Fluid flow, imaging

R. A. Waggoner, E. Fukushima 1996, (Velocity distribution of slow fluid flows in Bentheimer sandstone an NMRI and propagator study), Magn. Reson. Imag. 14, 1085. 

S. Sheppard, M. D. Mantle, A. J. Seder-man, M. L. Johns, L. F. Gladden 2003, (Magnetic resonance imaging study of complex fluid flow in porous media flow patterns and quantitative saturation profiling of amphiphilic fracturing fluid displacement in sandstone cores), Magn. Reson. Imag. 21, 365. 

Pore shape is a characteristic of pore geometry, which is important for fluid flow and especially multi-phase flow. It can be studied by analyzing three-dimensional images of the pore space [2, 3]. Also, long time diffusion coefficient measurements on rocks have been used to argue that the shapes of pores in many rocks are sheetlike and tube-like [16]. It has been shown in a recent study [57] that a combination of DDIF, mercury intrusion porosimetry and a simple analysis of two-dimensional thin-section images provides a characterization of pore shape (described below) from just the geometric properties. 

The molecular translations are spatially resolved by combining the velocity encoding sequence with the conventional spatial imaging encodings. In this velocity imaging", the phase shift of the spins reflects the information about their displacement as well as the spatial position. In this work, a flow-compensation [25] is implemented in the velocity imaging pulse sequence to eliminate the actifacts due to fluid flow. 

Figure 13.. Comparison of theoretical analysis and empirical NMR imaging of fluid flow during extrusion. Limiting cases for theoretical analysis (a), the velocity profile as a function of position with no pressure gradient in the z-direction (b), the velocity profile as a function of position with no net flow through the extruder. Limiting cases for empirical analysis by NMR flow imaging (c), no pressure gradient in the z-direction (die open) (d), no net flow through the extruder (die closed).[Reproduced with permission from Ref.61].

Figure 2 Diagram of a generalized 2D-PIV setup showing all major components flow channel with the particle seeded fluid flow, laser sheet pulses illuminating one plane in the fluid, a CCD camera imaging the particles in the laser-illuminated sheet in the area of interest, a computer with PIV software installed, a timing circuit communicating with the camera and computer and generating pulses to control the double-pulsed laser. The PIV software setups and controls the major components, and analyses the images to derive a vector representation of flow field (see Plate 4 in Color Plate Section at the end of this book).

The construction of a combined model starts with one image (created, supposed or seeded) where it is accepted that the flow into the device is composed of distinct zones which are coupled in series or parallel and where we have various patterns of flow flow zones with perfect mixing, flow zones with plug flow, zones with stagnant fluid (dead flow). We can complete this flow image by showing that we can have some by-pass connections, some recycled flow and some slip flow situations in the device. 

In one of our earlier applications, FCS diagnosed unanticipated micelle formation and led to the first development of confocal image microscopy for smaller focal volumes [3]. Recognizing the effective applications of fluorescent marker d mamics to understand cell membrane d mamics, we applied FCS to molecular diffusion on cell membranes, entering thereby into a long series of studies of the dynamics of membrane processes in life, which was at that time a quagmire of conflicting ideas [4]. Later, we also extended FCS theory to fluid flow analysis [9]. It has proven useful for a diversity of ultrafast chemical kinetics as well, c.f. [10-13]. 

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