Predicting RMS surface roughness using fractal dimension and PSD parameters

Durst, Phillip J.; Mason, George L.; McKinley, Burney; Baylot, Alex

doi:10.1016/j.jterra.2010.05.004

Cited by 29 publications

(13 citation statements)

References 9 publications

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“…For such surfaces, the rms-height s, the correlation length l, and the ACF do not exist [21,29] Thus, the surface modeling must be realized within the limits of the spatial frequencies (i.e., sampling rates) f min ≤ f ≤ f max . This is a semi-self-affine surface [13,30].…”

Section: Power-law Inputs For the Iemmentioning

confidence: 99%

“…where k is the wavenumber and α has a limited amount; D < α < D + 2, D is the topological dimension, and for the linear profile, it is considered one (D = 1) [29,33], representing the slope of the linear best-fit of the power spectral density (PSD) on a logarithmic scale [29,32]. It is noted that this linear best-fit must be applied to the trendless profile.…”

Section: Power-law Inputs For the Iemmentioning

confidence: 99%

“…It is noted that this linear best-fit must be applied to the trendless profile. In other words, the spectral slope (α) might not include the trend part of the PSD, the early part of the PSD [29].…”

Section: Power-law Inputs For the Iemmentioning

confidence: 99%

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Better Estimated IEM Input Parameters Using Random Fractal Geometry Applied on Multi-Frequency SAR Data

et al. 2017

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Abstract:Microwave remote sensing can measure surface geometry. Via the processing of the Synthetic Aperture Radar (SAR) data, the earth surface geometric parameters can be provided for geoscientific studies, especially in geological mapping. For this purpose, it is necessary to model the surface roughness against microwave signal backscattering. Of the available models, the Integral Equation Model (IEM) for co-polarized data has been the most frequently used model. Therefore, by the processing of the SAR data using this model, the surface geometry can be studied. In the IEM, the surface roughness geometry is calculable via the height statistical parameter, the rms-height. However, this parameter is not capable enough to represent surface morphology, since it only measures the surface roughness in the vertical direction, while the roughness dispersion on the surface is not included. In this paper, using the random fractal geometry capability, via the implementation of the power-law roughness spectrum, the precision and correctness of the surface roughness estimation has been improved by up to 10%. Therefore, the random fractal geometry is implemented through the calculation of the input geometric parameters of the IEM using the power-law surface spectrum and the spectral slope. In this paper, the in situ roughness measurement data, as well as SAR images at frequencies of L, C, and X, have been used to implement and evaluate the proposed method. Surface roughness, according to the operational frequencies, exhibits a fractal or a diffractal behavior.

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Section: Power-law Inputs For the Iemmentioning

confidence: 99%

Section: Power-law Inputs For the Iemmentioning

confidence: 99%

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Better Estimated IEM Input Parameters Using Random Fractal Geometry Applied on Multi-Frequency SAR Data

et al. 2017

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“…A complete list of the ride courses used for the study along with a more thorough discussion of FD and DC Offset calculation methods can be found in Ref. 11.…”

Section: Surface Roughness (Rms) Modelmentioning

confidence: 99%

Techniques for inferring terrain parameters related to ground vehicle mobility using UAV born IFSAR and lidar data

2011

Self Cite

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Predicting ground vehicle performance requires in-depth knowledge, captured as numeric parameters, of the terrain on which the vehicles will be operating. For off-road performance, predictions are based on rough terrain ride comfort, which is described using a parameter entitled root-mean-square (RMS) surface roughness. Likewise, on-road vehicle performance depends heavily on the slopes of the individual road segments. Traditional methods of computing RMS and road slope values call for high-resolution (inch-scale) surface elevation data. At this scale, surface elevation data is both difficult and time consuming to collect. Nevertheless, a current need exists to attribute large geographic areas with RMS and road slope values in order to better support vehicle mobility predictions, and high-resolution surface data is neither available nor collectible for many of these regions. On the other hand, meter scale data can be quickly and easily collected for these areas using unmanned aerial vehicle (UAV) based IFSAR and LIDAR sensors. A statistical technique for inferring RMS values for large areas using a combination of fractal dimension and spectral analysis of five-meter elevation data is presented. Validation of the RMS prediction technique was based on 43 vehicle ride courses with 30-centimeter surface elevation data. Also presented is a model for classifying road slopes for long road sections using five-meter elevation data. The road slope model was validated against one-meter LIDAR surface elevation profiles. These inference algorithms have been successfully implemented for regions of northern Afghanistan, and some initial results are presented.

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“…The degree of complexity of a working surface can be represented by a value called fractal dimension [20,21]. The fractal dimension is identified as an intrinsic property of such a multiscale structure and the Weierstrass-Mandelbrot (W-M) fractal function is used to introduce a simple method of roughness characterization [22,23]. Fractal analysis [24][25][26], an effective tool for friction and wear behavior of the contact surface [27], could provide new insights for surface analysis and characterization for ultrasonic motors.…”

Section: Introductionmentioning

confidence: 99%

Wear Characteristics of Metallic Counterparts under Elliptical-Locus Ultrasonic Vibration

Zhang

Wang

2016

Applied Sciences

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Wear behavior is influential to improve friction drive and wear lifespan of actuators or motors, which work at an elliptical locus vibration. Sliding wear tests of metallic friction pairs are conducted by a laboratory rig of ultrasonic vibration. Surfaces of the different metallic sliders are characterized using surface roughness, Abbott curves and fractal dimension. Results show that surface roughness is reduced to varying degrees in the metallic sliders due to ultrasonic polishing and/or micro-rolling effect. Variations in the fractal dimensions of contact surfaces are consistent with that of surface roughness. Wear traces demonstrate that plastic deformation and cracking are the primary failure modes. Where the driving tip on the slider is in intermittent contact followed by impact effects, ripples of 3~5 µm traces suggest the occurrence of fretting in duralumin sliders. Nodular cast iron showed a favorable performance during running of ultrasonic motor, exhibiting a stable output performance and durable wear life.

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Predicting RMS surface roughness using fractal dimension and PSD parameters

Cited by 29 publications

References 9 publications

Better Estimated IEM Input Parameters Using Random Fractal Geometry Applied on Multi-Frequency SAR Data

Better Estimated IEM Input Parameters Using Random Fractal Geometry Applied on Multi-Frequency SAR Data

Techniques for inferring terrain parameters related to ground vehicle mobility using UAV born IFSAR and lidar data

Wear Characteristics of Metallic Counterparts under Elliptical-Locus Ultrasonic Vibration

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