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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.

The key to the better design of an industrial scale wind turbine is to understand the influence of blade geometry and its dynamics on the complicated flow-structures. An industrial-scale wind turbine can be numerically represented using various approaches (from simpler 2D steady flow to complex 3D with moving mesh, with and without the inclusion of hubs) that can alter the results substantially. At the same time, to standardize and test methodologies, the reference industrial-scale NREL 5 MW wind turbine is gaining popularity. The wind energy community will learn through an improved understanding of the aerodynamics of this NREL 5 MW reference mega−watt size wind turbine by taking into consideration the various levels of geometric approximations. Hence, in this work the NREL 5MW reference wind turbine is used for both: (a) understanding the associated flow-complexities due to various geometric approximation, and (b) comparison and validation of the performance of different numerical approaches for flow-simulation. The various geometric approximations considered here are related to simulating the influence of the shape of turbines structure (blade, an inclusion of hub, tower) and motions of blades. It involves (a) influence of 2D, 2.5D and 3D turbine blade geometry to highlight the impact of section bluntness, (b) influence of steady frozen rotor and rotating rotor simulations and (c) influence of inclusion of hub through a full-scale geometry of the complete reference turbine. Furthermore, the key features of the flow dynamics of a rotor and full machine are identified and parametric studies are conducted to evaluate overall performance prediction under variable Tip Speed Ratios

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Design of industrial scale packed bed reactors can be aided by using computational fluid dynamics (CFD)-discrete element method (DEM) simulation for understanding the transport phenomena. However, conducting CFD-DEM simulation for the whole large-scale packed bed reactor is computationally prohibitive, which limits the usage of this tool. Hence, a methodology has been developed to identify the segments of the bed, which can serve as a good representative for the CFD simulation of large industrial scale packed bed reactors. Two segments, a cylindrical cut-segment and a wall-segment, have been used to represent the core central region of packed bed and the near wall region of packed bed, respectively. The methodology determines the size (diameter and height) of the cylindrical bed cut-segment that can be a good representative for the large-scale packed bed reactor. The segments are then used to obtain the information on transport phenomena (friction factor coefficients (pressure drop) and the heat transfer/mass-transfer coefficients) for packed beds made of spherical particles and non-spherical particles. CFD simulations have been conducted on the DEM generated packing segments for a wide particle Reynolds number range (from laminar to turbulent regime) to develop the correlations. The proposed methodology (on cut-segment and wallsegment) has been validated for spherical particle packed bed by comparing results to well-established correlations (such as Ergun equation for pressure drop, Wakao correlation and multiparticle Ranz−Marshal correlation for particle heat transfer coefficient, and Dixon−Lubua and Colledge−Paterson correlation for wall heat transfer coefficient). The methodology is then applied to understand the transport phenomena and to develop correlations for long cylindrical pellet (aspect ratio 7) and flutedring. The pressure drop correlation obtained for the long cylindrical pellet (aspect ratio of 7) packed bed has been compared to the correlation for cylindrical pellet with aspect ratio of 5.77 (obtained by Nemec; Nemec, D.; Levec, J. Chem. Eng. Sci. 2005, 60, 6947). The comparison shows that the effect of increasing aspect ratio on pressure drop is captured as per the expected trend. The methodology has been applied in a chemical looping combustion reactor based on the pressure drop and heat transfer results. Thus, the proposed CFD-DEM methodology offers a computationally efficient way of understanding the transport phenomena in an industrial scale reactor through simulating a methodically selected segment inside the reactor. This enables design and performance assessment of such reactors.

There have been several approaches in the literature to identify and characterize flow structures qualitatively as well as quantitatively. In the first part of this review, the methodologies and applications of various experimental fluid dynamics and computational fluid dynamics techniques, as well as mathematical techniques, have been discussed. Their chronological developments, and relative merits and demerits, have been presented to allow readers to make a judgment as to which techniques to adopt. In the present part of the review series, a stepwise procedure is suggested for the design of equipment using flow structure knowledge. An attempt has been made to relate the structure properties (such as age, penetration depth, size, shape, and energy content distribution) to the design parameters (such as mixing time, heat- and mass-transfer coefficient, drag coefficient, dissipation rate, etc.). This understanding of flow structures has brought improvements in the formulations of heuristic models of mass and heat transfer. This review makes an effort in developing insights into the views of earlier established analytic and heuristic theories of heat and mass transfer. The recently revealed dynamics of flow structures (as uncovered through the use of various techniques) has helped in furthering the efforts of developing new theories of heat, mass, and momentum transfer. Such an understanding between the structure dynamics and the transport phenomena has helped in the optimization of flow pattern (for instance, maximization of ratios of heat and mass transfer, as well as mixing, with respect to energy input). In this direction, some success stories have been described that have already been implemented in industry and have good potential for implementation.

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