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89 results for “surface roughness”
Non-linear variation in clinging performance with surface roughness in geckos
<p>Understanding the challenges faced by organisms moving within their environment is essential to comprehending the evolution of locomotor morphology and habitat use. Geckos have developed adhesive toe pads that enable exploitation of a wide range of microhabitats. These toe pads, and their adhesive mechanisms, have typically been studied using a range of artificial substrates, usually significantly smoother than those available in nature. Although these studies have been fundamental in understanding the mechanisms of attachment in geckos, it is unclear whether gecko attachment simply gradually declines with increased roughness as some researchers have suggested, or whether the interaction between the gekkotan adhesive system and surface roughness produces non-linear relationships. To understand ecological challenges faced in their natural habitats, it is essential to use test surfaces that are more like surfaces used by geckos in nature. We tested gecko shear force (i.e., frictional force) generation as a measure of clinging performance on three artificial substrates. We selected substrates that exhibit microtopographies with peak-to-valley heights similar to those of substrates used in nature, to investigate performance on a range of surfaces smooth (glass), and fine-grained (fine sandpaper) to rough (coarse sandpaper). We found that shear force did not decline monotonically with roughness, but varied non-linearly among substrates. Clinging performance was greater on glass and coarse sandpaper than on fine sandpaper, and clinging performance was not significantly different between glass and coarse sandpaper. Our results demonstrate that performance on different substrates varies, probably depending on the underlying mechanisms of the adhesive apparatus in geckos.</p>
The supplementary materials for "Roughness prediction of end milling surface for behavior mapping of digital twined machine tools".
<p>This is the supplementary materials for a paper named "Roughness prediction of end milling surface for behavior mapping of digital twined machine tools" published on the Digital Twin journal.</p>
Experimental measurements of the effects of surface roughness on large-scale downburst-like impinging jets at the WindEEE Dome laboratory
<p>Thunderstorm downbursts originate as negatively buoyant currents of cold air descending from cumulonimbus clouds. Upon impacting the ground, a strong radial outflow develops with maximum wind velocities occurring at the near-ground level. These types of flows pose serious hazard to the natural and built environment. Their restricted time and spatial extent as well as their intermittent and non-Gaussian fluctuating nature make them extremely challenging to be recorded and analyzed through classic full-scale measurements in nature. Alongside synoptic-scale extra tropical cyclones, downbursts govern the wind climate at the mid-latitude areas around the globe. Recent trends in climate change studies suggest both an increased intensity as well as frequency of occurrence of these events. Therefore, their scientific comprehension urges serious consideration.</p> <p>In the context of the project THUNDERR “Detection, simulation, modelling and loading of thunderstorm outflows to design wind-safer and cost-efficient structures”, financed by the European Research Council (ERC) Advanced Grant 2016 (grant No. 741273, P.I. Prof. Giovanni Solari, University of Genoa), an extensive experimental campaign was recently conducted at the WindEEE Dome wind chamber. This campaign focused on measuring downburst-like flows (DLFs) generated by large-scale impinging jets. The dataset presented here encompasses a portion of the measurements collected during this comprehensive experimental initiative. Specifically, this series of tests aimed to unravel the role of surface roughness in potentially altering the configuration and dynamics of the overall radial outflow. The term "surface roughness" pertains to the ground patch under examination, encompassing both the natural features of the terrain and any obstacles, such as buildings, present on the ground. While surface roughness plays a decisive role in changing the shape and magnitudes of the wind speed vertical profiles for extra-tropical cyclones, the scientific literature has not yet thoroughly addressed its impact on downburst winds, which are different being dominated by intense vortex dynamics.</p> <p>Impinging jets, considered representative for the simulation of downburst like flow (DLF), are here simulated as transient phenomena through the opening and closing of the bell-mouth that connects the test chamber and the upper plenum of the dome, the latter being pressurized before releasing the jet. As a result, the velocity records exhibit a distinct pattern, featuring a sudden ramp-up of velocity, followed by a velocity peak, a statistically-stationary phase, and ultimately, a gradual velocity deceleration—mirroring the behavior observed in real-world scenarios.</p> <p>The database consists of six ASCII tab-delimited text files, denoted as ‘windspeedDB89z0eq007.txt’, ‘windspeedDB89z0eq020.txt’, ‘windspeedDB89z0eq320.txt’, ‘windspeedDB124z0eq007.txt’, ‘windspeedDB124z0eq020.txt’, and ‘windspeedDB124z0eq320.txt’, aligning with the two jet intensities and three rough surfaces employed in the experiments. These filenames correspond to: (i) centerline jet velocities at the nozzle outlet section, with values of <em>Wjet</em> = 8.9 and 12.4 m/s (indicated as “<em>Wjet</em>” in the database files); (ii) equivalent full-scale roughness lengths <em>z0eq</em> = 0.007, 0.020, 0.32 m (“<em>z0eq</em>” in the database files, see details below). Each file encompasses wind speed timeseries, detailed as follows:</p> <p>The three-component velocity measurements were recorded by means of 11 Cobra probes (sampling frequency 2,500 Hz) mounted on a stiff mast. The heights (<em>z</em>) of the probes were <em>z</em> = 0.040, 0.070, 0.100, 0.125, 0.150, 0.200, 0.300, 0.400, 0.500, 0.700, 1.000 m above the surface. Within the database files, the wind speed linked to various heights is labeled as “<em>v_zXXXXmm</em>”. In this notation, '<em>v</em>' designates the velocity component: longitudinal ‘<em>U</em>’ (along the horizontal axis of the probe), corresponding to the radial outflow of the downburst, with a positive value when the flow is directed toward the probe. Transversal, ‘<em>V</em>’, represents the velocity component transverse to the probe's centerline axis, having a positive value when the flow is directed right-to-left concerning an observer facing the probe's head. The vertical component is denoted as ‘<em>W</em>’ with a positive value indicating an upward direction. The term “XXXX” signifies the height of the probe, specified in millimeters (mm). The mast with the Cobra probes was subsequently positioned at ten radial <em>r</em> distances with respect to the jet impingement position in the range <em>r/D</em> (<em>D</em> = 3.2 m is the jet diameter) between 0.2–2.0 with an increment of 0.2. Note that the position <em>r/D</em> = 0.8 was adjusted to <em>r/D</em> = 0.75. This modification was necessary due to irregularities on the chamber floor at <em>r/D</em> = 0.8, which could have otherwise introduced bias into the measurements. The radial distance is identified with “<em>r/D_distance</em>” in the dataset files. The ceiling height of the testing chamber is <em>H</em> = 3.75 m, which leads to <em>H/D</em> > 1 allowing for a full vertical development of the downburst radial outflow. For every <em>r/D</em> position, each experiment with the same initial condition (i.e., <em>Wjet</em>) was repeated 10 times (“<em>repetition#</em>” in the database files) to inspect the repeatability of the tests and their variance. Each velocity record lasted 12 s (12 × 2,500 = 30,000 samples) and the duration of the downburst-like part of the record varied between 3–5 s. Overall, 6,600 total time series (2 <em>Wjet</em> × 3 rough surface × 10 repetitions × 10 <em>r/D</em> positions × 11 heights <em>z</em>) of downburst-like outflows were recorded during this set of experimental tests.</p> <p>The reported accuracy of Cobra probes from the manufacturer is +/- 0.5 m/s and +/- 1° for velocity and yaw/pitch angles respectively, up to approximately 30% of turbulence intensity. All velocity magnitudes below 1 m/s were removed and converted to NaN (Not a Number) in the database due to the poor accuracy of Cobra probes for velocities below this threshold. In addition, some velocity values were reported as null in the instrument readings due to the incoming flow being outside the probe's spatial cone of measurement (+/- 45° in respect to the probe horizontal axis). These values are flagged as NULL values in the database. This notation aligns with that utilized in a preceding database of measurements collected within the same experimental campaign at the WindEEE Dome (Canepa et al., 2021; <a href="https://doi.org/10.1594/PANGAEA.931205">https://doi.org/10.1594/PANGAEA.931205</a>).</p> <p>DLFs were tested on three different surfaces: (i) WindEEE Dome bare floor; (ii) Carpet; (iii) Artificial grass. A 1 m × 8 m rectangular section was selected from each of the three surfaces for testing purposes. Each surface was positioned with a 1 m offset relative to the geometric location of the jet impingement. It was identified by an equivalent full-scale roughness length, “<em>z0eq</em>”based on matching atmospheric boundary layer profiles measured in WindEEE in boundary layer mode with standard ESDU (Engineering Science Data Unit) profiles. A total of 15 different Atmospheric Boundary Layer (ABL)-like profiles were tested inside the chamber by varying the rotation-per-minute (rpm) of the fans across the 4 rows of the 60-fan wall—a peripheral wall of the hexagonal WindEEE Dome chamber comprising a matrix of 4 × 15 (rows × columns) fans that is used to produce ABL -like flows. A specific configuration of the 60-fan wall and a length scale of 1:200 were chosen based on correlation analysis between physically reproduced ABL profiles and curve fitting through Eq. A1.8 of the ESDU 82026. This scale is deemed suitable for both ABL and downburst winds produced at the laboratory. Through a linear fitting of the measured data on the <em>U – ln(z)</em> chart, employing the logarithmic law-of-the-wall (dependent on roughness length <em>z0</em> and friction velocity <em>u*</em>), the equivalent roughness lengths for the three surfaces were determined: <em>z0eq</em> = 0.007, 0.020, 0.320 m for the WindEEE Dome bare floor, carpet, and artificial grass, respectively.</p> <p>In summary, each experimental velocity time series consists of 30,000 rows, with 33 columns detailing the three velocity components (<em>U</em>, <em>V</em>, <em>W</em>) across the 11 Cobra probe heights. Columns 34 to 37 provide information on the repetition number, radial position of measurement, equivalent full-scale roughness length, and jet intensity. The subsequent timeseries within the dataset refer to the parameters in columns 34 to 37, each one spanning its entire range in the specified order.</p> <p>Researchers can leverage this database to validate and calibrate numerical and analytical models of thunderstorm winds, in addition to interpreting full-scale measurements of the phenomenon. It also serves as a valuable resource for the fluid dynamics community, particularly those interested in the physical comprehension of downscaled flows or the surface flow dynamics of large Reynolds number impinging jets.</p>
The effects of surface roughness on the spectral (300-1400 nm) bidirectional reflectance distribution function (BRDF) of sea ice
<p>Please cite the following publication when using the data:</p> <p><br> Lamare, M. L., Hedley, J. D., and King, M. D.: The effects of surface roughness on the calculated, spectral, conical–conical reflectance factor as an alternative to the bidirectional reflectance distribution function of bare sea ice, The Cryosphere, 17, 737–751, https://doi.org/10.5194/tc-17-737-2023, 2023.</p> <p>"BRF_results" contains BRDF output files from the radiative-transfer model PlanarRad.</p> <p>BDRF was computed for three different types of sea ice with varying roughness parameters and <br> thicknesses. </p> <p>The folder tree is constructed with the following structure:</p> <p>BRF_results<br> - Solar Zenith angles<br> - Roughness parameters<br> - Sea ice thicknesses<br> - Wavelengths<br> - Sea ice types</p>
Influence of Inner Surface Roughness on the SIP Response - a Numerical Study
<p>Supporting data for publication</p> <p>This repository contains Matlab scripts, Comsol Multiphysics models, and numerical simulation data used to generate the plots in the manuscript. Comsol models are those with the <em>.mph</em> extension and were created with Comsol Multiphysics v 5.5. All numerical data used in the manuscript is available without running the Comsol simulation.</p>
MFMET A3.2.7 Documented example of surface roughness measurements
<p><span>Supplementary material to MFMET A3.2.7 Documented example of surface roughness measurements<br></span></p>
Data from the 'The Global Surface Roughness of 433 Eros from the NEAR Laser Rangefinder'
<p>See README.pdf to read the documentation of data.</p>
Data accompanying the paper "Improving the near-surface wind and turbulence at the edge of the orographic drag grey zone by tuning the roughness length"
<p>This repository contains outputs from simulations (with observations) launched with the ALARO-HR18 model utilizing the TOUCANS two-energy turbulence scheme and different roughness length configurations. The results are provided in three separate files, i.e., "acf_data.zip", "fluxes.zip", and "verif_data.zip".</p> <p>1) "acf_data.zip" contains four ASCII files with the name "acf_DATE_TYPE.txt", where DATE=YYYYMMDD and TYPE=mountain or flat. Each file includes the model data from surroundings of approx. 200 x 200 km around the chosen point (flat terrain or mountain), and provides data on longitude (LON), latitude (LAT), zonal wind (U-WIND), meridional wind (V-WIND) and wind speed (W). The data included in this file were used to compute the 2D spatial autocorrelation function for different cases and locations shown in Fig.3.</p> <p>2) "fluxes.zip" contains individual ASCII files with the name "FTYPE_EXP.txt", where FTYPE=uw_flux or vw_flux, and EXP is one of OBS, ORL, REF, EXP1, EXP2, EXP3 and EXP4. Each file contains turbulent momentum flux data at four vertical levels (5 m; UW005 and VW005, 60 m; UW060 and VW060; 100 m; UW100 and VW100 and 180 m; UW180 and VW180). The description of EXP is following:</p> <p>OBS; observed momentum fluxes from the Cabauw tower, the Netherlands, corresponding to five different cases, i.e., matching with 3D model simulations starting at 00 UTC on 6, 10 and 16th February 2020, as well as 16 and 25th August 2020. Each column corresponds to one vertical level, while the rows represent hourly values (up to +72 hours in advance), added case by case.</p> <p>ORL; predicted fluxes obtained with the old roughness length configuration described in the paper.</p> <p>REF; predicted fluxes obtained with the new roughness length (NRL) configuration as described in the paper and without tuning the vegetation roughness length (C1=0.25, C2=1.0, C3=6, FA=0 and OGWD scheme on).</p> <p>EXP1; is the same as REF but with C2=1.5.</p> <p>EXP2; is the same as REF but with C2=1.875.</p> <p>EXP3; is the same as REF but with C2=1.875 and C3=3.</p> <p>EXP4; is the same as REF but with C2=1.875 and C3=1.5.</p> <p>The data included in this file were used to prepare inputs to Table 2.</p> <p><strong>NOTE:</strong> The measurements taken at the Cabauw tower are under the care of "Koninklijk Nederlands Meteorologisch Instituut" (KNMI). They are added to this repository only to confirm related results presented in the paper. For any personal use of these data, contact the responsible KNMI staff.</p> <p>3) "verif_data.zip" contains individual ASCII files used to validate the impact of various tunable parameters and final settings of the NRL configuration on 10-m wind, i.e., for preparation of Fig. 6-8. and Fig. 9-12. Individual ASCII files are named "WS_EXP_SDATE_ENDDATE_all", where:</p> <ul> <li>a) EXP is one of C1s, C1f, C2s, C2f, C3s, C3f, ORL, NRLs, NRLf, FAs, FAf, GWDon and GWDoff. "s" and "f" correspond to the starting and the final setup related to sensitivity studies performed in the paper (Fig. 6-8.) or starting and final version of the NRL configuration (Fig. 10-12). The results from FAs, FAf, GWDon and GWDoff are only briefly commented on within the paper but not shown.</li> <li>b) SDATE=YYYYMMDD (starting date of the validation period), and</li> <li>c) EDATE=YYYYMMDD (ending date of the validation period).</li> </ul> <p>Each ASCII file contains the following fields (column-wise): SYNOP number of station (location; for explanation cf. "synop_list.txt"), latitude (lat), longitude (lon), altitude of the station (h), date to which data refers to (date), forecast lead time to which data refers to (lead time), predicted 10-m wind (fcst) and measured 10-m wind (obs).</p> <p><strong>CAUTION:</strong> Be aware that all times are given in UTC and that they are consistently used where predicted and observed data are used.</p> <p>In case you have any questions, contact the corresponding author.</p>
Experimental and Clinical Investigation of the Implant Surface Roughness Reduction Effect on Early-stage Fibrosis
ClinicalTrials.gov study NCT05648929. IPD Sharing: NO. Countries: 1. Publications: 2.
Microbial Adherence, Surface Roughness, Effectiveness and Impact on Patients Between Conventional and 3D Printed Twin Block Appliances
ClinicalTrials.gov study NCT06944860. IPD Sharing: NO. Countries: 1. Publications: 11.
Surface Roughness of a Dental Restorative Material and Biofilm Formation
ClinicalTrials.gov study NCT00256945. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Non-linear variation in clinging performance with surface roughness in geckos
Open the record for dataset details and reuse information.
Attachment force (mN) of bed bugs Cimex lectularius males on Perspex (PMMA) in relation to surface roughness and wettability
Open the record for dataset details and reuse information.
Optical topography of rough surfaces using vortex localization of fluorescent markers_experimental dataset
<p>This file contains the raw unprocessed experimental data for the results published in Petr Schovánek et al., Optical topography of rough surfaces using vortex localization of fluorescent markers, Optics Letters Vol. 45, Issue 16, pp. 4468-4471 (2020).</p>
Using generalized regression neural network to retrieve bare surface soil moisture from Radarsat-2 backscatter observations, regard less of roughness effect
<p>Description of soil moisture, correlation length, and RMS height from ground measurements of 147 sampling sites, full-polarized backscattering coefficients extracted from Radarsat-2 scene corresponding to those ground measurement sites.</p>
Data from: Adhesion force mapping on wood by atomic force microscopy: influence of surface roughness and tip geometry
This study attempts to address the interpretation of atomic force microscopy (AFM) adhesion force measurements conducted on the heterogeneous rough surface of wood and natural fibre materials. The influences of wood surface roughness, tip geometry and wear on the adhesion force distribution are examined by cyclic measurements conducted on wood surface under dry inert conditions. It was found that both the variation of tip and surface roughness of wood can widen the distribution of adhesion forces, which are essential for data interpretation. When a common Si AFM tip with nanometre size is used, the influence of tip wear can be significant. Therefore, control experiments should take the sequence of measurements into consideration, e.g. repeated experiments with used tip. In comparison, colloidal tips provide highly reproducible results. Similar average values but different distributions are shown for the adhesion measured on two major components of wood surface (cell wall and lumen). Evidence supports the hypothesis that the difference of the adhesion force distribution on these two locations was mainly induced by their surface roughness.
Multiscale modeling of lubrication between rough surfaces: Application to gas lubrication - Dataset
<p>Data set used for the paper entitled "Multiscale modeling of lubrication between rough surfaces: Application to gas lubrication"</p> <p>The associated paper is archived on an open access aichive: https://hal.archives-ouvertes.fr/hal-03402682</p>
Data for 'The Surface roughness of large craters on Mercury'
<p>A zip file containing the data for 'The surface roughness of large craters on Mercury'.</p>
Experimental investigation on effects of loading rate, surface roughness and normal stress on repeating stick-slip behaviors
<p>TEST DATA</p>
Experimental Investigation of Surface Roughness Effects and Transition on Wind Turbine Performance
<p>Aerodynamic experiments have been executed in the wind tunnel and on a wind turbine blade to measure the impact of roughness on the airfoil characteristics and the associated effect on rotor performance and to establish the transition location on a rotating blade. The wind tunnel tests have been performed in the low-speed, low-turbulence wind tunnel of TUDelft. The wind turbine tests were carried out at ECN&rsquo;s Wind Turbine Test Site. Roughness simulation material has been installed on the airfoil leading edge to measure the impact on airfoil performance. Microphones were mounted on the airfoil surface to detect the boundary layer laminar to turbulent transition position both on the wind tunnel model and on the wind turbine blade.</p>
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.