Friction of sliding surfaces carrying adsorbed lubricant layers

In concentrated contacts the behaviour of lubricants is much modified by the high local pressures: changes can arise both from molecular ordering within the very thin film lubricant layers present at the interface as well as from the deposition on the component surfaces of more solid-like polymeric boundary layers. These 'third bodies' separating the solid surfaces may have rheological or mechanical properties very different from those observed in the bulk. Classical elasto-hydrodynamic theory considers the entrapped lubricant to exhibit a piezo-viscous behaviour while the conventional picture of more solid boundary lubricant layers views their shear strength r as being linearly dependent on local pressure p, so that T = TO + ap where TO and a are constants. If TO is relatively small, then the coefficient of friction \i = T Ip ~ a and so Amonton's laws are recovered. However, the properties of adsorbed or deposited surface films, or indeed other third bodies such as debris layers, may be more complex than this. A preliminary study has looked quantitatively at the influence of the pressure dependence of the shear strength of any surface layer on the overall friction coefficient of a contact which is made up of an array of asperities whose height varies in a Gaussian manner. Individual contact points may be elastic or plastic. The analysis results in plots of coefficient of friction versus the service or load parameter PIH&NRa where P is the nominal pressure on the contact, HS the hardness of the deforming surface, N the asperity density, R the mean radius of curvature of the asperities, and a is the standard deviation of their height distribution. In principle, any variation oft withp can be incorporated into the model; however, in this initial study we have used data on colloidal suspensions from the group at the Ecole Centrale de Lyon as well as examining the effect of functional relationships of somewhat greater complexity than a simple linear form. Results of the analysis indicate that variations in fj. are possible as the load is varied which depend on the statistical spread of behaviour at individual asperity contacts. The value of this analysis is that it attempts to combine the behaviour of films on the molecular scale with the topography of real engineering surfaces and so give an indication of the effects at the full-size or macro-scale that can be achieved by chemical or molecular surface engineering.

Impedance control and internal model use during the initial stage of adaptation to novel dynamics in humans.

This study investigated the neuromuscular mechanisms underlying the initial stage of adaptation to novel dynamics. A destabilizing velocity-dependent force field (VF) was introduced for sets of three consecutive trials. Between sets a random number of 4-8 null field trials were interposed, where the VF was inactivated. This prevented subjects from learning the novel dynamics, making it possible to repeatedly recreate the initial adaptive response. We were able to investigate detailed changes in neural control between the first, second and third VF trials. We identified two feedforward control mechanisms, which were initiated on the second VF trial and resulted in a 50% reduction in the hand path error. Responses to disturbances encountered on the first VF trial were feedback in nature, i.e. reflexes and voluntary correction of errors. However, on the second VF trial, muscle activation patterns were modified in anticipation of the effects of the force field. Feedforward cocontraction of all muscles was used to increase the viscoelastic impedance of the arm. While stiffening the arm, subjects also exerted a lateral force to counteract the perturbing effect of the force field. These anticipatory actions indicate that the central nervous system responds rapidly to counteract hitherto unfamiliar disturbances by a combination of increased viscoelastic impedance and formation of a crude internal dynamics model.

The internal structure of igniting turbulent sprays as revealed by complex chemistry DNS

A parametric study of spark ignition in a uniform monodisperse turbulent spray is performed with complex chemistry three-dimensional Direct Numerical Simulations in order to improve the understanding of the structure of the ignition kernel. The heat produced by the kernel increases with the amount of fuel evaporated inside the spark volume. Moreover, the heat sink by evaporation is initially higher than the heat release and can have a negative effect on ignition. With the sprays investigated, heat release occurs over a large range of mixture fractions, being high within the nominal flammability limits and finite but low below the lean flammability limit. The burning of very lean regions is attributed to the diffusion of heat and species from regions of high heat release, and from the spark, to lean regions. Two modes of spray ignition are reported. With a relatively dilute spray, nominally flammable material exists only near the droplets. Reaction zones are created locally near the droplets and have a non-premixed character. They spread from droplet to droplet through a very lean interdroplet spacing. With a dense spray, the hot spark region is rich due to substantial evaporation but the cold region remains lean. In between, a large surface of flammable material is generated by evaporation. Ignition occurs there and a large reaction zone propagates from the rich burned region to the cold lean region. This flame is wrinkled due to the stratified mixture fraction field and evaporative cooling. In the dilute spray, the reaction front curvature pdf contains high values associated with single droplet combustion, while in the dense spray, the curvature is lower and closer to the curvature associated with gaseous fuel ignition kernels. © 2011 The Combustion Institute.

Temporal difference models describe higher-order learning in humans.

The ability to use environmental stimuli to predict impending harm is critical for survival. Such predictions should be available as early as they are reliable. In pavlovian conditioning, chains of successively earlier predictors are studied in terms of higher-order relationships, and have inspired computational theories such as temporal difference learning. However, there is at present no adequate neurobiological account of how this learning occurs. Here, in a functional magnetic resonance imaging (fMRI) study of higher-order aversive conditioning, we describe a key computational strategy that humans use to learn predictions about pain. We show that neural activity in the ventral striatum and the anterior insula displays a marked correspondence to the signals for sequential learning predicted by temporal difference models. This result reveals a flexible aversive learning process ideally suited to the changing and uncertain nature of real-world environments. Taken with existing data on reward learning, our results suggest a critical role for the ventral striatum in integrating complex appetitive and aversive predictions to coordinate behaviour.