147 resultados para Particle storm


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An experimental study on ignition and combustion of single particles was conducted at normal gravity (1-g) and microgravity (l-g) for three high volatile coals with initial diameter of 1.5 and 2.0 mm, respectively. The non-intrusive twin-color pyrometry method was used to retrieve the surface temperature of the coal particle through processing the images taken by a color CCD camera. At the same time, a mathematical model considering thermal conduction inside the coal particle was developed to simulate the ignition process. Both experiments and modeling found that ignition occurred homogeneously at the beginning and then heterogeneously for the testing coal particles burning at l-g. Experimental results confirmed that ignition temperature decreased with increasing volatile content and increasing particle size. However, contradicted to previous studies, this study found that for a given coal with certain particle size, ignition temperature was about 50–80 K lower at l-g than that at 1-g. The model predictions agreed well with the l-g experimental data on ignition temperature. The criterion that the temperature gradient in the space away from the particle surface equaled to zero was validated to determine the commence of homogeneous ignition. Thermal conduction inside the particle could have a noticeable effect for determining the ignition temperature. With the consideration of thermal conduction, the critical size for the phase transient from homogeneous to heterogeneous is about 700 lm at ambient temperature 1500 K and oxygen concentration 0.23. 2009 The Combustion Institute. Published by Elsevier Inc. All rights reserved.

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Particle velocity distribution in a blowing sand cloud is a reflection of saltation movement of many particles. Numerical analysis is performed for particle velocity distribution with a discrete particle model. The probability distributions of resultant particle velocity in the impact-entrainment process, particle horizontal and vertical velocities at different heights and the vertical velocity of ascending particles are analyzed. The probability distributions of resultant impact and lift-off velocities of saltating particles can be expressed by a log-normal function, and that of impact angle comply with an exponential function. The probability distribution of particle horizontal and vertical velocities at different heights shows a typical single-peak pattern. In the lower part of saltation layer, the particle horizontal velocity distribution is positively skewed. Further analysis shows that the probability density function of the vertical velocity of ascending particles is similar to the right-hand part of a normal distribution function, and a general equation is acquired for the probability density function of non-dimensional vertical velocity of ascending particles which is independent of diameter of saltating particles, wind strength and height. These distributions in the present numerical analysis are consistent with reported experimental results. The present investigation is important for understanding the saltation state in wind-blown sand movement. (C) 2009 Elsevier B.V. All rights reserved.

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Sand storm is a serious environmental threat to humans. Sand particles are transported by saltation and suspension, causing soil erosion in one place and deposition in another. In order to prevent and predict sand storms, the causes and the manners of particle motions must be studied in detail. In this paper a standard k-epsilon model is used for the gas phase simulation and the discrete element method (DEM) is used to predict the movements of particles using an in-house procedure. The data are summarized in an Eulerian-Eulerian regime after simulation to get the statistical particle Reynolds stress and particle collision stress. The results show that for the current case the Reynolds stress and the air shear stress predominate in the region 20-250 mm above the initial sand bed surface. However, in the region below 3 mm, the collision stress must be taken into account in predicting particle movement. (C) 2010 Chinese Society of Particuology and Institute of Process Engineering, Chinese Academy of Sciences. Published by Elsevier B.V. All rights reserved.

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