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1. The effect of shear sheltering on trailing edge noise

   https://doi.org/10.2514/6.2020-2515  

   Jimenez, Ignacio; Glegg, Stewart A.; Devenport, William J. (June 2020, AIAA AVIATION FORUM)

   Shear sheltering is defined as the effect of the mean flow velocity profile in a boundary layer on the turbulence caused by an imposed gust. It has been studied extensively in applications involving boundary layer transition, where the primary concern is flow instabilities that are enhanced by turbulence in the flow outside the boundary layer. In aeroacoustic applications turbulent boundary layers interacting with blade trailing edges or roughness elements are an important source of sound, and the effect of shear sheltering on these noise sources has not been studied in detail. Since the surface pressure spectrum below the boundary layer is the primary driver of trailing edge and roughness noise, we will consider the effect that shear sheltering has on the surface pressure spectrum below a boundary layer. We will model the incoming turbulence as vortex sheets at specified heights above the surface and show, using classical boundary layer profiles and approximations to numerical results, how the mean flow velocity can be manipulated to alter the surface pressure spectrum and hence the radiated trailing edge noise. 

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2. Comprehensive topography characterization of polycrystalline diamond coatings

   https://doi.org/10.1088/2051-672X/abe71f  

   Gujrati, Abhijeet; Sanner, Antoine; Khanal, Subarna R.; Moldovan, Nicolaie; Zeng, Hongjun; Pastewka, Lars; Jacobs, Tevis D. B. (March 2021, Surface Topography: Metrology and Properties)

   Abstract The surface topography of diamond coatings strongly affects surface properties such as adhesion, friction, wear, and biocompatibility. However, the understanding of multi-scale topography, and its effect on properties, has been hindered by conventional measurement methods, which capture only a single length scale. Here, four different polycrystalline diamond coatings are characterized using transmission electron microscopy to assess the roughness down to the sub-nanometer scale. Then these measurements are combined, using the power spectral density (PSD), with conventional methods (stylus profilometry and atomic force microscopy) to characterize all scales of topography. The results demonstrate the critical importance of measuring topography across all length scales, especially because their PSDs cross over one another, such that a surface that is rougher at a larger scale may be smoother at a smaller scale and vice versa. Furthermore, these measurements reveal the connection between multi-scale topography and grain size, with characteristic scaling behavior at and slightly below the mean grain size, and self-affine fractal-like roughness at other length scales. At small (subgrain) scales, unpolished surfaces exhibit a common form of residual roughness that is self-affine in nature but difficult to detect with conventional methods. This approach of capturing topography from the atomic- to the macro-scale is termedcomprehensive topography characterization, and all of the topography data from these surfaces has been made available for further analysis by experimentalists and theoreticians. Scientifically, this investigation has identified four characteristic regions of topography scaling in polycrystalline diamond materials. 

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3. The Empirical Dependence of Tornadogenesis on Elevation Roughness: Historical Record Analysis Using Bayes’s Law in Arkansas

   https://doi.org/10.1175/JAMC-D-18-0224.1  

   Hua, Zhanxiang; Chavas, Daniel R. (February 2019, Journal of Applied Meteorology and Climatology)

   Recent research suggests that surface elevation variability may influence tornado activity, though separating this effect from reporting biases is difficult to do in observations. Here we employ Bayes’s law to calculate the empirical joint dependence of tornado probability on population density and elevation roughness in the vicinity of Arkansas for the period 1955–2015. This approach is based purely on data, exploits elevation and population information explicitly in the vicinity of each tornado, and enables an explicit test of the dependence of results on elevation roughness length scale. A simple log-link linear regression fit to this empirical distribution yields an 11% decrease in tornado probability per 10-m increase in elevation roughness at fixed population density for large elevation roughness length scales (15–20 km). This effect increases by at least a factor of 2 moving toward smaller length scales down to 1 km. The elevation effect exhibits no time trend, while the population bias effect decreases systematically in time, consistent with the improvement of reporting practices. Results are robust across time periods and the exclusion of EF1 tornadoes and are consistent with recent county-level and gridded analyses. This work highlights the need for a deeper physical understanding of how elevation heterogeneity affects tornadogenesis and also provides the foundation for a general Bayesian tornado probability model that integrates both meteorological and nonmeteorological parameters. 

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4. Generation of Amorphous Silica Surfaces with Controlled Roughness

   https://doi.org/10.1021/acs.jpca.3c04955  

   Nguyen, Nuong P.; Laird, Brian B. (November 2023, The Journal of Physical Chemistry A)

   Amorphous silica (a-SiO2) surfaces, when grafted with select metals on the active sites of the functionalized surfaces, can act as useful heterogeneous catalysts. From a molecular modeling perspective, one challenge has been generating a-SiO2 slab models with controllable surface roughness to facilitate the study of the effect of surface morphology on the material properties. Previous computational methods either generate relatively flat surfaces or periodically corrugated surfaces that do not mimic the full range of potential surface roughness of the amorphous silica material. In this work, we present a new method, inspired by the capillary fluctuation theory of interfaces, in which rough silica slabs are generated by cleaving a bulk amorphous sample using a cleaving plane with Fourier components randomly generated from a Gaussian distribution. The width of this Gaussian distribution (and thus the degree of surface roughness) can be tuned by varying the surface roughness parameter α. Using the van Beest, Kramer, and van Santen (BKS) force field, we create a large number of silica slabs using cleaving surfaces of varying roughness (α) and using two different system sizes. These surfaces are then characterized to determine their roughness (mean- squared displacement), density profile, and ring size distribution. This analysis shows a higher concentration of surface defects (under-/overcoordinated atoms and strained rings) as the surface roughness increases. To examine the effect of the roughness on surface reactivity, we re-equilibriate a subset of these slabs using the reactive force field ReaxFF and then expose the slabs to water and observe the formation of surface silanols. We observe that the rougher surfaces exhibit greater silanol concentrations as well as bimodal acidity. 

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5. Surface roughness prediction through GAN-synthesized power signal as a process signature

   https://doi.org/10.1016/j.jmsy.2023.05.016  

   C. Cooper, J. Zhang (July 2023, Journal of manufacturing systems)

   Predicting machined surface roughness is critical for estimating a part’s performance characteristics such as susceptibility to fatigue and corrosion. Prior studies have indicated that power consumed at the tool-chip interface may represent an indicator for the surface integrity of the machining process. However, no quantita-tive association has been reported between the machining power and surface roughness due to a lack of data to develop predictive models. This paper presents a data synthesis method to address this gap. Specifically, a conditional generative adversarial network (CGAN) is developed to synthesize power signals associated with varying process parameter combinations. The quality of the synthesized signals is evaluated against experimentally measured power signals by examining the consistency in: 1) the spatial pattern of the signals induced by the cutting process as shown in the frequency domain, and 2) the temporal pattern as shown in the clustering of the synthesized and measured signals corresponding to the same parameter combination. The synthesized signals are then used to augment the measured signals and develop a convolutional neural network (CNN) for predicting the machined surface roughness. Experiments performed using H13 tool steel have shown that data augmentation by CGAN has effectively reduced the error of the surface roughness prediction from 58 %, when no synthetic data is used for CNN training, to 9.1 % when 250 synthetic samples are used. The results demonstrate the effectiveness of CGAN as a data augmentation method and CNN for mapping machining power to surface roughness. 

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