Abstract: The traction behavior of lubricants with the linear pressure-viscosity response in EHL line contacts is investigated numerically for smooth as well as rough surfaces. The analysis involves the simultaneous solution of Reynolds, elasticity and energy equations along with the computation of lubricant properties and surface temperatures. The temperature modified Doolittle-Tait equations are used to calculate viscosity and density as functions of fluid pressure and temperature, while Carreau model is used to describe the lubricant rheology. The surface roughness is assumed to be sinusoidal and it is present on the nearly stationary surface in near-pure sliding EHL conjunction. The linear P-V oil is found to yield much lower traction coefficients and slightly thicker EHL films as compared to the synthetic oil for a given set of dimensionless speed and load parameters. Besides, the increase in traction coefficient attributed to surface roughness is much lower for the former case. The present analysis emphasizes the importance of employing realistic pressure-viscosity response for accurate prediction of EHL traction.
Abstract: For a rigid body sliding on a rough surface, a range of
uncertainty or non-uniqueness of solution could be found, which is
termed: Painlevé paradox. Painlevé paradox is the reason of a wide
range of bouncing motion, observed during sliding of robotic
manipulators on rough surfaces. In this research work, the existence
of the paradox zone during the sliding motion of a two-link (P-R)
robotic manipulator with a unilateral constraint is investigated.
Parametric study is performed to investigate the effect of friction,
link-length ratio, total height and link-mass ratio on the paradox zone.
Abstract: Experimental study of natural convection heat transfer
inside smooth and rough surfaces of vertical and inclined equilateral
triangular channels of different inclination angles with a uniformly
heated surface are performed. The inclination angle is changed from
15º to 90º. Smooth and rough surface of average roughness (0.02mm)
are used and their effect on the heat transfer characteristics are
studied. The local and average heat transfer coefficients and Nusselt
number are obtained for smooth and rough channels at different heat
flux values, different inclination angles and different Rayleigh
numbers (Ra) 6.48 × 105 ≤ Ra ≤ 4.78 × 106. The results show that
the local Nusselt number decreases with increase of axial distance
from the lower end of the triangular channel to a point near the upper
end of channel, and then, it slightly increases. Higher values of local
Nusselt number for rough channel along the axial distance compared
with the smooth channel. The average Nusselt number of rough
channel is higher than that of smooth channel by about 8.1% for
inclined case at θ = 45o and 10% for vertical case. The results
obtained are correlated using dimensionless groups for both rough
and smooth surfaces of the inclined and vertical triangular channels.
Abstract: The overall objective of this paper is to retrieve soil
surfaces parameters namely, roughness and soil moisture related to
the dielectric constant by inverting the radar backscattered signal
from natural soil surfaces.
Because the classical description of roughness using statistical
parameters like the correlation length doesn't lead to satisfactory
results to predict radar backscattering, we used a multi-scale
roughness description using the wavelet transform and the Mallat
algorithm. In this description, the surface is considered as a
superposition of a finite number of one-dimensional Gaussian
processes each having a spatial scale. A second step in this study
consisted in adapting a direct model simulating radar backscattering
namely the small perturbation model to this multi-scale surface
description. We investigated the impact of this description on radar
backscattering through a sensitivity analysis of backscattering
coefficient to the multi-scale roughness parameters.
To perform the inversion of the small perturbation multi-scale
scattering model (MLS SPM) we used a multi-layer neural network
architecture trained by backpropagation learning rule. The inversion
leads to satisfactory results with a relative uncertainty of 8%.
Abstract: We developed a method based on quasi-molecular
modelling to simulate the fall of water drops on horizontally smooth
and rough surfaces. Each quasi-molecule was a group of particles
that interacted in a fashion entirely analogous to classical Newtonian
molecular interactions. When a falling water droplet was simulated at
low impact velocity on both smooth and rough surfaces, the droplets
moved periodically (i.e. the droplets moved up and down for a
certain period, finally they stopped moving and reached a steady
state), spreading and recoiling without splash or break-up. Spreading
rates of falling water droplets increased rapidly as time increased
until the spreading rate reached its steady state at time t ~ 0.25 s for
rough surface and t ~ 0.40 s for smooth surface. The droplet height
above both surfaces decreased as time increased, remained constant
after the droplet diameter attained a maximum value and reached its
steady state at time t ~ 0.4 s. However, rough surface had higher
spreading rates of falling water droplets and lower height on the
surface than smooth one.