Modelling of surfaces with a bimodal roughness distribution

Peng, Wei; Bhushan, Bharat

doi:10.1243/1350650001543331

Cited by 13 publications

(5 citation statements)

References 11 publications

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“…(2) Determine interval z with the given sampling number of equal intervals M using Eq. [17]. (3) Calculate the proportion for each interval using Eq.…”

Section: Discussionmentioning

confidence: 99%

Contact Analysis of a Nanocomposite Surface with Two Particle Size Distributions for Magnetic Particulate Tapes

Kim¹,

Bhushan²

2007

Tribology Transactions

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The coating layer of magnetic particulate tapes consists of magnetic and nonmagnetic particles of different sizes on a polymeric matrix, which makes the tape surface have a bimodal or trimodal roughness distribution. In this study, an algorithm is developed to generate composite surfaces with two size distributions of particles. The composite surface is generated by superimposing large and small particles with radii following a Gaussian distribution on a random Gaussian surface, with the centers of the large particles sitting on the mean plane of a rough surface and the centers of the small particles sitting on the superimposed surface of large particles and a rough surface. Statistical analysis is carried out for the generated composite surfaces to study the effects of the particle distribution on the surface's probability density functions (PDFs) and roughness parameters. Also, contact analysis is conducted to identify particle distribution for low friction and wear. Variation of the fractional contact area, the maximum contact pressure, and the relative meniscus force with four design variables as well as the mean radius ratio, the standard deviation ratio, and two particle densities are studied. It is found that a higher mean radius ratio and standard deviation ratio and lower particle densities give a lower contact area, which is desirable for low friction; for low wear, the converse is true. It is recommended that a composite surface with a higher mean radius ratio is most desirable for low friction. On the other hand, for low wear, a lower mean radius ratio is recommended.

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“…(2) Determine interval z with the given sampling number of equal intervals M using Eq. [17]. (3) Calculate the proportion for each interval using Eq.…”

Section: Discussionmentioning

confidence: 99%

Contact Analysis of a Nanocomposite Surface with Two Particle Size Distributions for Magnetic Particulate Tapes

Kim¹,

Bhushan²

2007

Tribology Transactions

View full text Add to dashboard Cite

show abstract

“…In the present notation, denotes the local roughness height as a function of wall-parallel coordinates . The bimodal distribution is obtained by combining two Gaussian distributions through (Peng & Bhushan 2000) where is the p.d.f. of the Gaussian distribution with randomized mean and randomized standard deviation .…”

Section: Methodsmentioning

confidence: 99%

“…In the present notation, k denotes the local roughness height as a function of wall-parallel coordinates (x, z). The bimodal distribution is obtained by combining two Gaussian distributions through (Peng & Bhushan 2000)…”

Section: Roughness Repositorymentioning

confidence: 99%

Prediction of equivalent sand-grain size and identification of drag-relevant scales of roughness – a data-driven approach

Yang,

Stroh,

Lee

et al. 2023

J. Fluid Mech.

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Despite decades of research, a universal method for prediction of roughness-induced skin friction in a turbulent flow over an arbitrary rough surface is still elusive. The purpose of the present work is to examine two possibilities; first, predicting equivalent sand-grain roughness size $k_s$ based on the roughness height probability density function and power spectrum (PS) leveraging machine learning as a regression tool; and second, extracting information about relevance of different roughness scales to skin-friction drag by interpreting the output of the trained data-driven model. The model is an ensemble neural network (ENN) consisting of 50 deep neural networks. The data for the training of the model are obtained from direct numerical simulations (DNS) of turbulent flow in plane channels over 85 irregular multi-scale roughness samples at friction Reynolds number $Re_\tau =800$ . The 85 roughness samples are selected from a repository of 4200 samples, covering a wide parameter space, through an active learning (AL) framework. The selection is made in several iterations, based on the informativeness of samples in the repository, quantified by the variance of ENN predictions. This AL framework aims to maximize the generalizability of the predictions with a certain amount of data. This is examined using three different testing data sets with different types of roughness, including 21 surfaces from the literature. The model yields overall mean error 5 %–10 % on different testing data sets. Subsequently, a data interpretation technique, known as layer-wise relevance propagation, is applied to measure the contributions of different roughness wavelengths to the predicted $k_s$ . High-pass filtering is then applied to the roughness PS to exclude the wavenumbers identified as drag-irrelevant. The filtered rough surfaces are investigated using DNS, and it is demonstrated that despite significant impact of filtering on the roughness topographical appearance and statistics, the skin-friction coefficient of the original roughness is preserved successfully.

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“…Peng and Bhushan [ 163 ] analyzed a contact between a rough surface of the Al 2 O 3 -TiC head and a flat diamond tool. They revealed that the contact area of two-process surfaces with negative skewness decreased with an increase in the Rpq/Rvq ratio.…”

Section: Impact Of Two-process Random Texturesmentioning

confidence: 99%

Two-Process Random Textures: Measurement, Characterization, Modeling and Tribological Impact: A Review

2021

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Two-process random textures seem to present better functional properties than one-process surfaces. There are many random two-process textures. Plateau-honed cylinder surfaces are the most popular example. Two-process surfaces are also created during the initial periods of life of machined elements. However, knowledge about two-process textures measurement, modeling, and behavior is low. Two-process surfaces are very sensitive to measurement errors. It is very difficult to model them. Special methods of their characterization were created. Their functional significance was studied in a small number of publications. In this paper, measurement, characterization, and modeling of two-process textures were presented. The functional impact of them was analyzed, the effects on contact mechanics and friction and wear were mainly studied. Finally, considerations of future challenges were addressed. The nature of two-process random textures should be taken into account during analyses of properties of machined elements. The plateau part decides about the asperity contact, and the valley portion governs the hydrodynamic lubrication.

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Modelling of surfaces with a bimodal roughness distribution

Cited by 13 publications

References 11 publications

Contact Analysis of a Nanocomposite Surface with Two Particle Size Distributions for Magnetic Particulate Tapes

Contact Analysis of a Nanocomposite Surface with Two Particle Size Distributions for Magnetic Particulate Tapes

Prediction of equivalent sand-grain size and identification of drag-relevant scales of roughness – a data-driven approach

Two-Process Random Textures: Measurement, Characterization, Modeling and Tribological Impact: A Review

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