217 resultados para Airborne-particle abrasion

em Cambridge University Engineering Department Publications Database


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The rates of erosive wear have been measured for a series of eight polyester-based one-component castable polyurethane elastomers, with widely varying mechanical properties. Erosion tests were conducted with airborne silica sand, 120μm in particle size, at an impact velocity of 50 ms-1 and impact angles of 30° and 90°. For these materials, which all showed similar values of rebound resilience, the erosion rate increased with increasing hardness, tensile modulus and tensile strength. These findings are at variance with those expected for wear by abrasion, perhaps because of differences in the strain rate or strain levels imposed on the elastomer during erosion and abrasion.

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Micro-scale abrasion (ball cratering) tests were performed with different combinations of ball and bulk specimen materials, under different test conditions, such as load and abrasive slurry concentration. Wear modes were classified into two types: with rolling particle motion and with grooving particle motion. Wear rates observed with rolling particle motion were relatively insensitive to test conditions, whereas with grooving motion they varied much more. It is suggested that rolling abrasion is therefore a more appropriate mode if reproducible test results are desired. The motion of the abrasive particles can be reliably predicted from the knowledge of hardnesses and elastic properties of the ball and the specimen, and from the normal load and the abrasive slurry concentration. General trends in wear resistance measured in the micro-scale abrasion test with rolling particle motion are similar to those reported in tests with fixed abrasives with sliding particle motion, although the variation in wear resistance with hardness is significantly smaller. © 2004 Published by Elsevier B.V.

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Vertically aligned carbon nanotubes were synthesized by plasma enhanced chemical vapor deposition using nickel as a metal catalyst. High resolution transmission electron microscopy analysis of the particle found at the tip of the tubes reveals the presence of a metastable carbide Ni3C. Since the carbide is found to decompose upon annealing at 600 degreesC, we suggest that Ni3C is formed after the growth is stopped due to the rapid cooling of the Ni-C interstitial solid solution. A detailed description of the tip growth mechanism is given, that accounts for the composite structure of the tube walls. The shape and size of the catalytic particle determine the concentration gradient that drives the diffusion of C atoms across and though the metal. (C) 2004 American Institute of Physics.

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Standard algorithms in tracking and other state-space models assume identical and synchronous sampling rates for the state and measurement processes. However, real trajectories of objects are typically characterized by prolonged smooth sections, with sharp, but infrequent, changes. Thus, a more parsimonious representation of a target trajectory may be obtained by direct modeling of maneuver times in the state process, independently from the observation times. This is achieved by assuming the state arrival times to follow a random process, typically specified as Markovian, so that state points may be allocated along the trajectory according to the degree of variation observed. The resulting variable dimension state inference problem is solved by developing an efficient variable rate particle filtering algorithm to recursively update the posterior distribution of the state sequence as new data becomes available. The methodology is quite general and can be applied across many models where dynamic model uncertainty occurs on-line. Specific models are proposed for the dynamics of a moving object under internal forcing, expressed in terms of the intrinsic dynamics of the object. The performance of the algorithms with these dynamical models is demonstrated on several challenging maneuvering target tracking problems in clutter. © 2006 IEEE.

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A study has been performed of the erosion of aluminium by silica sand particles at a velocity of 4.5 m s-1, both air-borne and in the form of a water-borne slurry. Measurements made under similar experimental conditions show that slurry erosion proceeds at a rate several times that of air-borne erosion, the ratio of the two rates depending strongly on the angle of impact. Sand particles become embedded into the metal surface during air-borne particle erosion, forming a composite layer of metal and silica, and provide the major cause of the difference in wear rate. The embedded particles giving rise to surface hardening and a significant reduction in the erosion rate. Embedment of erodent particles was not observed during slurry erosion. Lubrication of the impacting interfaces by water appears to have minimal effect on the wear of aluminium by slurry erosion.

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A pin on cylinder wear rig has been built with precision stepping motor drives to both rotary and axial motions which enable accurate positional control to be achieved. Initial experiments using sapphire indenters running against copper substrates have investigated the build up of a single wear groove by repeated sliding along the same track. An approximate three dimensional ploughing analysis is also presented and the results of theory and experiment compared.

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Sequential Monte Carlo methods, also known as particle methods, are a widely used set of computational tools for inference in non-linear non-Gaussian state-space models. In many applications it may be necessary to compute the sensitivity, or derivative, of the optimal filter with respect to the static parameters of the state-space model; for instance, in order to obtain maximum likelihood model parameters of interest, or to compute the optimal controller in an optimal control problem. In Poyiadjis et al. [2011] an original particle algorithm to compute the filter derivative was proposed and it was shown using numerical examples that the particle estimate was numerically stable in the sense that it did not deteriorate over time. In this paper we substantiate this claim with a detailed theoretical study. Lp bounds and a central limit theorem for this particle approximation of the filter derivative are presented. It is further shown that under mixing conditions these Lp bounds and the asymptotic variance characterized by the central limit theorem are uniformly bounded with respect to the time index. We demon- strate the performance predicted by theory with several numerical examples. We also use the particle approximation of the filter derivative to perform online maximum likelihood parameter estimation for a stochastic volatility model.

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We design a particle interpretation of Feynman-Kac measures on path spaces based on a backward Markovian representation combined with a traditional mean field particle interpretation of the flow of their final time marginals. In contrast to traditional genealogical tree based models, these new particle algorithms can be used to compute normalized additive functionals "on-the-fly" as well as their limiting occupation measures with a given precision degree that does not depend on the final time horizon. We provide uniform convergence results with respect to the time horizon parameter as well as functional central limit theorems and exponential concentration estimates. Our results have important consequences for online parameter estimation for non-linear non-Gaussian state-space models. We show how the forward filtering backward smoothing estimates of additive functionals can be computed using a forward only recursion.