Simulation of laser speckle patterns generated by random rough surfaces

A novel static friction model for the unlubricated contact of random rough surfaces at micro/nano scale is presented. This model is based on the energy dissipation mechanism that states that changes in the potential of the surfaces in contact lead to friction. Furthermore, it employs the statistical theory of two nominally flat rough surfaces in contact, which assumes that the contact between the equivalent rough peaks and the rigid flat plane satisfies the condition of interfacial friction. Additionally, it proposes a statistical coefficient of positional correlation that represents the contact situation between the equivalent rough surface and the rigid plane. Finally, this model is compared with the static friction model established by Kogut and Etsion (KE model). The results of the proposed model agree well with those of the KE model in the fully elastic contact zone. For the calculation of dry static friction of rough surfaces in contact, previous models have mainly been based on classical contact mechanics; however, this model introduces the potential barrier theory and statistics to address this and provides a new way to calculate unlubricated friction for rough surfaces in contact.

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High-frequency approximations of the full wave solutions to single and double scatter cross sections for two-dimensional random rough surfaces

10.1117/12.328459 ◽

1998 ◽

Author(s):

Ezekiel Bahar ◽

Magda El-Shenawee

Keyword(s):

Cross Sections ◽

High Frequency ◽

Rough Surfaces ◽

Two Dimensional ◽

Full Wave ◽

Wave Solutions ◽

Random Rough Surfaces

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Fourier transforms for fast and quantitative Laser Speckle Imaging

Scientific Reports ◽

10.1038/s41598-019-49570-7 ◽

2019 ◽

Vol 9 (1) ◽

Cited By ~ 2

Author(s):

J. Buijs ◽

J. van der Gucht ◽

J. Sprakel

Keyword(s):

Quantitative Data ◽

Fourier Transforms ◽

Imaging Technique ◽

Laser Speckle ◽

Frequency Range ◽

Laser Speckle Imaging ◽

Speckle Imaging ◽

New Approach ◽

Speckle Patterns ◽

New Processing

Abstract Laser speckle imaging is a powerful imaging technique that visualizes microscopic motion within turbid materials. At current two methods are widely used to analyze speckle data: one is fast but qualitative, the other quantitative but computationally expensive. We have developed a new processing algorithm based on the fast Fourier transform, which converts raw speckle patterns into maps of microscopic motion and is both fast and quantitative, providing a dynamnic spectrum of the material over a frequency range spanning several decades. In this article we show how to apply this algorithm and how to measure a diffusion coefficient with it. We show that this method is quantitative and several orders of magnitude faster than the existing quantitative method. Finally we harness the potential of this new approach by constructing a portable laser speckle imaging setup that performs quantitative data processing in real-time on a tablet.

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Measurement and Prediction of the Effects of Nonuniform Surface Roughness on Turbulent Flow Friction Coefficients

Journal of Fluids Engineering ◽

10.1115/1.3243567 ◽

1988 ◽

Vol 110 (4) ◽

pp. 380-384 ◽

Cited By ~ 20

Author(s):

R. P. Taylor ◽

W. F. Scaggs ◽

H. W. Coleman

Keyword(s):

Turbulent Flows ◽

Rough Surfaces ◽

Reynolds Numbers ◽

Base Diameter ◽

Friction Coefficients ◽

Friction Factors ◽

Random Rough Surfaces ◽

The Status ◽

Uniform Array ◽

Prediction Approach

The status of prediction methods for friction coefficients in turbulent flows over nonuniform or random rough surfaces is reviewed. Experimental data for friction factors in fully developed pipe flows with Reynolds numbers between 10,000 and 600,000 are presented for two nonuniform rough surfaces. One surface was roughened with a mixture of cones and hemispheres which had the same height and base diameter and were arranged in a uniform array. The other surface was roughened with a mixture of two sizes of cones and two sizes of hemispheres. These data are compared with predictions made using the previously published discrete element prediction approach of Taylor, Coleman, and Hodge. The agreement between the data and the predictions is excellent.

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