نتایج جستجو برای: predicting gas density

تعداد نتایج: 761424  

2000
Marcelo Alvarez Paul R. Shapiro Hugo Martel

Adaptive SPH and N-body simulations were carried out to study the effect of gasdynamics on the structure of dark matter halos that result from the gravitational instability and fragmentation of cosmological pancakes. Such halos resemble those formed in a hierarchically clustering CDM universe and serve as a test-bed model for studying halo dynamics. With no gas, the density profile is close to ...

Journal: :Physical review. E, Statistical, nonlinear, and soft matter physics 2010
Itamar Kolvin Eli Livne Baruch Meerson

We show that, in dimension higher than one, heat diffusion and viscosity cannot arrest thermal collapse in a freely evolving dilute granular gas, even in the absence of gravity. Thermal collapse involves a finite-time blowup of the gas density. It was predicted earlier in ideal, Euler hydrodynamics of dilute granular gases in the absence of gravity, and in nonideal, Navier-Stokes granular hydro...

1996
L. S. Nazarova P. T. O ’ Brien M. J. Ward

We present photoionization modelling of the Extended Narrow Line Region (ENLR) in Mkn 79 based on long-slit spectra presented in our previous paper (Nazarova et al. 1996). The ENLR has been modelled with two gas components: a system of high density clouds embedded in a low density gas envelope. The ENLR line fluxes have been calculated allowing for attenuation of the nuclear ionizing continuum ...

G.R Jalali-Naini S Sadeghian

Although knowing the time of the occurrence of the earthquakes is vital and helpful, unfortunately it is still unpredictable. By the way there is an urgent need to find a method to foresee this catastrophic event. There are a lot of methods for forecasting the time of earthquake occurrence. Another method for predicting that is to know probability density function of time interval between earth...

Journal: :Journal of the Mining and Metallurgical Institute of Japan 1961

Journal: :Machine learning and knowledge extraction 2023

Predicting emissions for gas turbines is critical monitoring harmful pollutants being released into the atmosphere. In this study, we evaluate performance of machine learning models predicting turbines. We compared an existing predictive model, a first-principles-based Chemical Kinetics against two developed based on Self-Attention and Intersample Attention Transformer (SAINT) eXtreme Gradient ...

2008
Daniela Tordella

A fluorescent image analysis procedure to determine the distribution of species concentration and density in a gas flow is proposed. The fluorescent emission is due to the excitation of atoms/molecules of a gas that is intercepted by an electron blade. The intensity of the fluorescent light is proportional to the local number density of the gas. When the gas flow is a mixture of different speci...

Journal: :Applied optics 2010
Isabelle Dicaire Jean-Charles Beugnot Luc Thévenaz

We present useful expressions predicting the filling time of gaseous species inside photonic crystal fibers. Based on the theory of diffusion, this gas-filling model can be applied to any given fiber geometry or length by calculating diffusion coefficients. This was experimentally validated by monitoring the filling process of acetylene gas in several fiber samples of various geometries and len...

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