Dispersions, dense, simulations

Ermak, D. L., S. T. Chan, D. L. Morgan, and L. K. Morris. Comparison of Dense Gas Dispersion Model Simulations with Burro Series LNG Spill Test Results, Journal cf Hazardous Materials, vol. 6, pp. 129-160, 1982. 

An analogy may be drawn between the phase behavior of weakly attractive monodisperse dispersions and that of conventional molecular systems provided coalescence and Ostwald ripening do not occur. The similarity arises from the common form of the pair potential, whose dominant feature in both cases is the presence of a shallow minimum. The equilibrium statistical mechanics of such systems have been extensively explored. As previously explained, the primary difficulty in predicting equilibrium phase behavior lies in the many-body interactions intrinsic to any condensed phase. Fortunately, the synthesis of several methods (integral equation approaches, perturbation theories, virial expansions, and computer simulations) now provides accurate predictions of thermodynamic properties and phase behavior of dense molecular fluids or colloidal fluids [1]. 

Abstract We use Nuclear Magnetic Resonance relaxometry (i.e. the frequency variation of the NMR relaxation rates) of quadrupolar nucleus ( Na) and H Pulsed Gradient Spin Echo NMR to determine the mobility of the counterions and the water molecules within aqueous dispersions of clays. The local ordering of isotropic dilute clay dispersions is investigated by NMR relaxometry. In contrast, the NMR spectra of the quadrupolar nucleus and the anisotropy of the water self-diffusion tensor clearly exhibit the occurrence of nematic ordering in dense aqueous dispersions. Multi-scale numerical models exploiting molecular orbital quantum calculations, Grand Canonical Monte Carlo simulations, Molecular and Brownian Dynamics are used to interpret the measured water mobility and the ionic quadrupolar relaxation measurements. 

Fig. 15.8. Distribution of charged particles in dense single-layer coating composed of mono-disperse nanoparticles (numerical simulation for dielectric constants e = 1 and e = 10, T = 100°C). Z is the charge multiplicity of nanoparticles.

The MCT-ITT approach thus provides a microscopic route to calculate the generalized shear modulus g t, y) and other quantities characteristic of the quiescent and the stationary state under shear flow. While MCT has been reviewed thoroughly, see, e.g., [2, 38, 39], the MCT-ITT approach shall be reviewed here, including its recent tests by experiments in model colloidal dispersions and by computer simulations. The recent developments of microscopy techniques to study the motion of individual particles under flow and the improvements in rheometry and preparation of model systems, provide detailed information to scrutinize the theoretical description, and to discover the molecular origins of viscoelasticity in dense colloidal dispersions even far away from thermal equilibrium. 

The reassuring agreement of ITT results on 5 with the data from simulations and experiments shows that in the ITT approach the correct expansion parameter Pe has been identified. This can be taken as support for the ITT-strategy to connect the non-linear rheology of dense dispersions with the structural relaxation studied at the glass transition. 

Alopaeus, V., Koskinen, J., Keskinen, K. I. Majander, J. 2002 Simulation of the population balances for liquid-liquid systems in a non ideal stirred tank. II. Parameter fitting and the use of the multiblock model for dense dispersions. Chemical Engineering Science 57, 1815-1825. 

Meroney, R. N., and D. E. Neff. 1986. Heat Transfer Effects during Cold Dense Gas Dispersion Wind-Tunnel Simulation of Cold Gas Spills, Journal of Heat Transfer, vol. 108, pp. 9-15. 

The Gaussian dispersion model has several strengths. The methodology is well defined and well validated. It is suitable for manual calculation, is readily computerized on a personal computer, or is available as standard software packages. Its main weaknesses are that it does not accurately simulate dense gas discharges, validation is limited from 0.1 to 10 km, and puff models are less well estabUshed than plume models. The predictions relate to 10 min averages (equivalent to 10 min sampling times). While this may be adequate for most emissions of chronic toxicity, it can underestimate distances to the lower flammable limit where instantaneous concentrations are of interest. More discussion will follow. 

Physical (scale) models employing wind tunnels or water channels have been used for dense gas dispersion simulation, especially for situations with obstructions or irregular terrain. Exact similarity in all scales and the re-creation of atmospheric stability and velocity distributions are not possible— very low air velocities are required to match large scale results. Havens et al (1995) attempted to use a 100-1 scale approach in conjunction with a finite element model. They found that measurements from such flows cannot be scaled to field conditions accurately because of the relative importance of the molecular difiusion contribution at model scale. The use of scale models is not a common risk assessment tool in CPQRA and readers are direaed to additional reviews by Mcroncy (1982), and Duijm et al. (1985). 

Dense gas mathematical models are widely employed to simulate the dispersion of flammable and toxic dense gas clouds. Early published examples of applications include models used in the demonstration risk assessments for Canvey Island (Health Safety Executive, 1978, 1981) and the Rijnmond Port Area (Rijmnond Public Authority, 1982), and required in the Department of Transport LNG Federal Safety Standards (Department of Transportation, 1980). While most dense gas models currently in use are based on specialist computer codes, equally good and versatile models are publicly available (c.g., DEGADIS, SLAB). The underlying dispersion mechanisms and necessary validation are more complex than any other area of consequence modeling. 

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