Footprint and Philosophy

  • Jinghai Li
  • Wei Ge
  • Wei Wang
  • Ning Yang
  • Xinhua Liu
  • Limin Wang
  • Xianfeng He
  • Xiaowei Wang
  • Junwu Wang
  • Mooson Kwauk
Chapter

Abstract

This chapter summarizes the footprint, philosophy and strategy of this book. The historical evolution of the energy minimization multiscale (EMMS) model is briefly introduced; the structure of the book is also outlined. To introduce the philosophy of this book, the spectrum of science and technology and the common multiscale nature of different disciplines are discussed. As meso-scales are identified as the critical issue in understanding complex systems, three such levels (material, reactor and system) in process engineering are analyzed in detail, emphasizing the importance of compromise between dominant mechanisms in generating meso-scale phenomena. Finally, the footprint of the EMMS model from a simple idea to a computational paradigm (the EMMS paradigm), and further to meso-science, is outlined to complete the overview of this book.

Keywords

Chemical engineering Competition Complexity Complex systems Compromise Coordination EMMS model EMMS paradigm Fluid dynamics Multi-phase flow Mesoscale Meso-scale Mesoscience Meso-science Multiphase Multiscale Multi-scale Stability condition Supercomputing Variational Virtual process engineering 

Notation

\( C_{\text{d}} \)

Drag coefficient, dimensionless

dcl

Cluster diameter, m

dp

Particle diameter, m

\( E_{\text{j}} \left( {\mathbf{x}} \right) \)

Objective function with respect to dominant mechanism j

\( E_{\text{OH}} \)

Hydrophilic potential in unit volume, m2/s3

\( E_{\text{WT}} \)

Lipophilic potential in unit volume, m2/s3

\( E_{\text{s}} \)

Surface energy in unit area, m2/s3

\( E_{{{\upmu}}} \)

Viscous dissipation rate in unit volume, m2/s3

Fi

Constraints condition i

f

Volume fraction of dense phase, dimensionless

\( g \)

Gravitational acceleration, m/s2

\( H \)

The height of the bed, m

\( H_{\text{a}} \)

Potential a in unit volume, m2/s3

\( H_{\text{b}} \)

Potential b in unit volume, m2/s3

\( L \)

Characteristic length of flow, m

\( N_{\text{surf}} \)

Rate of energy dissipation due to bubble breakage and coalescence per unit mass, W/kg

\( N_{\text{st}} \)

Rate of energy dissipation for transporting and suspending particles per unit mass, W/kg

\( N_{\text{turb}} \)

Rate of energy dissipation in turbulent liquid phase per unit mass, W/kg

\( R \)

Pipe radius, m

\( Re \)

Reynolds number, dimensionless

\( r \)

Radial coordinate, m

Uc

Gas in the dense phase superficial velocity, m/s

Uf

Gas in the dilute phase superficial velocity, m/s

Upc

Solid in the dense phase superficial velocity, m/s

Upf

Solid in the dilute phase superficial velocity, m/s

\( W_{\text{st}} \)

Energy consumption for transporting and suspending particles in unit volume, W/m3

\( \bar{W}_{\rm v} \)

Viscous shear dissipation rate in unit volume, W/m3

\( \bar{W}_{\text{te}} \)

Turbulent dissipation rate in unit volume, W/m3

X

State parameter

\( S \)

Surface energy in unit volume, m2/s3

\( Sh \)

Sherwood number, dimensionless

Greek Letters

\( \varepsilon \)

Local average voidage, dimensionless

εc

Voidage in dense phase, dimensionless

εf

Voidage in dilute phase, dimensionless

\( \varphi_{\rm r} \)

Dissipation rate of unit amount of kinetic energy across unit length, m2/s3

\( \eta \)

Kolmogorov microscales, m

\( \rho \)

Density, kg/m3

Subscripts

a

Index of particle a

b

Index of particle b

c

Dense phase

f

Dilute phase or fluid

g

Gas

mb

Minimum bubble

mf

Minimum fluidization

p

Particle

s

Solid

Abbreviations

CFB

Circulating fluidized bed

CFD

Computational fluid dynamics

CPU

Central processing unit

CUDA

Compute unified device architecture

EMMS

Energy-minimization multiscale

FD

Fluid-dominated

GPU

Graphics processing unit

MOV

Multi-objective variational

PD

Particle-dominated

PFC

Particle-fluid compromising

TFM

Two-fluid model

VPE

Virtual process engineering

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Jinghai Li
    • 1
  • Wei Ge
    • 1
  • Wei Wang
    • 1
  • Ning Yang
    • 1
  • Xinhua Liu
    • 1
  • Limin Wang
    • 1
  • Xianfeng He
    • 1
  • Xiaowei Wang
    • 1
  • Junwu Wang
    • 1
  • Mooson Kwauk
    • 1
  1. 1.Institute of Process EngineeringChinese Academy of SciencesBeijingPeople’s Republic of China

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