Al-Dahhan

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Muthanna H. AL-Dahhan

Chairman & Professor of Chemical & Biochemical

Engineering

Professor of Nuclear Engineering

Departments of Chemical & Biochemical Engineering

and Mining & Nuclear Engineering

Missouri S&T

Benchmarking Multiphase CFD Results

via

Sophisticated Experimental Measurement

Techniques & Methodologies

MISSOURI

S&TUniversity of

Science & Technology

Trends in Physical andNumerical Modeling for

Industrial Multiphase

Flows

Cargese, Corsica,

France

September 24-28, 2012

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Petroleum Refining

And Petrochemical

Processing

Polymerization,

Polymer

Processing

Wastes Treatment,

Environmental

Remediation, Benign

Processes

Syn gas, Natural &

Biogas Conversion

Bulk

Chemicals

Fine Chemicals,

Pharmaceuticals,Materials

Biomass

Conversion

Energy, Biomass,

Coal, Gas, Oil, Solar,

Nuclear, Fuel Cells

Green & Sustainable Processes Multiphase Reactors & Flow systems

Bio-processes,Biotechnology

BUBBLE COLUMN(GAS-LIQUID INTERACTION)

SLURRY BUBBLE COLUMN(GAS-LIQUID-FINE

SOLIDS INTERACTION)

GAS-SOLID FLUIDIZATION & CIRCULATING

FLUIDIZATION(GAS-SOLIDS INTERACTION)

LIQUID-SOLID RISER AND FLUIDIZATION

(LIQUID-SOLIDS INTERACTION)

EBULLATED BED(GAS-LIQUID-CATALYST

SOLIDS INTERACTION)

PACKED BEDS AND STRUCTUREDPACKING/MONOLITH BEDS(GAS-SOLIDS,

LIQUID-SOLIDS AND GAS-LIQUID-SOLIDS

INTERACTIONS)

STIRRED TANKS(GAS-LIQUID, LIQUID-SOLIDS

AND GAS-LIQUID-SOLIDS INTERACTIONS)

AIRLIFT COLUMNS(GAS-LIQUID AND GAS-

LIUQID-SOLIDS INTERACTIONS)

BIOREACTORS, DIGESTERS

ETC.

Bio-refinery and itsintegration

Development & Implementation of Advanced Techniques and

Facilities

MRPT, RPT, DSCT, CT, NGD, ECT, Optical Probes, Mass Transfer, Heat

Transfer, Gas Dynamics & RTD, Particle/liquid RTD, Conductivity probes,

multiphase flow facilities, High pressure and temperature multiphase flow

facilities, Kinetics measurement facilities, Mini-Micro reactors, On line

measurements, Analytical equipment, etc.

CFD & Multi-Scale Modeling & Quantification of

Kinetic-transport Interactions

Mechanistic Reactor Scale Models, Apparent and

Intrinsic Kinetic Models, ANN, CFD and Closures

Evaluation and Development, Integration of

Mechanistic models and CFD, optimization, etc.

Multiphase Flows and Catalytic Processes R&D for Sustainable and Clean

Energy, fuels, products and Environment

MultiphaseFlows and

Catalytic

Processes

Advancing Industrial Multiphase and Multiscale Processes (AIMMP)- Consortium!

Benchmarking CFD & Models

Novelty, Knowledge Advancement & Multidisciplinary Team work

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Nuclear Energy Thermal-Hydraulic

Development & Implementation of Advanced Techniques and

Facilities

MRPT, RPT, DSCT, CT, NGD, ECT, Optical Probes, Mass Transfer, Heat

Transfer, Gas Dynamics & RTD, Particle/liquid RTD, Conductivity probes,

multiphase flow facilities, High pressure and temperature multiphase flow

facilities, Kinetics measurement facilities, Mini-Micro reactors, On line

measurements, Analytical equipment, etc.

Multi-Scale Modeling & Quantification of

transports and Kinetic Interactions

Mechanistic Reactor Scale Models, Apparent and

Intrinsic Kinetic Models, ANN, CFD and Closures

Evaluation and Development, Integration of

Mechanistic models and CFD, optimization, etc.

Multiphase Flows and Catalytic Processes R&D for Sustainable and Clean

Energy, fuels, products and Environment

Advancing Industrial Multiphase and Multiscale Processes (AIMMP)Consortium!

Novelty, Knowledge Advancement & Multidisciplinary Team Work

Benchmarking CFD & Models

4thGeneration Nuclear Energy Pebble beds

Prismatic Beds

TRISO Nuclear Fuels

Other Nuclear Reactor Cores

3thGeneration Nuclear Energy - LWR PWR

LWR

SMR

4thGeneration Nuclear Energy Solids Dynamics

Gas dispersion

Bed Structure

Heat Transfer

Earthquake and upset conditions

3thGeneration Nuclear Energy - LWR 3D/2D Liquid Flow Field and Turbulence

Bubble Dynamics

Bed (Bubble/Gas I Liquid) Structure

3D/2D Phase Distribution

Heat Transfer

Interaction of bubbles and Heat Transfer

Earthquake and upset conditions Fuel rods vibration induced flow

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Petroleum, petrochemical and chemical processes

Catalytic Fischer-Tropsch (FT) slurry bubble columns via advanced

measurement techniques for renewable energy and chemicals

production Cleaner coal utilization for energy and chemical production

Microalgae growth in multiphase photo-bioreactors and harvesting for

bioenergy production and power plant flue gas treatment

Anaerobic digestion of animal and farm wastes for bio-energy

production and wastes treatment

New biocatalytic process technology for energy efficient bio-ethanol

production and driers emissions reduction via enzymatic waterremoval

Biomass and non-conventional feedstock gasification

Water and waste water treatment

4th generation nuclear energy, Light Water Reactor Sustainability and

Small Modular Reactors

Studies of Various Processes for

Energy and Environment Sustainability

Through

Development of Advanced Non-Invasive and Sophisticated

Experimental and Computing Techniques & Facilities

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Computational fluid dynamics (CFD) codes have been increasingly used to

simulate, design and scale up various processes. Such increased use has been

witnessed in conventional and non-conventional industry including nuclear industry

to predict their steady state and transient flows and transports for design

calculation, performance evaluation and safety assessment and analyses

Most (if not all) of the used models and closures needed for the CFD to simulate

these systems have not been based on physics or first principles.

NRCs CFD Best Practice Guidelines in Nuclear Reactor Safety applications:

Verification- To test if the code solves the equations accurately

Validation- To test if the models used in the code accurately represents reality

(CFD grade validation data)

Calibration -To test the ability of code to predict global quantities of interest

Challenges of CFD Implementation

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Global quantities such as pressure drop, temperature difference, Bulk temperature

can be measured easily using simple experimental techniquesBut they are not

sufficient for validation of models used in CFD

Therefore, validationand calibration of CFD models and closures against

trustworthy benchmark detailed 3D data as a function of time are essential. This is

a challenging task

Accordingly advanced measurement techniques are needed to provide these

detailed 3D hydrodynamics and thermal data (i.e., CFD grade validation data) Furthermore, design and development of scaled down experimental multi-phase

flows facilities mimicking actual phenomena and implementation of advanced flow

visualization techniques around it are required

These essential needs force close collaboration between multidisciplinary

computing groups and experimentalists In our laboratory such needed measurement techniques and facilities have been

developed, verified and implement to provide benchmark data for modeling,

simulating and optimizing various complex multiphase flow systems. They consist

of integral and separate effects test facilities & sophisticated techniques

radioisotopes and non-radioisotopes based techniques

Challenges of CFD Implementation

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CFD Conservation Equations

Continuity:

Momentum:

Where:

Constraint:

2

1

1

r

r: phase holdup distribution

m, eff: effective viscosity

m: molecular viscositymt,: turbulence eddy viscosity

M: Interface momentum transport

0

Urrt

m MUUrgrprUUrUrt

T

eff

)(,

Time varianceterm Convectionterm

Pressure

Gradient

term

Body forceterm

Reynolds Stress force

term

Interfacial

momentum

transfer term

...)ForcenDispersioTurbulence(

)ForceMassVirtual()Lift Force()ForceDrag(

TD

VMLD

M

MMMMM

Energy:

What Are the Issues of CFD?

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Turbulence Models

k equation:

Turbulence model: standard k-emodel

modified for two phase flow (Reynolds

shear stress model was also tested)

eequation:

)())((,

e

mm

PrkrkUrkrt k

Ts

)())((21

,

ee

e

e

ee

mmee CPC

krrUrr

t

Ts

mm

gUUUPt

TsT

Ts

Pr)(

,,Where:

||6.0

,

m UUdr bTb

m em

2, k

C

Ts

m

m

TT

T

eff mmm , +

Gas Phase:

Liquid Phase:

Shear induced turbulenceBubble induced turbulence

(Sato and Sekaguchi, 1975)

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Geometry and Grid (Example) 3D simulations

Draft Tube (having

been cut out from

the domain)

Rectangular insteadof real ring sparger

(4cm4cm)

Top: Degassing BC (for steady state simulations)

Constant Pressure (for transient simulations)

Fine griding: 271,360 elements with amesh size of 2.5 mm in the reactor

center

Coarse griding: 48,132 elements with a

mesh size of 5mm in the reactor center

Mesh in the radial and axial directions

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Vitruvius says that small models are of no avail for

ascertaining the effects of large ones; and I here

propose that this conclusion is a false one

Leonardo da Vinci

Vitruvian Man of Leonardo da Vinci (1513)representing the standard of humanphysical beauty. Ahmed Youssif, 2009

Is it the case for multiphase flow

systems?

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High Bay LaboratoriesMissouri S&TRadioisotopes Labs

Non Radioisotopes Labs

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Selected Non-Invasive and Sophisticated

Techniques

Dual Energy-Dual Source Computed Tomography

(DE-DSCT)

Determination of cross-sectional phase distribution

Radioactive Particle Tracking (RPT)

3-D Flow field visualization

Gamma Ray Densitometry (GRD)

Determination of line averaged phase distribution &flow regime identification

Advanced Liquid/Gas Tracer Technique

Quantification of Liquid/Gas phase dispersion and

extent of mixing

Optical Probe

Determination of solids hold-up and

velocity

Fast response Heat-Transfer Probe

Measurement of local heat transfer

coefficient

Four point fiber optical probe

Characterization of Bubble dynamics

Pressure Transducer

Pressure measurements

Mass Transfer Probe

Measurement of local mass transfer

coefficient

Radioisotopes Lab 1

Radioisotopes Lab 2

Radioisotopes Based

Techniques

Radioactive ParticlesPreparation

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Selected Separate Effects Experimental

Facilities & Computing Capabilities

Continuous Pebbles Recirculation

experimental set-upPacked Bed experimental set-up

Spouted beds

Fluidized bed reactor

Bubble Column with internals

Monolith (micro-reactors)

High Capacity Industrial Scale

Compressor

CFD & DEM codes

Pilot scale industrial bubble

column

High temperature and pressure

pilot plant

Separate effects 2D bubble

column

Industrial pilot plant internals inbubble/slurry bubble column

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Experimental and Computational Capabilities at Missouri S&T

Radioactive Particle Tracking (RPT) techniques and related calibration devices

Computed Tomography (CT)

Nuclear Gauge Densitometry (NGD)

Advanced gas/liquid tracer techniques and overall mass transfer coefficient

measurement

Gas-solid optical probes for solids dynamics

4-Point optical probes for bubble dynamics

Heat transfer coefficient probes (gas-liquid, gas-solid, gas-liquid-solids)

Mass transfer coefficient optical probes integrated with 4-point optical probe for

bubble dynamics

Array of single point and two points optical probes for bubble and liquid dynamics in

structured bed and flow regime identification

Array of two points optical probes for liquid velocity measurement and flow regime

identification in two phase flow packed beds

16 points conductivity mesh for liquid velocity distribution measurements in two

phase flow packed beds

Pressure transducers

Computing and modeling: CFD and EDEM codes; Chaotic and statistical signals

processing; artificial neural network; Reactor scale modeling by integrating kinetics

and transport

Missouri S&T Nuclear Test Reactor

In Addition to: X-Ray tomography, radiography and velocity measurements; Highspeed video Camera; PIV

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CT Technique Measurement of time-averaged cross-sectional

phase holdup(volume fraction)

distribution

Estimation-Maximization (EM)and Alternating Minimization

algorithm used for image

reconstruction


Seeing through the reactor

for phase distributions

Radiation

Source

Draft tube

column

0

29.0cm

59.7 cm

3.5

8.4cm

19.0cm

30.5 cm

Lead ShieldedCs-137 Source

Source

Collimator

DetectorCollimator

Lead Plugs withAperture

NaI(Tl) Detector

Detector

Collimator

3 D h ti f CT

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3-D schematic of CT

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1st Gamma Ray source

2nd Gamma Ray source

Three phase system(GLS)

Detectors

][ln lI

IA

o

m

ijllijl

ijggijg

LA

LA

][

][

,

,

m

m

)1()()()(,,,

o

sijg

o

sijsijsg ememm

ijgijkijK AAR

,,,

)(

,

)(

,

,

,

)(

,

)(

,

,)1(

I

ijL

I

ijslg

ijso

s

ijs

I

ijL

I

ijsg

ijgR

R

R

R ee

ee

)(,

)(

,

,

,

)(,

)(

,

,

)1(II

ijL

II

ijslg

ijsos

ijs

IIijL

II

ijsg

ijg R

R

R

R ee

e

e

o

s

II

ijl

I

ijlII

ijSG

o

s

I

ijSG

II

ijl

I

ijlII

ijSLG

I

ijSLG

ijs

R

RR

R

R

RRR

ee

e

)(

,

)(

,)(

,)(

,

)(

,

)(

,)(

,

)(

,

,

ijijsssijsijgijlijgijgslg LAAA ][)1(

,,,,,, emeee

Equation for a threephase system

Dual Source CT [Combining two single source -ray Tomography]

Varma and Al-Dahhan,

(2005)

D l t f DSCT t h i

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Development of DSCT technique

Locations for the

Source Collimator

devices of 137Cs and60Co sealed source

Detector Array

Plate

The Detector

Array Lead

Shield with

Detector Lead

Collimatorsinserted

Base

Plate

Circular Source

Plate

Ph t h f th DSCT S t

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Photograph of the DSCT Setup

Locations for the SourceCollimator devices of

137Cs and 60Co sealed

source

Detector ArrayPlate

The Detector Array

Lead Shield with

Detector Lead

Collimators inserted

Base Plate

Circular SourcePlate

The Detector Array

Lead Shield with

Detector Lead


The Detector Array

Lead Shield with

Detector Lead


The Detector Array

Lead Shield with

Detector Lead


The Detector Array

Lead Shield with

Detector Lead


Detector Plate

motors

GLS Phantom

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Data Processingof Radiation

Intensity Received by N

Detectors from a Single

Radioactive Sc-46 Particle

Intensity I for N detectors

(Photon counts)Calibration curves Ivs. D (distance)

Distance D from Particle to N

detectors

Weighted least

square regression

Particle Position Px,y,z (t)Filtering noise due tostatistical fluctuation

of raysusing

Wavelet Analysis

Filtered Particle position

Px,y,z(t), cells movement

Instantaneous Lagrangian

Velocities

Time Averaged velocities &

Turbulence Parameters

RPT Technique

Radioactive Scandium(Sc 46, 250mCi, emittingrays)embedded in 0.8~2.3 mm plolypropylene particle(neutrally buoyant with liquid)100~150mm for solids in a slurry bubble column

NaI detectors held by Alsupporter (notshown)

Power supplyConnect to data

acquisition

Active surface of detector

Distributor

Gas inlet

Example of Bubble column

Radioactive Particle Tracking (RPT)Simulating

the cells/liquid elements movement by a

radioactive particle

RPT on Pebble bed

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3-D schematic of RPT

R1

R2

Sc

Parylene N

Sc46particle coated with

parylene-N, trackingsolids

Sc46particle in

polypropylene ball,

tracking liquid

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Particle (Cell) Tracking

RPT vs. CFDDeveloped at

Washington

University by H. Lou

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Multiple RPT (MRPT)Vesvikar (2006)

Modified reconstruction algorithm for dual-particle tracking

Gamma peaks of Sc-46 and

Co-60 individually, together

and summation of individualcounts

L L/D B bbl Sl B bbl C l

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Low L/D Bubble-Slurry Bubble Column

Setup &Detectors arrangement

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Liquid/Solids phase mixing

Novel virtual tracer response method (RTD) using RPT

Liquid response curves, fitted with ADM

-Virtual response curves

-Injected tracer is almost

ideally distributed in time and

space

-Small axial variation of fitted

Dlvalues in fully developedzone

-Injection and sampling can

be designed anyway within

the CARPT experiment zone.

Particle counting process

z=2D

z=4D

z=6D

z=8D

t

count

0

z=8D

t

count

0

z=6D

t

count

0

z=4D

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Virtual tracer method applicationMechanistic, compartmental model for liquid phase

Recirculation and Cross Flow with Dispersion (RCFD) model

(Degaleesan et al., 1996 and 1997; Gupta et al., 2001 and 2002)

0 R

rg

rl

GasVelocity

Profile Liquid

Velocity

Profile

Liquid and Gas axial

velocity radial profile

R

L downwards

G downwards

L upwards

G upwards

L downwards

G upwards

Parameter to be fitted: Dx,u, Dx,d, Dr

u d

b

a

ul,u

Dx,u

ul,d

Dx,d

l,u Cl,ul,d

Cl,d

Liquid

Recirculation

Dr

l,b Cl,b

l,a Cl,a

b=hb/dc

a=ha/dc

r'2

l,u l,u l,u r l r r 'x,u l,u l,u l,d2

l,u

C C C 4(D )D u (C C )

t z r 'R z

e e

2l,d l,d l,d r l r r '

x,d l,d l,u l,d2 2 2l,d

C C C (D )4r'/RD u (C C )

t zz R r '

e e

Example equations:

Experimental Set upA Novel On-line Technique Using NGD

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CT has been successfully

implemented to identify regimetransition in bubble columns(Shaikh and Al-Dahhan, 2005)

NGD is available commerciallyfor liquid/slurry level measurement

and control in industry

Based on this, it has been

proposed to developNuclearGauge Densitometry(NGD)

to identifyflow regimetransition

Successful implementation of regime

transition methodology can benefit online

flow regime monitoring onindustrial scale

MultiorificeSparger Plate

35 cm

35 cm

0.625

35 cm

3.61" Nozzle

Detector

Experimental Set-up

Air-water system, D = 10 cm, P = 0.1 MPa

A Novel On-line Technique Using NGD

for Pinpointing Flow pattern (regime) in

Industrial Multiphase Flow Systems

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Gamma Ray Densitometry

Radial profile of phases, reduced tomography, flow

pattern identification and flow regime, on-line

diagnostic

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4thGeneration Nuclear Energy

Pebble bed modular reactor

Pebbles continuous

recirculation

LEU TRISO (Pressure

containment) fuel (8-10% U-

235 by wt.)

Inert helium coolant

High burn-up possible

Inherently safelarge

thermal inertia due to

graphite

Modular design Elimination of conventional

upper temperature limits- high

thermal efficiency (~45%)

29

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Pebble Bed / Moving Bed / Online Catalyst Replacement

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Pebble Bed Reactor Prototype -Cold

Flow Operation Video

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The Role of Bed Structure - Porosity

Distribution and Its Radial Profile

Local flow and transport properties of gas flowing through

voids - closely coupled with structural characteristics of a

packed bed

Radial distribution of particle centers - Input of bed structure toCFD analysis of packed bedsto simulate realistic packed beds

Radial and axial porosity profiles and associated solid phase

distributions are needed as input to hydrodynamic and thermal

models of packed beds

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Validation of EDEMs Packing Algorithm

This is essential as first step in DEM based analysis is to

pack properly the particles inside confined geometry

Packing algorithms available with commercial codes such

as EDEM are used as a Black-Box and are without any

detailed validation exercise In most cases, average porosity values are used to

validate numerical packing results with available

experimental results - not sufficient

Radial porosity variation profile, along with average porosityvalues, will be used as a means for this validation study

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Validation of EDEMs Packing Algorithm

34

Case 1 : Experimentally determined

interaction parameters is used

Case 13: Static friction between

particles and between particles and

the wall is considered

Case 16: Static friction between the

particle and the wall is considered inthe near wall region and the static

friction between particles is

considered in the region away from

wall

Case 15: Muellers benchmark data

Case 16 simulates benchmark data results to a greater extent and suggests a possibility

of the differential role played by static friction characteristics

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CT scanner machine with the pebble bed in the center

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Bed Structure of Pebble

Beds

d=0.5 inch

d=2 inch

d=1 inch

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Figure: Visualization of the packing structure inside

the cylindrical pebble bed showing the axial

and radial void distribution to all the 20 CT

cross-sectional slices for 1 inch pebble size

and 1 foot height and 1 foot diameter column

(a) 3D view of the analyzed bed (b) Vertical

cut section in the center of the bed.

(a)

(b)

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Comparison Between CT results and

Muellers Model (2011) Prediction

(Based on Geometrical Constraints)

0

0.2

0.4

0.6

0.8

1

1.2

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Voidfr

action

Radial distance (r/R)

Exp. CT

Mueller 2011

Radial void fraction profiles at level 3 (9 inch height) with D/d = 12

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39

Flow patterns in bunkers

Mass Flow

Funnel Flow

Ref.EN 19914.: Actions on structures. Silos and tanks (2006)

Deadzones

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EDEM Simulation-Results

40

Stream velocity profile

Mid-plane slice as viewed from Y direction)

Stream velocity profile

Mid-plane slice as viewed from X direction)

Plug flow zone in the top portion of cylinder whereas convergingzone exists towards exit opening, No stagnant zones observed

Particles close to wall are moving slowly as compared to rest ofthe particles

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EDEM Simulation Set-up

Initial filling of test reactor geometry was carried out by blocking

the bottom opening with a plate

Particles are generated randomly and allowed to settle down

under gravity until static equilibrium is reached

After proper filling, the bottom plate is removed and draining ofmarbles is initiated

Time step of 1.53E-05 sec, which is 59% of the critical time step ,

is used

Hertz-Mindlin contact model (with no-slip) is used

45and 60cone angles are simulated

41

DEM i l ti f 45

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DEM simulation for 45cone

angle

42

t= 0 sec t= 1 sec t= 2 sec

DEM i l ti f 60

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angle

43

t= 0 sec t= 1 sec t= 2 sec

DEM i l ti f 45

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angle

44

t= 0 sec t= 1 sec t= 2 sec t= 3 sec

DEM i l ti f 60

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angle

45

t= 0 sec t= 1 sec t= 2 sec t= 3 sec

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45cone angleVelocity profile

46

CV1

CV2

Simulation geometry

Velocity profile

Streamlines

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60cone angleVelocity profile

47

CV1

CV2

Simulation geometry

Velocity profile

Streamlines

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RPT position reconstruction algorithm -

Validation

Using the semi-empirical model in eqn.

(1), finer mesh points are established

for next cross-correlation based search

(r=10mm,=15, z=5mm )

Repeating cross-correlation based

search until convergence criterion of1 ,(0)0.005

Validation carried out by treating some

calibration points as unknown position

data

This approach has provided

reconstruction resolution of 5 mm. Thiscan be further reduced by iterative

search approach using finer mesh grid.

48

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3-D trajectory preliminary selected

results

Plug type flow in the top portion of

cylinder whereas converging flow

exists towards exit opening

Overall RTD of tracer seeded at

the center (8hr. 44 min) less than

the one seeded at radius 2 inch

from the center (10hr 40 min)

Mass flow type behavior

(Simultaneous motion of all

particles)

49

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RPT trajectory results

50

RPT lt t iti

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RPT resultstracer positions

vs. time

51

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RPT resultsvelocity profile

52

CV1

CV2

Simulation geometry

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Advanced Gaseous Tracer Technique & Experiment

Setup

FID

PCAmp

TCD

A/D

PumpPebble bed unit

Measurements Tracer

injection

Sampling

location

Dispersion zones measured

(i) C(i) I1 S1 Top sampling/analytical zone from S1

(ii) C(ii) I1 S2 Plenum zone + top sampling/analytical from S2

(iii) C(iii) I2 S3 Bottom sampling/analytical zone from S3

(iv) C(iv) I1 S3 Plenum zone + bed zone + sampling/analyticalzone from S3

Difficulties:

I. The system is non-ideal in a gas tracer

experiment.

II. Tracer input at the gas distributor is not a Diracdelta function as injected at the inlet.

III. Measured response does not represent the actual

profile at the bed outlet.

IV. Multiple measurements are needed tocharacterize each part.

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Schematic diagram of the convolution method and CSTR and ADM

models fit in this work (Han, 2007)

0/tTin

inj

CC e

C

The plenum and distributor zone is

assumed to be a continuous stirred tank

reactor (CSTR) model:

*

( )

0

( ) ( ') ( ') 't

in i inC t C t t C t dt

Convolution of Cinfor the regression of 0

2

2

gT T Ta

VC C CD

t z ze

Axial dispersion model (ADM) for gas phase

in the pebble bed:

0 00, V T

g in g T z a z

Cz C V C D

z

, 0T z LC

z Lz

0, 0Tt C

Modeling and Convolution Method

2

1

* ))()((1

jiij

n

jin tCtC

nError

Average squared error

function

input

Convoluted ADM

model prediction

Cout*

(d) ADM model prediction convoluted with the results of measurements (iii)

CoutPlenumCSTR

model

Sampling and

analytical zone

measured in (iii)

Bed zone

(ADM

prediction)

Cin

Regression of Dgin ADM model

function

input

Convoluted CSTRmodel prediction

Cin

(b) CSTR model prediction convoluted with the results of measurements (i)

CinPlenumCSTR model

Sampling and analyticalzone measured in (i)

C(iv)Plenum

zone

Sampling and

analytical zone

Overall

measurements

Injection

(c) Response of the whole system by measurements (iv)

Bed

zone

Plenum and

distributor zone

Sampling and

analytical zone

Results of

measurements (ii)

C(ii)

Injection

Regression of o

in CSTR model

(a) Response by measurements (ii)

P P;

Vt

;d d

g P

a

V d z

P D Ze e 2

2

1T T TC C C

Pe Z Z

Two extreme cases:

For perfectly plug flow ,dispersion number (1/Pe)=0, small amount of dispersionFor perfectly mixed flow, dispersion number (1/Pe)=,large amount of dispersion

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Sample of Results

Validation of CSTR Model

Mathematical representation by

ADM

0.0

0.2

0.4

0.6

0.8

1.0

0 10 20 30 40

C/Cmax(---)

Time, t (s)

(a) Laminar flow

Vg=0.08 m/s

o=0.62 s

Error= 7.6E-04

C(i)

CinCin*

C(ii)

0.0

0.2

0.4

0.6

0.8

1.0

0 10 20 30 40

C/Cmax(---)

Time (s)

(b) Turbulent flow

Vg=0.6 m/s

o=0.40 s

Error= 5.7E-04

C(i)

Cin

Cin*

C(ii)

0.0

0.2

0.4

0.6

0.8

1.0

0 10 20 30 40

C/Cmax(---)

Time (s)

(a) Laminar flow

Vg=0.08 m/s

Da=1.91 cm2/s

Error=3.91E-04

C(iii)

CoutCout*

C(iv)

0.0

0.2

0.4

0.6

0.8

1.0

0 10 20 30 40

C/Cmax(---)

Time, t (s)

(b) Turbulent flow

Vg=0.6 m/s

Da=4.89 cm2/s

Error=1.7E-04

C(iii)

Cout

Cout*

C(iv)

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Comparison with Empirical Correlations

Delgado (2006) rewrote Gunns correlation (1968)

Bischoff & Levenspiel (1962) , as given in most of the classic chemical

reaction engineering textbooks (Levenspiel, 1999; Fogler, 2005)

Wen and Fan (1975) proposed

0.0

0.2

0.4

0.6

0.8

1.0

1.2

0 440 880 1320 1760 2200

Dispersion

number,(1/Pe)

Particle Reynolds number, Re

(b) This workBischoff & Levenspiel, 1962b

Wen and Fan, 1975

Delgado, 2006

0.5

1.5

2.5

3.5

4.5

0 110 220 330 440 550

DispersiveP

ecletnumber,Pe

Molecular Peclet number, ReSc

(a) This workBischoff & Levenspiel, 1962b

Wen and Fan, 1975

Delgado, 2006

More and slow

dispersion, poor

extent of mixing

Less and rapid dispersion, better extent of mixing

Large deviation

from idealized

plug flow model

Small deviation from idealized plug flow model

= average porosity, recommended= average tortuosity

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Fast-ResponseGas-Solid Heat Transfer (HT) Probe

Heat transfer probe

DC Power

PC

DAQ

Amplifier

1 3 2 5

12 3 4 5

1- Teflon tube 4- Heater

2- Brass shell 5- Teflon

cap

3- Fast response (0.02s) heat flux sensor

bisi

i

i TT

Aq

h 1

1 n iave

i si bi

q A

h n T T

Based on the direct measurements of: Heat flux from the probe

Surface temperature of the probe

The heat transfer coefficients calculations:

simultaneouslyihAqi /

siT

b iT

n

Where:Instantaneous local heat transfer coefficient (kW/m2.K)

Instantaneous heat flux across the sensor (kW/m2)

Instantaneous surface temperature of the probe (K)

Instantaneous bulk temperature of the media (K)

Total number of data points, in this work 2,050 samples

2 34

1

1- Solid copper sphere 3- Heat flux sensor

2- Teflon tube 4- Cartridge heater

3

1

2

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Heat Transfer Experimental Setup

Heat transfer probe

DC Power

PC

DAQ

Amplifier Pebble bed unit

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4.0

4.5

5.0

5.5

6.0

6.5

7.0

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7

Heattransfercofficient,h(kW/m2.k)

Superfical gas velocty, Vg(m/sec)

a

Z/D=2.5

Z/D=1.5

Z/D=0.5

4.0

4.5

5.0

5.5

6.0

6.5

7.0

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7

Heattransfercofficient,h(kW/m2.k)

Superfical gas velocty, Vg(m/sec)

b

Z/D=2.5

Z/D=1.5

Z/D=0.5

Effect of Superficial gas velocity on Heat transfer Coefficient

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There are two approaches to simulate the system:

First approach: Porous medium model using commercial CFD code

(FLUENT package)

1) Packing structure is usually described by some statistical and lumped

parameters such as porosity and tortuosity.

2) Semi-empirical correlations (Ergun equation) to predict the pressure loss

3) The characterization of flow is assumed as ideal plug-flow model

4) Cannot capture many physics and local phenomena such as local hot spots

This homogenous porous medium approach will cause larger errors for packedpebble-bed and just simulates the system roughly

Second approach: Coupling DEM-CFD simulation using commercial EDEM-

FLUENT code.

1) High performance computing, it can considers all the particles in the packed

bed, and construct the grids of particles, therefore it becomes possible to

resolve flow in detail.

2) Discrete Element Method (DEM) is used to generate a realistic randompacking structure for the packed bed with spherical particles.

3) Importing the packing structure into the CFD preprocessor (Gambit) to

generate the mesh for the CFD simulation.

This approach is costly and time consuming

Third approach: Lattice Boltzmann method (LBM) using home code

This approach restricted to low Reynolds number of flow and isothermal conditions

Computational Fluid Dynamics (CFD) Simulations

Manufacturing TRISO Nuclear Fuel Particles for 4thGeneration Nuclear

E B CV D iti i G S lid S t d B d C t

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Energy By CV Deposition in Gas-Solid Spouted Bed Coater

CT for Spouted Bed

G S lid S t d B d

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Gas-Solid Spouted Beds

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Implementing for the first time a new advanced optical probe that can measuresimultaneously solids concentration, solids velocity and their fluctuations.

Developing a new reliable and simple methodology to calibrate the probe by correlating

the measured solids concentration with solids holdup.

Assess the dimensionless analysis based scale-up methodology and develop a new

mechanistic one that can be monitored online for hydrodynamics similarity

Implementing CFD as a tool to simulate the hydrodynamics of spouted bed

coater/reactor and validating using the new optical probe, CT, GRD and RPT

New Optical Probes 0.5*0.5mm and 0.8*0.8mm

respectively which can measure solids

concentration/holdup, solids velocity and theirfluctuations

New Optical probe techniquesManufactured by Chinese Academy of Sciences

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New Optical probe techniquesManufactured by Chinese Academy of Sciences

Distance

The addition of quartz

window eliminates the

blind region, giving a

good linear response

High precision

Can be used for high

concentration systems

Traditional OpticalProbes1stGeneration

Optical probe with

quartz window

Blind region without window

Ef fect of Bl ind region

I t was found that (Bi et al. 2003), the blind region in

the tradit ional probes aff ected the measurements

due to creation of dead zone. But the addition of

quar tz window eliminates this dead zone and

increasing the response and measur ing volume

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Simultaneous measure of solids velocity and concentration/holdup and their fluctuations

Solids velocity measur ement:

The average passing time is obtained from the peak of the

cross-correlation function:

The effective distance,Le (distance between any two light emitting or receiving fibers from one tip to another) is

known (given by vendorChinese Academy of Sciences)

The data is taken if the cross correlation coefficient is more than 0.7, rest is neglected (Liu et al., 2003)

Solids concentration/hold-up measurement: The probe tips measure the number of particles

in a measuring volume in front of them. Hence the measured voltage signal is related to solids

concentration. This needs to be translated to soli ds holdup via cali brati on.

Parti cle to probe diameter ratio is

importantSize of probe selected should be

>=2 the parti cle size under study

Particle selected must have good

ref lective properties (not black)

Parti cles should be non corrosive,

non reactive, non viscous etc.

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Validation of the Optical Probe measurements for Solids Velocity

Measurements from optical probe were validated against high speedcamera for velocity measurements

Programmable pump was used to feed solids at different flow rates

Solids passed through a funnel (tube length of 2.5mm diameter) as astring of particles

Equipment was covered with black cloth to stop any externalinterference of light, when using optical probe

FASTCAM high speed camera was used to record particle velocities

Pump

Optical Probe

Velocity validation (Optical Probe V/s High

Speed Camera)

0

0.2

0.4

0.60.8

1

1.2

1.4

1.6

0 5 10 15Flowrate (ml/min)

Velocit

y(m/s)

Optical Probe

High Speed Camera

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Verification of the Gas-Solid Optical Probe

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GRD experiments were

performed on 0.152 m

spouted bed for radial

profiles of solids holdup.

The solid phase was glass

beads of 2 mm with density

of 2500 Kg/m3.

The gas phase was

compressed air at a

velocity of 1.06 m/s. The measurement was

done at H/D = 1.5.

Results of both the

techniques compare well.

The deviation in the resultscan be attributed to the

intrusive nature of optical

probes.

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0 0.2 0.4 0.6 0.8 1

SolidsHoldup

Radial Position, r/R

GRD Technique

Optical Probe

Dual Source Computed Tomography (CT)

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6 inch Spouted Bed

Match conditions specified by He et

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Column diameter: 6inch

Height of particles in

spouted bed: 0.323m Temperature: 298K

Pressure: 101kPa

Solids: Glass Diameter of solids:

2mm

Density of solids:2450Kg/m3

Velocity of gas: 1.08

m/s H/Dc = 2.1

Dc/Di = 8

Dc/dp = 69.9 Reynolds number =

157

Match conditions specified by He et

al. (1997)

Gas holdup at Level 2 Gas holdup at Level 3

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p

Solids holdup at Level 2

p

Solids holdup at Level 3

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Comparing Results at level 2

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0 0.2 0.4 0.6 0.8 1 1.2

Solidholdup

Dimensionless radius (r/R)

Comparing solid holdup profile obtaining by optical probe and CT at L2

solid holdup L2 by CT

solid holdup L2 by optical probe

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Comparing Results at level 3

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0 0.2 0.4 0.6 0.8 1 1.2

Solidholdup


Comparing solid holdup profile obtaining by optical prob and CT at L3

solid holdup L3 by CT

solid holdup L3 by optical prob

Computational Fluid Dynamics (CFD)

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75

The two fluid model (TFM) approach hasbeen applied to the modeling of spoutedbeds to simulate the hydrodynamics in

FLUENT In TFM, the different phases are

mathematically treated as interpenetratingcontinua

CFD package Fluent V6.3.26 was used inthe simulations

Meshes were created by the CAD program

of GAMBIT 2.2.30 Two-dimensional axisymmetric model has

been assumed for the simulation studies

The Phase Coupled SIMPLE algorithm wasused for the pressure-velocity coupling

A first-order upwind differencing schemefor momentum and volume fraction

variables was used Very small time step (0.0001 s) with about

20 iterations per time step was used

A convergence criterion of 10-3 for eachscaled residual component was specified forthe relative error between two successiveiterations

Spouted bed Geometry 2D Spouted Bed Mesh

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2. Kinetic theory of granular flow equations

The granular temperature:

The solid phase stress:

The solid pressure:

The solid bulk viscosity:

The solid shear viscosity:

Closure of the solid phase momentum equation requires a description of the

solid phase stress. The granular kinetic theory derived is applied in this study.
http://www.sciencedirect.com/science?_ob=MathURL&_method=retrieve&_udi=B6TFK-4H9GRMG-1&_mathId=mml47&_cdi=5229&_pii=S0009250905006603&_issn=00092509&_acct=C000050731&_version=1&_userid=1036314&md5=f4a7f68271d7604b6801e7e07a1c5dfbhttp://www.sciencedirect.com/science?_ob=MathURL&_method=retrieve&_udi=B6TFK-4H9GRMG-1&_mathId=mml46&_cdi=5229&_pii=S0009250905006603&_issn=00092509&_acct=C000050731&_version=1&_userid=1036314&md5=20762d552f863c56fc0376040cea8a8fhttp://www.sciencedirect.com/science?_ob=MathURL&_method=retrieve&_udi=B6TFK-4H9GRMG-1&_mathId=mml43&_cdi=5229&_pii=S0009250905006603&_issn=00092509&_acct=C000050731&_version=1&_userid=1036314&md5=544c6ddb46b10ad00a8498569b48bfd2http://www.sciencedirect.com/science?_ob=MathURL&_method=retrieve&_udi=B6TFK-4H9GRMG-1&_mathId=mml36&_cdi=5229&_pii=S0009250905006603&_issn=00092509&_acct=C000050731&_version=1&_userid=1036314&md5=8ba748cf6664eaa16bfaa26ba2d5ebe2http://www.sciencedirect.com/science?_ob=MathURL&_method=retrieve&_udi=B6TFK-4H9GRMG-1&_mathId=mml35&_cdi=5229&_pii=S0009250905006603&_issn=00092509&_acct=C000050731&_version=1&_userid=1036314&md5=94a8aa22acd27bbff8cfb62f72488c4d

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3. Turbulence model

Turbulence predictions for the continuous phase are obtained using the standard

k

model supplemented with extra terms dealing with the interphase turbulentmomentum transfer.

Turbulence predictions of the continuous phase are obtained from the modifiedkmodel:
http://www.sciencedirect.com/science?_ob=MathURL&_method=retrieve&_udi=B6TFK-4H9GRMG-1&_mathId=mml49&_cdi=5229&_pii=S0009250905006603&_issn=00092509&_acct=C000050731&_version=1&_userid=1036314&md5=358a66fb47a58eec3e089faf573a1705http://www.sciencedirect.com/science?_ob=MathURL&_method=retrieve&_udi=B6TFK-4H9GRMG-1&_mathId=mml53&_cdi=5229&_pii=S0009250905006603&_issn=00092509&_acct=C000050731&_version=1&_userid=1036314&md5=48ef14b9bae84521ad27f301b4db8c3ehttp://www.sciencedirect.com/science?_ob=MathURL&_method=retrieve&_udi=B6TFK-4H9GRMG-1&_mathId=mml55&_cdi=5229&_pii=S0009250905006603&_issn=00092509&_acct=C000050731&_version=1&_userid=1036314&md5=95798c7b6ca69b6618a1ea0d96447f8fhttp://www.sciencedirect.com/science?_ob=MathURL&_method=retrieve&_udi=B6TFK-4H9GRMG-1&_mathId=mml54&_cdi=5229&_pii=S0009250905006603&_issn=00092509&_acct=C000050731&_version=1&_userid=1036314&md5=90e184d11416fb35e568c18ed7ab439chttp://www.sciencedirect.com/science?_ob=MathURL&_method=retrieve&_udi=B6TFK-4H9GRMG-1&_mathId=mml53&_cdi=5229&_pii=S0009250905006603&_issn=00092509&_acct=C000050731&_version=1&_userid=1036314&md5=48ef14b9bae84521ad27f301b4db8c3ehttp://www.sciencedirect.com/science?_ob=MathURL&_method=retrieve&_udi=B6TFK-4H9GRMG-1&_mathId=mml49&_cdi=5229&_pii=S0009250905006603&_issn=00092509&_acct=C000050731&_version=1&_userid=1036314&md5=358a66fb47a58eec3e089faf573a1705

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4. Drag model

The drag force acting on a particle in fluidsolids systems can be represented by

the product of a momentum transfer coefficient and the slip velocity between thetwo phases:

Gidaspow et al. employed the Ergun equation for dense phase calculation and the

WenYu (1966) equation for dilute phase calculation:
http://www.sciencedirect.com/science?_ob=MathURL&_method=retrieve&_udi=B6TFK-4H9GRMG-1&_mathId=mml77&_cdi=5229&_pii=S0009250905006603&_issn=00092509&_acct=C000050731&_version=1&_userid=1036314&md5=e26eacfbd0dc4b7287faae13115105a2http://www.sciencedirect.com/science?_ob=MathURL&_method=retrieve&_udi=B6TFK-4H9GRMG-1&_mathId=mml76&_cdi=5229&_pii=S0009250905006603&_issn=00092509&_acct=C000050731&_version=1&_userid=1036314&md5=1e7cae758b3201bd65ef1c0b24f24e35http://www.sciencedirect.com/science?_ob=MathURL&_method=retrieve&_udi=B6TFK-4H9GRMG-1&_mathId=mml74&_cdi=5229&_pii=S0009250905006603&_issn=00092509&_acct=C000050731&_version=1&_userid=1036314&md5=2a9c02352abf6ce15528377720e52279http://www.sciencedirect.com/science?_ob=MathURL&_method=retrieve&_udi=B6TFK-4H9GRMG-1&_mathId=mml68&_cdi=5229&_pii=S0009250905006603&_issn=00092509&_acct=C000050731&_version=1&_userid=1036314&md5=327dbdbaae22ce5748b6e2b8afd4f49c

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Models and Boundary conditions for CFD simulation

80

Granular Viscosity:Syamlal-Obrien Granular bulk viscosity:Lun et al

Frictional viscosity:Schaeffer Radial distribution:Lun et al

Solids pressure:Lun et al Drag Model: Gidaspow

The initial and boundary conditions are as follows:

The simulations start from a static bed

At the inlet, the uniform distribution is assumed for velocitycomponents

Gas is injected only in the axial direction, and solids velocityis zero

At the outlet, an outflow boundary condition is given, thevelocity gradient is zero, i.e., ux/x = 0 and the pressure isset at ambient atmosphere

At the wall, a no slip boundary condition is assumed

Volume fraction contours

for 2D spouted bed

Grid Convergence Studies

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81

3 grid sizes were studied coarse, medium and finely meshed grids

All 3 grids gave identical spout diameters, but coarse grid giving lesserfountain height

Voidage profiles and particles velocity profiles were very close for all 3 grids

Considering the accuracy and computational time, the medium sized grid hasbeen used in the current study

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Spout

Annulus

Bed

surface

Fountain

The simulated particle velocity vector and solid volume fraction

Validation on CFD model

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The simulated and experimental radial profiles of particle velocity and voidage

at different bed heights

Validation on CFD model

Vertical component of solid velocity in Spout

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84

Experimental v/s simulated solidsvelocity at different heights of spoutedbed at 60 deg conical base angle and at

1.1Ums for glass beads of 1mm

solids velocity at different conical anglesof spouted bed for 1.1 Ums at H = 0.02m forglass beads of 1mm

solids velocity at different conical anglesof spouted bed for 1.2 Umsat H = 0.02mfor glass beads of 1mm

0

1

2

3

4

5

6

0 0.05 0.1 0.15 0.2 0.25

Solidsvelocity,v(m/s)

radial distance, r(m)

Solids velocity in spout at different sections of spouted bed

experimental v/s simulated

0.02 m

0.06 m

0.08 m

0.1 m

0.14 m

0.2 m

0

1

2

3

4

5

6

0 0.05 0.1 0.15 0.2 0.25

solidsvelocity,v(m/s)

radial position, r(m)

vertical component of solid velocity at different conical base angles at

1.2Ums

45 degrees

30 degrees

60 degrees

0

1

2

3

4

5

6

0 0.05 0.1 0.15 0.2 0.25

solidsvelocity,v

(m/s)

radial position, r(m)

vertical component of solid velocity at different conical base angles

at 1.1 Ums

45 degrees

30 degrees

60 degrees

Solids Cross Flow

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85

Since we have the vertical and horizontal components of solids velocity, the velocity vectorswere calculated and also determined from CFD.

Spout

Annulus

Fountain

The Spout is not straight (as listed in the literature) but forms aneck at top part of the bed

Maximum solids cross flow occurs in two zones. One is near

the neck and the other is near the inlet The same trend was noticed in all 3 conical base angles studied Gas velocity does not affect the trend of the velocity vector, but

only the magnitude of these are greater at all positions of thebed at higher velocities

Solids cross flow apart from the above mentioned two zones islow and is confirmed by CFD as well

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The simulated solid volume fractions and particle velocity vectors

Cases A, S1, S2, S3 and S4.

Similarity and non-similarity in local hydrodynamics

S1 S2 A S3 S4

0.7

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0.0 0.2 0.4 0.6 0.8 1.00.1

0.2

0.3

0.4

0.5

0.6

0.7

H=0.084 mH=0.117 m

H=0.150 m

Volumefractio

nofsolid

r/R

H=0.084m

H=0.117m

H=0.150m

0.0 0.2 0.4 0.6 0.8 1.0-0.5

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

H=0.084 m

H=0.117 m

H=0.150 m

Solidvelocity,m

/s

r/R

RTD of Gas

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0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4

0.0

0.2

0.4

0.6

0.8

1.0

F(t)

t

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

E(t)

t tm=0.87 s

Gamma Ray Densitometry (GRD)

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Gamma Ray Densitometry (GRD) technique has

been designed and developed to measure the line-

averaged hold-up radial/diameter profiles of phasesalong the column diameter and along the height of

the bed.

Sealed source of Cs-137 (250 mCi energy) and a

NaI scintillation detector are mounted and aligned

with the source on the opposite side.

A focused beam of -radiation, coming from the

source, is transmitted through the column inventoryto the detector.

The amount of radiation reaching the detector is a

function of the line averaged density of the column

inventory.

The structure of GRD has been designed to provide

the flexibility to rotate the source/detector to have

different views of measurements and they can be

moved along the height of the column.

The designed structure allows utilizing GRD as

reduced tomography for industrial applications.

Gamma Ray Densitometry (with protective lead shielding)

applied on a 3 inch Spouted Bed column

Principle of Gamma Ray Densitometry (GRD)

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Gamma Ray Densitometry along with the computed and data

acquisition system

The attenuation () profile of any object is

quantified by the Beer Lamberts Law as follows

I = Io

. exp(-..l)

Measuring radial averaged density distribution by

measuring the attenuation distribution:

By processing the time series of counts received

by the detector, it is possible to identify the flow

pattern and regime of the multiphase flow

systems, channeling, bypassing, depositions,

cracks, etc.

ijijeffi

II

I

A ,0

)()ln( mijKijK

K

ijeff ,,, )()( emm

ln I

Io

= l= A

p y y ( )

Stepper motor to help GRDs

horizontal and vertical movement

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Detector lead shielding

with new NaI

scintillation detector

system

Protective box to prevent

operating personnel from

accidental gamma ray exposure

Wheels to help GRD

rotate 3600 around the

multiphase systems to

obtain different

measurement views

Cs 137 source used in GRD

technique

Ph t t f GRD

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Photon counts from GRD

Photon counts received by NaI

scintillation detector at minimum spouting

velocity (Ums) in 0.152 m ID spouted bed

at a axial height of 0.183 m

Photon counts received by NaI

scintillation detector in stable spouting

regime in 0.152 m ID spouted bed at a

axial height of 0.183 m at 0.76 m/s

Flow Regime Identification Cont.

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g

CFD simulations for flow regime

identification in 0.152 m ID spouted bed

for different superficial gas velocities

(a). 1.0 Ums; (b). 1.1 Umsand (c). 1.2 Ums (a) (b)(c)

The transitions velocities

obtained by CFD simulation were

in agreement with experimental

results from pressure transducer

measurements and from GRD

technique.

The minimum spouting velocity

in the simulation for 0.152 m ID

spouted bed was found to be 0.72m/s, which was the same for the

experimental results.

The transition velocity for

unstable spouting regime was

found to be 0.79 m/s.

Solids Holdup Profiles 0.7Line averaged radial profile of solids hold-up using densitometry

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p

0

0.1

0.2

0.3

0.4

0.5

0.6

-1 -0.5 0 0.5 1

Solidshold-up,

s


Sol

idsholdup

Radial Profiles of solids holdup using CFD

Gamma ray densitometry (GRD) was

developed as a non-invasive radioactive

technique to monitor on-line the

performance of multiphase systems(spouted bed in the present study).

GRD, custom built to provide a focused

point beam of radiation to obtain live

averaged radial profiles of solids holdup.

This was successfully demonstrated using

GRD for the conditions studied in spouted

bed.

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

-1 -0.5 0 0.5 1

SolidsHoldup


1.1

Ums

Radial Profiles of solids holdup using optical probe

Flow Regime Identification

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g

100

120

140

160

180

200

220

0.5 0.6 0.7 0.8 0.9 1 1.1 1.2

Mean

Superficial gas velocity, m/s

I

II

III

0

100

200

300

400

500

600

700

0.5 0.6 0.7 0.8 0.9 1 1.1 1.2

Variance


I II

III

Mean versus superficial gas velocity

using GRD technique showing differentflow regimes for 0.152 m ID spouted bed

using 1mm glass beads with density of

2450 kg/m3(I = Packed bed; II = Stable

spouting regime and III = unstable

spouting regime)

Variance versus superficial gas velocity

using GRD technique showingdifferent flow regimes for 0.152 m ID

spouted bed using 1mm glass beads

with density of 2450 kg/m3(I = Packed

bed; II = Stable spouting regime and III

= unstable spouting regime)

Flow Regime Identification Cont.

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0

0.5

1

1.5

2

2.5

3

3.5

0.5 0.6 0.7 0.8 0.9 1 1.1 1.2

Ratio(I)=Variance/Mean


I

IIIII

Ratio (I) = Variance/Mean versus superficial gas velocity using GRD

technique showing different flow regimes for 0.152 m ID spouted bed using1mm glass beads with density of 2450 kg/m3(I = Packed bed; II = Stable

spouting regime and III = unstable spouting regime)

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Benchmarking CFD

Results Using The NewOptical Probes in Gas-

Solid Fluidized Bed

Reactors

97

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Generally a CFD simulation can be subdividedinto three parts: preprocessing, simulation, post

processing

Eulerian multiphase model using FLUENT has

been used to simulate the solids and the gas

dynamics in the two sets of fluidized beds (6

inch and 18 inch) using the operating

conditions that provide match and mismatchin hydrodynamics.

Meshes were created by the program of

GAMBIT 2.4.6

Fluent V13.0.0 was used in simulations.

Use small time steps (0.001) to capture

important flow features.

Convergence criterion of (10^-3) for eachscaled residual component was specified for

the relative error between successive

iterations Fluidized Bed Geometry

2D Fluidized Bed Mesh

The three steps of a CFD simulation

3D Fluidized Bed Mesh

98

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6 inch 18 inch

Models and Boundary conditions for

CFD simulation

Models

Granular Viscosity: Syamlal-Obrien, Gidaspow

Granular bulk viscosity: Lun et al

Frictional viscosity: Schaeffer

Granular temperature: Algebraic

Solids pressure: Lun et al

Radial distribution: Lun et al Drag Model: Gidaspow

Boundary conditions

Assumed uniform gas distribution in the axial direction.

The pressure and temperature out let set at ambient

conditions.

Started the simulations in the begining as fixed bed( see next

slide).

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Figure shows a contour plot of solids fraction of a typical result using the Syamlal-O'Brien drag model for 6 inch FB

5 Sec0.025Sec 4sec1.5 Sec1Sec0.75 Sec0.55 Sec0.36 Sec 100

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0.02 Sec 0.62 Sec 1.02Sec 1.42Sec 2.02Sec 2.82Sec 4.42Sec

Figure shows a contour plot of solids fraction of a typical result using the Syamlal-O'Brien drag model for 18 inch FB

101

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102

0

0.1

0.2

0.3

0.4

0.5

0.6

0 0.2 0.4 0.6 0.8 1 1.2

Solid Holdup V/S Radial Position (Ug=0.25) 6 inch at Z/D=0.644

Solidholdu


Optical Probe

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0 0.2 0.4 0.6 0.8 1

Radial position, r/R

CFD


0

0.1

0.2

0.3

0.4

0.5

0.6

0 0.2 0.4 0.6 0.8 1

Solidholdup(-)


CFD

Optical probe


0

0.1

0.2

0.3

0.4

0.5

0.6

0 0.2 0.4 0.6 0.8 1

Solidh

oldup(-)

Dimensionless Radius (r/R)

CFD

Optical probe


Clean Alternative Fuels, FT Synthesis, Methanol Synthesis

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GTL: Gas to liquid fuels and

chemicals

SlurryBubble

Column

ReactorPacked

Bed

Tubular

Reactors

222

2222HCOOHCO

OHCHHCO

0

298

0

298

165,000 /

41,000 /

H J mol

H J mol

Fischer-Tropsch Synthesis (FTS)

Chemistry

Co,H2/CO ~ 2.15 Fe,H2/CO ~ 1.7Group VIII transition metal oxidesCatalyst

Low T

High T

GTL Reactors

Bubble Columns with / without Internals for Clean

Alt ti E A d Ch i l

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Alternative Energy And Chemicals

Experimental Setup - Internals airwater system

Gas velocities: 5 cm/s - 45 cm/s ( homogenous and

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Plexiglas column of 5.5 inch (14 cm) internal

diameter with a height of 6 ft (1.83m) is used .

( g

heterogonous regimes )

Compressed and filtered air

Rotameter

Plexiglas column

Vertical internals (0.5

diameter)

1.83m

15.24 cm

Configuration of internals

25% occluded area

Triangular pitch = 2.2 cm

(30 rods)

Perforated plate distributor

Number of holes: 121

Size of holes: 1.32 mm

Layout: triangular pitch

Total free area: 1.09%

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Single source gamma ray Computed Tomography (CT) technique is part of

the dual source gamma ray computed tomography (DSCT). The CT is usedto quantitatively determine the time-averaged phase holdup distribution of

the phases in a dynamic system.

CT is equipped with Cs-137 of initial strength of 300 mCi housed in a

Source Collimator Device which is made of lead.

The electronics and data acquisition for CT consist of the detectors,preamplifier, pulse processors and stepper motors which automate

motions involved in the CT system (Kumar, 1994)

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Radioactive Particle Tracking (RPT).

28 detectors are needed and 2 detectorson each level situated 180 degree away

from other. The 49 locations at each calibration level are grouped at four radial

locations

Ring 0: r = 0.00 cm , single central location

Ring 1: r = 2.0 cm , 8 azimuthal locations 45.0oapart



z0= 23 cm

Nz= 45Dz = 2.54 cm

zmax= 140 cm

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Radioactive Particle Tracking (RPT)

A fully automatic calibration device will be used to provide highly accurate RPTmeasurements. A noninvasive RPT facility will provide essential information for

determining the liquid velocity field and turbulence parameters (Reynolds

stresses, turbulent kinetic energy, and Eddy diffusivities )

RPT experiment comprises two steps:

a) RPT calibration ( a static experiment)

b) RPT experiment (a dynamic experiment.

C t d T h (CT) R lt ith t i t l

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Computed Tomography (CT) Results: without internals

CT was used to measure the time averaged cross-sectional two phases

holdup distribution .

The radial gas holdup in bubble columns

without internals was measured to benchmark

the gas holdup with internals.

0

0.1

0.2

0.3

0.4

0.5

0.6

0 0.2 0.4 0.6 0.8 1

Radialgas

holdup


45 cm/s

30 cm/s

20 cm/s

15 cm/s

8 cm/s

5 cm/s

45 cm/s30 cm/s

15 cm/s 20 cm/s

5 cm/s 8 cm/s

Effect of internals and superficial gas velocities based on empty column

on the radial profiles of gas holdup

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-0.05

0.05

0.15

0.25

0.35

0.45

0.55

0.65

0 0.2 0.4 0.6 0.8 1

Radialgasholdup


internals at 8 cm/s based on

empty column

no internals at 8 cm/s based

on empty column

internals at 45 cm/s based

on empty column

no internals at 45 cm/sbased on empty column

With/ without internals

25% occluded area

Triangular pitch = 2.2 cm (30 rods)

(F-T synthesis)

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Effect of Internals on the Radial Profiles of Gas Holdup

0

0.1

0.2

0.3

0.4

0.5

0.6

0 0.2 0.4 0.6 0.8 1

Gasholdup(-)


5 cm/s based on free CAS with

internals

5 cm/s based on total CSA with

internals

5 cm/s without internals


5 cm/s based on free CSA

At 5 cm/s, the internals have significant effect on the redial gas holdup profile

5 cm/s based on total CSA

Eff t f I t l th R di l P fil f G H ld

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0

0.1

0.2

0.3

0.4

0.5

0.6

0 0.2 0.4 0.6 0.8 1

Gasholdup

(-)


8 cm/s based on total CSA with

internals

8 cm/s based on free CSA with

internals


Scatterplot of Gas holdup against D at 8 cm/s without internals

0 2 4 6 8 10 12 14 16

D (cm)

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

G

asholdup(-)

Scatterplot of Gas holdup against D at 8 cm/s based on free CSA with Plixglas internals

0 2 4 6 8 10 12 14 16

D (cm)

0.0

0.1

0.2

0.3

0.4

0.5

0.6

Gasholdup(-)

Scatterplot of gas holdup against D at 8 cm/s baed on total CSA with Plixglas internals

0 2 4 6 8 10 12 14 16

D(cm)

0.0

0.1

0.2

0.3

0.4

0.5

0.6

Gasholdup(-)

At 8 cm/s, the internals still have significant effect .

Eff t f I t l th R di l P fil f G H ld

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0

0.1

0.2

0.3

0.4

0.5

0.6

0 0.5 1

Gasholdup(-)


15 cm/s based

on total CSA

with internals

15 cm/s based

on free CSA with

internals

0

0.1

0.2

0.3

0.4

0.5

0.6

0 0.5 1

Gasholdup(-)


20 cm/s based on

total CSA with

internals

20 cm/s based on

free CSA with

internals

0

0.1

0.2

0.3

0.4

0.5

0.6

0 0.5 1

Gasholdup(-)


30 cm/s based

on total CSA

withinternals

30 cm/s based

on free CSA

with internals

30 cm/s without

internals0

0.1

0.2

0.3

0.4

0.5

0.6

0 0.5 1

Gasholdup(-)


45 cm/s based on

free CSA with

internals

45 cm/s based on

total CSA with

internals

45 cm/s without

internals

At high gas velocity ,the internals effect are diminished due to strong turbulence.

Ops!!,

theinter

nalshaveinsignificanteffectathigherVg

basedonfree

CSA

Eff f T f I l M i l G H ld R di l P fil

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Effect of Type of Internals Material on Gas Holdup Radial Profile

-0.1

6E-16

0.1

0.2

0.3

0.4

0.5

0.6

0 0.2 0.4 0.6 0.8 1

Gasholdup(-)



with steel internals


with plexiglas internals



with steel internals


with internals

-0.1

6E-16

0.1

0.2

0.3

0.4

0.5

0.6

0 0.2 0.4 0.6 0.8 1

Gasholdup(-)


8 cm/s based on free area with Plixglas internals

8 cm/s based on free area with steel internals


The Plexiglas internals enhance

the gas holdup due to flexibility of

the internals which give the

opportunity for bubbles to break

and change their size during the

bubbles rise

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CFD Simulation of Bubble Column

Equipped with Internals

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Eulerian Model Eulerian Model is selected in my work because of limitations of the VOF and

Mixture Models.

EulerianEulerian (Two-Fluid) Model:

In this model, the continuous and dispersed phases are considered to be

interpenetrating continua.

The mass balance :

Where are the volume fraction, the phase density and velocity of

each phase.

kidS

kkikkkkkkkkk Sdnuu

du

t

')(1

= 0 in absence of interphone

exchange

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Eulerian ModelMomentum equation:

Interphase Forces:

Drag is caused by relative motion between phases

kkkkkkkkkkkkkkkkkkk FPuuu

t

SdnPuuud kidS

kkkkikk

ki

'

)(1

Interaction term

,0)(1

n

i

kiik uuK

ik

drag

kkik

fK

i

kkik

d

m

18

2

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Eulerian Model

In this work, the drag force is considered and other

forces are neglected because of following:

1) Virtual mass effect is significant when the second phase density is much smaller than

the primary phase density (i.e., bubble column)

2) Lift force usually insignificant compared to drag force except when the phases

separate quickly and near boundaries

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S l ti d th lt

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Solution and the results

The results and comparison between CT and CFD ?

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The results and comparison between CT and CFD ?

Help is needed !

0

0.1

0.2

0.3

0.4

0.5

0.6

0 0.2 0.4 0.6 0.8 1

Gasholdup(-)


CFD

CT

Similar steps are applied for bubble column without internals except number of

bubbles in population balance model

Comparison the radial gas holdup profile between CT and CFD at 20 cm/s (200s)

F-T Syngas Conversion Research conducted at

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y g

Washington University and continued at Missouri S&T

Objectives

Investigations

Models

Techniques

and methodsCARPT/CT

Phases back-

mixingGas-liquid mass

transfer

Mimicked FT system

Air-C9C11-FT catalyst

Velocity and

turbulent

parameters

Phaseholdup

distribution

Hydrodynamics

Gas phase

back-mixing

Liquid phase

back-mixing

Solids phase

back-mixing

kla of other

gases (CH4,

CO2, Ar)

O2 masstransfer

coefficient

Sedimentation-

dispersion

model

Axial

dispersion

model

Mechanistic,

compartment

model

Virtual tracer

method

Optical oxygen

probe

Gaseous tracer

technique

Stirred tank

(CSTR)

System

Bubble Dynamics

Bubble sizes,

velocities, ...

4-point probe

technique

Heat transfer

h, heattransfer

coefficient

Heat transfer

probe

Air-water-glass beads;

Air-Therminol-glass beads

Air-water/C9C11/

Therminol

Flow regime

transition

Scale-up of

BCRs

-ray densitometry

Regime

identification

Artificial neural

network (ANN)

CFD

Two-Fluid CFD of 3D Bubble ColumnsUsing FLUENT - P. Chen CFD work(Under Dudukovic) Vs. A. Sheikh

experimental work (Under Al-Dahhan(Wash. U.)

1.000.95

0.90

0.85

0.80

0.75

0.70

0.65

0 60

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(Wash. U.)

-40

-30

-20

-10

0

10

20

30

40

50

0 0.2 0.4 0.6 0.8 1

Dimensionless Radius

AxialLiquidVelocit

y,cm/s

CARPT Data

Two-Fluid

ASMM

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

0 0.2 0.4 0.6 0.8 1


GasHoldup

CT Data

Two-Fluid

ASMM

Multiphase k-

Implementation of Breakup and

Coalescence Models

19 cmID

12 cm/s

0.60

0.55

0.50

0.45

0.40

0.35

0.30

0.25

0.200.15

0.10

0.05

0.00

(a) (b) (c) (d) (e)

-60.0

-40.0

-20.0

0.0

20.0

40.0

60.0

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0


Time-averagedLiquidAxialVelocity,cm/s

CARPT

Two-fluid

ASMM, DV

ASMM, SV

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

DimensionlessRadius

Time-averagedHoldup

CT

Two-fluid

ASMM, DV

ASMM, SV

44 cmID

Ug = 10 cm/s

Ug, cm/s 10 14 30 30 30

P, Bar 1 1 1 4 10

CREL

Heat Transfer and Bubble Dynamics

F i t fib ti l b (X 2004 F ijli k 1989)

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Four points fiber optical probe, (Xue 2004, Frijlink 1989)

1 3 2 5

1 2 3 4 5

1. Tube2. Brass shell

3. Heat flux sensor

4. Heater

5. Teflon cap

Heat Transfer Probe

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Heat Transfer Probe Mounted on the Internals

Effect of Internals

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0

10

20

30

40

50

60

70

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

No Internals Ug = 20 cm/s

Internals Ug = 20 cm/s

No Internals Ug = 45 cm/s

Internals Ug = 45 cm/s

Dimensionless radius, r/R(-)

Localgasholdup,(%)

18 inch

Radial profiles of gas holdup

Radial Profile of specific

interfacial area

0.6

1

1.4

1.8

2.2

2.6

33.4

3.8

4.2

4.6

5

5.4

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

No Internals Ug = 20 cm/s Internals Ug = 20 cm/s


Dimensionless radius, r/R(-)

Specificinterfacialarea,(cm2/cm3

18 Inch

160

180

200

,Ub(cm/s) 18 inch

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Axial Bubble Velocity

40

60

80

100

120

140

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1



Dimensionless radius, r/R -

Axialbubblevelocity,

5

5.5

6

6.5

7

7.5

8

0 5 10 15 20 25 30 35 40 45 50

No InternalsInternals

Superficial gas velocity, Ug(cm/s)

Heattransfercoefficient,hw

(kW/m2.K)

18 inch

Heat Transfer

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Other Selected Examples

To advance understanding and design ofanaerobic digesters by integrating hydrodynamics andperformance via implementing and developing advanced measurement and computational

techniques; systematically investigate operating and design parameters using the developed

Anaerobic Digestion for Bioenergy Production and Animal Wastes Treatment

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Single particle CARPT

Effect of geometry and operating conditions o

Flow pattern

Velocity profiles

Turbulence quantities

Impact of scale on mixing intensity

MP -CARPT

Overcoming the shortcomings of

single particle CARPT in digester

Development

Validation

Implementation

Performance studies

(lab -

Impact of mixing intensity and scale

on performance

Biogas (methane) production

TS, VS and VFA

Single particle RPT

and Single Source CT

Laboratory and pilot plant scales,

Effect of design and operating variables on

Flow pattern

Velocity profiles

Turbulence quantities phase distribution and dead zones etc.


-Overcoming the shortcomings of

single particle RPT and single source CT for

digesters

Development

Testing and Validation

Implementation

CFD

Modeling of anaerobic digester flow field

Closures evaluation

Validation

Effect of geometry and operating conditions

on the flow field


CFD

Modeling of anaerobic digester flow field

Closures evaluation

Validation

Effect of geometry and operating conditions

on the flow field


Performance and kinetics studies

Impact of mixing intensity and scale, on performance,

biogas(methane) production TS, VS and VFA

Biogas (methane) production

Kinetics

Commercial scale design and preparation

(lab - scale and pilot scale)(lab - scale and pilot scale)

Phase distribution

MRPT and DSCT

Lab, pilot plant and commercial scales

Overall Accomplishments on Bioenergy (Biogas) from Animal/Farm

Wastes Project ~ over 2.1 million dollars from DOE (~2002-2007)

techniques; systematically investigate operating and design parameters using the developed

techniques

Gas inlet pipe

(Diameter 0.64 cm)

Sealing

Gas Recirculation

Hangers

Biogas Production by Anaerobic Digestion

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20.32 cm

29.5cm

22.2cm

Sludge level

4.4 cm

14cm

Draft tube

(Diameter 4.4

cm)

RPT/CT IN ANAEROBICDIGESTER

(Results at a glance)

Simulated Digester

Particle Trajectories

(each color indicates trajectory

for 20 seconds)

Azimuthally averaged velocity

vector (Gas flow rate = 3 SCFH)

Draft tube

Gas hold up distributions at the centre

of the draft tube

8

8

Solids hold up distributions at the

centre of the draft tube

Details of 3D CFD SimulationCFD software: CFX 5.7.1

Multiphase System

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Dispersed phase: Air (Average bubble diameter

=10mm)

Continuous phase: Water

Two fluid Euler-Euler modelTurbulence closure models

Air: Zero equation model Water: k-emodel

Drag Force (dominant): Grace Model (Ranade, 2002)

Numerical Scheme: Finite Volume Technique

Surface Mesh: Delaunay mesh (Typically more than 100,000 volume elements werecreated by volume meshing

)

Time step and length scales: Automatic, generated by codeSimulations were performed for different bubble sizes ranging from 2 to 12 mm in diameter, no appreciable difference in the predictions was

observed.

The solution is mesh independent for the applied meshing.

0

Urrt

Continuity:

Momentum: m MUUrprUUrUrt

T

eff

)(,

...)ForceDispersionTurbulence(

)ForceMassVirtual()ForceLift()ForceDrag(

TD

VMLD

M

MMMM

MM

3D CFD simulations were performed using CFX 5.7.1. k-e

turbulence model was used and only drag force was considered

CFD Predictions versus RPT Results

0.152 cm below draft tube (CFD)

2 cm below draft tube (CARPT)

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-0.05

0.00

0.05

0.10

0.0 0.2 0.4 0.6 0.8 1.0r/R

AxialVe

locity(m/s

2 cm below draft tube (CARPT)

center of draft tube (CFD)

center of draft tube (CARPT

2 cm above draft tube (CFD)

2 cm above draft tube (CARPT)

CFD Predictions

RPT results

-0.05

0.00

0.05

0.10

0.15

0.20

0.25

0.0 0.2 0.4 0.6 0.8 1.0

r/R

AxialVelocity(m/s)

Ug=0.024 cm/s (CFD)

Ug=0.048 cm/s (CFD)

Ug=0.072 cm/s (CFD)

Ug=0.024 cm/s (CARPT)



Location of draft tube

Axial liquid velocity profile (3 lpm)

Effect of gas flow rate (center of draft tube)

CFD predictions showed good qualitative agreement with RPT

data and the quantitative agreement was reasonable

A B

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C D

Velocity field and streamlines for 25 degree conical

bottomed digester (A & B) without hanging baffle,

and (C & D) with hanging baffle.

Velocity field and streamlines for 45 degree conical

bottomed digester (A & B) without hanging

baffle, and (C & D) with hanging baffle

A New Approach for PBR Analysis, Modeling & Optimization

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Modeling

Irradiance

Distribution,

I (x, y, z)

Fundamentally based modeling approach

for PBR performance evaluation, design,

scale-up, and process intensification

CFD Simulation?

Dynamic

Photosynthetic

Rate Model

Experimental Techniques: CARPT and CT

Local multiphase flow dynamics:

Microorganism cells movements (x(t), y (t), z(t))

Liquid flow dynamics (Velocity profile, Turbulent intensity,

Shear Stresses, Macro-mixing, etc.)

Local phase distributions

Calculation of the temporal irradiance patterns, I(t)

Characterization of the interactions between hydrodynamics and

Photosynthesis

Model Evaluation

by Real Culturing

Experiments

Verification

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RPT

Cells Movement Dynamic Photosynthetic growth rate Model

Column wall directradiation

Irradiance Model

3211 )( xxxtI

dt

dx

Assumption: photosynthetic factory (PSF)

has three states: resting state (x1), activated

state (x2) and inhibited state (x3)

Differential equations:

(1)

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RPT

Experiments

CFD

Simulation

0

10

20

30

40

50

60

0 50 100 150 200 250 300

Time, hr

Cellconcentr

ation(*106c

Date post:	03-Jun-2018
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