Modeling Approaches and Computational Methods for Particle-laden Turbulent Flows

1st Edition - October 20, 2022
Editors: Shankar Subramaniam, S. Balachandar
Language: English
Paperback ISBN:
9 7 8 - 0 - 3 2 3 - 9 0 1 3 3 - 8
eBook ISBN:
9 7 8 - 0 - 3 2 3 - 9 0 1 3 4 - 5

Modelling Approaches and Computational Methods for Particle-laden Turbulent Flows introduces the principal phenomena observed in applications where turbulence in particle-… Read more

Modeling Approaches and Computational Methods for Particle-laden Turbulent Flows

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Modelling Approaches and Computational Methods for Particle-laden Turbulent Flows introduces the principal phenomena observed in applications where turbulence in particle-laden flow is encountered while also analyzing the main methods for analyzing numerically. The book takes a practical approach, providing advice on how to select and apply the correct model or tool by drawing on the latest research. Sections provide scales of particle-laden turbulence and the principal analytical frameworks and computational approaches used to simulate particles in turbulent flow. Each chapter opens with a section on fundamental concepts and theory before describing the applications of the modelling approach or numerical method.

Featuring explanations of key concepts, definitions, and fundamental physics and equations, as well as recent research advances and detailed simulation methods, this book is the ideal starting point for students new to this subject, as well as an essential reference for experienced researchers.

Cover image
Title page
Table of Contents
Copyright
Contributors
About the editors
Preface
References
Acknowledgment
1: Introduction
Abstract
1.1. Physical description
1.2. Scope
1.3. Deterministic descriptions
1.4. Statistical descriptions
1.5. Important non-dimensional quantities
1.6. Multiscale nature of turbulent particle-laden flows
1.7. Outline of the book
Nomenclature
References
2: Particle dispersion and preferential concentration in particle-laden turbulence
Abstract
2.1. Introduction
2.2. Particle dispersion
2.3. Preferential concentration of particles by turbulence
2.4. Turbophoresis
References
3: Physics of two-way coupling in particle-laden homogeneous isotropic turbulence
Abstract
3.1. Introduction
3.2. Particle-laden flows with dp<η
3.3. Particle-laden flows with dp>η
Appendix 3.A. Governing equations
Appendix 3.B. Equations of conservation of linear and angular momenta for a solid particle moving in an incompressible fluid
References
4: Coagulation in turbulent particle-laden flows
Abstract
Acknowledgements
4.1. Introduction
4.2. Geometric collision kernel
4.3. Collision efficiency
4.4. Modeling the evolution of particle size distribution
4.5. A specific application: turbulent collision coalescence of cloud droplets and its impact on warm rain precipitation
4.6. Summary and outlook
References
5: Efficient methods for particle-resolved direct numerical simulation
Abstract
Acknowledgement
5.1. Introduction
5.2. The immersed boundary method in Navier–Stokes-based solvers
5.3. Distributed Lagrange multiplier methods
5.4. Boltzmann equation-based mesoscopic methods
5.5. Reference datasets
5.6. Comparing PR-DNS methods: a difficult exercise
5.7. Conclusion and outlook
References
6: Results from particle-resolved simulations
Abstract
6.1. Introduction
6.2. PR-DNS of dense fluidized systems for drag force parameterizations based on dynamic simulations
6.3. PR-DNS of unbounded flows in the dilute regime
6.4. PR-DNS of wall-bounded shear flows
6.5. Conclusions and outlook
References
7: Modeling of short-range interactions between both spherical and non-spherical rigid particles
Abstract
7.1. Introduction
7.2. Motion of a non-spherical rigid body
7.3. Geometric description of a non-spherical rigid body and the problem of collision detection of non-spherical rigid bodies
7.4. Non-collisional short-range hydrodynamic interactions: lubrication in dilute regime
7.5. Methods for Lagrangian tracking of non-spherical rigid bodies with collisions
7.6. Efficient and parallel implementation of granular dynamics solvers and their parallel coupling to the fluid solver
7.7. Test cases
7.8. Outlook
References
8: Improved force models for Euler–Lagrange computations
Abstract
Acknowledgements
8.1. Introduction
8.2. Undisturbed quantities
8.3. Stochastic effects in Euler–Lagrange simulation for unresolved fields
8.4. Fluid equations for dilute flows modeled with the Euler–Lagrange method
8.5. Particle equation of motion
8.6. Eulerian–Lagrangian data transfer
8.7. Correction schemes for the undisturbed quantities
8.8. Summary and future directions
8.9. Discussion questions
References
9: Deterministic extended point-particle models
Abstract
9.1. Motivation to go beyond the point-particle model
9.2. Neighbor influence
9.3. Undisturbed flow prediction
9.4. Deterministic particle force prediction using the PIEP model
9.5. Beyond pairwise approximation using machine learning
9.6. Concept and statement of the force coupling method
9.7. FCM results for individual particles
9.8. Examples of FCM applications
9.9. Comments
References
10: Stochastic models
Abstract
10.1. Motivation for stochastic models
10.2. Dispersion of inertial particles from a point source
10.3. Lagrangian particle description
10.4. Challenges in modeling turbulent particle-laden flow
10.5. Models for inertial particles in turbulence
10.6. Numerical considerations
10.7. Summary and extensions
Appendix 10.A. Details of numerical integration of SDEs
Appendix 10.B. Fast and slow variables
References
11: Volume-filtered Euler–Lagrange method for strongly coupled fluid–particle flows
Abstract
11.1. Strongly coupled fluid–particle flows
11.2. Microscale description
11.3. Volume-filtering
11.4. Closure modeling
11.5. Numerical implementation
11.6. Application to the study of strongly coupled particle-laden flows
11.7. Extensions
11.8. Concluding remarks
References
12: Quadrature-based moment methods for particle-laden flows
Abstract
12.1. Introduction
12.2. The kinetic equation and its generalization
12.3. Generalities on moment methods
12.4. Quadrature-based moment closures
12.5. Anisotropic Gaussian closure for monodisperse flows
12.6. Anisotropic Gaussian closure for polydisperse flows
12.7. Closure
References
13: Eulerian–Eulerian modeling approach for turbulent particle-laden flows
Abstract
13.1. Introduction
13.2. Derivation of the Eulerian–Eulerian model for fluid–solid flows
13.3. Probability density function
13.4. Closure relations
13.5. Outlook and conclusions
References
14: Multiscale modeling of gas-fluidized beds
Abstract
Acknowledgement
14.1. Introduction
14.2. Multiscale modeling
14.3. Outlook
References
15: Future directions
Abstract
15.1. Future directions
15.2. Mapping the high-dimensional parameter space
15.3. Discovery and quantification of flow physics
15.4. Theoretical challenges
15.5. Modeling needs
15.6. Need for collaborative efforts
References
Index

Shankar Subramaniam

Shankar Subramaniam is a Professor in the Department of Mechanical Engineering at Iowa State University and Founding Director of the Center for Multiphase Flow Research and Education. He received his B. Tech. in aeronautical engineering from the Indian Institute of Technology, Bombay (Mumbai) in 1988 and is a recipient of the President’s Silver Medal. He earned his PhD at Cornell University, after an MS in Aerospace Engineering at the University of Notre Dame, USA. Prior to joining the ISU faculty in 2002, Subramaniam was an assistant professor at Rutgers University. He is a recipient of the US Department of Energy’s Early Career Principal Investigator award. His areas of expertise are theory, modelling and simulation of multiphase flows including sprays, particle-laden flows, colloids and granular mixtures, turbulence, mixing, and reacting flows.

Affiliations and expertise

Professor, Department of Mechanical Engineering, Iowa State University, Ames, Iowa, United States of America; Founding Director, Center for Multiphase Flow Research and Education, Ames, Iowa, USA

S. Balachandar

S. Balachandar is currently the William F. Powers Professor and University Distinguished Professor in the Department of Mechanical and Aerospace Engineering at the University of Florida. From 2005 to 2011, he was the Chairman of the department. He is the inaugural Director of the Herbert Wertheim College of Engineering Institute for Computational Engineering (ICE). Professor Balachandar’s expertise is in computational multiphase flow, direct and large eddy simulations of transitional, turbulent flows, and integrated multiphysics simulations of complex problems. He is a fellow of the American Physical Society and the American Society of Mechanical Engineers. Professor Balachandar received the Francois Frenkiel Award from the American Physical Society Division of Fluid Dynamics in 1996 and the Arnold O. Beckman Award and the University Scholar Award from the University of Illinois.

Affiliations and expertise

William F. Powers Professor and Distinguished Professor, Department of Mechanical and Aerospace Engineering, University of Florida, Gainesville, Florida, USA

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