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E-raamat: From Multiscale Modeling to Meso-Science: A Chemical Engineering Perspective

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  • Formaat: PDF+DRM
  • Ilmumisaeg: 22-Mar-2013
  • Kirjastus: Springer-Verlag Berlin and Heidelberg GmbH & Co. K
  • Keel: eng
  • ISBN-13: 9783642351891
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From Multiscale Modeling to Meso-Science: A Chemical Engineering PerspectiveSuurem pilt
  • Formaat: PDF+DRM
  • Ilmumisaeg: 22-Mar-2013
  • Kirjastus: Springer-Verlag Berlin and Heidelberg GmbH & Co. K
  • Keel: eng
  • ISBN-13: 9783642351891
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This book surveys three decades of research into the energy-minimization multi-scale (EMMS) model for multi-phase systems in chemical engineering. Explores philosophy, principles, modeling, computation and applications, and the emerging field of meso-science.

Multiscale modeling is becoming essential for accurate, rapid simulation in science and engineering. This book presents the results of three decades of research on multiscale modeling in process engineering from principles to application, and its generalization for different fields. This book considers the universality of meso-scale phenomena for the first time, and provides insight into the emerging discipline that unifies them, meso-science, as well as new perspectives for virtual process engineering.

Multiscale modeling is applied in areas including:

  • multiphase flow and fluid dynamics
  • chemical, biochemical and process engineering
  • mineral processing and metallurgical engineering
  • energy and resources
  • materials science and engineering

Jinghai Li is Vice-President of the Chinese Academy of Sciences (CAS), a professor at the Institute of Process Engineering, CAS, and leader of the EMMS (Energy-minimizing multiscale) Group.

Wei Ge, Wei Wang, Ning Yang and Junwu Wang are professors at the EMMS Group, part of the Institute of Process Engineering, CAS.

Xinhua Liu, Limin Wang, Xianfeng He and Xiaowei Wang are associate professors at the EMMS Group, part of the Institute of Process Engineering, CAS.

Mooson Kwauk is an emeritus director of the Institute of Process Engineering, CAS, and is an advisor to the EMMS Group.

1 Footprint and Philosophy
1(46)
1.1 Footprint and Profile
3(7)
1.1.1 History
5(3)
1.1.2 Synopsis
8(2)
1.2 Meso-Scales: A Common Challenge
10(7)
1.3 Meso-Scales in Process Engineering
17(6)
1.3.1 Material Level
19(1)
1.3.2 Reactor Level
20(2)
1.3.3 System Level
22(1)
1.3.4 Correlation Between Levels
23(1)
1.4 Complexity at Meso-Scales
23(5)
1.4.1 Gas-Solid Systems
23(1)
1.4.2 Turbulence
24(1)
1.4.3 Materials
25(1)
1.4.4 Proteins
26(1)
1.4.5 Emulsions
27(1)
1.4.6 Other Systems
27(1)
1.5 Universality of Compromise at Various Meso-Scales
28(3)
1.6 Transdisciplinarity at Meso-Scales
31(2)
1.7 Meso-Scale Modeling: The EMMS Model
33(4)
1.8 The EMMS Strategy: From Model to Paradigm
37(10)
1.8.1 Universality of Compromise
37(1)
1.8.2 From Local to Global
38(1)
1.8.3 From-Individual to General
39(1)
1.8.4 From Physical Modeling to Computation Paradigm
40(1)
1.8.5 From Modeling to Hardware
41(1)
1.8.6 From Computer to VPE
41(1)
1.8.7 Towards Meso-Science
41(2)
References
43(4)
2 Meso-Scale Modeling: The EMMS Model for Gas-Solid Systems
47(44)
2.1 Background
49(6)
2.1.1 Designation
49(1)
2.1.2 Structural Characteristics
50(4)
2.1.3 Modeling Methodology
54(1)
2.2 Formulation of the EMMS Model
55(12)
2.2.1 Multiscale Analysis
55(5)
2.2.2 Conservation Equations
60(4)
2.2.3 Stability Condition
64(3)
2.3 Solution of the EMMS Model
67(8)
2.3.1 Analytical Solution of the Original EMMS Model
67(2)
2.3.2 Numerical Solution
69(2)
2.3.3 Critical Conditions for Choking
71(2)
2.3.4 Regime and Operation Diagram for Gas-Solid Systems
73(2)
2.4 The EMMS Drag for CFD
75(5)
2.4.1 Deficiencies of Traditional Drag Models
76(1)
2.4.2 EMMS Drag
77(3)
2.5 The Overall EMMS Model
80(6)
2.5.1 Radial EMMS Model
80(3)
2.5.2 Axial EMMS Model
83(3)
2.6 Problems to be Solved
86(5)
References
87(4)
3 Verification of the EMMS Model with Pseudo-Particle Modeling
91(20)
3.1 Pseudo-Particle Modeling
92(4)
3.1.1 Fundamentals and Formulation
93(1)
3.1.2 Boundary Conditions for Gas-Solid Flow
94(2)
3.2 Simulation Setup and Analysis Methods
96(4)
3.3 Verification of the EMMS Model with PPM
100(2)
3.4 Scale-Dependence of the Stability Criterion
102(4)
3.5 Stability at Different Density Ratios
106(5)
References
108(3)
4 Extension of the EMMS Model to Gas-Liquid Systems
111(36)
4.1 Introduction
112(3)
4.2 The DBS Model: An Extended EMMS Model for Gas-Liquid Systems
115(6)
4.2.1 Partition of Energy Dissipation
116(3)
4.2.2 Stability Condition
119(1)
4.2.3 Model Equations
120(1)
4.3 Physical Understanding of Macro-Scale Phenomena
121(10)
4.3.1 Jump Change and Regime Transition
121(2)
4.3.2 Physical Essence of the Jump Change
123(3)
4.3.3 Effects of Viscosity
126(1)
4.3.4 Effects of Surface Tension
127(2)
4.3.5 Regime Map
129(1)
4.3.6 Comparison of DBS, TBS and MBS Models
129(2)
4.4 Intrinsic Similarities Between Gas-Solid and Gas-Liquid Systems
131(3)
4.5 EMMS-Based CFD Approach for Bubble Columns
134(13)
4.5.1 Model Description
134(2)
4.5.2 Simulation Settings
136(1)
4.5.3 Effect of Bubble Diameter and Correction Factor
136(5)
4.5.4 Simulation with the EMMS Drag
141(2)
References
143(4)
5 From EMMS Model to EMMS Paradigm
147(38)
5.1 Universality
149(3)
5.2 The EMMS Principle: From Individual to General
152(15)
5.2.1 Extension to Gas/Liquid Flow
153(1)
5.2.2 Extension to Turbulent Flow in Pipes
153(7)
5.2.3 Extension to Foam Drainage
160(3)
5.2.4 Extension to Emulsions
163(2)
5.2.5 Extension to Granular Flow
165(2)
5.3 Compromise: Possibly a Universal Law
167(3)
5.4 The EMMS Paradigm
170(15)
5.4.1 Universality in Physics
170(2)
5.4.2 Universality in Mathematics
172(2)
5.4.3 The Framework of the EMMS Paradigm
174(2)
5.4.4 Three Modes of the EMMS Paradigm
176(1)
5.4.5 Definition of the Top-Down Mode
177(2)
5.4.6 Requirements for Hardware
179(2)
References
181(4)
6 Partial Realization of the EMMS Paradigm
185(76)
6.1 EMMS-Based Multi-Fluid Model
188(13)
6.1.1 Structure-Dependent Conservation Equations
189(4)
6.1.2 Reduction to the TFM
193(2)
6.1.3 Restoration to the EMMS Model
195(2)
6.1.4 Simplified Solution with EMMS Drag
197(4)
6.2 Simulation with EMMS Paradigm: Global Distribution
201(1)
6.3 Simulation with EMMS Paradigm: Local Evolution
202(12)
6.3.1 Determination of Meso-Scale Structure: The First Step
204(7)
6.3.2 Determination of EMMS Drag: The Second Step
211(3)
6.4 Applications of EMMS Paradigm
214(14)
6.4.1 3D Full-Loop Simulation of a CFB
214(6)
6.4.2 Flow Regime Diagram: Intrinsic Versus Apparent
220(8)
6.5 Challenges of the TFM
228(5)
6.5.1 Comparison of Periodic Domain Simulations
229(3)
6.5.2 Direct Comparison: Simulations of Risers
232(1)
6.6 Multiscale Mass Transfer
233(15)
6.6.1 EMMS/Mass Model
235(8)
6.6.2 Application to Reactive Flow Simulation
243(5)
6.7 Further Development
248(7)
6.7.1 EMMS/Bubbling Model
248(3)
6.7.2 Realization of an Alternative EFM Model
251(3)
6.7.3 MP-PIC with EMMS Drag
254(1)
6.8 Summary
255(6)
References
256(5)
7 Complete Realization of the EMMS Paradigm
261(50)
7.1 Structural Consistency
262(9)
7.1.1 Phenomena
264(2)
7.1.2 Physical Models
266(1)
7.1.3 Simulation Methods
267(3)
7.1.4 Hardware Architecture
270(1)
7.2 Simulation at Micro-Scales with Discrete Methods
271(9)
7.2.1 First-Principles Methods
272(1)
7.2.2 Coarse-Grained Methods
273(1)
7.2.3 Collective Methods
274(1)
7.2.4 Interphase Interactions
275(1)
7.2.5 General Algorithm
276(4)
7.3 Developing Hardware with Current Technology
280(8)
7.3.1 General Architecture
281(1)
7.3.2 Hardware Development
282(2)
7.3.3 Configuration
284(1)
7.3.4 Performance
285(3)
7.4 Implementation of the EMMS Paradigm
288(13)
7.4.1 Global Distribution
289(4)
7.4.2 Dynamic Structural Evolution in a Whole Reactor
293(1)
7.4.3 Detailed Structural Evolution Down to Particle Scale
294(3)
7.4.4 Evolution of Structures Below Particles
297(4)
7.5 Future of the EMMS Paradigm
301(10)
7.5.1 Further Development of Models for Solids
301(2)
7.5.2 From Top-Down to Bottom-Up
303(1)
7.5.3 From GPUs to xPUs
304(1)
References
305(6)
8 Applications of EMMS Drag in Industry
311(48)
8.1 Prediction of Choking
312(1)
8.2 Fluid Catalytic Cracking
313(15)
8.2.1 Phase 1: Design of an MIP Reactor
314(5)
8.2.2 Phase 2: Troubleshooting with CFD Simulations
319(8)
8.2.3 Phase 3: VPE for MIP Reactors
327(1)
8.2.4 Lessons from MIP Simulation
328(1)
8.3 Circulating Fluidized Bed Combustion
328(18)
8.3.1 Simulation of a CFBB
329(3)
8.3.2 CFD Simulation of a 150 MWe CFBB
332(10)
8.3.3 CFD Simulation of an Experimental CFBB
342(3)
8.3.4 Lessons from CFBB Simulation
345(1)
8.4 Fischer-Tropsch Synthesis
346(9)
8.4.1 Background
346(1)
8.4.2 Hydrodynamic Models
346(4)
8.4.3 Reaction Simulation
350(2)
8.4.4 Lessons from Simulation of FT Synthesis
352(3)
8.5 Conclusion
355(4)
References
355(4)
9 Academic Applications of EMMS Drag
359(18)
9.1 Coexistence of Dense-Bottom and Dilute-Top Zones in High Solid-Flux Risers
359(3)
9.2 Estimation of Mass Transfer Coefficient
362(2)
9.3 Hydrodynamics of CFB Boilers
364(2)
9.4 Sub-grid Drag Closure for a Riser at PSRI
366(3)
9.5 Extension to Geldart B Particles and Heterogeneity Index
369(1)
9.6 Effect of Cluster Diameter
370(7)
References 4
373(4)
10 Many-Core Programming
377(26)
10.1 Introduction
377(3)
10.1.1 Background of CUDA
378(1)
10.1.2 Applications of CUDA
379(1)
10.2 Programming Model and Interface
380(6)
10.2.1 Basic Concepts
380(1)
10.2.2 Structure of CUDA Hardware
381(2)
10.2.3 Structure of CUDA Software
383(1)
10.2.4 Hierarchy of CUDA Memory
384(2)
10.2.5 Asynchronous Concurrent Execution
386(1)
10.3 Application and Performance Guidelines
386(17)
10.3.1 Programming and Optimization Techniques
386(3)
10.3.2 Implementation of Applications on GPU
389(4)
10.3.3 Coupled LBM and DEM for Large-Scale DNS
393(7)
References
400(3)
11 Software
403(28)
11.1 Introduction
403(1)
11.2 EMMS Software
404(2)
11.3 FCC Online Simulator
406(3)
11.4 GPU-MD: A MD Simulation Software Package with GPU Implementation
409(10)
11.4.1 Introduction
409(3)
11.4.2 Model
412(1)
11.4.3 GPU-Based Algorithms
413(1)
11.4.4 Implementation
414(2)
11.4.5 Usage of GPU-MD
416(2)
11.4.6 Example: Crystallization Simulation of a PE System
418(1)
11.5 Granular Flow DEM Simulation Software
419(3)
11.5.1 Introduction
419(1)
11.5.2 Usage of DEMMS
420(2)
11.5.3 Example: Simulation of a Rotary Kiln
422(1)
11.6 In Situ Particle Visualization Software: ParticleEye
422(5)
11.6.1 Introduction
422(1)
11.6.2 Usage
423(2)
11.6.3 Example: Visualization for GPU-Based DEM Simulation of a Rotating Drum
425(2)
11.7 Summary
427(4)
References
427(4)
12 Experimental Characterization of Meso-Scale Processes
431(30)
12.1 Fluid Dynamics
433(13)
12.1.1 Characteristics of Meso-Scale Processes
433(6)
12.1.2 Particle Clustering Dynamics
439(7)
12.2 Mass Transfer
446(8)
12.2.1 Mass Transfer from Static Particle Clusters
446(4)
12.2.2 Mass Transfer from Dynamic Particle Clusters
450(4)
12.3 Gas Backmixing in High-Velocity Fluidization
454(2)
12.4 Virtual Process Engineering Platform
456(5)
References
459(2)
13 Perspectives: Meso-Science and Virtual Process Engineering
461(16)
13.1 Meso-Science
461(3)
13.2 Virtual Process Engineering
464(3)
13.3 Future Scenarios of Chemical Engineering
467(2)
13.4 Important Research Topics
469(5)
13.5 Research Philosophy
474(1)
13.6 Education Curriculum
474(3)
References
476(1)
Subject Index 477(4)
Co-worker Index 481
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