999 resultados para Particle bombardment


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We use the Lippman-Schwinger scattering theory to study nonequilibrium electron transport through an interacting open quantum dot. The two-particle current is evaluated exactly while we use perturbation theory to calculate the current when the leads are Fermi liquids at different chemical potentials. We find an interesting two-particle resonance induced by the interaction and obtain criteria to observe it when a small bias is applied across the dot. Finally, for a system without spatial inversion symmetry, we find that the two-particle current is quite different depending on whether the electrons are incident from the left or the right lead.

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Under lubricated conditions, Al-graphite particle composite is a good antiseizure bearing and antifriction material possessing properties which inhibit excessive temperature rise in bearings. The present study characterizes the dry wear properties of the composite. The dry wear characteristics of the Al-(2.7%–5.7% graphite particle) (50–200μm) composite were found to deteriorate with the addition of graphite, load and sliding distance. Both micro structural and microhardness studies of the worn subsurfaces and analysis of wear debris show that the reductions in strength and ductility of the composite due to graphite addition are the most likely causes of deterioration in the wear properties of the composite.

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A direct method of preparing cast aluminium alloy-graphite particle composites using uncoated graphite particles is reported. The method consists of introducing and dispersing uncoated but suitably pretreated graphite particles in aluminium alloy melts, and casting the resulting composite melts in suitable permanent moulds. The optical pretreatment required for the dispersion of the uncoated graphite particles in aluminium alloy melts consists of heating the graphite particles to 400° C in air for 1 h just prior to their dispersion in the melts. The effects of alloying elements such as Si, Cu and Mg on the dispersability of pretreated graphite in molten aluminium have also been reported. It was found that additions of about 0.5% Mg or 5% Si significantly improve the dispersability of graphite particles in aluminium alloy melts as indicated by the high recoveries of graphite in the castings of these composites. It was also possible to disperse upto 3% graphite in LM 13 alloy melts and retain the graphite particles in a well distributed fashion in the castings using the pre-heat-treated graphite particles. The observations in this study have been related to the information presently available on wetting between graphite and molten aluminium in the presence of different elements and our own thermogravimetric analysis studies on graphite particles. Physical and mechanical properties of LM 13-3% graphite composite made using pre-heat-treated graphite powder, were found to be adequate for many applications, including pistons which have been successfully used in internal combustion engines.

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Considers the magnetic response of a charged Brownian particle undergoing a stochastic birth-death process. The latter simulates the electron-hole pair production and recombination in semiconductors. The authors obtain non-zero, orbital diamagnetism which can be large without violating the Van Leeuwen theorem (1921).

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The damping capacity of cast graphitic aluminum alloy composites has been measured using a torsion pendulum at a constant strain amplitude. It was found that flake-graphite particles dispersed in the matrix of aluminum alloys increased the damping capacity; the improvement was greater, the higher the amount of graphite dispersed in the matrix. At sufficiently high graphite contents the damping capacity of graphitic aluminum composites approaches that of cast iron. The ratio between the damping capacity and the density of graphitic aluminum alloys is higher than cast iron, making them very attractive as light-weight, high-damping materials for possible aircraft applications. Machinability tests on graphite particle-aluminum composites, conducted at speeds of 315 sfm and 525 sfm, showed that the chip length decreased with the amount of graphite of a given size. When the size of graphite was decreased, at a given machining speed, the chip length decreased. Metallographic examination shows that graphite particles act as chip breakers, and are frequently sheared parallel to the plane of the

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The amplification mechanism for the side bands which accompany a large amplitude electron wave on a plasma column are shown to arise due to two mode interaction between negative and positive energy waves.

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Copper- and nickel-coated graphite particles can be successfully introduced into aluminium-base alloy melts as pellets to produce cast aluminium-graphite particle composites. The pellets were made by pressing mixtures of nickel- or copper-coated graphite particles and aluminium powders together at pressures varying between 2 and 20 kg mm–2. These pellets were dispersed in aluminium alloy melts by plunging and holding them in the melts using a refractory coated mild steel cone, until the pellets disintegrated and the powders were dispersed. The optimum pressure for the preparation of pellets was 2 to 5 kg mm–2 and the optimum size and percentage of aluminium powder were 400 to 1000mgrm and 35 wt% respectively. Under optimum conditions the recovery of the graphite particles in the castings was as high as 96%, these particles being pushed into the last freezing interdendritic regions. The tensile strength and the hardness of the graphite aluminium alloys made using the pellet method are comparable to those of similar composites made using gas injection or the vortex method. The pellet method however has the advantage of greater reproducibility and flexibility. Dispersion of graphite particles in the matrix of cast aluminium alloys using the pellet method increases their resistance to wear.

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The effect of a particle size distribution on the fractional reaction has been analysed. The analysis shows that for non-isothermal TG the activation energy and frequency factor evaluated from the fractional reaction by conventional method depend on the particle size distribution, and this may lead to a kinetic compensating effect. Particle size distribution may also lead to an erroneous conclusion about the change in the mechanism of reaction.

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Particle filters find important applications in the problems of state and parameter estimations of dynamical systems of engineering interest. Since a typical filtering algorithm involves Monte Carlo simulations of the process equations, sample variance of the estimator is inversely proportional to the number of particles. The sample variance may be reduced if one uses a Rao-Blackwell marginalization of states and performs analytical computations as much as possible. In this work, we propose a semi-analytical particle filter, requiring no Rao-Blackwell marginalization, for state and parameter estimations of nonlinear dynamical systems with additively Gaussian process/observation noises. Through local linearizations of the nonlinear drift fields in the process/observation equations via explicit Ito-Taylor expansions, the given nonlinear system is transformed into an ensemble of locally linearized systems. Using the most recent observation, conditionally Gaussian posterior density functions of the linearized systems are analytically obtained through the Kalman filter. This information is further exploited within the particle filter algorithm for obtaining samples from the optimal posterior density of the states. The potential of the method in state/parameter estimations is demonstrated through numerical illustrations for a few nonlinear oscillators. The proposed filter is found to yield estimates with reduced sample variance and improved accuracy vis-a-vis results from a form of sequential importance sampling filter.

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Tribology of small inorganic nanoparticles in suspension in a liquid lubricant is often impaired because these particles agglomerate even when organic dispersants are used. In this paper we use lateral force microscopy to study the deformation mechanism and dissipation under traction of two extreme configurations (1) a large MoS2 particle (similar to 20 mu m width) of about 1 mu m height and (2) an agglomerate (similar to 20 mu m width), constituting 50 nm MoS2 crystallites, of about 1 mu m height. The agglomerate records a friction coefficient which is about 5-7 times that of monolithic particle. The paper examines the mechanisms of material removal for both the particles using continuum modeling and microscopy and infers that while the agglomerate response to traction can be accounted for by the bulk mechanical properties of the material, intralayer and interlayer basal planar slips determine the friction and wear of monolithic particles. The results provide a rationale for selection of layered particles, for suspension in liquid lubricants.

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Swarm Intelligence techniques such as particle swarm optimization (PSO) are shown to be incompetent for an accurate estimation of global solutions in several engineering applications. This problem is more severe in case of inverse optimization problems where fitness calculations are computationally expensive. In this work, a novel strategy is introduced to alleviate this problem. The proposed inverse model based on modified particle swarm optimization algorithm is applied for a contaminant transport inverse model. The inverse models based on standard-PSO and proposed-PSO are validated to estimate the accuracy of the models. The proposed model is shown to be out performing the standard one in terms of accuracy in parameter estimation. The preliminary results obtained using the proposed model is presented in this work.

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The crucial role of oxide surface chemical composition on ion transport in "soggy sand" electrolytes is discussed in a systematic manner. A prototype soggy sand electrolytic system comprising aerosil silica functionalized with various hydrophilic and hydrophobic moieties dispersed in lithium perchlorate-ethylene glycol solution was used for the study. Detailed rheology studies show that the attractive particle network in the case of the composite with unmodified aerosil silica (with surface silanol groups) is most favorable for percolation in ionic conductivity, as well as rendering the composite with beneficial elastic mechanical properties: Though weaker in strength compared to the composite with unmodified aerosil particles, attractive particle networks are also observed in composites of aerosil particles with surfaces partially substituted with hydrophobic groups. The percolation in ionic conductivity is, however, dependent on the size of the hydrophobic moiety. No spanning attractive particle network was formed for aerosil particles with surfaces modified with stronger hydrophilic groups (than silanol), and as a result, no percolation in ionic conductivity was observed. The composite with hydrophilic particles was a sol, contrary to gels obtained in the case of unmodified aerosil, and partially substituted with hydrophobic groups.

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Optimal allocation of water resources for various stakeholders often involves considerable complexity with several conflicting goals, which often leads to multi-objective optimization. In aid of effective decision-making to the water managers, apart from developing effective multi-objective mathematical models, there is a greater necessity of providing efficient Pareto optimal solutions to the real world problems. This study proposes a swarm-intelligence-based multi-objective technique, namely the elitist-mutated multi-objective particle swarm optimization technique (EM-MOPSO), for arriving at efficient Pareto optimal solutions to the multi-objective water resource management problems. The EM-MOPSO technique is applied to a case study of the multi-objective reservoir operation problem. The model performance is evaluated by comparing with results of a non-dominated sorting genetic algorithm (NSGA-II) model, and it is found that the EM-MOPSO method results in better performance. The developed method can be used as an effective aid for multi-objective decision-making in integrated water resource management.

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The problem of identifying parameters of nonlinear vibrating systems using spatially incomplete, noisy, time-domain measurements is considered. The problem is formulated within the framework of dynamic state estimation formalisms that employ particle filters. The parameters of the system, which are to be identified, are treated as a set of random variables with finite number of discrete states. The study develops a procedure that combines a bank of self-learning particle filters with a global iteration strategy to estimate the probability distribution of the system parameters to be identified. Individual particle filters are based on the sequential importance sampling filter algorithm that is readily available in the existing literature. The paper develops the requisite recursive formulary for evaluating the evolution of weights associated with system parameter states. The correctness of the formulations developed is demonstrated first by applying the proposed procedure to a few linear vibrating systems for which an alternative solution using adaptive Kalman filter method is possible. Subsequently, illustrative examples on three nonlinear vibrating systems, using synthetic vibration data, are presented to reveal the correct functioning of the method. (c) 2007 Elsevier Ltd. All rights reserved.