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Keywords = cove-edges

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25 pages, 9825 KB  
Article
Noise Reduction Mechanism and Spectral Scaling of Slat Gap Filler Device at Low Angle of Attack
by Yingzhe Zhang, Peiqing Liu and Baohong Bai
Aerospace 2025, 12(6), 541; https://doi.org/10.3390/aerospace12060541 - 15 Jun 2025
Viewed by 876
Abstract
Slat noise poses a significant challenge during aircraft landing. Slat gap filler (SGF) technology has shown promise in mitigating slat noise, yet its noise reduction mechanisms and characteristics remain unclear. This study numerically investigates the noise reduction mechanisms of SGF and analyzes its [...] Read more.
Slat noise poses a significant challenge during aircraft landing. Slat gap filler (SGF) technology has shown promise in mitigating slat noise, yet its noise reduction mechanisms and characteristics remain unclear. This study numerically investigates the noise reduction mechanisms of SGF and analyzes its noise characteristics using the high-lift common research model (CRM-HL). The lattice Boltzmann solver simulates the unsteady flow field, and the Ffowcs-Williams and Hawkings (FW-H) equation predicts far-field noise. The computed results exhibit a satisfactory concordance with experimental measurements. Furthermore, the near-field flow dynamics have been elucidated through proper orthogonal decomposition. The findings demonstrate that the SGF alters the distribution patterns of flow dynamics and pressure fluctuations, thereby effectively attenuating the mode energy. Moreover, our findings demonstrate that SGF significantly reduces slat noise. The noise reduction mechanism can be attributed to decreased surface pressure fluctuations on the leading edge of the main wing, and a shifted broadband noise peak to a lower frequency due to the enlarged slat cove flow vortex caused by SGF. Finally, a scaling analysis of the slat noise spectra indicates that the SGF noise spectra align well with baseline slat noise spectra when the characteristic length scale is determined by the vortex structure. Full article
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17 pages, 4218 KB  
Article
Operational Robustness of Amino Acid Recognition via Transverse Tunnelling Current Across Metallic Graphene Nano-Ribbon Electrodes: The Pro-Ser Case
by Giuseppe Zollo
Computation 2025, 13(2), 22; https://doi.org/10.3390/computation13020022 - 21 Jan 2025
Viewed by 1242
Abstract
Asymmetric cove-edged graphene nano-ribbons were employed as metallic electrodes in a hybrid gap device structure with zig-zag graphene nano-ribbons terminations for amino acid recognition and peptide sequencing. On a theoretical basis, amino acid recognition is attained by calculating, using the non equilibrium Green [...] Read more.
Asymmetric cove-edged graphene nano-ribbons were employed as metallic electrodes in a hybrid gap device structure with zig-zag graphene nano-ribbons terminations for amino acid recognition and peptide sequencing. On a theoretical basis, amino acid recognition is attained by calculating, using the non equilibrium Green function scheme based on density functional theory, the transversal tunnelling current flowing across the gap device during the peptide translocation through the device. The reliability and robustness of this sequencing method versus relevant operations parameters, such as the bias, the gap size, and small perturbations of the atomistic structures, are studied for the paradigmatic case of Pro-Ser model peptide. I evidence that the main features of the tunnelling signal, that allow the recognition, survive for all of the operational conditions explored. I also evidence a sort of geometrical selective sensitivity of the hybrid cove-edged graphene nano-ribbons versus the bias that should be carefully considered for recognition. Full article
(This article belongs to the Section Computational Chemistry)
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20 pages, 4168 KB  
Article
Comparing PlanetScope to Landsat-8 and Sentinel-2 for Sensing Water Quality in Reservoirs in Agricultural Watersheds
by Abubakarr S. Mansaray, Andrew R. Dzialowski, Meghan E. Martin, Kevin L. Wagner, Hamed Gholizadeh and Scott H. Stoodley
Remote Sens. 2021, 13(9), 1847; https://doi.org/10.3390/rs13091847 - 9 May 2021
Cited by 73 | Viewed by 10504
Abstract
Agricultural runoff transports sediments and nutrients that deteriorate water quality erratically, posing a challenge to ground-based monitoring. Satellites provide data at spatial-temporal scales that can be used for water quality monitoring. PlanetScope nanosatellites have spatial (3 m) and temporal (daily) resolutions that may [...] Read more.
Agricultural runoff transports sediments and nutrients that deteriorate water quality erratically, posing a challenge to ground-based monitoring. Satellites provide data at spatial-temporal scales that can be used for water quality monitoring. PlanetScope nanosatellites have spatial (3 m) and temporal (daily) resolutions that may help improve water quality monitoring compared to coarser-resolution satellites. This work compared PlanetScope to Landsat-8 and Sentinel-2 in their ability to detect key water quality parameters. Spectral bands of each satellite were regressed against chlorophyll a, turbidity, and Secchi depth data from 13 reservoirs in Oklahoma over three years (2017–2020). We developed significant regression models for each satellite. Landsat-8 and Sentinel-2 explained more variation in chlorophyll a than PlanetScope, likely because they have more spectral bands. PlanetScope and Sentinel-2 explained relatively similar amounts of variations in turbidity and Secchi Disk data, while Landsat-8 explained less variation in these parameters. Since PlanetScope is a commercial satellite, its application may be limited to cases where the application of coarser-resolution satellites is not feasible. We identified scenarios where PS may be more beneficial than Landsat-8 and Sentinel-2. These include measuring water quality parameters that vary daily, in small ponds and narrow coves of reservoirs, and at reservoir edges. Full article
(This article belongs to the Special Issue Remote Sensing for Water Resources Assessment in Agriculture)
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15 pages, 9321 KB  
Article
Atomistic Modelling of Size-Dependent Mechanical Properties and Fracture of Pristine and Defective Cove-Edged Graphene Nanoribbons
by Daniela A. Damasceno, R.K.N.D. Nimal Rajapakse and Euclides Mesquita
Nanomaterials 2020, 10(7), 1422; https://doi.org/10.3390/nano10071422 - 21 Jul 2020
Cited by 17 | Viewed by 4675
Abstract
Cove-edged graphene nanoribbons (CGNR) are a class of nanoribbons with asymmetric edges composed of alternating hexagons and have remarkable electronic properties. Although CGNRs have attractive size-dependent electronic properties their mechanical properties have not been well understood. In practical applications, the mechanical properties such [...] Read more.
Cove-edged graphene nanoribbons (CGNR) are a class of nanoribbons with asymmetric edges composed of alternating hexagons and have remarkable electronic properties. Although CGNRs have attractive size-dependent electronic properties their mechanical properties have not been well understood. In practical applications, the mechanical properties such as tensile strength, ductility and fracture toughness play an important role, especially during device fabrication and operation. This work aims to fill a gap in the understanding of the mechanical behaviour of CGNRs by studying the edge and size effects on the mechanical response by using molecular dynamic simulations. Pristine graphene structures are rarely found in applications. Therefore, this study also examines the effects of topological defects on the mechanical behaviour of CGNR. Ductility and fracture patterns of CGNR with divacancy and topological defects are studied. The results reveal that the CGNR become stronger and slightly more ductile as the width increases in contrast to normal zigzag GNR. Furthermore, the mechanical response of defective CGNRs show complex dependency on the defect configuration and distribution, while the direction of the fracture propagation has a complex dependency on the defect configuration and position. The results also confirm the possibility of topological design of graphene to tailor properties through the manipulation of defect types, orientation, and density and defect networks. Full article
(This article belongs to the Special Issue Advanced Mechanical Modeling of Nanomaterials and Nanostructures)
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