Non-Gaussian surface roughness distribution of magnetic media for minimum friction/stiction

1996 ◽  
Vol 79 (8) ◽  
pp. 5794 ◽  
Author(s):  
Bharat Bhushan ◽  
Sameera Chilamakuri
Keyword(s):  
Surface Roughness ◽  
Magnetic Media ◽  
Non Gaussian ◽  
Gaussian Surface

Numerical Investigations of Various Surface Roughness Parameters on the Performance of Profiled Hydrostatic Thrust Bearing

2005 ◽  
Author(s):  
Kirat Shah ◽  
Robert E. Johnson ◽  
Harish P. Cherukuri
Keyword(s):  
Surface Roughness ◽  
Thrust Bearings ◽  
Lubrication Equation ◽  
Load Carrying ◽  
Minimum Film Thickness ◽  
Order Of Magnitude ◽  
Auto Correlation Function ◽  
Non Gaussian ◽  
Gaussian Surface ◽  
Lubrication Conditions

Measurements of surface roughness on hydrostatic bearing slipper indicate that the surfaces are not always Gaussian. Previous studies in this area were primarily concerned with Gaussian surfaces. In this research the effects of non-Gaussian surface roughness on the performance of profiled hydrostatic thrust bearings are analyzed. This study is applicable to the lubrication conditions where the surface roughness is of the same order of magnitude as the minimum film thickness. Surfaces with different skewness, kurtosis, mean, auto-correlation function and standard deviation are generated numerically using a combination of Fast Fourier Transform (FFT) and Johnson translatory system. The finite difference method is used to solve the Reynolds lubrication equation. The effect of roughness on the load carrying capacity is investigated and compared with the results for ideal smooth surfaces.

The Influence of Surface Topography on Energy Dissipation and Compliance in Tangentially Loaded Elastic Contacts

2012 ◽  
Vol 134 (1) ◽  
Author(s):  
Simon Medina ◽  
Andrew V. Olver ◽  
Daniele Dini
Keyword(s):  
Surface Roughness ◽  
Energy Dissipation ◽  
Partial Slip ◽  
Fast Calculation ◽  
Numerical Solution Method ◽  
Correlation Lengths ◽  
Loading Method ◽  
Non Gaussian ◽  
Gaussian Surface ◽  
Tangential Directions

The influence of non-Gaussian surface roughness on elastic contacts loaded in both normal and tangential directions has been investigated. A numerical solution method based on the multilevel scheme and incorporating the theorem of Ciavarella/Jaeger has been implemented, which allows fast calculation of partial slip loading conditions, including the energy dissipation for a fully reversed tangential loading cycle. The effect of varying roughness rms, skewness, kurtosis, and correlation lengths on contact areas, stiffness values, and energy dissipation is presented, and the significance of these parameters and of the loading method are discussed. It was found that the energy dissipation can be greatly increased by greater surface roughness. Maps showing how the energy dissipation is distributed within the contact are presented, which provide some explanation for this observation and the scatter that may occur for surfaces of nominally similar roughness. The suitability of these parameters for predicting the contact behavior of rough surfaces is also considered.

scholarly journals Radar Imaging Statistics of Non-Gaussian Rough Surface: A Physics-Based Simulation Study

2022 ◽  
Vol 14 (2) ◽  
pp. 311
Author(s):  
Cheng-Yen Chiang ◽  
Kun-Shan Chen ◽  
Ying Yang ◽  
Yang Zhang ◽  
Lingbing Wu
Keyword(s):  
Surface Roughness ◽  
Rough Surface ◽  
Rough Surfaces ◽  
Parameters Estimation ◽  
Statistical Characteristics ◽  
Height Distribution ◽  
Sar Imaging ◽  
Non Gaussian ◽  
Physics Based Simulation ◽  
Gaussian Surface

This paper investigates the radar image statistics of rough surfaces by simulating the scattered signal’s dependence on the surface roughness. Statistically, the roughness characteristics include the height probability density (HPD) and, to the second-order, the power spectral density (PSD). We simulated the radar backscattered signal by computing the far-field scattered field from the rough surface within the antenna beam volume in the context of synthetic aperture radar (SAR) imaging. To account for the non-Gaussian height distribution, we consider microscopic details of the roughness on comparable radar wavelength scales to include specularly, singly, and multiply scatterers. We introduce surface roughness index (RSI) to distinguish the statistical characteristics of rough surfaces with different height distributions. Results suggest that increasing the RMS height does not impact the Gaussian HPD surface but significantly affects the Weibull surface. The results confirm that as the radar frequency increases, or reaches a relatively larger roughness, the surface’s HPD causes significant changes in incoherent scattering due to more frequent multiple scattering contributions. As a result, the speckle move further away from the Rayleigh model. By examining individual RSI, we see that the Gaussian HPD surface is much less sensitive to RMS height than the Weibull HPD surface. We demonstrate that to retrieve the surface parameters (both dielectric and roughness) from the estimated RCS, less accuracy is expected for the non-Gaussian surface than the Gaussian surface under the same conditions. Therefore, results drawn from this study are helpful for system performance evaluations, parameters estimation, and target detection for SAR imaging of a rough surface.

A simple model of a one-dimensional, randomly rough, non-Gaussian surface

2016 ◽  
Author(s):  
E. R. Méndez ◽  
G. D. Jiménez ◽  
A. A. Maradudin
Keyword(s):  
Simple Model ◽  
One Dimensional ◽  
Non Gaussian ◽  
Gaussian Surface

Decomposing the Non-Gaussian Surface in Sum of Gaussian Surfaces

2012 ◽  
Vol 580 ◽  
pp. 170-174
Author(s):  
Zhang Xing Qi ◽  
Zhen Sen Wu ◽  
Zi Wen Yu ◽  
Hai Ying Li
Keyword(s):  
Cross Section ◽  
Electromagnetic Scattering ◽  
Radar Cross Section ◽  
Kirchhoff Approximation ◽  
Munk Function ◽  
The Difference ◽  
Non Gaussian ◽  
Gaussian Surface

The decomposition of the multivariate Non-Gaussian PDF in the sum of a Gaussian PDF instead of the Gram-Charlier series is investigated. Four parameters need to be found by minimizing the integrated square of the difference between Cox-Munk function and its approximation. The backscattering radar cross section (RCS) of the surface is calculated by the Kirchhoff approximation (KA) under different value of k using the formula of decomposition of the Non-Gaussian. The condition of KA satisfying electromagnetic scattering scale from Gaussian and Non-Gaussian surfaces is taken into account by computing the backscattering coefficients in HH and VV polarity.

Study on the mutual influence of surface roughness and texture features of rough-textured surfaces on the tribological properties

2020 ◽  
pp. 135065012094021
Author(s):  
Chunxing Gu ◽  
Xianghui Meng ◽  
Shuwen Wang ◽  
Xiaohong Ding
Keyword(s):  
Surface Roughness ◽  
Tribological Properties ◽  
Gaussian Distribution ◽  
Mutual Influence ◽  
Surface Texturing ◽  
Texture Features ◽  
Tribological Performance ◽  
Mixed Lubrication ◽  
Non Gaussian ◽  
Skewness And Kurtosis

In recent years, the efforts to better control friction and wear have focused on surface topography modification through surface texturing. To study the mutual influence of surface roughness and texture features, this paper developed one comprehensive mathematical model of mixed lubrication to study the tribological performance of the rough-textured conjunction. The typical ring-liner conjunction was chosen as the research object. In particular, the effects of skewness and kurtosis were considered based on the non-Gaussian distribution of asperity height. In this way, the influences of non-Gaussian distribution properties and surface texturing on the tribological performance were analyzed. The results show that the influences of skewness and kurtosis on the tribological performance are nontrivial and should not be neglected in the mixed lubrication. Compared to the Gaussian distribution, considering the non-Gaussian distribution can represent the physical rough surfaces more accurately. Surfaces with negative skewness were found to generally result in better tribological properties. Moreover, the tribological performance improved by surface texturing can also be improved or reduced by the effect of skewness and kurtosis. As a result, the optimization of surface texturing should take the effects of roughness parameters into account.

Influence of Non-Gaussian-Distributed Surface Roughness on the Static Performance of Slider Bearings

2016 ◽  
Vol 60 (4) ◽  
pp. 739-752 ◽  
Author(s):  
Jian Li ◽  
Guojun Zhang ◽  
Yu Huang ◽  
Runchang Chen ◽  
Shengda Yan ◽  
...  
Keyword(s):  
Surface Roughness ◽  
Slider Bearings ◽  
Static Performance ◽  
Non Gaussian

A Microscopic Wear Measurement Method for General Surfaces

2002 ◽  
Vol 124 (4) ◽  
pp. 829-833 ◽  
Author(s):  
Yeau-Ren Jeng ◽  
Zhi-Way Lin ◽  
Shiuh-Hwa Shyu
Keyword(s):  
Surface Roughness ◽  
Measurement Method ◽  
Current Method ◽  
Current Approach ◽  
Height Distribution ◽  
Initial Surface ◽  
Roughness Parameters ◽  
Wear Measurement ◽  
Non Gaussian ◽  
Worn Surfaces

A method was developed to measure the wear of general engineering surfaces based on the roughness parameters of the worn surfaces. This method does not require any information of the initial surface. The surface height distribution is described using Johnson translatory system where the loss of surface height is attributed to wear. Experiments of engine running in were conducted to validate the method. The results show that the current method can determine wear comparable to surface roughness. The current approach simplifies the profilometrical wear measurement and extends such a measurement to non-Gaussian surfaces.

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