Exploring Complex Systems in Chemical Engineering - National
Transcript of Exploring Complex Systems in Chemical Engineering - National
Meso-scale Structure and Multi-scale Strategy in Simulating Multiphase Systems
Meso-scale Structure and Multi-scale Strategy in Simulating Multiphase Systems
Jinghai Li,Wei Ge, Wei Wang, Ning Yang, and Jingdong He
State Key Laboratory of Multi-Phase Complex SystemsInstitute of Process Engineering, Chinese Academy of Sciences
Beijing, P. R. China
NETL 2009 Workshop on Multiphase Flow Science
Mogantown, WV, April 22~23, 2009
ChallengesChallenges
Modeling
Computation
Multi-scale Discrete
+ Commonnature
Software
Hardware
Multi-ScaleParallel Computation
Progresses Applications Perspectives
Similarity in structure
Meso-scale
StrategyStrategy
Outline of presentation
Low efficiencyHigh cost
1.0 Peta flops
3
Factory Environment(~100m, ~1000m )
Molecule~1A
Assembly~1 nm
Particle(~1μm)
Cell(~1mm)
Cluster(~1cm)
Component(~ 1m)
Apparatus(~10m)
Product
Bottleneck:Multi-scale structure
Multi-scale and multi-level features of process engineering
Process Material
SystemProcess
Multi-scale:
Multi-level:
5
0.5 0.6 0.7 0.8 0.9 1.01E-3
0.01
0.1
1
10
β/β 0
Voidage, εg
Deviation between different methods
3~4 orders of difference
Mass transfer
Re
Sh 6~7 orders of difference
Drag Coefficient
Reaction
+
Almost impossible
6
EQUIPMENT SCALE
MESO-SCALE
MICRO-SCALE
Critical influence on transport
CD= 18.6 CD= 5.43
?
Axial and radial distribution
Flow, mass transfer and reaction around a particle
Process
Challenge in physicalmodeling is poor predictability
Understanding of meso-scale structure is the bottleneck
9
10
100 101 102 10310-4
10-3
10-2
10-1
100
101
102
Sc=2.51
Sh ov
r
Re0
1
3
2
5
4
7
6
8
single sphere
02
46
810
020
4060
80100
-8
-6
-4
-2
0
2
4
Ug, m/sGs, kg/m2s
log(
Shov
r)
Dong et al, Chem. Eng. Sci., 63,2008, 2798-2823
Meso-scale structure is the key to achieve high predictability:
11
ChallengesChallengesIn ComputationIn Computation
High cost & low efficiencyHigh cost & low efficiency
12
100MIndigo
100GPC cluster
1
1MVAX1
1
10TDawning 4000A
10 100 1000 … CPUs
Computation capability
Computer capacity
Cap
acity
/ ca
pabi
lity
Big gap?
80’s
90’s
2000
2005
2009
100TDawning 5000A
13
DisparityStructure of problems
Configuration of software
Architecture of computers:
Diversity:
Multi-scale:
Single scale
Single-scale architecture
Long-range correlation
Global communication
14
NNmodelsmodels
MMPhenomenaPhenomena
M×N algorithms
Universal
Diversity in algorithms
Hardware
Specific
?
Software
Application
Hardware
Software
Application
Low efficiency
high cost
15
StrategyStrategyIn Physical ModelingIn Physical Modeling
VarationalVarational MultiMulti--scale scale MethodolgyMethodolgy
16
“Multi-scale science ”
Glimm and Sharp, SIAM News, 1997Multi-scale Science
– the challenge of 21st century
Krumhansl, Material Science Forum, 2000Multi-scale Science
– Material Science of 21st century
“Multi-scale” OR “Multiscale” within Keywords/Title/Abstract http://isiknowledge.com/ on Nov. 14, 2008
0
500
1000
1500
2000
2500
1950 1960 1970 1980 1990 2000 2010
All FieldChemical Engineering
18
Complex system
Resolution
Description of individual scales
Mech. 1 ……Mech. 2 Mech. k
With respect to dominant mechs.
Identification of extremum tendencies of each dominant mechanisms and their compromise
Stability
Modeling
Correlation between scales
Scale 1Scale 2 Scale n…...With respect to scales
Variational multi-scale methodology
19
Equilibrium: Max. Entropy
Linear: Min. Entropy Production
Non-linear: ?Non-Equilibrium
Stability criterion
Chemical Processes
Multi-scale Structure !
Complexity science
21
Insufficiency of conservation equationsInsufficiency of conservation equations
Cluster-scale
Particle-scale:in dense-phase
Stability ?
6 equations
Dilute phase
Gas velocity UcSolid velocity UpcVoidage εcVolume fraction fCluster diameter dcl
Gas velocity UfSolid velocity UpfVoidage εf
8 variablesDense phase
in dilute-phase
22
Physical Concept of Physical Concept of EMMS ModelModel
EMMSmodel
Operating Conditions
6 equations
Dilute phase
Gas velocity UcSolid velocity UpcVoidage εcVolume fraction fCluster diameter dcl
Gas velocity UfSolid velocity UpfVoidage εf
8 Variables
Densephase
Energy-minimization multi-scale
Particle-fluid compromiseWst = min|ε=min
Particle dominatedε = min
Fluid dominatedWst = min
Mathematical FormulationMathematical Formulation
max
2
3
4
5
6
( )1 1
(
( ) (1 )( ) 0
( ) (1 )( ) 0
( ) /(1 ) 0
( ) (1 ) 0
( ) (1 ) 0
( )
p p mf
p mf
mf
p p m
st mf
p f
c c i i c p f
f f f p f
f f i i c c
p pf pc
g f c
cl
U Ud U g
UN U
F m F f m F f g
F m F g
F m F m F f m F
F U U f U f
F U U f U f
F d
ε
ε ε
ρ ε
ρ ρ
ε ρ ρ
ε ρ ρ
− + ⋅− −
− +−
= + − − − =
= − − − =
= + − − =
= − − − =
= − − − =
⎡ ⎤⎢ ⎥⎣ ⎦= −
X
X
X
X
X
X
1
)1
0f
mf
gε
⋅−
=
(1- )st
stWNε ρ
=
To find:X={ Upc, Uc , εc, f , dcl , Upf, Uf, εf }
Minimizing:
23
s.t. Fi(X)=0
24
Local structural parameters
Previously:Gas velocity UcSolid velocity UpcVoidage εcVolume fraction fCluster diameter dcl
Gas velocity UfSolid velocity UpfVoidage εf
8 variables
Radial and axial distributions
Regime diagram
0.5 0.6 0.7 0.8 0.9 1.060
65
70
75
80
85
90
95
100
}FD(dilute)
900
620
500
400
300
200
Gs=100
Voidage in clusters εc
E
nerg
y co
nsum
ptio
n fo
r sus
pend
ing
and
trans
porti
ng p
artic
les
Nst (J
/kg
s)
PFC(dense)
25
Prediction of chockingGe&Li, Chem. Eng. Sci. 2002, Vol. 56; Wang et al Chem. Eng. Sci. 2007 Vol. 62
26
0.01 0.1
10
100
100018
12 108
6.0
4.0
3.0
5.0
1.52
Ug=1m/s
Gs,
kg/m
2 s
εs0
2.1
Intrinsic flow regime predicted by EMMS
Solids inventory
http://pevrc.ipe.ac.cn/emms/emmsmodel.php3
Sol
ids
flux
27
Online servicehttp://pevrc.ipe.ac.cn/emms/emmsmodel.php3
Whether or not ?
If yes, why?Nst = min ?
29
PseudoPseudo--ParticleParticle
Micro-scale description
Macro-scale phenomena
30
W(r)
aiVgV Δ+∇′−= νρk&
aii ai
aia r
W r∑=∇ 2|ρ
aii ai
ia Wr∑=Δ 2
m
2 V|V a ρ
Numerical Operators “Interactions” between “Particles”
Particles
Physical
Fluid Pseudo-particles
Macro-scale Pseudo-Particle Modeling
Mathematical
Ge & Li: CFB5, 1996; Chem. Eng. Sci., 58, 2003, 1565; Powder Tech. 137:99
32
0 1 2 3 4 5 6 71
2
3
4
5
6
7
8
9
10
11
12
Nst
(J/
(kg.
s))
Time (s)
1.5 2.0 2.5 3.0 3.5 4.0 4.51
2
3
4
5
6
7
8
9
10
11
12
Nst
(J/
(kg.
s))
Time (s)
1.5 2.0 2.5 3.0 3.5 4.0 4.51
2
3
4
5
6
7
8
9
10
11
12
Nst
(J/
(kg.
s))
Time (s)
0 1 2 3 4 5 6 71
2
3
4
5
6
7
8
9
10
11
12
Nst
(J/
(kg.
s))
Time (s)
Li et al., Chem. Eng. Sci. Vol.59,2004,1687 ~ 1700
Region D Region G
min(1 )
stst
p
WNε ρ
= =−
( )1 (v) 1 (v) v min(1 )
st stV
N N dV
εε
= ⋅ ⋅ − =− ⋅ ∫
Fluid
Particle-Fluid Systems
Region D
Region G
EMMS model Radial EMMS model
globalcompromise
Point A
Point B
st
=
W min
e
=
min
temporal compromise(at any single point)
regional compromise
stW =
spatial compromise(at any single instant)
min
→ minstN→ minstN
Nst min was verified in 2004
Local (micro-scale) structure:Point A & B
Meso-scale structure:Region D
Global (macro-scale) structure:Region G
34
Stability ?
3 equationsLarge
bubbles
Gas velocity Ug,S
Volume fraction fSBubble diameter dS
6 Variables
Smallbubbles
Large bubble
Small bubble
Gas velocity Ug,L
Volume fraction fLBubble diameter dL
Liquid
Insufficiency of conservation equations:Insufficiency of conservation equations:
TN
breakN
35
Liquid
Energy consumption dueto meso-scale structuralchange:
Path of energy transfer and dissipation
Small bubble
Large bubble
Liquid-dominated
Gas-dominated
Compromise
+ →turbsurf minN N
Large/Small
Turbulent Dissipation
Interface Oscillationturb min→N
surf min→N
Gas
Zhao, Ge & Li, Chem. Eng. Sci., 2007
36
Physical ModelPhysical Model
DBSmodel
Operating Conditions
Compromisebetween dominant
mechanisms
Stability ?Correlation between scales
3 equationsLargebubbles
Gas velocity Ug,SVolume fraction fSBubble diameter dS
6 Variables
Smallbubbles
Dual-bubble-size model
Large bubble
Small bubble
Gas velocity Ug,LVolume fraction fLBubble diameter dL
Stability Condition:
surf,S+L turb min .N N+ →
Liquid ?
minWν →
maxWte →
Viscosity
Inertia
39
Wv , Wte fluctuating Wv / Wte min
75 100 125 150 175
0.0000
0.0004
0.0008
0.0012
75 100 125 150 175
0.00004
0.00006
0.00008
temporal compromise (at any point)
spatial compromise(at any instant)
Time step
Wν
Wte
50 100 150 200 250 300 3500
5
10
15
20
25
Time step
WWteν
minWWteν →
Point A
Point B
Region G
Region G
Point A & B Region G
Extension 3: Turbulent FlowCompromise between Viscosity and Inertia
40
Lipophile
Hydrophile
minOHE →
minWTE →
3x105 4x105 5x105
20
25
30
3x105 4x105 5x105
18
24
30
minOHE =
minWTE =
minOHE =minrepulse WT OHE E E= + →
2.0x105 4.0x105
300
400
500
Region C Point ARegion C
Point B
temporal compromise(at any single point)
spatial compromise(at any single instant)
Point A & B Region C
EWT , EOH fluctuating Erepulse min
H: Hydrophile group (red) T: Lipophile group (blue) W: Water (green) O: Oil (yellow)
Extension 4: MicroemulsionCompromise between Hydrophile and Lipophile
41
Extension 5: Granular Flow
Stream a Stream a dominantdominant
Ha , Hb fluctuating Ha+Hb min
Fa
Fb
Point A & B Region G
Region G
200 300 4000.500
0.504
0.508
0.512
Ha
t
Point B
200 300 4000.500
0.504
0.508
0.512
Hb
Point A
Ha=min
Hb=min
Ha=min
temporal compromise(at any single point)
spatial compromise(at any single instant)
Ha+Hb min( )
Ha+
Hb
Region G
200 400 600 8000.500
0.504
0.508
0.512
t
Fb
Stream b Stream b dominantdominant
Compromise Between Two Streams of Granular Flow
42
ES , Eμ fluctuating Es/Eμ min
9000 10000 11000 1200018
20
22
24
26
E s / E μ
Time step
9000 10000 11000 12000
18
20
22
24
26
E s / E μ
Time step
0 5000 10000 1500010
20
30
40
50
E s/Eμ
Time step
Point A & B
Region G
Liquid Point A
Point B
temporal compromise(at any single point)
spatial compromise(at any single instant)
Region G
Region G
Surface energy
Viscous dissipation
minsE →
minEμ →
Extension 6: Foam DrainageCompromise Between Surface Energy and Viscosity
43
Extension 7: Nano Gas-liquid FlowCompromise Between Interfacial Potential and Viscosity
a b c d e f g h
0 2000 4000 6000 8000 10000
1.0
1.5
2.0
1.0
1.2
1.4ϕr
ϕ r
Time t (-)
1.0
1.5
2.0
2.5
3.0
ϕ r×S/
S 0
S/S0S/S
0
r minSϕ →
Compromise
a b c d e f g h
S
∫∫∫=
A mm
AA mr
dAAVA
dAdAAVF
)()(21
)(
3ρϕ
rϕ The “cost–benefit” ratio of the energy transportation.
Interfacial area
44
2. Dominant mechanisms
Compromise
Definition aH --- potential a
bH --- potential b
Compromise
Compromise
Compromise
Compromise
Compromise
Definition
Definition
Definition
Definition
Definition
( )b
a minmin
HH
==
te =max( min) WW =ν
teW --- turbulent dissipation
Wν --- viscous dissipation
st min( min)W ε ==
stW --- volume specific energyconsumption for transporting andsuspending particles
ε--- local voidage of the identified area
surfturb min( min) NN ==
turbN --- dissipation liquid in the turbulent
surfN --- surface dissipation
s min( min) EEμ =
=
Eμ--- viscous dissipation
sE --- surface energy
OHWT min( min) EE ==
WTE --- lipophilic potential
OHE --- hydrophilic potential
Compromise
Definition
r S min( min) ϕ ==
rϕ --- dissipation associated with the transportation of unit amount of kinetic energy across unit length
S --- surface energy in the system
1. Systems
Crossflow of
granular materials
Gas-solidsystem
Turbulentflow
Emulsion
Foamdrainage
Turbulentgas-liquid
flow
Nanogas-liquidpipe flow
3. Local extremumexistence indication
121518
21
1.0x105 2.5x1052.0x1051.5x105
EWT
369121518
1.0x105 1.5x105 2.0x105 2.5x105
EOH
No
No
No
No
No
No
No
existence indication4. Global extremum
0.00.0020.003
0.004
0.0050.006
0.007
0.008
8.0x105 1.6x106 2.4x106
Nst min
0.0 0.5 1.0 1.5 2.00.46
0.50
0.54
0.58
0.62
N st
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Summary: Ge et al Chem. Eng. Sci. 2007
45
Compromise Compromise between between dominant mechanismsdominant mechanisms
Generality?Generality?
DependenceDependencebetween scalesbetween scalesGas-solid flow
Gas-liquid flow
Turbulent flow
Nano flow
Foam drainage
Microemulsion
Granular flow
Stability:Stability:Extremum tendency of mechanism 1 Extremum tendency
of mechanism 2
46
Mathematical model of complex systemsMathematical model of complex systems
J. Li et al., Chem. Eng. Sci., 2003, 58, 521- 535
Ej (X)min
s.t. Fi(X)=0, i=1, 2, …, m
X = { x1, x2, ……, xn }
… Multi-objective variational problem
Ek (X)
47
Strategy Strategy in Computationin Computation
Multi-scale structureProblems
SoftwareHardware
Similarity
48
Multi-objective variation
Ej (X)┇�
Ek (X)min
s.t. Fi(X)=0, i=1, 2, …, m
X = { x1, x2, ……, xn }
New computation approach
Different problems in engineering
Common nature
Multi-scale
Discrete
Computer
Physical model
Low cost
High efficiency
Generality
Software
49
Computer software computation
Chemical engineers
physical models software computer computation
Computer scientists
Computer scientists
Chemical engineers
Two different strategies:
50
Parallel
Computation
CPUpopular
CPU+GPUTo be optimized
PU?:
BorrowingGPU
Low cost
Structuralsimilarity
ProblemsSoftwareHardware
Ordinary hardware
Super capability
CapacityCapability
Gap between
51
Software structure
CPUMacro
GPU
Variational multi-scale
Lower scale variables
Pseuo-particle
Meso
120 T
Micro:
Computer structure
Physical structure
54
The 1.0 Peta flops systemLenovo Co. /NV, 200T, Cuda
Dawning Co./AMD, 200T, Brook/CAL
Cooperation
1P flops
200T(Dawning ) 200T(Lenovo) 150T(IPE) 450T(IPE)
150T / AMD built by IPE
Upgrading 120T 450T
56
Real performance in couette-cavity flow
Grooved micro-channel 1024x1024x4
Computational load
.5×1×2×5×
Performance (flops)
51T102T162T324T
Mode member
150T / AMD+
450T / NV
Efficiency
8.5%17%27%54%
57
Comparison of capability
120T (GPU)
1000T GPU
Variational multi-scale
2 Hours
2 Minutes
1 Day
Simulation of gas-solid system
59
1024 Particles
Oil-field
Sample of rock
20000 Particles
Experimental apparatus
Industrial apparatus
60
100MIndigo
100GPC cluster
100TDawing 5000A
1
1MVAX11
10TDawning 4000A
10 100 1000 … CPUs
VariationalMulti-scale
Computatio
Actual capability
Theoretical Capacity
Big gap
80’s
90’s
2000
2005
2009
Huge potential
Cap
acity
/ ca
pabi
lity
62
Direct simulation of gas/solid system
1024 Particles1024CPU
2006 2008
25 K Particles200GPU
Ma et al., Chem. Eng. Sci., 61: 7096,2006.
Ma et al., Chem. Eng. Sci., 64: 43,2008.
2.5M Particles240GPU
2009
Direct simulation of industrial stirred tank
64
Commercial software
2D steady
This system
3D Dynamic200GPU
Parallel computation
67
Metallurgy process
2007-08:10CPU/2GPU 2009:20GPU
3D simulation of granular flow in
blast furnace
Slurry of steel
71
Polymer dynamics Vesicle formation
1392656 water3375 dipalmiteyl phosphatidyl choline
NPT Ensemble
1200 polyethylene chainsChain length:300 CH2
NVT Ensemble
72
Silicon crystal for solar cell
Solar cell
GPU:bulk CPU:interfance
Atom number:1010
Scale :1μm3
Thickness on silicon film
Photon
interfance
From Angstroms to Microns:MD-PPM simulation of interactions
73
2D simulation ofliquid film rupture40nm×35nm,solid velocity 3.2m/s LJ/PP fluid at 60K, constant P & V
10000 particles,3.2G Intel CPU(1core)
Chen et al., Sci. in China,52,2008,372-380
74
3D simulation: bubble-particle in liquid0.1*0.1*0.15µm, bubble mean velocity 3m/s
LJ/PP fluid at 60K, NVT ensemble7M particles, 2 GPUs
77
Two-fluid model Smaller grid size
LimitationAverage in grid
Constitutive model
OptionsMeso-structure Correlation
Stability
7878
Two-fluid models
Local parameters Cd for local cells
Start Final
Spatial-temporal correlation
Average approach
EMMS
Structure parameters
& acceleration
79
1 10 1000.5
1.0
1.5
2.0
2.5
3.0
λ/dp
Dim
ensi
onle
ss s
lip v
eloc
ity
1 10 1003
4
5
6
7
8
Dim
ensi
onle
ss s
lip v
eloc
ity
λ/dp
0.7 0.8 0.9 1.00
2
4
6
8
10
Bed
hei
ght (
m)
Voidage
Bed
hei
ght (
m)
0.7 0.8 0.9 1.00
2
4
6
8
10
Voidage
Fine-grid TEM, big deviation from experiments
EMMS+CFD, good agreement and mesh
independent
CFD + EMMS
80
0 5 10 15 20 25 30
0
20
40
60
80
100
120
Experimental
Simulation
Output solid flux (kg/m2s)Empirical correlations+CFD
Time (s)0 5 10 15 20 25 30
0
20
40
60
80
100
120
ExperimentalSimulation
Output solid flux (kg/m2s)EMMS+CFD
Time (s)
Solid flux: comparison between experiment & simulation
Simulation:with only CFX
Simulation:CFX + EMMS
Yang, Wang, Ge & Li, Chem. Eng. J., 2003
81
0
1
2
3
4
5
6
7
8
9
0.0 0.1 0.2 0.3 0.4
Solid Volume Fraction
Hei
ght (
m)
Exp.15-3-4 Sim.15-3-4 Sim.15-3-4-100w
-0.2 -0.1 0.0 0.1 0.2
0.0
0.1
0.2
0.3
0.4
0.5
Sol
id V
olum
e Fr
actio
n
x (m)
Exp.15-3-4 Sim.15-3-4 Sim.15-3-4-100w
-0.2 -0.1 0.0 0.1 0.2
-4
-3
-2
-1
0
1
2
3
4
5
6
Solid
Vol
ume
Frac
tion
x (m)
Exp.15-3-4 Sim.15-3-4 Sim.15-3-4-100w
Comparison between experiment & simulation
0.72m
2m
4m
6.5m
7.6m
Ug = 3.5 m/s Hinit = 1.70m
82
0.0 0.3 0.6 0.9 1.2 1.5 1.8 2.1 2.4 2.7 3.00
30
60
90
120
150
180
210
240
S
olid
Flu
x (k
g/m
2 .s)
Hini (m)
Ug=3.5m/sUg=2.5m/s
83
Riser Height
0.00 0.05 0.10 0.15
100
200
300
εs0
Gs
0.01 0.1
10
100
1000
Gs Ug=18m/s 10
1.5 m/s
εs0
0.00 0.05 0.10 0.15
50
100
150
εs0
Gs
0.00 0.05 0.10 0.15
20406080
100120
εs0
2.1
Gs
2.8
Ug=4.0m/s
8
10m 20m ...
Crit
ical
poi
nt
Ug*
, Gs*
Intrinsic critical point
Riser height is a key factor !O
pera
tion
flexi
bilit
y
84
εgc, εgf, f,
dcl, ugc, ugf,
usc, usf,
ac, af, ai
Shdynamic
Shstatic
Shdilute
Shdense
Structure-dependent CD
Meso-scale
Micro-scale
Structure-dependent Sh(Ug, Gs)
EMMS/mass: Sub-grid mass transfer
Multiscale flow Multiscale mass transfer
c/c 0
r/r0
c/c 0
r/r0
85
Comparison between CFD computation and experiments
c/c 0
r/r0
Averaged flow & averaged mass transfer
Multi-scale flow &averaged mass transfer
Multi-scale flow multi-scale mass transfer
c/c 0
r/r0
c/c 0
r/r0
c/c 0
r/r0
Dong et al, Chem. Eng. Sci., 63,2008,2798-2823
88
SINOPEC Stage 1 :MIP (max. iso-paraffins) process
Shade of color: concentrationShade of color: concentration
Novel FCC RiserHeight: 40 mDiameter: 1∼3.5 m
Determine design parameterDiametervelocityInventory
89
SINOPEC SINOPEC Stage 2Stage 2 ::Further optiFurther optimization of MIP processation of MIP process
91
98 169 390
SINOPEC: the influence ofSINOPEC: the influence ofOrifice numberOrifice number
Distributor shapeDistributor shape
OutletsOutlets
Distributor shapeDistributor shape OutletOutletOrifice numberOrifice number
Upright SidewardArc Basin Cone
93
XinhuiXinhui Power plantPower plant
Solid volume fraction
Height:36.5 mWidth:15.3 mDepth:7.22 m
480t/h (150MW)CFB boiler480t/h (150MW)CFB boiler
Slag processing in metallugry
95
Hot model with phase transition
Cool model
Coarse particle Fine particle/multi particle1 billion particles
98
Astro- Sciences
Earth, Space & Oceanic Sciences
Multi-scale StructureA common challenge for different areas
ProcessNano
Molecular
Atom
Basic LawsMathematics
System Science
ResourcesEnvironment Buildings
Agriculture Life
& H
ealth
Information
Economics
ManufactureTraffic
Mat
eria
ls
Energy
Nan
o-st
ruct
ure
Multi-Scale
Mol
ecul
es
Clu
ster
s
Part
icle
s
Rea
ctor
s
Fact
ory
Ato
ms
Part
icle
s
Rea
ctor
s
Part
icle
s
99
CPU+GPUBottleneck: Application
PU?:
BorrowingGPU
Simple Hardware, Low cost
Structuralsimilarity
ApplicationSoftwareHardware
Ordinary hardware
Super capability
Breakthrough of computation capability is expected in near future
100
Classified design
Flexible design
Application-oriented
Hardware Software ApplicationUniversal
Easy to use
Application
Software Hardware
High efficiency
Common nature
Particularity
Dive
rsity
Generality
Software Hardware
High efficiencySpecial purposeHigh cost
Conditionally universal
Principles for designing variationalmulti-scale computer systems
CPU-a
CPU-b + m GPU-j
CPU-c + n GPU-k
Parameters:a,b,c,n,m,j,k
Virtual Process Engineering: Dream reality
Breakthrough in understanding meso-scale structures:
Ug=3.5m/sGs=40Kg/m2s
Ug=2.5m/sGs=40Kg/m2s
Ug=2.5m/sGs=60Kg/m2s
Acknowledgement
NSFC (Natural Science Foundation of China)
MOST (Ministry of Science and Technology)CAS (Chinese Academy of Sciences)
Supports from:
Coworkers:
Idea: Multi-scale
Model Method ExtensionTurbulence
G-L-S
1999
2001
NanoReactionEmulsionGranularProtein……
2004Modeling:
Multi-objective variationalproblem
Software
2005
Nst = minverified
Preliminaryverification
Verification
Pseudo Particle
Nst = min?
Computer: Multi-scale
discrete parallel system
2008
Application
YankuangPetroChinaSINOPEC BaosteelABBBHPBCSIROUnilever
Con
fiden
ce!
Stim
ulat
ed Confidence on Generalization
compromise
1984 1987 1994
1993 1997
Enco
urag
emen
t
2003
Extension continued
Futu
re
Virtual reality in process engineering
Similarity
Retrospect and prospect: