scholarly journals The significant impact of ribs and small-scale roughness on cylinder drag crisis

2020 ◽  
Vol 202 ◽  
pp. 104192 ◽  
Author(s):  
Arne Kilvik Skeide ◽  
Lars Morten Bardal ◽  
Luca Oggiano ◽  
R. Jason Hearst
Keyword(s):  
Small Scale ◽  
Scale Roughness ◽  
Cylinder Drag

Reverberation Intensity Decaying in Range-Dependent Waveguide

2019 ◽  
Vol 27 (03) ◽  
pp. 1950007
Author(s):  
J. R. Wu ◽  
T. F. Gao ◽  
E. C. Shang
Keyword(s):  
Large Scale ◽  
Back Propagation ◽  
Normal Modes ◽  
Small Scale ◽  
Signal Pulse ◽  
First Order ◽  
Orthogonal Property ◽  
Short Signal ◽  
Scale Roughness ◽  
Special Case

In this paper, an analytic range-independent reverberation model based on the first-order perturbation theory is extended to range-dependent waveguide. This model considers the effect of bottom composite roughness: small-scale bottom rough surface provides dominating energy for reverberation, whereas large-scale roughness has the effect of forward and back propagation. For slowly varying bottom and short signal pulse, analytic small-scale roughness backscattering theory is adapted in range-dependent waveguides. A parabolic equation is used to calculate Green functions in range-dependent waveguides, and the orthogonal property of local normal modes is employed to estimate the modal spectrum of PE field. Synthetic tests demonstrate that the proposed reverberation model works well, and it can also predict the reverberation of range-independent waveguide as a special case.

Inferring Ice/Ocean Surface Roughness from Horizontal Current Measurements

1989 ◽  
Vol 111 (2) ◽  
pp. 155-159 ◽  
Author(s):  
M. G. McPhee
Keyword(s):  
Boundary Layer ◽  
Surface Roughness ◽  
Local Stress ◽  
Horizontal Stress ◽  
Current Speed ◽  
Small Scale ◽  
Horizontal Current ◽  
Similarity Model ◽  
Scale Roughness ◽  
Shear Production

Turbulence measurements in the underice boundary layer from two Arctic drift stations are used to develop a method for estimating the small-scale roughness, zo, of the ice underside from horizontal current and current variance, sampled at one level. Horizontal variance is shown to be well correlated with turbulent kinetic energy (TKE). Measurements also indicate that at depths where turbulence is fully developed to the surface roughness, shear production of TKE is approximately in balance with viscous dissipation, so that the magnitude of local horizontal stress is proportional to flow variance. A similarity model is used to extrapolate local stress to the interface, and zo is estimated from the logarithmic profile for current speed. The method has application for using remote data buoys, equipped with “smart” current meters, for mapping the underice roughness.

scholarly journals A MODIFIED TWO-SCALE MICROWAVE SCATTERING MODEL FOR A GAUSSIAN-DISTRIBUTED CONDUCTING ROUGH SURFACE

2018 ◽  
Vol XLII-3 ◽  
pp. 2141-2146
Author(s):  
F. Yu ◽  
H. Wang ◽  
Z. Y. Chen
Keyword(s):  
Surface Roughness ◽  
Rough Surface ◽  
Large Scale ◽  
Incident Angle ◽  
Wavelet Packet ◽  
Small Scale ◽  
Backscattering Coefficient ◽  
Microwave Scattering ◽  
Scattering Model ◽  
Scale Roughness

A modified two-scale microwave scattering model (MTSM) was presented to describe the scattering coefficient of natural rough surface in this paper. In the model, the surface roughness was assumed to be Gaussian so that the surface height <i>z(x, y)</i> can be split into large-scale and small-scale components relative to the electromagnetic wavelength by the wavelet packet transform. Then, the Kirchhoff Model (KM) and Small Perturbation Method (SPM) were used to estimate the backscattering coefficient of the large-scale and small-scale roughness respectively. Moreover, the ‘tilting effect’ caused by the slope of large-scale roughness should be corrected when we calculated the backscattering contribution of the small-scale roughness. Backscattering coefficient of the MTSM was the sum of backscattering contribution of both scale roughness surface. The MTSM was tested and validated by the advanced integral equation model (AIEM) for dielectric randomly rough surface, the results indicated that, the MTSM accuracy were in good agreement with AIEM when incident angle was less than 30&amp;deg; (<i>&amp;theta;<sub>i</sub></i>&amp;thinsp;&amp;lt;30&amp;deg;) and the surface roughness was small (<i>ks</i>&amp;thinsp;=&amp;thinsp;0.354).

scholarly journals Radio-Echo Power Profiling

1976 ◽  
Vol 17 (77) ◽  
pp. 527-530 ◽  
Author(s):  
C. S. Neal
Keyword(s):  
Small Scale ◽  
Water Interface ◽  
Glacial Ice ◽  
Power Profiling ◽  
Reflecting Surface ◽  
Ice Water ◽  
Scale Roughness

A method of continuously recording the r.f. power returned from a sub-glacial ice/rock or ice/water interface is described. Illustrations of the records produced are given and their relevance to the reconstruction of the small scale roughness of a reflecting surface is discussed.

Decoupling small-scale roughness and long-range features on deep reactive ion etched silicon surfaces

2013 ◽  
Vol 114 (11) ◽  
pp. 113506 ◽  
Author(s):  
Frank W. DelRio ◽  
Lawrence H. Friedman ◽  
Michael S. Gaither ◽  
William A. Osborn ◽  
Robert F. Cook
Keyword(s):  
Long Range ◽  
Silicon Surfaces ◽  
Small Scale ◽  
Etched Silicon ◽  
Scale Roughness

scholarly journals Conditional simulation of Thwaites Glacier (Antarctica) bed topography for flow models: Incorporating inhomogeneous statistics and channelized morphology

2014 ◽  
Vol 60 (222) ◽  
pp. 635-646 ◽  
Author(s):  
John A. Goff ◽  
Evelyn M. Powell ◽  
Duncan A. Young ◽  
Donald D. Blankenship
Keyword(s):  
Conditional Simulation ◽  
Rapid Change ◽  
Radar Data ◽  
Small Scale ◽  
Flow Models ◽  
Bed Topography ◽  
Error Characterization ◽  
Glacier Flow ◽  
Global Sea Level ◽  
Scale Roughness

AbstractThwaites Glacier, Antarctica, is experiencing rapid change and its mass could, if disgorged into the ocean, lead to ∼1 m of global sea-level rise. Efforts to model flow for Thwaites Glacier are strongly dependent on an accurate model of bed topography. Airborne radar data collected in 2004/05 provide 35 000 line km of bed topography measurements sampled every 20 m along track. At ∼15 km track spacing, this extensive dataset nevertheless misses considerable important detail, particularly: (1) resolution of mesoscale channelized morphology that can guide glacier flow; and (2) resolution of small-scale roughness between the track lines that is critical for determining topographic resistance to flow. Both issues are addressed using a conditional simulation that merges a stochastic realization (an unconditional simulation) with a deterministic surface. A conditional simulation is a non-unique interpolation that reproduces observed statistical behavior without affecting data values. Channels are resolved in the deterministic surface using an interpolation algorithm designed for sinuous channels. Small-scale roughness is resolved using a statistical analysis that accounts for heterogeneity, including an abrupt transition between ‘lowland’ and ‘highland’ morphology. Multiple realizations of the unconditional simulation can be generated to sample the probability space and allow error characterization in flow modeling.

Sign in / Sign up

Export Citation Format

Share Document