2 resultados para particle size distribution
em AMS Tesi di Laurea - Alm@DL - Università di Bologna
Resumo:
Particle concentration is a principal factor that affects erosion rate of solid surfaces under particle impact, such as pipe bends in pneumatic conveyors; it is well known that a reduction in the specific erosion rate occurs under high particle concentrations, a phenomenon referred to as the “shielding effect”. The cause of shielding is believed to be increased likelihood of inter-particulate collisions, the high collision probability between incoming and rebounding particles reducing the frequency and the severity of particle impacts on the target surface. In this study, the effects of particle concentration on erosion of a mild steel bend surface have been investigated in detail using three different particulate materials on an industrial scale pneumatic conveying test rig. The materials were studied so that two had the same particle density but very different particle size, whereas two had very similar particle size but very different particle density. Experimental results confirm the shielding effect due to high particle concentration and show that the particle density has a far more significant influence than the particle size, on the magnitude of the shielding effect. A new method of correcting for change in erosivity of the particles in repeated handling, to take this factor out of the data, has been established, and appears to be successful. Moreover, a novel empirical model of the shielding effects has been used, in term of erosion resistance which appears to decrease linearly when the particle concentration decreases. With the model it is possible to find the specific erosion rate when the particle concentration tends to zero, and conversely predict how the specific erosion rate changes at finite values of particle concentration; this is critical to enable component life to be predicted from erosion tester results, as the variation of the shielding effect with concentration is different in these two scenarios. In addition a previously unreported phenomenon has been recorded, of a particulate material whose erosivity has steadily increased during repeated impacts.
Resumo:
The Standard Cosmological Model is generally accepted by the scientific community, there are still an amount of unresolved issues. From the observable characteristics of the structures in the Universe,it should be possible to impose constraints on the cosmological parameters. Cosmic Voids (CV) are a major component of the LSS and have been shown to possess great potential for constraining DE and testing theories of gravity. But a gap between CV observations and theory still persists. A theoretical model for void statistical distribution as a function of size exists (SvdW) However, the SvdW model has been unsuccesful in reproducing the results obtained from cosmological simulations. This undermines the possibility of using voids as cosmological probes. The goal of our thesis work is to cover the gap between theoretical predictions and measured distributions of cosmic voids. We develop an algorithm to identify voids in simulations,consistently with theory. We inspecting the possibilities offered by a recently proposed refinement of the SvdW (the Vdn model, Jennings et al., 2013). Comparing void catalogues to theory, we validate the Vdn model, finding that it is reliable over a large range of radii, at all the redshifts considered and for all the cosmological models inspected. We have then searched for a size function model for voids identified in a distribution of biased tracers. We find that, naively applying the same procedure used for the unbiased tracers to a halo mock distribution does not provide success- full results, suggesting that the Vdn model requires to be reconsidered when dealing with biased samples. Thus, we test two alternative exten- sions of the model and find that two scaling relations exist: both the Dark Matter void radii and the underlying Dark Matter density contrast scale with the halo-defined void radii. We use these findings to develop a semi-analytical model which gives promising results.