Dynamics of Flow Structures and Transport Phenomena, 1. Experimental and Numerical Techniques for Identification and Energy Content of Flow Structures

Abstract

Most chemical engineering equipment is operated in the turbulent regime. The flow patterns in this equipment are complex and are characterized by flow structures of wide range of length and time scales. The accurate quantification of these flow structures is very difficult and, hence, the present design practices are still empirical. Abundant literature is available on understanding of these flow structures, but in very few cases efforts have been made to improve the design procedures with this knowledge. There have been several approaches in the literature to identify and characterize the flow structures qualitatively as well as quantitatively. In the last few decades, several numerical as well as experimental methods have been developed that are complementary to each other with the onset of better computational and experimental facilities. In the present work, the methodologies and applications of various experimental fluid dynamics (EFD) techniques (namely, point measurement techniques such as hot film anemometry, laser Doppler velocimetry, and planar measurement techniques such as particle image velocimetry (PIV), high speed photography, Schlieren shadowgraphy, and the recent volume measurement techniques such as holographic PIV, stereo PIV, etc.), and the computational fluid dynamics (CFD) techniques (such as direct numerical simulation (DNS) and large eddy simulation (LES)) have been discussed. Their chronological developments, relative merits, and demerits have been presented to enable readers to make a judgment as to which experimental/numerical technique to adopt. Also, several notable mathematical quantifiers are reviewed (such as quadrant technique, variable integral time average technique, spectral analysis, proper orthogonal decomposition, discrete and continuous wavelet transform, eddy isolation methodology, hybrid POD−Wavelet technique, etc.). All three of these tools (computational, experimental, and mathematical) have evolved over the past 6−7 decades and have shed light on the physics behind the formation and dynamics of various flow structures. The work ends with addressing the present issues, the existing knowledge gaps, and the path forward in this field.

Study on effect of process parameters and mixing on morphology of ammonium diuranate

“…Various investigators [16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35] have reported that the fluid dynamics or mixing plays an important role in controlling supersaturation profile, hence determining particle sizes and the morphology. Both mixing between fluid and particles, and particles and particles affect the crystallization process.…”

CFD Simulation of Annular Centrifugal Extractors

“…The LES simulations also give information on turbulent structures in terms of their size, shape, velocity, and energy distributions [64,[171][172][173][174][175][176][177][178][179][180]. These structures properties can then be related to the mechanisms of heat, mass, and momentum transfer [70,73,181,182]. (11) The mixing and the separation zones contain liquidliquid dispersions with possibly a drop size distribution.…”

Development of a novel mobile industrial-scale fluidized adsorption process for emergency treatment of water polluted by aniline: CFD simulation and experiments

“…Since experiments on an industrial-scale LSCFB are often prohibitively expensive and complex, it would be very advantageous to mimic the hydrodynamics of a large commercial unit using a lab-scale model [10,11]. The flow structures in the lab-based fluidized beds were complex [12,13] and accurate quantification of these flow structures needed to be obtained in a wide range of time and expenses scales by experimental methods [14][15][16][17][18]. In the last few years, computational fluid dynamics (CFD) has been developed to understand such intricacies because it is more flexible than experimental studies and less expensive, and it allowed process engineers to predict, manipulate, and realize the desired fluid dynamics in process equipment [19,20], which was complementary to experimental methods with the onset of better computational and experimental facilities [14,[21][22][23][24][25][26][27][28][29][30][31][32][33].…”