Standard Deviation Of Surface Height Research Articles

Wear and vibration behavior of ZDDP-Containing oil considering scuffing failure

The use of appropriate lubricant additives can reduce surface damage, extend machine lifetimes, improve machining accuracy, and reduce power consumption. In this study, the effects of zinc dialkyl dithiophosphates (ZDDP) additives in lubricating oil on the tribological performance, vibration characteristics, and scuffing thermal mechanism were experimentally investigated using a four-ball tribotester. The impact of the running-in process with ZDDP-containing lubricants on scuffing resistance was examined by varying the applied normal loads. The results showed that the addition of ZDDP and presence of the running-in process could effectively increase the scuffing load, which is in good agreement with previous research. This paper further describes the factors breaking the energy equilibrium caused by scuffing, including the lubricant temperature, friction, vibration, and wear particles. For different weight percentages of the ZDDP in the running-in and step-loading processes, the difference in wear curves was relatively small. The decrease in the number of large particles (>1 μm) before scuffing resulted in lower wear losses than that of the lubricant without ZDDP. The influence of oil in surface roughness valleys, represented by a void volume parameter, on the scuffing failure phenomenon was taken into account. A new modified friction power intensity criterion—which considers the effect of microcontact and oil flow characteristics, according to the ratio of the void volume parameter and the standard deviation of surface heights—is proposed to predict the onset of scuffing failure.

Remote Sensing of Precipitation Using Reflected GNSS Signals: Response Analysis of Polarimetric Observations

For the first time, rain effects on the polarimetric observations of the global navigation satellite system reflectometry (GNSS-R) are investigated. The physical feasibility of tracking the modifications in the surface roughness by rain splash and the surface salinity by the accumulation of freshwater is theoretically discussed. An empirical analysis is carried out using measurements of a coastal GNSS-R station with two side-looking antennas in right- and left-handed circular polarizations (RHCP and LHCP). Discernible drops in RHCP and LHCP powers are observed during rain over a calm sea. The power drop becomes larger at higher elevation angles. The average LHCP power drops by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\approx ~5$ </tex-math></inline-formula> dB at an elevation angle of 45°. The amplitude of the correlation sum shows a dampening, responding to rain rate systematically. The LHCP observations show higher sensitivity to rainfall compared to RHCP observations. The retrieved standard deviation of surface heights shows a steady increase with the rain rate. The derived surface salinity shows a decrease at rains higher than 10 mm/h. This study confirms the potential under environmental conditions of the GNSS-R ground-based station, e.g., with salinity mostly lower than 30 psu, over a calm sea, being a starting point for future investigations.

New empirical backscattering models for estimating bare soil surface parameters

ABSTRACT Various models have been proposed to estimate the degree of backscatter in Synthetic Aperture Radar (SAR) images. However, it is still necessary to calibrate these models based on the characteristics of different study areas and to propose new models to achieve the highest possible accuracy in estimating the backscattering coefficient () SAR. In this study, three empirical models, including Champion, Sahebi and Zribi/Dechambre, were initially calibrated for two SAR datasets (i.e. The Airborne Synthetic Aperture Radar (AIRSAR) and Canadian Space Agency radar satellite (RADARSAT-1)) acquired over two bare soil study areas with various soil characteristics. The Zribi/Dechambre model was then modified by revising the roughness parameter to obtain higher accuracy in estimating over a larger range of incidence angles (θ). A new empirical model was also proposed by combining the four parameters of Soil Moisture (SM), standard deviation of surface height -root mean square- (rms), correlation length (l), and θ. To this end, the most appropriate form of the regression model was investigated and used for each of these parameters to obtain the highest correlation between the in-situ data and values. A comparison of the empirical models showed that the modified Zribi/Dechambre had the highest accuracy in predicting values with the Root Mean Square Errors (RMSE) of 1.20 dB and 1.59 dB over Oklahoma and Quebec, respectively. Furthermore, coefficients values of the new proposed model remained stable in the two datasets unlike the other investigated models. In this study, the effects of l on the accuracy of the new proposed model were also assessed. It was concluded that l had a considerable impact on the accuracy of the proposed model and including this parameter can improve the accuracy by up to 1 dB.

Combined Use of Sentinel-1 SAR and Landsat Sensors Products for Residual Soil Moisture Retrieval over Agricultural Fields in the Upper Blue Nile Basin, Ethiopia.

The objective of this paper is to investigate the potential of sentinel-1 SAR sensor products and the contribution of soil roughness parameters to estimate volumetric residual soil moisture (RSM) in the Upper Blue Nile (UBN) basin, Ethiopia. The backscatter contribution of crop residue water content was estimated using Landsat sensor product and the water cloud model (WCM). The surface roughness parameters were estimated from the Oh and Baghdadi models. A feed-forward artificial neural network (ANN) method was tested for its potential to translate SAR backscattering and surface roughness input variables to RSM values. The model was trained for three inversion configurations: (i) SAR backscattering from vertical transmit and vertical receive (SAR VV) polarization only; (ii) using SAR VV and the standard deviation of surface heights (), and (iii) SAR VV, , and optimal surface correlation length (). Field-measured volumetric RSM data were used to train and validate the method. The results showed that the ANN soil moisture estimation model performed reasonably well for the estimation of RSM using the single input variable of SAR VV data only. The ANN prediction accuracy was slightly improved when SAR VV and the surface roughness parameters ( and ) were incorporated into the prediction model. Consequently, the ANN’s prediction accuracy with root mean square error (RMSE) = 0.035 cm3/cm3, mean absolute error (MAE) = 0.026 cm3/cm3, and r = 0.73 was achieved using the third inversion configuration. The result implies the potential of Sentinel-1 SAR data to accurately retrieve RSM content over an agricultural site covered by stubbles. The soil roughness parameters are also potentially an important variable to soil moisture estimation using SAR data although their contribution to the accuracy of RSM prediction is slight in this study. In addition, the result highlights the importance of combining Sentinel-1 SAR and Landsat images based on an ANN approach for improving RSM content estimations over crop residue areas.

Modeling L- and X-band backscattering of wheat and tests over fields of Pampas

ABSTRACTA discrete scattering model and a detailed set of ground measurements are used to simulate the backscattering coefficients of wheat fields during the whole growth cycle. Simulations are carried out at L- and X-band, and at HH, VV, and HV polarizations. Wheat fields are located in Pampas (Argentina), and are characterized by low values of plant density. Simulations show that the backscattering coefficient is driven by variations of soil moisture at L-band, particularly for HH polarization, with low vegetation effects. Conversely, the attenuation of vegetation is dominant in producing variations of backscattering coefficients at X-band, particularly for VV polarization. Simulations are compared against experimental data collected over the same Pampas region, using airborne SARAT SAR at L-band and COSMO-SKYMED at X-band. Assuming a surface height standard deviation in a 0.4–0.7 cm range, the simulations generally agree with experimental data, with an RMSE lower than about 2 dB at L-band and X-band, except a limited number of cases. Discrepancies observed in specific conditions are discussed. Overall, the results indicate that a joint use of L- and X-band has a good potential to monitor both soil moisture and vegetation growth.

Influence of Surface Roughness Measurement Scale on Radar Backscattering in Different Agricultural Soils

Soil surface roughness strongly affects the scattering of microwaves on the soil surface and determines the backscattering coefficient ( $\sigma ^{0})$ observed by radar sensors. Previous studies have shown important scale issues that compromise the measurement and parameterization of roughness especially in agricultural soils. The objective of this paper was to determine the roughness scales involved in the backscattering process over agricultural soils. With this aim, a database of 132 5-m profiles taken on agricultural soils with different tillage conditions was used. These measurements were acquired coinciding with a series of ENVISAT/ASAR observations. Roughness profiles were processed considering three different scaling issues: 1) influence of measurement range; 2) influence of low-frequency roughness components; and 3) influence of high-frequency roughness components. For each of these issues, eight different roughness parameters were computed and the following aspects were evaluated: 1) roughness parameters values; 2) correlation with $\sigma ^{0}$ ; and 3) goodness-of-fit of the Oh model. Most parameters had a significant correlation with $\sigma ^{0}$ especially the fractal dimension, the peak frequency, and the initial slope of the autocorrelation function. These parameters had higher correlations than classical parameters such as the standard deviation of surface heights or the correlation length. Very small differences were observed when longer than 1-m profiles were used as well as when small-scale roughness components ( 100 cm) were disregarded. In conclusion, the medium-frequency roughness components (scale of 5–100 cm) seem to be the most influential scales in the radar backscattering process on agricultural soils.

Effect of roughness on surface force distributions measured by newly developed surface force apparatus with ultra-high accuracy

A pull-off force between a sphere and a flat plate is precisely investigated using a newly developed surface force apparatus (SFA). In this system, (1) a pull-off force between a spherical glass probe and a sample plate is measured in vacuum, (2) the probe is directly pulled off from the sample by an electromagnetic force, and (3) the pull-off force and displacement of the probe are measured with ultra-high resolution of 0.4 nN and 0.3 nm, respectively, which are electrical noise levels. The pull-off process, which appears as a part of force curve, is clearly measured by this system, and pull-off force is measured with high reproducibility and accuracy. Pull-off force distributions on flat surfaces of Si, SrTiO3, glassy carbon, and diamond-like carbon are measured. It is shown that despite the differences between these materials, for all four for them, the distribution strongly depends on the surface roughness, such that the relative standard deviation of pull-off force is proportional to the surface roughness ( Ra and Rz). The Greenwood and Williamson (GW) model is then used for the analysis of the pull-off force. Comparing with SFA experiments, it is shown that the pull-off force and separation displacement can be predicted by the GW model and that the pull-off force strongly depends on the standard deviation of surface height, causing the broad distribution of pull-off force at rough surfaces.

Massively Parallel Computation of Soil Surface Roughness Parameters on A Fermi GPU

Surface roughness is description of the surface micro topography of randomness or irregular. The standard deviation of surface height and the surface correlation length describe the statistical variation for the random component of a surface height relative to a reference surface. When the number of data points is large, calculation of surface roughness parameters is time-consuming. With the advent of Graphics Processing Unit (GPU) architectures, inherently parallel problem can be effectively solved using GPUs. In this paper we propose a GPU-based massively parallel computing method for 2D bare soil surface roughness estimation. This method was applied to the data collected by the surface roughness tester based on the laser triangulation principle during the field experiment in April 2012. The total number of data points was 52,040. It took 47 seconds on a Fermi GTX 590 GPU whereas its serial CPU version took 5422 seconds, leading to a significant 115x speedup.

Fast computation of bare soil surface roughness on a Fermi GPU

Surface roughness is an important factor in bare soil microwave radiation for the observation of the Earth. Correlation length and standard deviation of surface height are the two statistical parameters that describe surface roughness. However, when the number of data points is large, the calculation of surface roughness parameters becomes time-consuming. Therefore, it is desired to have a high-performance computing facility to execute this task. A Graphics Processing Unit (GPU) provides hundreds of computing cores along with a high memory bandwidth. To carry out a parallel implementation of the algorithms, Compute Unified Device Architecture (CUDA) provides researchers with an easy way to execute multiple threads in parallel on GPUs. In this paper, we propose a GPU-based parallel computing method for 2D surface roughness estimation. We use an NVIDIA GeForce GTX 590 graphics card to run the CUDA implementation. The experimental input data is collected by our in-house surface roughness tester which is designed based on the laser triangulation principle, giving sample data points of up to 52,040. According to the experimental results, the serial CPU version of the implementation takes 5422s whereas our GPU implementation takes only 47s, resulting a significant 115× speedup.

Focused Ion Beam Fabrication and Atomic Force Microscopy Characterization of Micro/Nanoroughness Artifacts With Specified Statistic Quantities

Series of roughness patterns with predetermined statistic quantities, such as standard deviation of surface heights, autocorrelation length, and height distribution were fabricated by focused ion beam (FIB) lithography and characterized by atomic force microscopy (AFM). Template-matching analysis and comparisons of surface parameters were performed to ascertain the fabrication and measurement qualities. Results show that the root-mean-square (rms) residuals are approximately 4.8, 5.2, and 15.7 nm for the fabricated Gaussian, negatively skewed, and positively skewed surfaces with the dimension of 5120 nm × 5120 nm × 121 nm. For the surfaces with the same skewness but different autocorrelation lengths, the rms residuals have no significant differences. The surface parameters of the fabricated artifacts are in close agreements with their expected values. To further elucidate the geometric interactions between the tip and roughness structure in AFM measurements, blind tip estimations were carried out on the scanned images. The tip estimation deviation increases with the increase of autocorrelation length for the Gaussian surfaces. The skewed structures help to improve the estimation accuracy. By a proper design of the surface quantities, the artifacts can serve as reference areal roughness standards at the nanoscale and a kind of tip characterizers.

Evaluating the Semiempirical $H$– $Q$ Model Used to Calculate the L-Band Emissivity of a Rough Bare Soil

In this paper, a numerical modeling approach was used to evaluate the semiempirical H-Q model used in the Soil Moisture and Ocean Salinity (SMOS) retrieval algorithm to account for roughness effects over bare soil. The H-Q model uses four parameters, H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</sub> , Q <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</sub> , N <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RH</sub> , and N <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RV</sub> , which are usually calibrated at the ground scale for different surface types. The aim of this paper is to investigate whether these empirical parameters could be linked to the physical roughness parameters of standard deviation of surface heights σ and autocorrelation length L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">c</sub> . First, a numerical modeling approach was used to calculate rough soil emissivities for different roughness and soil moisture conditions. Second, H -Q model parameters were retrieved by minimizing a cost function between these emissivities and those calculated by the H -Q model. It was found that the retrieved H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</sub> could be related directly to Zs = σ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> /L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">c</sub> and that Q <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</sub> , N <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RV</sub> , and N <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RH</sub> were dependent on H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</sub> . H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</sub> was found to have a negligible dependence on soil moisture. Based on these results, a new model was proposed where the four H-Q model parameters were calibrated to Zs. This model was tested on the PORTOS 1993 data set and found to yield a root-mean-square difference between the retrieved and measured soil moisture values of ~ 0.03 m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> /m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> , which was within the desired 0.04-m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> /m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> error margin for the SMOS mission.

Evaluating the L-MEB Model From Long-Term Microwave Measurements Over a Rough Field, SMOSREX 2006

The present paper analyzes the effects of roughness on the surface emission at L-band based on observations acquired during a long-term experiment. At the Surface Monitoring of the Soil Reservoir Experiment site near Toulouse, France, a bare soil was plowed and monitored over more than a year by means of an L-band radiometer, profile soil moisture and temperature sensors, and a local weather station, accompanied by 12 roughness campaigns. The aims of this paper are the following: 1) to present this unique database and 2) to use this data set to investigate the semiempirical parameters for the roughness in L-band Microwave Emission of the Biosphere, which is the forward model used in the Soil Moisture and Ocean Salinity soil moisture retrieval algorithm. In particular, we studied the link between these semiempirical parameters and the soil roughness characteristics expressed in terms of standard deviation of surface height (σ) and the correlation length (LC). The data set verifies that roughness effects decrease the sensitivity of surface emission to soil moisture, an effect which is most pronounced at high incidence angles and soil moisture and at horizontal polarization. Contradictory to previous studies, the semiempirical parameter Qr was not found to be equal to 0 for rough conditions. A linear relationship between the semiempirical parameters N and σ was established, while NH and NV appeared to be lower for a rough (NH ~ 0.59 and NV ~ -0.3) than for a quasi-smooth surface. This paper reveals the complexity of roughness effects and demonstrates the great value of a sound long-term data set of rough L-band surface emissions to improve our understanding on the matter.

Estimation of surface soil moisture and roughness from multi-angular ASAR imagery in the Watershed Allied Telemetry Experimental Research (WATER)

Abstract. Radar remote sensing has demonstrated its applicability to the retrieval of basin-scale soil moisture. The mechanism of radar backscattering from soils is complicated and strongly influenced by surface roughness. Additionally, retrieval of soil moisture using AIEM (advanced integrated equation model)-like models is a classic example of underdetermined problem due to a lack of credible known soil roughness distributions at a regional scale. Characterization of this roughness is therefore crucial for an accurate derivation of soil moisture based on backscattering models. This study aims to simultaneously obtain surface roughness parameters (standard deviation of surface height σ and correlation length cl) along with soil moisture from multi-angular ASAR images by using a two-step retrieval scheme based on the AIEM. The method firstly used a semi-empirical relationship that relates the roughness slope, Zs (Zs = σ2/cl) and the difference in backscattering coefficient (Δσ) from two ASAR images acquired with different incidence angles. Meanwhile, by using an experimental statistical relationship between σ and cl, both these parameters can be estimated. Then, the deduced roughness parameters were used for the retrieval of soil moisture in association with the AIEM. An evaluation of the proposed method was performed in an experimental area in the middle stream of the Heihe River Basin, where the Watershed Allied Telemetry Experimental Research (WATER) was taken place. It is demonstrated that the proposed method is feasible to achieve reliable estimation of soil water content. The key challenge is the presence of vegetation cover, which significantly impacts the estimates of surface roughness and soil moisture.

Effective versus measured correlation length for radar‐based surface soil moisture retrieval

At present, radar‐based surface soil moisture (SM) retrieval is hampered by the influence of surface roughness on the backscattering coefficient (σ0). Surface roughness is typically represented by two parameters, namely the standard deviation of surface heights (s) and the surface correlation length (l). The latter is a very problematic parameter, since it is extremely variable and very difficult to measure adequately. Therefore, several authors proposed calibrating it using backscattering models yielding optimum or effective l values. Baghdadi et al. found that those effective l were related to the parameter s and the configuration of the sensor, and proposed an approach to calculate it. The objective of this study is to evaluate the validity of that approach using data acquired on a complementary test site. RADARSAT‐1 scenes acquired over an experimental watershed are used. Soil moisture and surface roughness parameters were measured in detail, coinciding with satellite overpasses. The effective l values calculated from the equations of Baghdadi et al. (2006) are used to perform forward and inverse simulations using the Integral Equation Model that are compared with radar observations and ground measurements of SM. The results obtained highlight the potential of the evaluated approach towards an operational radar based soil moisture estimation.

ＳＡＲデータを用いた非湛水水田の土壌水分量推定におけるモデルパラメータの校正

This paper reports the calibration of model parameters to estimate soil moisture of non-inundated paddy fields using SAR (Synthetic Aperture Radar) data. The IEM (Integral Equation Method) model, theoretical model to represent surface scattering in the microwave region, was examined. Surface roughness parameters (standard deviation of surface height, autocorrelation function of the surface height, and autocorrelation length) are important parameters for microwave backscattering. However, the surface roughness of the paddy fields was found to change, and the autocorrelation length is quite difficult to accurately measure on the surface. Therefore, the two parameters, standard deviation of surface height, and autocorrleation length, were calibrated with actual soil moisture. As a result, it was found that the objective function to minimize the sum of residuals between estimated soil moisture and actual soil moisture produced the most optimal estimation results. In this case, The root mean square of errors between the estimated volumetric soil moisture and the actual soil moisture used for the calibration was 9.4%. This can be acceptable to consider the fluctuation of the surface roughness parameters during the noninundated season.

ＡＬＯＳ／ＰＡＬＳＡＲとＪＥＲＳ‐１／ＳＡＲデータを用いた非湛水水田の土壌水分推定

This paper reports a methodology to estimate soil moisture of non-inundated paddy fields on conditions that the field data synchronously measured is not available. Two L-band synthetic aperture radar (SAR) sensor data, i.e. Japanese Earth Resources Satellite-1 (JERS-1) /SAR, and Advanced Land Observing Satellite (ALOS) /Phased Array type L-band Synthetic Aperture Radar (PALSAR), were used. First, parameters of a model used for the soil moisture estimation were calibrated. Integral Equation Method (IEM) model, theoretical model to represent surface scattering in the microwave region, has several parameters. Among those parameters, two parameters, standard deviation of surface height, and autocorrelation length, were calibrated with actual volumetric soil moisture measured in 2007, and PALSAR backscatter coefficients. Then, the calibrated IEM model was applied to estimate volumetric soil moisture in 1990s using JERS-1/SAR. Finally, the volumetric soil moisture distribution maps in different years of 1990s were produced. While it is impossible to validate the estimated volumetric soil moisture because of the absence of field measurement data, it was found that the trend of the estimated volumetric soil moisture is similar to the trend of the actual volumetric soil moisture. It is concluded that the proposed methodology is quite effective to estimate soil moisture of non-inundated paddy fields on conditions that the field data synchronously measured is not available.

Sensitivity of radar backscatter to desert surface roughness

Synthetic aperture radar (SAR) data have proved useful in remote sensing studies of deserts, enabling different surfaces to be discriminated by differences in roughness properties. Roughness is characterized in SAR backscatter models using the standard deviation of surface heights (σ), correlation length (L) and autocorrelation function (ρ(ξ)). Previous research has suggested that these parameters are of limited use for characterizing surface roughness, and are often unreliable due to the collection of too few roughness profiles, or under‐sampling in terms of resolution or profile length (Lp ). This paper reports on work aimed at establishing the effects of Lp and sampling resolution on SAR backscatter estimations and site discrimination. Results indicate significant relationships between the average roughness parameters and Lp , but large variability in roughness parameters prevents any clear understanding of these relationships. Integral equation model simulations demonstrate limited change with Lp and under‐estimate backscatter relative to SAR observations. However, modelled and observed backscatter conform in pattern and magnitude for C‐band systems but not for L‐band data. Variation in surface roughness alone does not explain variability in site discrimination. Other factors (possibly sub‐surface scattering) appear to play a significant role in controlling backscatter characteristics at lower frequencies.

Adhesion Forces for Sub-10 nm Flying-Height Magnetic Storage Head Disk Interfaces

A quasi-dynamic adhesion model is used to calculate the intermolecular adhesion forces present in ultra low flying Head Disk Interfaces (HDI’s). The model is a continuum-based micromechanics model that accounts for realistic surfaces with roughness, molecularly thin lubricants, and is valid under both static and dynamic sliding conditions. Several different levels of surface roughness are investigated ranging from extremely smooth surfaces having a standard deviation of surface heights σ=2 Å to rougher interfaces with several nanometer roughness. It is found that when the flying-height is greater than 5 nm, there are no significant adhesive forces, whereas for flying-heights less than 5 nm, adhesion forces increase sharply, which can be catastrophic to the reliability of low flying HDI’s. In addition to roughness, the apparent area of contact between the flying recording slider and the magnetic disk is also found to significantly affect the magnitude of the adhesion forces. The adhesion model is validated by direct comparisons with adhesion “pull-off” force measurements performed using an Atomic Force Microscope with controlled probe tip areas and magnetic disks having different lubricant thickness.

An empirical calibration of the integral equation model based on SAR data, soil moisture and surface roughness measurement over bare soils

The retrieval of surface parameters demands the use of well calibrated models and unfortunately, none of the existing models provide consistently good agreement with the measured data. The overall objective of this article is to propose a semi-empirical calibration of the Integral Equation Model (IEM) so as to better reproduce the backscattering coefficient measured from SAR images over bare soils. As correlation length is not only the least accurate parameter but also the most difficult to measure, we propose its empirical estimation from an experimental data set of SAR images and soil parameter measurements. Based on a first data set, correlation length behaviour was studied in terms of f rms (where f is the wave number and rms the standard deviation of surface height) and radar configuration (polarization and incidence angle). Exponential relationships between optimal correlation length and rms height were found for each radar configuration. The IEM was then tested on another set of measured data in order to validate the calibration procedure. The new calibrated version of the IEM, corresponding to the original IEM with a coupling of the empirical function of correlation length, shows a very good agreement with the backscattering measurements provided by space-borne SAR systems. This adapted version of the IEM can be used in inversion techniques for retrieving rms height and/or soil moisture from radar observations.

Three-dimensional contact analysis of layered elastic/plastic solids with rough surfaces

A three-dimensional numerical model is presented to investigate the contact behavior of layered elastic/plastic solids with rough surfaces. The surface deformation and pressure distributions are obtained based on a variational principle with a fast Fourier transform (FFT)-based scheme. The surface and subsurface stresses in the layer and the substrate are determined and a von Mises yield criterion is used to determine onset of yield. The model is applicable for both single-asperity contact and multiple-asperity contact. Three-dimensional layered elastic/plastic solids with rough surfaces are generated on the computer with specified standard deviation of surface heights, correlation length, layer thickness, stiffness and hardness ratio of the layer to the substrate. The contact statistics of layered solids under a designated normal load are predicted, namely, the fractional contact area, contact pressures, surface and subsurface stress, and relative meniscus force. These contact statistics are used to investigate friction, stiction, and wear problems such as debris generation, brittle failure, and delamination. The results yield insight into the effects of the layer, surface roughness, and normal load on the tribological performance of layered elastic/plastic solids. It allows the specification of layer properties to realize elastic contact and to reduce wear of materials. Optimum layer parameters are identified to provide low friction, stiction and wear.