p>The subfigures show the drag coefficient C<sub>D</sub> (<b>a</b>,<b>c</b>) and lift coefficient C<sub>L</sub> (<b>b</b>,<b>d</b>) for different azimuthal angles (<b>a</b>,<b>b</b>) and angles of attack (<b>c</b>,<b>d</b>). The presented data shows comparison of aerodynamic coefficients for 1-bladed, 2-bladed, 3-bladed, and 4-bladed configurations.</p>
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<p>Effect of the pitch angle on the normal and tangential force coefficients, as well as on the local angle of attack and relative velocity, as a function of azimuth <span class="html-italic">θ</span> for a 2-bladed rotor configuration at a tip-speed ratio of 4.5.</p>
Full article ">Figure 18
<p>The subfigures show the drag coefficient C<sub>D</sub> (<b>a</b>,<b>c</b>) and lift coefficient C<sub>L</sub> (<b>b</b>,<b>d</b>) for different azimuthal angles (<b>a</b>,<b>b</b>) and angles of attack (<b>c</b>,<b>d</b>). The data presented above shows aerodynamic coefficient comparison for varying blade pitch angles (<span class="html-italic">β</span> = −10°, 0°, 10°).</p>
Full article ">
17 pages, 4729 KiB  
Article
Performance Validation of Control Algorithm Considering Independent Generator Torque Control in PCS
by Dongmyoung Kim, Min-Woo Ham, Insu Paek, Wirachai Roynarin and Amphol Aphathanakorn
Appl. Sci. 2024, 14(24), 11598; https://doi.org/10.3390/app142411598 - 12 Dec 2024
Viewed by 296
Abstract
This study designed and validated a power control algorithm tailored to the unique characteristics of a 100 kW wind turbine equipped with an independent generator torque control system within the Power Conversion System (PCS). Unlike conventional power control methods based on the Programmable [...] Read more.
This study designed and validated a power control algorithm tailored to the unique characteristics of a 100 kW wind turbine equipped with an independent generator torque control system within the Power Conversion System (PCS). Unlike conventional power control methods based on the Programmable Logic Controller (PLC), the independent generator torque control in the PCS estimates the generator speed based on the voltage signal and performs the generator torque control without any interference from the PLC. The conventional power control algorithm in the PLC was modified so that the PLC could perform the power control by the blade pitch control only. Furthermore, a mode transition algorithm was designed to improve the shortcomings of the mode switch previously used for transitioning between the torque and pitch control modes. To verify the performance, a simulation environment similar to actual control conditions was established using a commercial analysis program, and dynamic simulations were conducted. Additionally, for the experimental validation, the proposed control algorithm was applied to a scaled wind turbine, and wind tunnel tests were performed. The results of the simulations and wind tunnel tests confirmed the operational performance of the PCS’s torque control and the improved control transition logic. The proposed control algorithm could be specifically applied to medium-sized wind turbines employing a sensorless generator speed estimation method and independent generator torque control in the PCS. Full article
(This article belongs to the Topic Advances in Wind Energy Technology)
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<p>Target wind turbine (100 kW model).</p>
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<p>Overall schematic of the power control algorithm considering the PCS torque control.</p>
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<p>Structure of the pitch PI control algorithm [<a href="#B13-applsci-14-11598" class="html-bibr">13</a>].</p>
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<p>Structure of the mode switch [<a href="#B13-applsci-14-11598" class="html-bibr">13</a>].</p>
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<p>Overall schematic of the proposed power control algorithm.</p>
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<p>Structure of the pitch PI control algorithm with SPS.</p>
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<p>Comparison of the performance between the reference power control and power control considering the independent generator torque control. (<b>a</b>) Simulation results under an average wind speed of 10 m/s; (<b>b</b>) Simulation results under an average wind speed of 12 m/s.</p>
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<p>Performance comparison of the power control considering the mode switch and Set Point Smoother in the independent generator torque control. (<b>a</b>) Simulation results under average wind speed conditions of 10 m/s; (<b>b</b>) Simulation results under average wind speed conditions of 12 m/s.</p>
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<p>Scaled wind turbine for the wind tunnel test.</p>
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<p>Experimental environment of the wind tunnel center.</p>
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<p>Comparison of the simulation results for the scaled wind turbine (6 m/s).</p>
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<p>Comparison of the wind tunnel test results for the scaled wind turbine.</p>
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18 pages, 3635 KiB  
Article
Diagnostic Approach and Tool for Assessing and Increasing the Sustainability of Renewable Energy Projects
by Jing Tian, Sam Culley, Holger R. Maier, Aaron C. Zecchin and James Hopeward
Sustainability 2024, 16(24), 10871; https://doi.org/10.3390/su162410871 - 11 Dec 2024
Viewed by 524
Abstract
The imperative of achieving net zero carbon emissions is driving the transition to renewable energy sources. However, this often leads to carbon tunnel vision by narrowly focusing on carbon metrics and overlooking broader sustainability impacts. To enable these broader impacts to be considered, [...] Read more.
The imperative of achieving net zero carbon emissions is driving the transition to renewable energy sources. However, this often leads to carbon tunnel vision by narrowly focusing on carbon metrics and overlooking broader sustainability impacts. To enable these broader impacts to be considered, we have developed a generic approach and a freely available assessment tool on GitHub that not only facilitate the high-level sustainability assessment of renewable energy projects but also indicate whether project-level decisions have positive, negative, or neutral impacts on each of the sustainable development goals (SDGs). This information highlights potential problem areas and which actions can be taken to increase the sustainability of renewable energy projects. The tool is designed to be accessible and user-friendly by developing it in MS Excel and by only requiring yes/no answers to approximately 60 diagnostic questions. The utility of the approach and tool are illustrated via three desktop case studies performed by the authors. The three illustrative case studies are located in Australia and include a large-scale solar farm, biogas production from wastewater plants, and an offshore wind farm. Results show that the case study projects impact the SDGs in different and unique ways and that different project–level decisions are most influential, highlighting the value of the proposed approach and tool to provide insight into specific projects and their sustainability implications, as well as which actions can be taken to increase project sustainability. Full article
(This article belongs to the Section Energy Sustainability)
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<p>The proposed approach for identifying the project–level decisions that have to be made during the development of renewable energy projects that affect SDGs.</p>
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<p>Developed relationships between Project–level Decision Themes and the SDGs. This consists of a Sankey diagram [<a href="#B29-sustainability-16-10871" class="html-bibr">29</a>] showing which project–level decision themes impact different SDGs, colour-coded by SDG, enabling project–level decisions that require attention to be identified. Note that the widths of the lines indicate the number of questions in the developed questionnaire that either fall within the category of project–level decision themes or potentially cause an impact (positive or negative) on an SDG.</p>
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<p>The proposed approach for identifying the relationships between Project–level Decision Themes and the SDGs for specific renewable energy projects, as well as screenshots of required user inputs via the MS Excel-based implementation tool.</p>
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<p>Illustrative result figures plotted by the MS Excel-based implementation tool. (<b>a</b>) presents the high-level sustainability assessments for the energy project under consideration. (<b>b</b>) presents the identification of project actions most suited to increasing sustainability.</p>
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<p>High-level summary of the key characteristics of the three illustrative case studies used to demonstrate the application and benefit of the proposed approach and MS Excel tool.</p>
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<p>Summary of high-level SDG impacts for the three illustrative case studies.</p>
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<p>Impact of project–level decision themes on relevant SDGs for the three illustrative case studies considered. The “traffic light” indicators on the right-hand side of the figure summarise the impact on a particular SDG due to project–level decisions. The traffic light indicators on the left-hand side of the figure summarise the contribution of a particular project–level decision theme to the overall impact of a particular SDG.</p>
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21 pages, 4072 KiB  
Article
Effect of Adjuvants on Physical–Chemical Properties, Droplet Size, and Drift Reduction Potential
by Sérgio Basílio, Marconi Ribeiro Furtado Júnior, Cleyton Batista de Alvarenga, Edney Leandro da Vitória, Beatriz Costalonga Vargas, Salvatore Privitera, Luciano Caruso, Emanuele Cerruto and Giuseppe Manetto
Agriculture 2024, 14(12), 2271; https://doi.org/10.3390/agriculture14122271 - 11 Dec 2024
Viewed by 413
Abstract
Adjuvants alter the physical–chemical properties of pesticide formulations, influencing either the droplet size or drift phenomenon. Selecting the appropriate adjuvant and understanding its characteristics can contribute to the efficiency of Plant Protection Product (PPP) application. This reduces drift losses and promotes better deposition [...] Read more.
Adjuvants alter the physical–chemical properties of pesticide formulations, influencing either the droplet size or drift phenomenon. Selecting the appropriate adjuvant and understanding its characteristics can contribute to the efficiency of Plant Protection Product (PPP) application. This reduces drift losses and promotes better deposition on the crop. The objective of this study was to evaluate the effects of four commercial adjuvants based on mineral oil (Agefix and Assist), vegetable oil (Aureo), and polymer (BREAK-THRU) on the physical–chemical properties (surface tension, contact angle, volumetric mass, electrical conductivity, and pH), droplet size, and drift, using pure water as the control treatment (no adjuvant). Surface tension and contact angle were measured with a DSA30 droplet shape analyzer, while droplet size measurements were determined through a laser diffraction particle analyzer (Malvern Spraytec), using a single flat fan spray nozzle (AXI 110 03) operating at 0.3 MPa. Drift reduction potential was evaluated inside a wind tunnel with an air speed of 2 m s−1. All adjuvants reduced surface tension and contact angle compared to water. volumetric median diameter (VMD) increased for Aureo, Assist, and Agefix, generating coarse, medium, and medium droplets, respectively, while BREAK-THRU formed fine droplets, similar to those generated by water. Aureo had the greatest reduction in Relative Span Factor (RSF), with a reduction of 30.3%. Overall, Aureo, Assist, and Agefix adjuvants significantly reduced the percentage of droplets <100 µm and increased those >500 µm. Drift reduction potential was achieved for all adjuvants, with Aureo showing the highest reduction of 59.35%. The study confirms that selecting the appropriate adjuvant can improve PPP application and promote environmental sustainability in agricultural practices. Full article
(This article belongs to the Special Issue Pesticides in the Environment: Impacts and Challenges in Agriculture)
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<p>Main instruments used during the first stage of experiments to measure the physical–chemical properties.</p>
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<p>General overview of the laser diffraction equipment (Malvern Spraytec).</p>
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<p>General schematic view of the wind tunnel and arrangement of the collectors for ground measurement tests.</p>
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<p>Effects of the four adjuvant solutions, including the control treatment (water), used for measuring volumetric mass (<b>A</b>), electrical conductivity (<b>B</b>), pH (<b>C</b>), surface tension (<b>D</b>), and contact angle (<b>D</b>). Means sharing the same letters do not differ statistically at <span class="html-italic">p</span>-level = 0.05 using Tukey’s test; * represents statistically significant differences with respect to water using Dunnet’s test at <span class="html-italic">p</span>-level = 0.05; ns indicates no significant difference with respect to water. Errors bars represent standard deviations.</p>
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<p>Effects of the four adjuvant solutions, including the control treatment (water), used for measuring volumetric diameters <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>D</mi> </mrow> <mrow> <mi>v</mi> <mn>0.1</mn> </mrow> </msub> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>D</mi> </mrow> <mrow> <mi>v</mi> <mn>0.5</mn> </mrow> </msub> </mrow> </semantics></math>, and <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>D</mi> </mrow> <mrow> <mi>v</mi> <mn>0.9</mn> </mrow> </msub> </mrow> </semantics></math> (<b>A</b>) and Relative Span Factor (<math display="inline"><semantics> <mrow> <mi>R</mi> <mi>S</mi> <mi>F</mi> </mrow> </semantics></math>) values (<b>B</b>). Means sharing the same letters for each volumetric diameter do not differ statistically at <span class="html-italic">p</span>-level = 0.05 using Tukey’s test; * represents statistically significant differences with respect to water using Dunnet’s test at <span class="html-italic">p</span>-level = 0.05; ns indicates no significant difference with respect to water. Errors bars represent standard deviations.</p>
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<p>Cumulative volume curves as a function of adjuvants and water.</p>
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<p>Percentages of the total volume of droplets with a diameter smaller than 100 μm (V &lt; 100) and greater than 500 μm (V &gt; 500) across the studied solutions. Means sharing the same letter do not differ statistically from each other at <span class="html-italic">p</span>-level = 0.05 using Tukey’s test; * represents statistically significant differences with respect to water using Dunnet’s test at <span class="html-italic">p</span>-level = 0.05; ns indicates no significant difference with respect to water. Errors bars represent standard deviations.</p>
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<p>(<b>A</b>) Adjuvant drift and (<b>B</b>) drift potential reduction. * Represents statistically significant differences with respect to water using Dunnet’s test at <span class="html-italic">p</span>-level = 0.05.</p>
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23 pages, 8949 KiB  
Article
Optimized Design and Test of Geometrically Nonlinear Static Aeroelasticity Model for High-Speed High-Aspect-Ratio Wing
by Xing Li, Wei Qian, Ling Xiao, Xinyu Ai and Jun Liu
Aerospace 2024, 11(12), 1015; https://doi.org/10.3390/aerospace11121015 - 10 Dec 2024
Viewed by 346
Abstract
Large transport aircraft tend to adopt a wing layout with a high aspect ratio and swept-back angle due to the requirement of a high lift-to-drag ratio. Composite material is typically employed to ensure the light weight of the structure, causing serious static aeroelasticity [...] Read more.
Large transport aircraft tend to adopt a wing layout with a high aspect ratio and swept-back angle due to the requirement of a high lift-to-drag ratio. Composite material is typically employed to ensure the light weight of the structure, causing serious static aeroelasticity problems to the aircraft. When the airplane is flying in the transonic region, its aerodynamic load is very complex, and the large load leads to large deformation of the wing, triggering geometric nonlinear effects, which further affects the static aerodynamic elasticity characteristics of the wing. In this study, in order to study the static aeroelastic characteristics of the transonic flow of a high-aspect-ratio airfoil, a new design method of the scaled similar optimization model is described, and the change in the model lift coefficient due to geometrically nonlinear static aeroelasticity effects when the angle of attack is changed was investigated by using simulation and wind tunnel test methods. In order to ensure the accuracy of the wing shape when the model was deformed greatly, this study employed the structural design scheme of the wing with the skin as the main stiffness component, and the thicknesses of different regions of the skin were used as the design variables for the stiffness optimization design. The engineering algorithm of nonlinear finite elements was used in this study to calculate the curve of lift with the angle of attack considering the geometric nonlinear static aeroelasticity effect. The results show that the similarity optimization process employed in this study can be used to complete the design of the high-speed aerostatic wing test model, and the wind tunnel test results show that geometric nonlinearity has a large impact on the lift coefficient of the wing. Full article
(This article belongs to the Special Issue Aircraft Design and System Optimization)
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<p>Flowchart for static aeroelasticity modeling.</p>
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<p>Flowchart of optimized design for scaling.</p>
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<p>Structural design of wind tunnel test model: (<b>a</b>) the structural design of the model; (<b>b</b>) the area divided by the skin; (<b>c</b>) the structural design of the pressure measurement; (<b>d</b>) the position of the pressure measurement structure in the wing.</p>
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<p>Nonlinear finite element iterative methods.</p>
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<p>Constraints for model design: (<b>a</b>) the parameters of the original model; (<b>b</b>) a photo of the wind tunnel.</p>
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<p>Target flexibility matrix of the original model.</p>
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<p>Electronic prototype of wind tunnel test model: (<b>a</b>) the finite element model; (<b>b</b>) the structural design model.</p>
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<p>Optimization of design-related materials: (<b>a</b>) Flexibility matrix of the simulation model, (<b>b</b>) Control points of the flexibility matrix.</p>
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<p>Target flexibility matrix for the target model.</p>
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<p>Optimization flowchart of the case.</p>
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<p>The result of the optimization, and 1–8 are the numbers of the different areas of the skin, the area numbers and thicknesses are shown in <a href="#aerospace-11-01015-t006" class="html-table">Table 6</a>.</p>
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<p>Error in optimization results. Errors greater than 3% but less than 5% are shown in yellow, errors greater than 5% are shown in red.</p>
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<p>Wing model test: (<b>a</b>) photo of the stiffness test; (<b>b</b>) photo of the profile inspection.</p>
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<p>Flexibility matrix error results for test and simulation models, and the red font color is the main diagonal of the flexibility matrix.</p>
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<p>Strength structure of the wing model: (<b>a</b>) the results of the finite element strength calculation; (<b>b</b>) the results of the strength test.</p>
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<p>Curves of load and deformation for strength tests.</p>
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<p>Process and results of ground vibration tests: (<b>a</b>) the MAC value of the shape; (<b>b</b>) the force hammer method to test the structural dynamics properties.</p>
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<p>Results of structural dynamics characterization of the wing model: (<b>a</b>) the FEM calculation of first vertical bend shape; (<b>b</b>) the test result of first vertical bend shape; (<b>c</b>) the FEM calculation of second vertical bend shape; (<b>d</b>) the test result of second vertical bend shape; (<b>e</b>) the FEM calculation of third vertical bend shape; (<b>f</b>) the test result of third vertical bend shape; (<b>g</b>) the FEM calculation of torsion shape; (<b>h</b>) the test result of torsion shape.</p>
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<p>Deflection results.</p>
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<p>Results of static aeroelasticity of wing models: (<b>a</b>) the simulated static aeroelastic properties; (<b>b</b>) the wind tunnel test static aeroelastic properties.</p>
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23 pages, 43085 KiB  
Article
Effects of Turbulence Modeling on the Simulation of Wind Flow over Typical Complex Terrains
by Guolin Ma, Linlin Tian, Yilei Song and Ning Zhao
Appl. Sci. 2024, 14(23), 11438; https://doi.org/10.3390/app142311438 - 9 Dec 2024
Viewed by 451
Abstract
The correct prediction of the wind speed and turbulence levels over complex terrain is essential for accurately assessing wind turbine wake recovery, power production, safety, and wind farm design. In this paper, two modified RANS turbulence models are proposed, which are innovative variants [...] Read more.
The correct prediction of the wind speed and turbulence levels over complex terrain is essential for accurately assessing wind turbine wake recovery, power production, safety, and wind farm design. In this paper, two modified RANS turbulence models are proposed, which are innovative variants of the conventional SST k-ω model and the linear Reynolds stress model (RSM) featuring optimized closure constants. Then, these two modified models and their origin models are applied to compare and analyze wind flows from a 3D hill wind tunnel experiment and two field measurements over typical complex terrain, including Askervein hill and Bolund island, with the aim of analyzing the sensitivity of wind flows to different RANS turbulence models. The study focuses on analyzing the effects of different turbulence models on the self-sustainability of wind speed and turbulent kinetic energy upstream of the computational domain and on the accuracy of wind flow prediction over complex terrain. The results show that our modified RSM model shows better agreement with the available experimental data on the upstream and leeward sides of all simulated hills. The wind speed on the leeward slope is particularly sensitive to the turbulence model, with a maximum difference in the relative root mean square error (RRMSE) that can reach 11% among the four models. The accuracy of the turbulent kinetic energy depends on the self-sustainability of the upstream turbulent kinetic energy and the predictive ability of the turbulence model for separated flows, and the maximum difference in the RRMSE of the four models can reach 47%. In addition, the advantages and disadvantages of the tested models are discussed to provide guidance for model selection during wind flow simulations in complex terrain. Full article
(This article belongs to the Special Issue Recent Advances in Wind Engineering and Applied Aerodynamics)
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<p>Schematic of the geometrical configuration of the 3D hill model.</p>
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<p>Illustration of the geographical location of the meteorological masts on Askervein hill.</p>
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<p>Illustration of the measurement locations on Bolund island.</p>
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<p>Schematic of the computational domain and validation cases grid. (<b>a</b>) Computational domain boundary conditions; (<b>b</b>) Wind tunnel 3D hill model case; (<b>c</b>) Askervein hill case; (<b>d</b>) Bolund island case.</p>
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<p>Vertical profiles of the mean streamwise velocity and turbulence intensity in the upstream of Case 1, where the reference wind speed <span class="html-italic">U</span><sub>ref</sub> = 5.8 m/s, and the probe location is at <span class="html-italic">x</span>/<span class="html-italic">L</span> = −24. (<b>a</b>) Dimensionless wind speed (<span class="html-italic">U</span>/<span class="html-italic">U</span><sub>ref</sub>); (<b>b</b>) Turbulence intensity in the streamwise direction (<span class="html-italic">I<sub>u</sub></span>).</p>
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<p>Vertical profiles of the mean streamwise velocity and turbulence intensity on the central plane of the hill in Case 1, where the reference wind speed <span class="html-italic">U</span><sub>ref</sub> = 5.8 m/s, and <span class="html-italic">x</span>/<span class="html-italic">L</span> ranges from −1 to 2.5. (<b>a</b>) Dimensionless wind speed (<span class="html-italic">U</span>/<span class="html-italic">U</span><sub>ref</sub>); (<b>b</b>) Turbulence intensity in the streamwise direction (<span class="html-italic">σ<sub>u</sub></span>/<span class="html-italic">U</span><sub>ref</sub>).</p>
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<p>Vertical profiles of the mean streamwise velocity and turbulence intensity on the central plane of the hill in Case 1, where the reference wind speed <span class="html-italic">U</span><sub>ref</sub> = 5.8 m/s, and <span class="html-italic">x</span>/<span class="html-italic">L</span> ranges from −1 to 2.5. (<b>a</b>) Dimensionless wind speed (<span class="html-italic">U</span>/<span class="html-italic">U</span><sub>ref</sub>); (<b>b</b>) Turbulence intensity in the streamwise direction (<span class="html-italic">σ<sub>u</sub></span>/<span class="html-italic">U</span><sub>ref</sub>).</p>
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<p>Statistical error of the dimensionless wind speed (<span class="html-italic">U</span>/<span class="html-italic">U</span><sub>ref</sub>) and streamwise direction turbulence intensity (<span class="html-italic">σ<sub>u</sub></span>/<span class="html-italic">U</span><sub>ref</sub>) in <a href="#applsci-14-11438-f006" class="html-fig">Figure 6</a>, considering all measurement locations around the hill (about 102 in total) in Case 1. (<b>a</b>) Mean absolute error of <span class="html-italic">U</span>/<span class="html-italic">U</span><sub>ref</sub> and <span class="html-italic">σ<sub>u</sub></span>/<span class="html-italic">U</span><sub>ref</sub>; (<b>b</b>) Relative root mean square error of <span class="html-italic">U</span>/<span class="html-italic">U</span><sub>ref</sub> and <span class="html-italic">σ<sub>u</sub></span>/<span class="html-italic">U</span><sub>ref</sub>.</p>
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<p>Contours of the streamwise velocity in the vertical midplane of the domain. The positions of the separation and reattachment points are shown by vertical solid lines, respectively. The positions of the separation and reattachment points for the experiment were <span class="html-italic">x<sub>s</sub></span>/<span class="html-italic">H</span> = 0.9 and <span class="html-italic">x<sub>r</sub></span>/<span class="html-italic">H</span> = 2.5, respectively.</p>
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<p>Numerical results at the RS location in the Askervein experiment case. ‘Cup’, ‘Sonic’, ‘Tilted Gill UVW’, ‘Vertical Gill UVW’, and ‘BRE tala kite’ stand for the different types of experimental data and fitting the Sonic data with the log-law wind speed profile. (<b>a</b>) Vertical distribution of the wind speed; (<b>b</b>) TKE profile.</p>
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<p>TKE contour plots of the vertical slice along the <span class="html-italic">x</span>-direction through the RS point in Case 2. (<b>a</b>) TKE decay in the upstream region from the inlet boundary to the front of the hill; (<b>b</b>) TKE around the hill.</p>
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<p>Numerical results of the Askervein experiment. Normalized wind speed and TKE along lines A, AA, and B at 10 m from the ground, where <span class="html-italic">U</span><sub>ref</sub> = 9.1 m/s, <span class="html-italic">k</span><sub>ref</sub> = 1.76 m<sup>2</sup>/s<sup>2</sup>, and <span class="html-italic">z</span><sub>ref</sub> = 10 m. ‘Exp’ stands for experimental data and ‘<span class="html-italic">H</span>/<span class="html-italic">H</span><sub>top</sub>’ stands for dimensionless hill height. (<b>a</b>) Wind speed-up on line A; (<b>b</b>) TKE increase ratio on line A; (<b>c</b>) Wind speed-up on line AA; (<b>d</b>) TKE increase ratio on line AA; (<b>e</b>) Wind speed-up on line B.</p>
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<p>Statistical error of the wind speed (<span class="html-italic">U</span>) and turbulent kinetic energy (TKE) in <a href="#applsci-14-11438-f011" class="html-fig">Figure 11</a>, considering all of the sites of Askervein hill (wind speed measurement sites: 40 in total; TKE measurement sites: 12 in total) in Case 2. (<b>a</b>) Mean absolute error of <span class="html-italic">U</span> and <span class="html-italic">TKE</span>; (<b>b</b>) Relative root mean square error of <span class="html-italic">U</span> and <span class="html-italic">TKE</span>.</p>
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<p>Vertical profiles of wind speed and TKE at location M0 (Bolund island case, 270° wind direction); <span class="html-italic">z</span> is the height above the ground, which, for M0, is above sea level. (<b>a</b>) Wind speed profile; (<b>b</b>) Turbulent kinetic energy profile.</p>
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<p>Vertical profiles of the wind speed and TKE (Bolund island case, 270° wind direction). ‘Cup’ and ‘Sonic’ stand for different types of experiment data; <span class="html-italic">z</span> is the height above the ground and ‘<span class="html-italic">H</span>/<span class="html-italic">H</span><sub>top</sub>’ stands for the dimensionless island height. (<b>a</b>) Wind speed profile along line B; (<b>b</b>) Wind speed profile along line A; (<b>c</b>) Turbulent kinetic energy profile along line B; (<b>d</b>) Turbulent kinetic energy profile along Line A; (<b>e</b>) Measurement locations along line B; (<b>f</b>) Measurement locations along line A.</p>
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<p>Vertical cross-section of the wind speed and TKE along line B (Bolund island case, 270° wind direction). The values of the wind speed (<b>a</b>) and TKE (<b>b</b>) for the white contour are 0 m/s and 3 m<sup>2</sup>/s<sup>2</sup>, respectively.</p>
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<p>Vertical profiles of the wind speed and TKE at location M0 (Bolund island case, 239° wind direction); <span class="html-italic">z</span> is the height above the ground, which, for M0, is above sea level. (<b>a</b>) Wind speed profile; (<b>b</b>) Turbulent kinetic energy profile.</p>
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<p>Vertical profiles of the wind speed and TKE (Bolund island case, 239° wind direction). ‘Cup’ and ‘Sonic’ stand for the different types of experimental data; <span class="html-italic">z</span> is the height above the ground. (<b>a</b>) Wind speed profile along line A; (<b>b</b>) Wind speed profile along line B; (<b>c</b>) Turbulent kinetic energy profile along line A; (<b>d</b>) Turbulent kinetic energy profile along line B.</p>
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14 pages, 2537 KiB  
Article
The Role of Radiation in Mixed Convection Heat Transfer from a Rectangular Fin Heat Sink: Experimental Investigation
by Mahmoud Rasti, Mohammad Hossein Kashefi, Amirreza Shahsavari, Mirae Kim, Wonseop Chung, Kyung Chun Kim and Se Chul Oh
Symmetry 2024, 16(12), 1628; https://doi.org/10.3390/sym16121628 - 8 Dec 2024
Viewed by 726
Abstract
Nowadays, effective thermal management is essential to prevent overheating in high-power devices. The utilization of high-emissivity materials plays a crucial role in enhancing heat transfer efficiency in both natural and mixed convection systems. This study presents an experimental investigation of a rectangular fin [...] Read more.
Nowadays, effective thermal management is essential to prevent overheating in high-power devices. The utilization of high-emissivity materials plays a crucial role in enhancing heat transfer efficiency in both natural and mixed convection systems. This study presents an experimental investigation of a rectangular fin heat sink’s thermal performance, exploring the effect of mixed convection and radiation heat transfer on two symmetrical fins with an aspect ratio of S*= 0.4 and 0.8. The experiment was carried out in a laboratory-scale wind tunnel, where the inlet fluid velocity was maintained at a constant value of u = 0.3 m/s across a range of Richardson number (0.6–5) and Rayleigh number (1.09–9.15 ×105), corresponding to the variation of heat loads 18–100 W. High-emissivity paint (ε = 0.85) was applied to the heat sink fins and compared to a low-emissivity paint (ε = 0.05) to assess the effect of performance. The results reveal that the high emissivity fin dissipated heat more effectively, with radiation and convection contributing approximately 25% and 75%, respectively, at the highest Rayleigh number. The study also revealed that increased fin spacing enhanced the view factor, although radiation heat transfer was higher for lower fin spacing due to a greater number of fins. Additionally, fin effectiveness was influenced more by fin spacing compared to surface emissivity, with effectiveness decreasing at higher Rayleigh numbers across all conditions. Infrared (IR) imaging confirmed that the high-emissivity coating allowed the heat sink to dissipate up to 30 °C from the heated surface, underscoring the substantial impact of high-emissivity materials in thermal management applications. Full article
(This article belongs to the Section Mathematics)
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<p>Schematic view of (<b>a</b>) experimental apparatus (<b>b</b>) rectangular fins heat sink.</p>
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<p>IR camera records of the heat sink surface temperature in different S* (<b>a</b>,<b>b</b>) 75 W; (<b>c</b>,<b>d</b>) 100 W.</p>
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<p>(<b>a</b>) Schematic of virtual channel and virtual walls (number of walls 1–6); (<b>b</b>) variation of view factor as a function of emissivity ε.</p>
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<p>The relative contribution of convection and radiation heat transfer in different Ra numbers.</p>
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<p>Convective heat transfer coefficient as a function of input heat load (Q).</p>
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<p>Fin efficiency as a function of Rayleigh number.</p>
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<p>Fin effectiveness as a function of Rayleigh number.</p>
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13 pages, 3709 KiB  
Article
Simulations on Evacuation Strategy and Evacuation Process of the Subway Train Under the Fire
by Xingji Wang, Bin Liu, Weilian Ma, Yuehai Feng, Qiang Li and Ting Sun
Fire 2024, 7(12), 464; https://doi.org/10.3390/fire7120464 - 6 Dec 2024
Viewed by 509
Abstract
This study focuses on the safe evacuation strategy and evacuation process in the subway train under the fires. The subway station evacuation mode should be adopted if the power system of a subway train is normal on fire. While, the tunnel evacuation mode [...] Read more.
This study focuses on the safe evacuation strategy and evacuation process in the subway train under the fires. The subway station evacuation mode should be adopted if the power system of a subway train is normal on fire. While, the tunnel evacuation mode should be adopted if the power system of the train fails because of the effects of fire. Under the tunnel evacuation mode, the direction of tunnel smoke should be opposite to that of most passengers, and passengers should be evacuated toward the fresh wind. By using the numerical simulation software Pathfinder and PyroSim, the passenger evacuation time under different conditions is calculated, and the safety of the evacuation process is evaluated. The results show that the evacuation time of the station evacuation mode is obviously shorter than that of the tunnel evacuation mode. With the same conditions, the evacuation time of the tunnel evacuation mode is 2193 s, which is about four times as much as the evacuation time of the station evacuation mode (526 s). The total evacuation time increases with the total number of passengers and the proportion of older people and children. Under an oil pool fire, which is an extreme fire condition, the fire environment inside the train may reach a level threatening the passengers’ safety before the evacuation is complete, even before the door opens; therefore, special attention should be paid to the safety issues in stage from the fire begins to the evacuation complete. Full article
(This article belongs to the Special Issue Fire Numerical Simulation, Second Volume)
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<p>Nine typical evacuation modes under the tunnel evacuation conditions (<span class="html-italic">s</span>: evacuate distance that passengers need to walk to the safety exit; <span class="html-italic">l</span>: length of the tunnel between the two contact channels).</p>
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<p>Simulation models of the subway train, the tunnel, and the platform of the subway station.</p>
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<p>Evacuation process in the subway train and the platform in Case 1 (Unit: person/m<sup>2</sup>).</p>
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<p>Curve of the evacuation passengers versus time in Cases 1 to 3.</p>
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<p>Temperature profiles inside the subway train in the baggage and oil pool fire conditions before the door opened (Range: the fire carriage and its adjacent carriages; Unit: °C).</p>
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<p>Distribution of the passengers inside the carriages under different personnel densities.</p>
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<p>Evacuation process for passengers in a subway train and the tunnel in Case 4 (Unit: People/m<sup>2</sup>).</p>
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<p>Curve of the evacuation passengers versus time in Case 4.</p>
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<p>Smoke movement and temperature distribution in a tunnel for luggage fire and oil pool fire with different smoke exhaust conditions.</p>
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<p>The curve of evacuees versus time in Cases 5 to 9.</p>
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25 pages, 7795 KiB  
Article
Assessing Particulate Matter Deposition and Resuspension by Living Wall Systems in a Wind Tunnel Setup
by Tess Ysebaert, Kyra Koch, Roeland Samson and Siegfried Denys
Sustainability 2024, 16(23), 10733; https://doi.org/10.3390/su162310733 - 6 Dec 2024
Viewed by 409
Abstract
This study examines the particulate matter (PM) capture capacity of living wall systems (LWSs), focusing on leaf traits that facilitate PM deposition. Six LWS designs, differing in structure and substrate, were tested under constant airflow conditions with and without additional PM. Results showed [...] Read more.
This study examines the particulate matter (PM) capture capacity of living wall systems (LWSs), focusing on leaf traits that facilitate PM deposition. Six LWS designs, differing in structure and substrate, were tested under constant airflow conditions with and without additional PM. Results showed that planter-based LWSs reduced PM0.1 by 2% and PM2.5 by 4%, while a textile LWS reduced PM0.1 by 23% and PM2.5 by 5%, though geotextile textile increased PM by 11% for both fractions. A moss substrate LWS worsened air quality, raising PM0.1 by 2% and PM2.5 by 5%. Magnetic analysis of leaf-deposited PM (SIRM) revealed species-specific differences (p < 0.001), with SIRM values ranging from 5 ± 1 µA to 260 ± 1 µA and higher PM accumulation in plants with lower specific leaf areas. No differences were observed in SIRM between deposition and resuspension phases, indicating the PM source lacked sufficient magnetisable particles. The findings highlight the potential of LWSs in urban environments for air quality improvement but underscore the importance of selecting suitable LWS structures and plant species. Full article
(This article belongs to the Special Issue Benefits of Green Infrastructures on Air Quality in Urban Spaces)
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Figure 1
<p>Plant composition used in all LWS panels. The pictured LWS consists of planters stacked on top of each other.</p>
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<p>Wind tunnel setup for measuring the influence of different LWS panels on PM concentrations (1: inlet of air, 2: inlet of generated particles, 3: flow conditioning section (honeycomb and screens), 4: test section, 5: extraction ventilator, 6: outlet). Wind speed (U) and PM concentration (C) measurements were taken at the inlet (C<sub>in</sub>, U<sub>in</sub>) and outlet (C<sub>out,1</sub>, U<sub>out,1</sub>) of the test section and in front of the fan (C<sub>out,2</sub>, U<sub>out,2</sub>).</p>
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<p>Example of how the LWS setups were positioned in the plant section of the wind tunnel, i.e., parallel to the incoming air stream (going from right to left in the picture and indicated by the white arrows). The LWS on the picture is Planter 1.</p>
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<p>SIRM value (µA m<sup>2</sup>) of different amounts of Arizona Fine Test dust (g), including a linear regression with its equation and R<sup>2</sup> value. Points represent measurements of replicated quantities.</p>
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<p>Collection efficiency (%) corrected for an empty wind tunnel for PM<sub>0.1</sub> and PM<sub>2.5</sub> when placing different setups in the wind tunnel at constant wind speed without PM addition (<b>resuspension</b>). Both <span class="html-italic">CE</span><sub>1</sub> between <span class="html-italic">C<sub>in</sub></span> and <span class="html-italic">C<sub>out</sub></span><sub>,1</sub> and <span class="html-italic">CE</span><sub>2</sub> between <span class="html-italic">C<sub>in</sub></span> and <span class="html-italic">C<sub>out</sub></span><sub>,2</sub> are shown. Triangles represent the average collection efficiency, and dots represent those of each experiment separately (<span class="html-italic">n</span> = 2–3).</p>
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<p>Collection efficiency (%) corrected for an empty wind tunnel for PM<sub>0.1</sub> and PM<sub>2.5</sub> when placing different types of LWS in the wind tunnel at constant wind speed with PM addition (deposition). Both <span class="html-italic">CE</span><sub>1</sub> between <span class="html-italic">C<sub>in</sub></span> and <span class="html-italic">C<sub>out</sub></span><sub>,1</sub> and <span class="html-italic">CE</span><sub>2</sub> between <span class="html-italic">C<sub>in</sub></span> and <span class="html-italic">C<sub>out</sub></span><sub>,2</sub> are shown. Triangles represent the average collection efficiency, and dots represent those of each experiment separately (<span class="html-italic">n</span> = 2–3).</p>
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<p>Leaf area-normalised SIRM values (µA), presented on a logarithmic scale, per replicate for all plant species and setups of the different sampling time points: outdoor (before doing the wind tunnel experiment), resuspension (after 3 h in the wind tunnel subjected to a constant air flow) and deposition (after 3 h in the wind tunnel subjected to a constant air flow with the addition of PM sources).</p>
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<p>Leaf area-normalised SIRM values (µA), presented on a logarithmic scale, aggregated for all treatments for each tested plant species and for the different setups (Moss, Planter 1–2, Textile 1–3).</p>
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<p>Leaf area-normalised SIRM values (µA), presented on a logarithmic scale, of the different plant species tested, aggregated for all treatments (outdoor, resuspension and deposition) and setups (LWS types from different manufacturers). Letters A–D indicate significant groups (<span class="html-italic">p</span> ≤ 0.05).</p>
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<p>Biplot of the principal component analysis (PCA) on the morphological parameters measured at leaf level of the tested plant species (n = 7): leaf dissection index (LDI, -), functional leaf size (FLS, -), average single leaf area (LA, m<sup>2</sup>), specific leaf area (SLA, g m<sup>−2</sup>), drop contact angle at abaxial (ab, °) and adaxial (ad, °). Principal components 1 (PC1) and 2 (PC2) explained 41.1% and 32.8% of the variance, respectively.</p>
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<p>Leaf area-normalised SIRM values (µA), presented on a logarithmic scale, of all tested plant species, averaged for all setups and replicates as a function of specific leaf area (SLA, g m<sup>−2</sup>) after resuspension (top) and deposition (bottom). The relationship between SIRM and SLA is shown, together with the R<sup>2</sup> value.</p>
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<p>Leaf area-normalised deposited PM mass (µg) calculated from the difference in SIRM (µA m<sup>2</sup>) between deposition and resuspension aggregated for all setups (LWS types from different manufacturers). No significant groups were distinguished.</p>
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<p>Area-normalised SIRM values (µA) or mass-normalised SIRM values (µA m<sup>2</sup> g<sup>−1</sup>) in the case of the moss system, presented on a logarithmic scale, per replicate of the supporting structure of the different setups, aggregated for all treatments (outdoor, resuspension and deposition).</p>
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10 pages, 2083 KiB  
Article
Validity and Reliability of Wind Speed Calculated by Notio in Comparison with a Hot-Wire Anemometer
by Manuel Ordiñana-Pérez, Manuel Mateo-March, Ainoa Roldan, David Barranco-Gil, Mikel Zabala and Cristina Blasco-Lafarga
Designs 2024, 8(6), 131; https://doi.org/10.3390/designs8060131 - 6 Dec 2024
Viewed by 452
Abstract
Optimizing aerodynamic efficiency is crucial in competitive cycling, where aerodynamic resistance significantly limits performance. Devices like Notio have emerged to calculate the coefficient of drag area (CDA) considering dynamic pressure data calculated by an integrated Pitot-static tube. This study aimed to [...] Read more.
Optimizing aerodynamic efficiency is crucial in competitive cycling, where aerodynamic resistance significantly limits performance. Devices like Notio have emerged to calculate the coefficient of drag area (CDA) considering dynamic pressure data calculated by an integrated Pitot-static tube. This study aimed to evaluate the validity and reliability of Pitot-static tube calculations through wind speed (WS) data against a hot-wire anemometer (HWA). Sixty recordings were made, lasting 30 s each, in a closed-circuit wind tunnel at four different WS (≈30 to ≈60 km/h), and at five different yaw angles (0° to 20°). Initially, Notio showed WS 6.44% higher than HWA. The calibration process recommended by the Notio manufacturer reduced the differences to a non-significant 0.76%. Comparison of the WS of Notio calibrated and HWA only showed significant differences in the WS group of ≈60 km/h. There were no significant differences in the comparison of yaw angles groups. The reliability of Notio was worse than that of the HWA. In conclusion, Notio calibrated at a speed close to its use allows for reliable and accurate calculation of WS over a wide range of yaw angles under controlled wind tunnel conditions without the presence of a cyclist and bicycle. However, due to the influence of WS on aerodynamic drag, small errors in WS could translate into considerable values of CDA for cycling performance. Full article
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<p>Three-dimensional representation of the wind tunnel, with the two test sections shaded, the low-speed test section on the left and the high-speed test section on the right. Original image of the wind tunnel manual with permission (Oritia &amp; Boreas).</p>
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<p>Notio on the left and hot-wire anemometer Kanomax Anemomaster 6036-CE on the right. Original images from the manufacturer manuals with permission.</p>
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<p>Scaled graphical representation of the arrangement of Notio and hot-wire anemometer in the high-speed test section of the wind tunnel.</p>
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<p>Comparative boxplot between Notio, Notio_C, and HWA wind speed (WS), including medians, interquartile ranges, and outliers, with statistical significance analysis: ns, non-significant; *** <span class="html-italic">p</span> &lt; 0.001. Created with GraphPad Prism 10.</p>
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<p>Agreement between Notio_C and HWA wind speed (WS) Bland–Altman plot. Created with GraphPad Prism 10.</p>
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22 pages, 5957 KiB  
Article
Influence of Across-Wind on Rectangular Tall Buildings
by Subramaniam Shanthi, Ramamurthy Vidjeapriya and Krishnan Prabhakaran Jaya
Buildings 2024, 14(12), 3902; https://doi.org/10.3390/buildings14123902 - 6 Dec 2024
Viewed by 355
Abstract
This study centered on investigating the effect of wind loads on tall rectangular buildings, particularly with various aspect ratios. The aim was to assess the across-wind base shear and moment of tall rectangular buildings using both experimental methods and an Ansys Fluent 2024 [...] Read more.
This study centered on investigating the effect of wind loads on tall rectangular buildings, particularly with various aspect ratios. The aim was to assess the across-wind base shear and moment of tall rectangular buildings using both experimental methods and an Ansys Fluent 2024 analysis. The results were compared between different sections with various aspect ratios according to Australian and Indian building codes. Six models with rectangular sections were employed, with three models measuring 20 m × 60 m and the remaining measuring 20 m × 80 m, to analyze the force measurements at different pitch angles ranging between 0° and 90°. All six models with various aspect ratios (1:3:7, 1:3:8, 1:3:9, 1:4:7, 1:4:8, and 1:4:9) were used to test the forces in both open and urban terrains. In conclusion, this study highlights the major factors that impact the design of tall rectangular structures. A new formula was developed to estimate the across-wind spectrum coefficient, which was followed in the across-wind force and moment calculations in IS 875-Part III:2015. From this study, it is evident that, through advanced techniques such as computational fluid dynamics (CFD) simulations, designers can gain insights into pressure distributions and make informed decisions to optimize the performance of buildings against wind loads. Full article
(This article belongs to the Section Building Structures)
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<p>Wind tunnel parts and model.</p>
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<p>Model orientation on the turn table with open terrain.</p>
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<p>Models with terrain blocks and turbulence separators on the table.</p>
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<p>Across-wind vortices.</p>
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<p>(<b>a</b>) Force comparison using the Australian code. (<b>b</b>) Force comparison using the Indian code.</p>
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<p>Pressure distribution of the CAARC building.</p>
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<p>(<b>a</b>) Pressure distribution of the 20 × 60 × 180 section. (<b>b</b>) Flow around the building sized 20 × 60 × 180. (<b>c</b>) Pressure distribution of the 20 × 60 × 160 section. (<b>d</b>) Flow around the building sized 20 × 60 × 160. (<b>e</b>) Pressure distribution of the 20 × 60 × 140 section. (<b>f</b>) Flow around the building sized 20 × 60 × 140. (<b>g</b>) Pressure distribution of the 20 × 80 × 180 section. (<b>h</b>) Flow around the building sized 20 × 80 × 180. (<b>i</b>) Pressure distribution of the 20 × 80 × 160 section. (<b>j</b>) Flow around the building sized 20 × 80 × 160. (<b>k</b>) Pressure distribution of the 20 × 80 × 140 section. (<b>l</b>) Flow pattern around the building sized 20 × 80 × 140.</p>
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<p>(<b>a</b>) Pressure distribution of the 20 × 60 × 180 section. (<b>b</b>) Flow around the building sized 20 × 60 × 180. (<b>c</b>) Pressure distribution of the 20 × 60 × 160 section. (<b>d</b>) Flow around the building sized 20 × 60 × 160. (<b>e</b>) Pressure distribution of the 20 × 60 × 140 section. (<b>f</b>) Flow around the building sized 20 × 60 × 140. (<b>g</b>) Pressure distribution of the 20 × 80 × 180 section. (<b>h</b>) Flow around the building sized 20 × 80 × 180. (<b>i</b>) Pressure distribution of the 20 × 80 × 160 section. (<b>j</b>) Flow around the building sized 20 × 80 × 160. (<b>k</b>) Pressure distribution of the 20 × 80 × 140 section. (<b>l</b>) Flow pattern around the building sized 20 × 80 × 140.</p>
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<p>(<b>a</b>) Pressure distribution of the 20 × 60 × 180 section. (<b>b</b>) Flow around the building sized 20 × 60 × 180. (<b>c</b>) Pressure distribution of the 20 × 60 × 160 section. (<b>d</b>) Flow around the building sized 20 × 60 × 160. (<b>e</b>) Pressure distribution of the 20 × 60 × 140 section. (<b>f</b>) Flow around the building sized 20 × 60 × 140. (<b>g</b>) Pressure distribution of the 20 × 80 × 180 section. (<b>h</b>) Flow around the building sized 20 × 80 × 180. (<b>i</b>) Pressure distribution of the 20 × 80 × 160 section. (<b>j</b>) Flow around the building sized 20 × 80 × 160. (<b>k</b>) Pressure distribution of the 20 × 80 × 140 section. (<b>l</b>) Flow pattern around the building sized 20 × 80 × 140.</p>
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<p>(<b>a</b>) Pressure distribution of the 20 × 60 × 180 section. (<b>b</b>) Flow around the building sized 20 × 60 × 180. (<b>c</b>) Pressure distribution of the 20 × 60 × 160 section. (<b>d</b>) Flow around the building sized 20 × 60 × 160. (<b>e</b>) Pressure distribution of the 20 × 60 × 140 section. (<b>f</b>) Flow around the building sized 20 × 60 × 140. (<b>g</b>) Pressure distribution of the 20 × 80 × 180 section. (<b>h</b>) Flow around the building sized 20 × 80 × 180. (<b>i</b>) Pressure distribution of the 20 × 80 × 160 section. (<b>j</b>) Flow around the building sized 20 × 80 × 160. (<b>k</b>) Pressure distribution of the 20 × 80 × 140 section. (<b>l</b>) Flow pattern around the building sized 20 × 80 × 140.</p>
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<p>(<b>a</b>) Pressure distribution of the 20 × 60 × 180 section. (<b>b</b>) Flow around the building sized 20 × 60 × 180. (<b>c</b>) Pressure distribution of the 20 × 60 × 160 section. (<b>d</b>) Flow around the building sized 20 × 60 × 160. (<b>e</b>) Pressure distribution of the 20 × 60 × 140 section. (<b>f</b>) Flow around the building sized 20 × 60 × 140. (<b>g</b>) Pressure distribution of the 20 × 80 × 180 section. (<b>h</b>) Flow around the building sized 20 × 80 × 180. (<b>i</b>) Pressure distribution of the 20 × 80 × 160 section. (<b>j</b>) Flow around the building sized 20 × 80 × 160. (<b>k</b>) Pressure distribution of the 20 × 80 × 140 section. (<b>l</b>) Flow pattern around the building sized 20 × 80 × 140.</p>
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<p>Flowchart describing the analytical procedure.</p>
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<p>(<b>a</b>) Base shear of the 20 × 60 × 140/160/180 section in the open and urban terrains. (<b>b</b>) Base shear of the 20 × 80 × 140/160/180 section in the open and urban terrains. (<b>c</b>) Moment of the 20 × 60 × 140/160/180 section in the open and urban terrains. (<b>d</b>) Moment of the 20 × 80 × 140/160/180 section in the open and urban terrains.</p>
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<p>(<b>a</b>) Base shear of the 20 × 60 × 140/160/180 section in the open and urban terrains. (<b>b</b>) Base shear of the 20 × 80 × 140/160/180 section in the open and urban terrains. (<b>c</b>) Moment of the 20 × 60 × 140/160/180 section in the open and urban terrains. (<b>d</b>) Moment of the 20 × 80 × 140/160/180 section in the open and urban terrains.</p>
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<p>(<b>a</b>) Base force comparison of the 20 × 60 × 140/160/180 section in the open and urban terrains. (<b>b</b>) Base force comparison of the 20 × 80 × 140/160/180 section in the open and urban terrains.</p>
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<p>Comparison of the base forces obtained using the IS and Australian codes with CFD.</p>
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<p>(<b>a</b>) Crosswind force spectrum for the 7:1:3, 8:1:3, and 9:1:3 rectangular sections. (<b>b</b>) Crosswind force spectrum for the 7:3:1, 8:3:1, and 9:3:1 rectangular sections.</p>
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