Pore‐scale modeling of longitudinal dispersion

By Branko Bijeljic, A. Muggeridge, Martin J. Blunt. 2004

203 citations recorded by the source corpus.

Subfields: Environmental Engineering, Nuclear and High Energy Physics, Ocean Engineering

View Paper: https://www.semanticscholar.org/paper/08e074cbf32154514318d4f290d7e1bc7b393847

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In Environmental Engineering

In Nuclear and High Energy Physics

In Ocean Engineering

Citations in the corpus

Modeling non‐Fickian transport in geological formations as a continuous time random walk (2006, methodology): “They obtained numerically the spatial moments (variance s2) of a particle transported through flow (average velocity v) and molecular diffusion (Dm) in a pore-scale model [Bijeljic et al., 2004] and then computed the dispersion coefficients according to D = s2/2t.”
Pore‐scale modeling and continuous time random walk analysis of dispersion in porous media (2006, methodology): “The model accurately captures the experimentally determined Peclet number dependence of the asymptotic longitudinal dispersion coefficient, DL(Pe) (Figure 1) [Bijeljic et al., 2004].”
Experimental investigation of the thermal dispersivity term and its significance in the heat transport equation for flow in sediments (2012, result): “For slower flows, the slope is slightly steeper [e.g., Bijeljic et al., 2004] as is experimentally confirmed in Figure 10.”
Simulation of Flow and Dispersion on Pore-Space Images (2012, methodology): “At higher NPe, we see a transition regime followed by an approximate power-law regime with DL NPed , with an exponent d¼ 1.2, consistent with measurements and other analyses (Brigham et al. 1961; Salter and Mohanty 1982; Kinzel and Hill 1989; Bijeljic et al. 2004).”
Perspective on theories of non-Fickian transport in heterogeneous media (2009, background): “Several experimental [13,47] and numerical [49,10] investigations concluded that the underlying stochastic transport process must likewise be non-Fickian regardless of how small one takes x to be.”
Numerical Algorithms for Network Modeling of Yield Stress and other Non-Newtonian Fluids in Porous Media (2012, background): “…space (and therefore the flow physics), simulations are often found to closely match experimental data (Bryant and Pallatt 1996; Bakke and Øren 1997; Valvatne and Blunt 2004; Balhoff and Wheeler 2009; Joekar-Niasar et al. 2010) For modeling non-Newtonian flow, the fluid is usually described by a…”