2D and 3D Roughness Characterization

Magsipoc, Earl; Zhao, Qi; Grasselli, Giovanni

doi:10.1007/s00603-019-01977-4

Cited by 88 publications

(49 citation statements)

References 94 publications

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“…The variety of describing the roughness of a surface offers even more possibilities: Semi-Variograms (e.g., Lianheng et al 2018;Babadagli and Develi 2003), power-spectral density (e.g., Babadagli and Develi 2003;Kanafi and Tuononen 2017) or wavelet decomposition (e.g., Mehrishal and Sharifzadeh 2013;Li et al 2019) are among them. Magsipoc et al (2020) and Gadelmawla et al (2002) provide an overview about surface roughness models.…”

Section: State Of the Art: (Semi-) Analytical Solutionsmentioning

confidence: 99%

Simulation of direct shear tests using a forces on fracture surfaces (FFS) approach

2021

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The aim of this work is to provide a complete data set of direct shear tests and to propose a corresponding simulation approach. Tests have been conducted on crystalline rock samples applying constant normal load (CNL) and constant normal stiffness (CNS) boundary conditions. A physical consistent algorithm which explicitly calculates the forces acting on the fracture surface (FFS) has been developed. This FFS approach can explain the occurrence of surface degradation and shows the main shear characteristics. After all, shearing of rough rock joints remains a complex process and the differences between laboratory and simulation results are still significant in some cases. All data and input files are provided free for download and testing.

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Section: State Of the Art: (Semi-) Analytical Solutionsmentioning

confidence: 99%

Simulation of direct shear tests using a forces on fracture surfaces (FFS) approach

2021

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“…Moreover, solely the largest amplitude over the length of the pin diameter is measured. The device, therefore, acts as a low-cut filter and band-pass filters the original surface trace (Maerz et al 1990). Indeed, all measuring approaches let them be contactless optical devices or tactile procedures do have a limited resolution and low-cut filtering is always happening.…”

Section: Measurement Quality Of the Original Type-profilesmentioning

confidence: 99%

“…Besides, individual authors used other statistical parameters, such as the standard deviation of the slope angles of each sampling step σ i (Yu and Vayssade 1991) or the ultimate slope of the profile λ, as a measure of the peak amplitude of the profile versus its projected length (Barton and de Quadros 1997). Examples of other parameters used more often than σ i and λ are the roughness profile index R p (Tatone and Grasselli 2010;Yu and Vayssade 1991;Maerz et al 1990) and the so-called Structure Function of the profile, termed SF (Tse and Cruden 1979;Yu and Vayssade 1991;Yang et al 2001). R p is defined as the ratio between the actual versus the projected length of the profile whereas SF can be calculated from Z 2 by introducing the sampling interval.…”

Section: Statistical Parametersmentioning

confidence: 99%

“…In the already mentioned, most recent work by Stigsson and Mas Ivars (2019) an important fact is underlined: fractal calculation schemes are meaningful only if an additional magnitude parameter is incorporated. Magsipoc et al (2019) recognized this simultaneously, identifying power spectral density and the root-mean-square correlation method (RMS-COR) as being eligible for roughness characterisation. Consequently, possible correlations of fractal measures with JRC need another parameter apart from H or D to be fully constrained.…”

Section: Fractal Measuresmentioning

confidence: 99%

“…Especially for the fractal dimension, the discrepancies are large since several calculation routines have been used in the past (c.f. Magsipoc et al 2019). Moreover, in this context, confusion exists regarding the applicability of the different fractal calculation approaches with respect to the self-affine nature of rock discontinuity profiles.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Comparative Evaluation of Statistical and Fractal Approaches for JRC Calculation Based on a Large Dataset of Natural Rock Traces

Marsch

Fernández-Steeger

2021

Rock Mech Rock Eng

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After the publication of the type-profiles for the estimation of the joint roughness coefficient (JRC) a discussion evolved about how to adequately use these traces. Based on the chart numerous researchers assembled mathematical correlations with various parameters seeking objectivity in the determination of JRC. Within these works differences concerning the database and the mathematical implementations exist. Consequently, each correlation, although predominantly the same parameters are used, leads to different JRC values. In theory, for any arbitrary profile, irrespective of the particular calculation approach, the same JRC should result. This is a requisite because of the referencing of all correlations to the 10 type-profiles. However, it is shown in this study that in most cases equal or even satisfactorily similar results are not obtained. The discrepancies are vast when non-standard profiles are evaluated, in this case, more than 40,000 traces from six different rock surfaces that cover a broad range of roughness categories. The simple intuitive parameter Z2 served as an agent for the statistical methods because of its broad use and consequently good comparability. On the part of the fractal approaches, three definitions were used. However, JRC inferred from fractal correlations are very much dependent on the particular calculation routine. In fact, the theory of fractals is overly complex for the sparse and low-resolution type-profiles. In summary, fractal approaches do not produce safer or more reliable estimates of roughness compared to simple statistical means and using Z2 perfectly suffices to determine the class of JRC.

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Experimental Study on Bubble Entrainment by a Free Fall Jet in an Argon‐Filled Continuous Casting Tundish

Chang

Song

Liu

et al. 2022

steel research int.

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Inert gas entrainment by the impact of free fall jet can avoid the bubble grow‐up on nonwetting interface between liquid steel and refractory gas nozzle, which produces bubbles as small as 0.37 mm for deep cleaning the liquid steel in an argon‐filled tundish. The time‐dependent vibration of free fall jet is described by a high‐speed camera combined with image postprocessing software. The effect of liquid flow rate and free fall length on jet vibration is investigated. The equivalent surface roughness is proposed to characterize the surface wave of free fall jet without fixed amplitude and frequency. The entrained gas flow rate shows a good correlation with the equivalent surface roughness. Gas entrainment cannot happen with a free fall jet shorter than 2 mm, as the jet vibration is insufficient to make the bubbles separate from the gas sheath. The surface fluctuation caused by the impact of free fall jet can be well isolated within a submerged shield that remains a stable slag layer outside of the shield. Besides, the strong mixing flow caused by free fall jet is beneficial to dissipate the turbulence kinetic energy of the entry flow, which improves the tundish flow field.

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2D and 3D Roughness Characterization

Cited by 88 publications

References 94 publications

Simulation of direct shear tests using a forces on fracture surfaces (FFS) approach

Simulation of direct shear tests using a forces on fracture surfaces (FFS) approach

Comparative Evaluation of Statistical and Fractal Approaches for JRC Calculation Based on a Large Dataset of Natural Rock Traces

Experimental Study on Bubble Entrainment by a Free Fall Jet in an Argon‐Filled Continuous Casting Tundish

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