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12 pages, 14593 KiB  
Article
Comparative Analysis of Magnetic Field Distribution Characteristics of Two Shapes of Air-Core Bridge Arm Reactors
by Tao Jiang and Zhe Yang
Energies 2024, 17(18), 4652; https://doi.org/10.3390/en17184652 - 18 Sep 2024
Viewed by 928
Abstract
The application of air-core reactors in power systems is extensive and primarily aimed at enhancing system stability, limiting short-circuit currents, and providing reactive power compensation. Currently, the type most commonly used in power systems is the cylindrical-shaped air-core reactor (CAR), known for its [...] Read more.
The application of air-core reactors in power systems is extensive and primarily aimed at enhancing system stability, limiting short-circuit currents, and providing reactive power compensation. Currently, the type most commonly used in power systems is the cylindrical-shaped air-core reactor (CAR), known for its stable mechanical structure and mature manufacturing process. However, the external magnetic field generated by this reactor propagates over a considerable distance in the air, which can interfere with the normal operation of many power electronic devices. This paper presents a comparative analysis between a novel annular-shaped air-core bridge arm reactor (AABAR) and the widely used cylindrical-shaped air-core bridge arm reactor (CABAR) within a DC transformer system. The comparison focuses on the magnetic field distribution, including magnetic flux density, magnetic field radiation range, and magnetic field energy, as well as the attenuation characteristics of these physical quantities. The concept of magnetic clearance (MC) is introduced as a quantitative metric. Through finite element simulation software (AEDT 2021 R1), it is demonstrated that the annular-shaped air-core reactor design can significantly improve spatial utilization and reduce the actual usage space of the reactors in DC transformer systems. Full article
(This article belongs to the Section F1: Electrical Power System)
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Figure 1

Figure 1
<p>3D models of two shapes of bridge arm air-core reactor: (<b>a</b>) CABAR; (<b>b</b>) AABAR.</p>
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<p>Topology of the DC transformer and the configuration positions of the BARs.</p>
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<p>Schematic diagrams of the winding coils constituting the two BARs: (<b>a</b>) front view of the winding coil; (<b>b</b>) side view of the winding coil.</p>
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<p>Geometric views and parameter annotations of the CABAR and AABAR: (<b>a</b>) front view and parameter annotations of the CABAR; (<b>b</b>) top view and parameter annotations of the CABAR; (<b>c</b>) front view and parameter annotations of the AABAR; (<b>d</b>) top view and parameter annotations of the AABAR. <math display="inline"><semantics> <mrow> <msub> <mi>r</mi> <mi>i</mi> </msub> </mrow> </semantics></math> is the inner radius of the winding coils constituting both shapes of BARs; <math display="inline"><semantics> <mrow> <msub> <mi>r</mi> <mi>o</mi> </msub> </mrow> </semantics></math> is the outer radius of the winding coils constituting both shapes of BARs; <math display="inline"><semantics> <mi>T</mi> </semantics></math> is the thickness of the winding coil; <math display="inline"><semantics> <mrow> <msub> <mi>R</mi> <mi>i</mi> </msub> </mrow> </semantics></math> is the inner radius of both shapes of BARs; <math display="inline"><semantics> <mrow> <msub> <mi>R</mi> <mi>o</mi> </msub> </mrow> </semantics></math> is the outer radius of both shapes of BARs; <math display="inline"><semantics> <mi>H</mi> </semantics></math> is the height of both shapes of BARs; <math display="inline"><semantics> <mi>d</mi> </semantics></math> is the inter-turn distance of the CABAR; <math display="inline"><semantics> <mrow> <msub> <mi>d</mi> <mrow> <mi>min</mi> </mrow> </msub> </mrow> </semantics></math> is the minimum inter-turn distance of the AABAR; <math display="inline"><semantics> <mrow> <msub> <mi>d</mi> <mrow> <mi>max</mi> </mrow> </msub> </mrow> </semantics></math> is the maximum inter-turn distance of the AABAR.</p>
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<p>Vertical section schematic and magnetic field physical quantities diagram of the AABAR: (<b>a</b>) vertical section schematic; (<b>b</b>) magnetic flux density cloud diagram of the vertical section; (<b>c</b>) 3D distribution diagram of magnetic field energy.</p>
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<p>Simulation process flowchart of two shapes of BARs.</p>
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<p>Schematic diagram of data extraction lines: (<b>a</b>) transverse data extraction line for CABAR; (<b>b</b>) longitudinal data extraction line for CABAR; (<b>c</b>) transverse data extraction line for AABAR; (<b>d</b>) longitudinal data extraction line for AABAR.</p>
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<p>Simulation results of magnetic flux density magnitudes for the two types of air-core reactors: (<b>a</b>) transverse magnetic flux density of CABAR; (<b>b</b>) transverse magnetic flux density of AABAR; (<b>c</b>) longitudinal magnetic flux density of CABAR; (<b>d</b>) longitudinal magnetic flux density of AABAR.</p>
Full article ">Figure 8 Cont.
<p>Simulation results of magnetic flux density magnitudes for the two types of air-core reactors: (<b>a</b>) transverse magnetic flux density of CABAR; (<b>b</b>) transverse magnetic flux density of AABAR; (<b>c</b>) longitudinal magnetic flux density of CABAR; (<b>d</b>) longitudinal magnetic flux density of AABAR.</p>
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<p>Comparison of flux densities of reactors with different shapes.</p>
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<p>Simulation cloud diagrams of the radiation range for the two shapes of BARs: (<b>a</b>) front view of the radiation range of CABAR; (<b>b</b>) front view of the radiation range of AABAR; (<b>c</b>) top view of the radiation range of CABAR; (<b>d</b>) top view of the radiation range of AABAR.</p>
Full article ">Figure 10 Cont.
<p>Simulation cloud diagrams of the radiation range for the two shapes of BARs: (<b>a</b>) front view of the radiation range of CABAR; (<b>b</b>) front view of the radiation range of AABAR; (<b>c</b>) top view of the radiation range of CABAR; (<b>d</b>) top view of the radiation range of AABAR.</p>
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<p>Simulation results of magnetic field energy density for the two shapes of air-core reactors: (<b>a</b>) transverse magnetic field energy density of CABAR; (<b>b</b>) transverse magnetic field energy density of AABAR; (<b>c</b>) longitudinal magnetic field energy density of CABAR; (<b>d</b>) longitudinal magnetic field energy density of AABAR.</p>
Full article ">Figure 11 Cont.
<p>Simulation results of magnetic field energy density for the two shapes of air-core reactors: (<b>a</b>) transverse magnetic field energy density of CABAR; (<b>b</b>) transverse magnetic field energy density of AABAR; (<b>c</b>) longitudinal magnetic field energy density of CABAR; (<b>d</b>) longitudinal magnetic field energy density of AABAR.</p>
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13 pages, 3324 KiB  
Article
The Effect of Buckwheat Resistant Starch on Intestinal Physiological Function
by Zhan-Bin Sun, Xiao Zhang, Yi Yan, Jia-Liang Xu, Xin Lu and Qing Ren
Foods 2023, 12(10), 2069; https://doi.org/10.3390/foods12102069 - 21 May 2023
Cited by 6 | Viewed by 4346
Abstract
Resistant starch appears to have promising effects on hypertension, cardiovascular and enteric illness. The influence of resistant starch on intestinal physiological function has drawn great attention. In this study, we first analyzed the physicochemical characteristics, including the crystalline properties, amylose content, and anti-digestibility [...] Read more.
Resistant starch appears to have promising effects on hypertension, cardiovascular and enteric illness. The influence of resistant starch on intestinal physiological function has drawn great attention. In this study, we first analyzed the physicochemical characteristics, including the crystalline properties, amylose content, and anti-digestibility among different types of buckwheat-resistant starch. The influence of resistant starch on the physiological functions of the mouse intestinal system, contained defecation, and intestinal microbes were also evaluated. The results showed that the crystalline mold of buckwheat-resistant starch changed from A to B + V after acid hydrolysis treatment (AHT) and autoclaving enzymatic debranching treatment (AEDT). The amylose content in AEDT was higher than in AHT and raw buckwheat. Moreover, the anti-digestibility of AEDT was also stronger than that in AHT and raw buckwheat. The buckwheat-resistant starch can promote bowel intestinal tract movement. The quantity of intestinal microbe was regulated by buckwheat-resistant starch. Our research demonstrates an effective preparation method for improving the quality of buckwheat-resistant starch and found that buckwheat-resistant starch has the role of adjusting the distribution of the intestinal flora and maintaining the health of the body. Full article
(This article belongs to the Special Issue Food Quality Control: Microbial Diversity and Metabolic Regulation)
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Figure 1
<p>X-ray diffraction molds of raw and treated starches. a indicates buckwheat starch; b indicates resistant starch prepared using the acid hydrolysis method; c represents resistant starch prepared using the autoclaving enzymatic debranching method.</p>
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<p>In vitro digestion rates of raw and resistant starches. The X-axis indicates digestion time; the Y-axis indicates digestion rates. RS① indicates preparation by the acid hydrolysis method with a resistant starch content of 29%; RS②–⑤ indicates preparation by the autoclaving enzymatic debranching method with the resistant starch contents of 31.7%, 35.6%, 39.7%, and 45.5%, respectively.</p>
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<p>The effects of buckwheat resistance starch on the quantity of <span class="html-italic">Citrobacter</span> on feces (<b>A</b>), colon (<b>B</b>), and caecum (<b>C</b>). NG: negative control group; BS: buckwheat starch group; LBS: low dosage of buckwheat resistance starch group; MBS: middle dosage of buckwheat resistance starch group; HBS: high dosage of buckwheat resistance starch group.</p>
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<p>The effects of buckwheat resistance starch on the quantity of <span class="html-italic">Enterococcus</span> on feces (<b>A</b>), colon (<b>B</b>), and caecum (<b>C</b>). NG: negative control group; BS: buckwheat starch group; LBS: low dosage of buckwheat resistance starch group; MBS: middle dosage of buckwheat resistance starch group; HBS: high dosage of buckwheat resistance starch group.</p>
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<p>The effects of buckwheat resistance starch on the quantity of <span class="html-italic">Bifidobacterium</span> on feces (<b>A</b>), colon (<b>B</b>), and caecum (<b>C</b>). NG: negative control group; BS: buckwheat starch group; LBS: low dosage of buckwheat resistance starch group; MBS: middle dosage of buckwheat resistance starch group; HBS: high dosage of buckwheat resistance starch group.</p>
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<p>The effects of buckwheat resistance starch on the quantity of <span class="html-italic">Lactobacillus</span> on feces (<b>A</b>), colon (<b>B</b>), and caecum (<b>C</b>). NG: negative control group; BS: buckwheat starch group; LBS: low dosage of buckwheat resistance starch group; MBS: middle dosage of buckwheat resistance starch group; HBS: high dosage of buckwheat resistance starch group.</p>
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12 pages, 6138 KiB  
Article
Unveil Overcharge Performances of Activated Carbon Cathode in Various Li-Ion Electrolytes
by Xianzhong Sun, Yabin An, Xiong Zhang, Kai Wang, Changzhou Yuan, Xiaohu Zhang, Chen Li, Yanan Xu and Yanwei Ma
Batteries 2023, 9(1), 11; https://doi.org/10.3390/batteries9010011 - 24 Dec 2022
Cited by 12 | Viewed by 2581
Abstract
Typically, the practical lithium-ion capacitor (LIC) is composed of a capacitive cathode (activated carbon, AC) and a battery-type anode (graphite, soft carbon, hard carbon). There is a risk of the LIC cell overcharging to an unsafe voltage under electrical abuse conditions. Since the [...] Read more.
Typically, the practical lithium-ion capacitor (LIC) is composed of a capacitive cathode (activated carbon, AC) and a battery-type anode (graphite, soft carbon, hard carbon). There is a risk of the LIC cell overcharging to an unsafe voltage under electrical abuse conditions. Since the anode potential is usually quite low during the charging process and can be controlled by adjusting the amount of anode materials, the overcharge performances of LIC full-cell mainly depend on the AC cathode. Thus, it is necessary to independently investigate the overcharge behaviors of the AC cathode in nonaqueous Li-ion electrolytes without the interference of the anode electrode. In this work, the stable upper potential limits of the AC electrode in three types of lithium-ion electrolytes were determined to be 4.0−4.1 V via the energy efficiency method. Then, the AC//Li half-cells were charged to 5.0 V and 10.0 V, respectively, to investigate the overcharge behaviors. For the half-cells with propylene carbonate (PC)-based electrolytes, the voltage increased sharply to 10.0 V with a vertical straight line at the end of the overcharging process, indicating that the deposits of electrolyte decomposition had separated the AC electrode surface from the electrolytes, forming a self-protective passivation film with a dielectric capacitor behavior. The dense and compact passivation film is significant in separating the AC electrode surface from the electrolytes and preventing LIC cells from volume expansion and explosion risks under electrical abuse and overcharging conditions. Full article
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Figure 1
<p>Galvanostatic charging and discharging curves of AC//Li half-cells upon the voltage ranges from <span class="html-italic">pzc</span> to various vertex voltages in different Li-ion electrolytes: (<b>a</b>) EDD, (<b>c</b>) EPD, and (<b>e</b>) PCE; the corresponding EE, CE, and VE values as functions of the vertex voltage: (<b>b</b>) EDD, (<b>d</b>) EPD, and (<b>f</b>) PCE.</p>
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<p>Overcharged to 5.0 V of AC//Li half-cells using EDD, EPD, and PCE electrolytes, respectively: (<b>a</b>) Voltage versus time curves, (<b>b</b>) d<span class="html-italic">Q/</span>d<span class="html-italic">V</span> versus voltage curves.</p>
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<p>Overcharged to 10.0 V of AC//Li half-cells: (<b>a</b>) voltage versus time profiles, and photos of the half-cells after overcharging process with different electrolytes: (<b>b</b>) EPD, (<b>c</b>) PCE and (<b>d</b>) EDD.</p>
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<p>SEM images of (<b>a</b>) fresh AC electrode and the AC electrodes from the AC//Li half-cell charged to the high voltage of 10.0 V with different electrolytes; (<b>b</b>,<b>c</b>) PCE; (<b>d</b>) EPD.</p>
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<p>EDS spectra of AC electrodes obtained from the AC//Li half-cell with PCE electrolyte after overcharging to the high voltage of 10.0 V: (<b>a</b>) zone 1; (<b>b</b>) zone 2.</p>
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<p>Identification and quantification of emitted gases: (<b>a</b>) gas chromatograph spectrum; (<b>b</b>) normalized relative volume ratio of the released gas products.</p>
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<p>(<b>a</b>,<b>b</b>) SEM image of AC electrode after overcharging, and (<b>c</b>) EDS analysis.</p>
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<p>Schematic diagram of the decomposition of electrolyte on AC surface upon overcharging.</p>
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16 pages, 4583 KiB  
Article
Comparison of the Aircraft Noise Calculation Programs sonAIR, FLULA2 and AEDT with Noise Measurements of Single Flights
by Jonas Meister, Stefan Schalcher, Jean-Marc Wunderli, David Jäger, Christoph Zellmann and Beat Schäffer
Aerospace 2021, 8(12), 388; https://doi.org/10.3390/aerospace8120388 - 10 Dec 2021
Cited by 21 | Viewed by 4168
Abstract
As aircraft noise affects large areas around airports, noise exposure calculations need to be highly accurate. In this study, we compare noise exposure measurements with calculations of several thousand single flights at Zurich and Geneva airports, Switzerland, of three aircraft noise calculation programs: [...] Read more.
As aircraft noise affects large areas around airports, noise exposure calculations need to be highly accurate. In this study, we compare noise exposure measurements with calculations of several thousand single flights at Zurich and Geneva airports, Switzerland, of three aircraft noise calculation programs: sonAIR, a next-generation aircraft noise calculation program, and the two current best-practice programs FLULA2 and AEDT. For one part of the flights, we had access to flight data recorder (FDR) data, which contain flight configuration information that sonAIR can account for. For the other part, only radar data without flight configuration information were available. Overall, all three programs show good results, with mean differences between calculations and measurements smaller than ±0.5 dB in the close range of the airports. sonAIR performs clearly better than the two best-practice programs if FDR data are available. However, in situations without FDR data (reduced set of input data), sonAIR cannot exploit its full potential and performs similarly well as FLULA2 and AEDT. In conclusion, all three programs are well suited to determine averaged noise metrics resulting from complex scenarios consisting of many flights (e.g., yearly air operations), while sonAIR is additionally capable to highly accurately reproduce single flights in greater detail. Full article
(This article belongs to the Special Issue Aircraft Noise)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Noise monitoring terminals (NMT) in close range to ZRH with all flight trajectories used for this study, colored by procedure. The black circles around each terminal represent spatial gates, which the flight trajectories have to penetrate to be considered (basemap: swissALTI3D LV95, swisstopo; source: Federal Office of Topography).</p>
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<p>Noise monitoring terminals (NMT) in close range to GVA with all flight trajectories used for this study, colored by procedure. The black circles around each terminal represent spatial gates, which the flight trajectories have to penetrate to be considered (basemap: swissALTI3D LV95, swisstopo; source: Federal Office of Topography).</p>
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<p>Measuring stations of the measurement campaign in the far range to ZRH with all flight trajectories used for this study. Approaches on runway 28 (A28) are depicted in blue and on runway 34 (A34) in purple. The black lines represent spatial gates, which the flight trajectories have to penetrate to be considered (basemap: swissALTI3D LV95, swisstopo; source: Federal Office of Topography).</p>
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<p>Scatter plots of simulation vs. measurements for aircraft with FDR data in the close range to the airports (ZRH and GVA combined), grouped by procedure (D: Departure, A: Approach, SD: Standard deviation). (<b>a</b>) sonAIR, (<b>b</b>) FLULA2, (<b>c</b>) AEDT.</p>
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<p>Scatter plots of simulation vs. measurements for aircraft without FDR data in the close range to the airports (ZRH and GVA combined), grouped by procedure (D: Departure, A: Approach, SD: Standard deviation). (<b>a</b>) sonAIR, (<b>b</b>) FLULA2, (<b>c</b>) AEDT.</p>
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<p>Scatter plots of simulation vs. measurements for approaches of aircraft with FDR data in the far range of ZRH. A: Approach, SD: Standard deviation. Note that no departures were recorded (<a href="#sec2dot3-aerospace-08-00388" class="html-sec">Section 2.3</a>). (<b>a</b>) sonAIR, (<b>b</b>) FLULA2, (<b>c</b>) AEDT.</p>
Full article ">Figure 7
<p>Box-whisker-plots of differences ∆L<sub>AE,t10</sub> for sonAIR and FLULA2 and ∆L<sub>AE</sub> for AEDT (simulation minus measurements) for all scenarios, grouped by procedure (D: Departure, A: Approach). (<b>a</b>) ZRH &amp; GVA, FDR, close range; (<b>b</b>) ZRH &amp; GVA, nonFDR, close range; (<b>c</b>) ZRH, FDR, far range.</p>
Full article ">Figure 8
<p>Scatter plots of the calculated event levels L<sub>AEt10</sub> (L<sub>AE</sub> for AEDT) between the three models for aircraft with FDR data in the close range (ZRH and GVA combined), grouped by procedure (D: Departure, A: Approach, SD: Standard deviation). (<b>a</b>) sonAIR vs. FLULA2; (<b>b</b>) FLULA2 vs. AEDT; (<b>c</b>) sonAIR vs. AEDT.</p>
Full article ">Figure 9
<p>Scatter plots of the calculated event levels L<sub>AE, t10</sub> (L<sub>AE</sub> for AEDT) between the three models for aircraft without FDR data in the close range (ZRH and GVA combined), grouped by procedure (D: Departure, A: Approach, SD: Standard deviation). (<b>a</b>) sonAIR vs. FLULA2; (<b>b</b>) FLULA2 vs. AEDT; (<b>c</b>) sonAIR vs. AEDT.</p>
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19 pages, 9541 KiB  
Article
Selection of Vertiports Using K-Means Algorithm and Noise Analyses for Urban Air Mobility (UAM) in the Seoul Metropolitan Area
by Junyoung Jeong, Minjun So and Ho-Yon Hwang
Appl. Sci. 2021, 11(12), 5729; https://doi.org/10.3390/app11125729 - 21 Jun 2021
Cited by 31 | Viewed by 6210
Abstract
In this study, a combination of well-established algorithms and real-world data was implemented for the forward-looking problem of future vertiport network design in a large metropolitan city. The locations of vertiports were selected to operate urban air mobility (UAM) in the Seoul metropolitan [...] Read more.
In this study, a combination of well-established algorithms and real-world data was implemented for the forward-looking problem of future vertiport network design in a large metropolitan city. The locations of vertiports were selected to operate urban air mobility (UAM) in the Seoul metropolitan area based on the population of commuters, and a noise priority route was created to minimize the number of people affected by noise using Aviation Environmental Design Tool (AEDT) software. Demand data were analyzed using survey data from the commuting population and were marked on a map using MATLAB. To cluster the data, the K-means algorithm function built in MATLAB was used to select the center of the cluster as the location of the vertiports, and the accuracy and reliability of the clustering were evaluated using silhouette techniques. The locations of the selected vertiports were also identified using satellite image maps to ensure that the location of the selected vertiports were suitable for the actual vertiport location, and if the location was not appropriate, final vertiports were selected through the repositioning process. A helicopter model was then used to analyze the amount of noise reduction achieved by the noise priority route, which is the route between the selected K-UAM vertiports compared to the shortest distance route. As a result, it was shown that the noise priority route that minimized the amount of noise exposure was more efficient than the business priority routes. Full article
(This article belongs to the Special Issue Urban Air Mobility/Advanced Air Mobility Using eVTOL Aircraft)
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Figure 1
<p>Commuter data expressed in MATLAB for each district.</p>
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<p>Administrative districts represented by squares.</p>
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<p>(<b>a</b>) Result of clustering when the number of vertiports is 40; (<b>b</b>) result of clustering when the number of vertiports is 100.</p>
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<p>(<b>a</b>) Evaluation result of the silhouette technique when the number of vertiports is 40; (<b>b</b>) evaluation result of the silhouette technique when the number of vertiports is 100.</p>
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<p>Seoul metropolitan area green belt.</p>
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<p>Location of repositioned vertiports.</p>
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<p>Location of repositioned vertiports.</p>
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<p>Examples of final 100 vertiports.</p>
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<p>(<b>a</b>) Result of clustering when the number of vertiports is 10; (<b>b</b>) result of clustering when the number of vertiports is 40.</p>
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<p>(<b>a</b>) Virtual noise width of eVTOL passing through the actual city; (<b>b</b>) virtual noise width of eVTOL passing through a virtual city modeled in an ideal space.</p>
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<p>K-UAM mission profile.</p>
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<p>Two methods of route generation from Gimpo airport to COEX.</p>
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<p>Each city and district modeled in the ideal space.</p>
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<p>Noise maps for two different scenarios.</p>
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<p>Noise analysis results for two different scenarios in the modeled area.</p>
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10 pages, 889 KiB  
Proceeding Paper
Validating Aircraft Noise Models
by Ran Giladi and Eliav Menachi
Proceedings 2020, 59(1), 12; https://doi.org/10.3390/proceedings2020059012 - 3 Dec 2020
Cited by 2 | Viewed by 2400
Abstract
Aircraft noise, especially at takeoffs and landings, became a major environmental nuisance and a health hazard for the population around metropolitan airports. In the battle for a better quality of life, wellbeing, and health, aircraft noise models are essential for noise abatement, control, [...] Read more.
Aircraft noise, especially at takeoffs and landings, became a major environmental nuisance and a health hazard for the population around metropolitan airports. In the battle for a better quality of life, wellbeing, and health, aircraft noise models are essential for noise abatement, control, enforcement, evaluation, policy-making, and shaping the entire aviation industry. Aircraft noise models calculate noise and exposure levels based on aircraft types, engines and airframes, aircraft flight paths, environment factors, and more. Validating the aircraft noise model is a mandatory step towards the model credibility, especially when these models play such a key role with a huge impact on society, economy, and public health. Yet, no validation procedure was offered, and it turns out to be a challenging task. The actual, measured, aircraft noise level is known to be subject to statistical variation, even for the same aircraft type at the same situation and flight phase, executing the same flight procedure, with similar environmental factors and at the same place. This study tries to validate the FAA’s AEDT aircraft noise model, by trying to correlate the specific flight path of an aircraft with its measured noise level. The results show that the AEDT noise model underestimates the actual noise level, and four validation steps should be performed to correct or tune aircraft noise databases and flight profiles. Full article
(This article belongs to the Proceedings of 8th OpenSky Symposium 2020)
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Figure 1
<p>NMTs location at Heathrow Airport.</p>
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<p>Noise map of A320 at SERFR direct approach for landing ILS 28 R/L at SFO.</p>
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<p>Flight path variations and correlation to the SEL: (<b>a</b>) distribution of aircraft altitude and lateral distance from NMT 404 in Palo-Alto when LAmax was recorded; (<b>b</b>) LAmax in dB(A) vs. slant distance when LAmax was recorded.</p>
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<p>TLV airport with two sets of MNTs. Map obtained using Google Maps.</p>
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<p>AEDT calculated SEL in dB(A) vs. Measured SEL. the red line presents a perfect match, the dotted black line presents the trendline of the linear regression: (<b>a</b>) comparison at NMT3; (<b>b</b>) at NMT5; (<b>c</b>) at NMT4; (<b>d</b>) at NMT 9.</p>
Full article ">
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