role of modeling and simulation in used fuel recycling
TRANSCRIPT
Role of Modeling and Simulation in gUsed Fuel Recycling
David DePaoliDavid DePaoliNuclear Science and Technology Division gy
Oak Ridge National Laboratory
Presented at:Introduction to Nuclear Chemistry and Fuel Cycle Separations
Managed by UT-Battellefor the Department of Energy
SeparationsVanderbilt UniversityDec. 17, 2008
Outline
• Introduction• Some applications to date• Some applications to date
– Solvent extraction– Plant-level modelingg– Agent design
• Key research needsy• Advancing to the future
– NEAMS visionNEAMS vision– Separations M&S development
This talk will focus on a subset of modeling and simulation of nuclear fuel cycle
From K. McCarthy presentation at Office of Nuclear Energy FY2009 University Program Workshop:http://www.energetics.com/meetings/univworkshopaug08/reports.html
Used Nuclear Fuel Recycling Entails Many Interconnected Steps
Productsmeeting stringent
ifi ti
Used fuelvaried composition (b li ti ) specifications
shearing
(burn-up, cooling time) Environmental, safety, accountability constraints
solvent extraction l
Fuel
dissolution
voloxidation waste treatment
solidification
cycles
recovery systemsoff-gas treatment
feed & accountability
Need new processes to meet future goals
Waste forms
Emerging modeling and simulation capabilities can improve development and implementation (better, cheaper, faster)
Benefits of modeling and simulation of nuclear reprocessing systemsp g y
• Reduced cost of process development by guiding and minimizing the amount of experimental and piloting work requiredpiloting work required – Compare different separation and fuel cycle strategies– Develop new chemical processes with lower cost and waste
generationgeneration
• Optimized system designs, with reduced design margins
Scale up with confidence– Scale up with confidence – Reduce hot-cell footprint (surge capacity, throughput)– Process control
• Increase safety and acceptance of regulatory bodies• Reduced risk of material diversion by providing
accurate predictions of materials streams p
Modeling and simulation
• Modeling is the development of an approximate mathematical description of physical and chemical processes at a given level of sophistication andprocesses at a given level of sophistication and understanding.
• Simulation utilizes computational methods to obtain Important Note:ppredictions of process performance.
• “Together modeling and simulation
pAdvanced modeling and simulation do not replace the need for theory or
– enhance understanding of known systems, – provide qualitative/quantitative insights and
guidance for experimental work, and
experiments!!!
guidance for experimental work, and – produce quantitative results that replace difficult,
dangerous, or expensive experiments.”(Basic Research Needs for Advanced Nuclear Energy Systems(Basic Research Needs for Advanced Nuclear Energy Systems,
http://www.sc.doe.gov/bes/reports/abstracts.html#ANES)
Some applications for modeling and simulation
• Guidance for experimental development work • Process design optimization and confirmation • Safeguards/materials accountability studies• Plant licensing • Criticality safety studies • Development of operational envelopes • Understanding and recovery of off-normal events • Process instrumentation studies • Process control • Operator training
Outline
• Introduction
• Some applications to date• Some applications to date– Solvent extraction– Plant-level modelingg– Agent design
• Key research needsKey research needs
• Advancing to the future– NEAMS visionNEAMS vision– Separations M&S development
Modeling and simulation of nuclear separations has primarily focused on solvent extraction
• Original predictions:predictions:– Graphical stage
calculations using experimentalexperimental equilibrium data and operating lines
A. D. Ryon, ORNL-3045, 1961
Equations for interphase mass transfer and material balances converted to computer codes
TBPbNOM iai•)( 3
TBP TBP
M ia+ M ia+−3NO
−3NO
)(3)()(3)( )( orgiaorgiaqiaaq TBPbNOMTBPbNOaM i
i •++ ⇔−+
Overview of SEPHIS model of solvent extraction stages[ ]
[ ] [ ]i orgia TBPbNOM
K•
=3)(
[ ] [ ] [ ] iii borg
aaqaq
aiTBPNOM
K−+
=3
Existing models are based on empirical fits to experimental data
[ ][ ] [ ] [ ]22
32
2
232' 2)(
orgaqaq
orgU
TBPNOUO
TBPNOUOK
−+
•=
[ ][ ] [ ] [ ]34
34
43' 3)(
orgaqaq
orgTh
TBPNOTh
TBPNOThK
−+
•=
[ ]O 3[ ][ ] [ ] [ ]
orgaqaq
orgH
TBPNOH
TBPHNOK
−+
•=
3
3' 3
+++= 32
2321
' CCCCKU μμμ
[ ] [ ] [ ]+++ ++≡
+++=
+++=
422
312
211109
'
38
2765
'
2321
103 ThUOH
CCCCK
CCCCK
H
Th
U
μ
μμμ
μμμ
μμμ
[ ] [ ] [ ]2μ
With good input data good predictions are obtained within conditions of fit
Rainey and Watson, 1975
With good input data, good predictions are obtained – within conditions of fit
AMUSE Models Solvent Extraction
SASPEO iActivities
Distribution Ratio
Calculation
Aqueous Phase
Speciation
Speciation
[ ] { } { }{ }( )nnKKKOHHNOTBPfMD ββ ,...,,...,,,,)( 11023+−= 0.1
1
10
100
1000
D v
alue
Input
FeedCompositions
Organic Phase
Speciation
Activities
Mass Balance
0.001
0.01
0.1 1 10 100
[HNO3 ], M
SASSEProcessParameters
Flowsheet Module
MassBalance
Balance
Org.
Aq
Org.
Aq
Org.
Aq
y1i y2i y3i
yi
y1o y3oy2o
yo
xo xi
Output
CalculatedFlowsheet
x1i x2i x3ix1o x3ox2o
1 00E 04
1.00E-02
1.00E+00
1 00E 04
1.00E-02
1.00E+00
Org
Aq
1.00E-18
1.00E-16
1.00E-14
1.00E-12
1.00E-10
1.00E-08
1.00E-06
1.00E-04
Con
c., M
St
Org
Aq
1.00E-18
1.00E-16
1.00E-14
1.00E-12
1.00E-10
1.00E-08
1.00E-06
1.00E-04
Con
c., M
StStageStage
M. Regalbuto and C. Pereira, ANL
AMUSE has been used for process upset and product diversion analysis
AMUSE was used to bracket the operational window for a plant conceptual designp g– Four fuel compositions were used as the initial process feed
• High and low burn-up; long- and short-cooled fuels– Results showed little difference with cooling time but stronger effect g g
due to burnup differencesMore recently AMUSE has been used to examine the effect of changing specific process parameters on the behavior of different elements– Design of instrumentation to track material– Process control– Product purity determination– Product diversion detection
M. Regalbuto and C. Pereira, ANL
Example: Changes in scrub stream can cause “pinching” of metals in the extraction section
The build up, or “pinching” of metal in the extraction sectionSteady-state concentrationsResults need to be verified experimentallyp yIn most cases no indication of a problem at the outletsSmall, possible indication of a problem at the product outlet
1.300-1.4001.200-1.3001 100 1 200
1.0001.1001.2001.3001.400
1.100-1.2001.000-1.1000.900-1.0000.800-0.900
The image ca… 0.700-0.8000.600-0.7000.500-0.6000.400-0.5000.300-0.4000.200-0.300
4.50E-02-5.00E-024.00E-02-4.50E-023.50E-02-4.00E-02
0 0000.1000.2000.3000.4000.5000.6000.7000.8000.900
Conc. Metal, aq.
0.100-0.2000.000-0.100
0 025
0.030
0.035
0.040
0.045
0.050
c. o
f Met
al
3.00E-02-3.50E-022.50E-02-3.00E-022.00E-02-2.50E-021.50E-02-2.00E-021.00E-02-1.50E-025.00E-03-1.00E-020.00E+00-5.00E-03
0.000
Inc. ScrubConcentration
, qStage
0.000
0.005
0.010
0.015
0.020
0.025
Org
. Con
c
Stage Product
14
RaffinateDec. ScrubConcentration
M. Regalbuto and C. Pereira, ANL
Example of the application of AMUSE to determine the effect of fuel composition on D-values
Here the data show that the fuel composition has a minor effect on the measured D-values– The process proves to be robust in terms of feed variance p p
1.01
1.02
0.98
0.99
1.00
D V
alue
SolventTBP in dodecane
Fuel Derived FeedAqueous HNO3
ScrubAqueous HNO3
StripAqueous HNO3
SolventTBP in dodecane
Fuel Derived FeedAqueous HNO3
ScrubAqueous HNO3
StripAqueous HNO3
0.95
0.96
0.97
Rel
ativ
e
GWD 5-year cooled 25
GWD 50-year cooled 25
GWD 10-year cooled 40
GWD 5 l d 100
Extraction Section Scrub Section Strip Section
RaffinateAqueous HNO3
ProductAqueous HNO3
Spent Solvent(Out at Stage 40)
Extraction Section Scrub Section Strip Section
RaffinateAqueous HNO3
ProductAqueous HNO3
Spent Solvent(Out at Stage 40)
0.93
0.94
Stage
GWD 5-year cooled 100
GWD 50-year cooled 100
q 3Pu, FP
q 3U, Tc
( g )q 3Pu, FP
q 3U, Tc
( g )
M. Regalbuto and C. Pereira, ANL
Changes in feed composition lead to changes in the concentration profiles in the aqueous and organic phases
Automated computations valuable. For this example –– If the six concentrations have no co-dependency that results in 30 calculations– If the six concentrations have a co-dependency that results in 15,625 calculations– For the thirteen variables on a co-dependent basis results in 1.22 x 109 calculations
Large impact from different fuel feeds
-1-0
-2--1
-3--2
-4--3
5 4 .
-1-0-2--1-3--2-4--3-5--4
Met
al, a
q. c
onc.
-5--4
-6--5
-7--6
-8--7
-9--8
-10--9
-11--10
-12--11M
etal
, org
. con
c. -6--5-7--6-8--7-9--8-10--9-11--10-12--11
Stage Scrub conc.Stage\
HNO3, feed
-13--12-14--13
16M. Regalbuto and C. Pereira, ANL
Outline• Introduction• Introduction
• Some applications to dateSolvent extraction– Solvent extraction
– Plant-level modeling• 1980’s example1980 s example• Current efforts
– Separations– Safeguards
– Agent design
• Key research needs
• Advancing to the future– NEAMS vision– Separations M&S development
1980’s – a full plant model• Consolidated Fuel Reprocessing Program - a complete plant simulation run in p g g p p
the Advanced System for Process ENgineering (ASPEN) simulator
• 52 components tracked throughout a preconceptual design of a plant containing 32 systems and approximately 700 streams, including:
– fuel cleaning and storagefuel cleaning and storage– disassembly and shearing– dissolution and feed preparation– hulls drying– feed clarification– feed preparation and accountability– solvent extraction – solvent extraction ancillary systems (concentration, backcycle, storage, high-activity
waste concentration, solvent recovery)– process support (acid and water recovery and recycle, process steam, and sump)p pp ( y y , p , p)– product conversion– cell atmosphere cooling and purification– process off-gas treatment– vitrification – vitrification off-gas treatment
• Large simulation for computers of the time – broken down into three segments that were executed separately to achieve a steady-broken down into three segments that were executed separately to achieve a steady
state material balance for the complete plant.
R. T. Jubin, ORNL
Top Level Plant-scale Flowsheet (UREX+3a)
HIERARCHY
Organic Waste
Waste WaterTreatment
Segment of UREX Solvent Extraction Module
HIERARCHY
HIERARCHY
Assembly
Chopper
DissolverFeed Adjustment
Organic Waste
Treatment
S l tWPRD-S
WASHSOLVSOLRECY
SOLADD
SOLSPLT
AMUSE
S Solvent
Segment of UREX Solvent Extraction Module
HIERARCHY
HIERARCHY
HIERARCHY
HIERARCHY
HIERARCHY HIERARCHYHIERARCHYHIERARCHY
Hardware Wash
Hull Wash
UREX CCD-PEG
U/Tc Ion Exchange
NPEX TRUEX
Feed Adjustment
TALSPEAKSolventRecycleLoop
UREX-INUREX-IN(IN)
URXSCRB
URXSTRP
URXSOLV
URXSPSLVWASH-S
SOLTANK
AMUSEInterface
HIERARCHY
HIERARCHY
HIERARCHY
HIERARCHY
HIERARCHY
HIERARCHY
HIERARCHY
HIERARCHY
HIERARCHYNitric AcidRecovery
Tc Solidification U Solidification Cs/Sr SolidificationU/Np/Pu Solidification
FP Solidification TRU Solidification
ProductRaffinate
URXRAFF URXPROD
WASH-PWASH-R
SOLVX
HIERARCHY HIERARCHY
Aspen/AMUSE UREX+3a Flowsheet Version 1.1
Head End Off-gasTreatment
Off-gas
Treatment
WashRaffinate
Wash
UREX-PRD(OUT)
URX-RAF
UREX-RAF(OUT)WPRD-PWPRD-R
M. Regalbuto and C. Pereira, ANL
Safeguards Performance Model
Dissolver + Hulls
Surge TankAccountability
TankUREX FeedAdjust Tank
Wash + Centrifuge
Measurement of a tank typically requires a level indicator and an analytical sample.
The model uses the MatLab Simulink platform, which is used routinely in laboratories, universities, and industry.The model tracks bulk flow rates, cold chemicals, elemental concentrations, and solids
– Solvent extraction splits based on data generated by ANL’s AMUSE spreadsheet.Measurement blocks simulate a material measurement—such as a level indicator on aMeasurement blocks simulate a material measurement such as a level indicator on a tank or a mass spec measurement of a sample.
B. Cipiti and N. Ricker, SNL
Material Tracking in the Model
Solvent RecycleMixer-Setter
CCD-PEG Extract
CCD-PEG Scrub CCD-PEG Strip
C /S H ldiCs/Sr HoldingTank
B. Cipiti and N. Ricker, SNL
Traditional Accounting Instrumentation (Baseline)
KMP 1
KMP 2
KMP 3
KMP 4
KMP 5KMP 5
KMP 6KMP 6
B. Cipiti and N. Ricker, SNL
Additional Accounting Instrumentation (Advanced)
KMP 1
KMP 2
KMP 8KMP 12 KMP 13
KMP 3
KMP 9
KMP 4
KMP 3
KMP 10KMP 14 KMP 15
KMP 4
KMP 5
KMP 11KMP 16Red
Green
Traditional Accounting
Process MonitoringKMP 5
Blue Advanced Monitoring
KMP 6
B. Cipiti and N. Ricker, SNL
Pu Inventory Difference
(Baseline) (Advanced)
B. Cipiti and N. Ricker, SNL
Diversion Scenario
(Baseline) (Advanced)
B. Cipiti and N. Ricker, SNL
Current state of separations process modeling
S l t t ti t d l d• Solvent extraction most developed– Useful aid in process development and
analysis– Semi-empirical fits
Example of transient output from SEPHIS process model for U/Pu solvent extraction step.
– Many species not modeled well, or not at all
– Leading codes use equilibrium stages– Current codes do not predict well:p
• Mass transfer and reaction kinetics effects
• Effects of micellization, third-phase formation, radiolysis, etc.
– Few transient codes– Few transient codes
• Other important processes not well modeled
– Legacy modules for some important unit operations are available• e.g., dissolver, acid recovery
• Full plant models are crudeSimple descriptions of important unit
Height of bars indicates concentration of uranium in organic (top) and aqueous (bottom) phases in a– Simple descriptions of important unit
operations– Not fully transient
in organic (top) and aqueous (bottom) phases in a 48-stage mixer settler bank. Time indicates the time in minutes from the start of operation with zero concentration throughout the system.
Outline
• Introduction• Some applications to date• Some applications to date
– Solvent extraction– Plant-level modelingg– Agent design
• Key research needsy• Advancing to the future
– NEAMS visionNEAMS vision– Separations M&S development
Sequestering agents are the basis for separations
OPO OO
S
SH
ClOP
+
OPO OO
S
SH
ClOP
+
OO
O
PUREX (HANFORD, 1950s)
SH
ClSANEX
(Europe, 1990s)OO
O
PUREX (HANFORD, 1950s)
SH
ClSANEX
(Europe, 1990s)
O O
OO
O O
POO
NN
N N
O O
OO
O O
POO
NN
N NO
O
O
O
O
O
O
TRUEX (ANL, 1980s)
NN N
NSANEX
(Europe, 2000s)
O
O
O
O
O
O
O
TRUEX (ANL, 1980s)
NN N
NSANEX
(Europe, 2000s)
OO
O
OOO
OO
N N
OO
O
OOO
OO
N N
CSEX (ORNL,1990s)
SREX(ANL, 1990s)
DIAMEX(Europe, 1990s)
CSEX (ORNL,1990s)
SREX(ANL, 1990s)
DIAMEX(Europe, 1990s)
B.Hay, ORNL
Experimental development is slow and expensive
Identify candidate Ligand synthesisstructures
g y
Structural characterization
Optimize:• binding affinity• selectivity• solubility
Design
Structural characterizationincluding X-ray analysis
solubility• stability• cost
gcriteria Thermodynamic studies,
Binding constant measurement
Testing under process conditions
B.Hay, ORNL
Influence of ligand architecture
Large effects on binding affinity:
OO O
O O O
O OO
OOO
OO
OO
OO
OO
OO
OO
1010 106 105 103
N N NN
and significant impacts on selectivity:
N N
N N
N
N
N
N
Hg2+/Cu2+ = 5 Cu2+/Hg2+ = 104
N NHO2C CO2H
HO2C CO2HN N
HO2C CO2H
HO2C CO2H
Eu3+/Al3+ = 13 Al3+/Eu3+ = 500
B.Hay, ORNL
Computer-aided ligand development
Ligand synthesisIdentify
candidate Computational Ligand synthesiscandidatestructures
St t l h t i ti
screening
Molecular
Design
Structural characterizationincluding X-ray analysis
modeling
Design criteria Thermodynamic studies,
Binding constant measurement
Testing under process conditions
B.Hay, ORNL
Electronic structure (quantum mechanics) models
cone partial cone
O O
O O -22.57 kcal mol-1 -31.10 kcal mol-1
1,3 alternate 1,2 alternate -35.14 kcal mol-1-33.98 kcal mol-1
Geometry optimized B3LYP/6-31+G*Single point energies MP2/aug-cc-pVDZ
50,000 cpu/hr on EMSL MPP1
-41.65 kcal mol-1-37.07 kcal mol-1(6 cpu/yr for 10 structures)
B.Hay, ORNL
Experimental validation
15
1,67
0
Sr
2
log 10
D
3
-1
4
-210 12 14 16
²U kcal/molΔU kcal/molU, kcal/mol
Extraction of Sr2+ from 1M HNO3 using 0.1 M ligand in n-octanol, 25 °CB.Hay, ORNL
ΔU, kcal/mol
Computer-aided ligand design
Ligand synthesisIdentify
candidate Computationalscreening
StructureG t structures
Structural characterization
screening
Moleculard li
Generator
Design
Structural characterizationincluding X-ray analysis
modeling
gcriteria Thermodynamic studies,
Binding constant measurement
Testing under process conditions
B.Hay, ORNL
Outline
• Introduction• Some applications to date• Some applications to date
– Solvent extraction– Plant-level modelingg– Agent design
• Key research needsy• Advancing to the future
– NEAMS visionNEAMS vision– Separations M&S development
Recent workshops have identified key needs for contributions by modeling and simulation • Scientific Grand Challenges
– Resolving the f-electron challenge to master the chemistry and physics of actinides and actinide-bearing materials.
– Developing a first-principles, multiscale description of material properties in comple materials nder e treme conditionsin complex materials under extreme conditions.
– Understanding and designing new molecular systems to gain unprecedented control of chemical selectivity during processing.
• Priority Research Directions – Physics and chemistry of actinide-bearing materials and the f-electron
challenge. – Microstructure and property stability under extreme conditions. – Mastering actinide and fission product chemistry under all chemical
conditions.– Exploiting organization to achieve selectivity at multiple length scales.– Adaptive material-environment interfaces for extreme chemical conditions.– Fundamental effects of radiation and radiolysis in chemical processes.– Predictive multiscale modeling of materials and chemical phenomena in
multi-component systems under extreme conditions
• Crosscutting Research ThemesCrosscutting Research Themes – Tailored nanostructures for radiation-resistant functional and structural
materials. – Solution and solid-state chemistry of 4f and 5f electron systems. – Physics and chemistry at interfaces and in confined environments. – Physical and chemical complexity in multi-component systems.
http://www.sc.doe.gov/bes/reports/abstracts.html#ANES
Physical and chemical complexity in multi component systems.
Recent workshops have identified key needs for contributions by modeling and simulation
S ti Ch ll• Separations Challenges– Plant-scale simulation
• integrated toolset to enable full-scale simulation of a plant – chemistry, mass transport, energy input, and physical layout
• dynamic plant modelsy p– Computational fluid dynamics
• Multiple fluid phases, fully developed turbulence, non-Newtonian flows, interfacial phenomena, radical chemical processes due to the presence of ionizing radiation
– Predictive methods for thermodynamics and kinetics data as input to process simulatorsto process simulators• extend currently limited thermodynamics data reliably into broader ranges of
parameter spaces• incorporate limited experimental data and use computational chemistry
approaches– Rational design of the separations system from first-principlesRational design of the separations system from first principles
physics and chemistry• predict what molecules will have the desired properties and can be synthesized• reliably predict the properties of liquids, solvation, and kinetics in solution
– Connecting/crossing time and length scales, with uncertainty quantificationquantification• access longer times without dramatic changes in theoretical and algorithmic
approaches• span spatial regimes; critical regime is the mesoscale (1 nm-1 μm)
– Below 1 nm, computational chemistry; above 1 μm, continuum approaches
Data management and visualization
http://www-fp.mcs.anl.gov/anes/SMANES/gnep06-final.pdf
– Data management and visualization• Data must be captured, managed, integrated, and mined from a wide range of
sources to enable the optimal design and operation of separation processes• Computer resources and access• Export control issues
Outline
• Introduction• Some applications to date• Some applications to date
– Solvent extraction– Plant-level modelingg– Agent design
• Key research needsy• Advancing to the future
– NEAMS visionNEAMS vision– Separations M&S development
Improving Our Vision into Complex Physical ProcessesPhysical Processes
Understanding of Complex Physical Process
Limited Insight Gained from Theory, Experiments and Empirical Based Modeling and Simulation St
art Finish
Understanding of Complex Physical Process
Well Wellble
Insi
ght
Understood Initial
Conditions
Well Characterized
Effects
Limited Theoretical and Experimental Insight Into Physical Processes
evel
of P
ossi
bLe
A. Larzelere, DOE-NE
Improving Our Vision into Complex Physical Processes
F
Understanding of Complex Physical Process
Physical Processes
Improved Insight by Adding Science (1st Principles) Based Advance Modeling and Simulation to Theory and Experiments St
art Finish
Advanced Science Based Well Wellbl
e In
sigh
t
Modeling and Simulation
Understood Initial
Conditions
Well Characterized
Effects
Limited Theoretical and Experimental Insight Into Physical Processes
evel
of P
ossi
b
It is Important to Note That Advanced Modeling and
Le
Advanced Modeling and Simulation Does Not Replace the Need for
Theory or Experiments
A. Larzelere, DOE-NE
Stepping Up to the New Level of Understanding!Understanding!
NEAMS VisionTo rapidly create, and deploy next generation, verified and validated nuclear energy modeling p y , p y g , gy g
and simulation capabilities for the design, implementation, and operation future nuclear energy systems to improve the U.S. energy security future.
• 3D, 2D, 1D3D, 2D, 1D• Science Based Physical
Behaviors• High Resolution• Integrated Systems• Advanced Computing
Science Based Nuclear Energy
Systems
• Low Dimensionality• Test Based Physical
Behaviors• Low Resolution• Uncoupled Systems
W k t ti C ti
2008 2030• Workstation Computing
2018
A. Larzelere, DOE-NE
Nuclear Energy Advanced Modeling and Simulation (NEAMS)FY-10 Proposed Program OverviewFY 10 Proposed Program Overview
StrategiesIntegrated Performance & Safety Codes (IPSC) –Hi h l ti 3D i t t d t d t di t fHigh resolution, 3D, integrated systems codes to predict performance
Fundamental Methods and Models (FMM) – Lower length scale performance understanding
Verification, Validation & Uncertainty Quantification (VU) – Understanding the “believability” of simulation results
Nuclear FuelsReactor Core & SafetySeparations and SafeguardsWaste Forms and Near Field
Capability Transfer (CT) – Moving modeling and simulation tools out of the research environment
Enabling Computational Technologies (ECT) –Computer science resources needed to make the vision a reality
Waste Forms and Near Field Repositories
ApproachBuilt on a robust experimental program for model development and V&VAppropriate flexibility so that the simulation tools
Major MilestonesYear 1
– Create product requirement documents for integrated codes– Initiate robust interaction with NRC on V&V and UQ– Establish overall plans and processes for FMM, CT and ECT el
are applicable to a variety of nuclear energy system options and fuel cyclesContinuously deliver improved modeling and simulation capabilities relevant to existing and future nuclear systems (in the near, mid, and long
)
Year 3– Deliver initial versions of integrated codes and modeling and
simulation interoperability frameworkYear 5
– Deliver integrated codes with proper V&V and UQ pedigreesYear 7
term)– Deliver codes with empirical “knobs” removedYear 10
– Deliver predictive, science based modeling and simulation capabilities for new nuclear energy systems
A. Larzelere, DOE-NE
Outline
• Introduction• Some applications to datepp
– Solvent extraction– Plant-level modeling– Agent designAgent design
• Key research needs• Advancing to the future• Advancing to the future
– NEAMS vision– Separations M&S development
Solvent extraction• Solvent extraction– Continuum– Molecular-level
• Visualization with interactive models
Separations M&S developmentA primary goal is the development of an integrated plant model that allows dynamic simulations of the operation of separations plants of various configurations and operating conditions. Subscale models to
ents
operation of separations plants of various configurations and operating conditions. Subscale models to provide required fidelity in chemical and physical processes.
Needs
I t t d t i t l tInteg
rated plan
t elem
en
Integrated, transient plant simulatorModern, expandable architectureBridging to detailed models
Process developmentDesign and evaluationSafety analysis
Plant-le
vel, 0-
d
Improved models of
nit op
eratio
ns, 1-
d
Equipment designSafety
Improved models of multicomponent, multiphase unit operations• Solvent extraction contactors, dissolvers, etc.• Electrochemical separators• Voloxidizers, calciners, etc.Cost
Unitt-p
rincip
les; 3-
d
QuantumSolvent structure designThermo-chemistry
Classic atomisticSolvent-diluent interactionSolvent-solvate complexationInterfacial transport
ContinuumMultiphase sharp interface
Averaged ContinuumClassic multiphaseTurbulence modelingFlow regime dependent
Mixing zoneSeparation zoneTransition zone
Averaged ContinuumClassic multiphaseTurbulence modelingFlow regime dependent
Mixing zoneSeparation zoneTransition zone
Agent designThermodynamicsTransport
Detailed models • Thermodynamic and physical properties• Chemistry fundamentals• Transport in multiphase, reactive flows
First-p
Å nm μm mm cm m
MultispeciesMass-action reactive
reactive flows
Some Priority Unit OperationsVoloxidation –treatment of chopped oxide fuel with oxygen or ozone at high temperature to drive offoxygen or ozone at high temperature to drive off tritium and other volatile fission products. Technical issues – uncertainty in design/scale-up of process and operating conditions to ensure sufficient FP volatilization from a variety of fuels; validate opportunity to simplify flow sheet.M&S issues: Many common to fuel performance;
voloxidation
M&S issues: Many common to fuel performance; fuel chemistry and reactions, grain structure evolution, fission-product transport, etc.
Solvent extraction – contacting of solutions containing dissolved fuel components with immiscible solutions containing selective separating agents.
Technical issues: uncertainty in design of process and operating conditions to ensure scale-up
M&S i lti t lti h tiM&S issues: multicomponent, multiphase, reactive transport; performance affected by molecular-level processes, including interfacial phenomena, degradation of separating agents by radiolysis and hydrolysis, etc.
Calcination – high-temperature conversion of g psolutions containing separated species into solids suitable for fuel and waste formfabrication.
Technical issues – design and scale-up of process for production of solids with desired particle size, density, homogeneity, etc. p , y, g y,
M&S issues –multiphase (solid/liquid/gas) flow, heat transfer, nucleation and growth of solids
Outline
• Introduction• Some applications to datepp
– Solvent extraction– Plant-level modeling– Agent designAgent design
• Key research needs• Advancing to the future• Advancing to the future
– NEAMS vision– Separations M&S development
Solvent extraction• Solvent extraction– Continuum– Molecular-level
• Visualization with interactive models
Simulation of Solvent Extraction—How can it all work?
Fluid flowsimulation
Liquid–liquidflow simulation
Solvent Extraction = liquid–liquid
flow simulation+ chemistry
↑continuum scale
↓
Liquid–liquidMD simulation
Mass transfer
↓molecular scale
Mass transfer coefficientsMolecular model
development &MD simulation
K. Wardle, ANL
Modern M&S for Solvent Extraction
Comparison of effect of vane geometry on mixing
4-Vane 8-Vane Curved4 Vane 8 Vane Curved
K. Wardle, ANL
Flow Regime VisualizationState-of-the-art high-speed digital video imaging, g p g g g,solid-state light, and optics provide needed insight
Sub-millimeter flow regime never seen before
1 mmRealistic system and operating conditions
Reveals significant time and length scales
• Contactor rotation: 2500 rpm• Aqueous flow rate: 300 mL/min• Organic flow rate: 300 mL/ming• Organic: 30% by volume TBP in dodecane • Aqueous: 1 M HNO3
• Framing: 5400 fps (185 μs between frames)• Exposure: 24 μs• Field of view: ~3.5 mm• Spatial resolution: ~7 μm seeing is believing• Spatial resolution: ~7 μm• Elapsed time: 37 ms (200 frames)
seeing is believing . . .
V. de Almeida, ORNL
Flow Regime Samples1:5 O/A flow ratio 5:1 O/A flow ratio
1 mm 1 mm
• Elapsed time: 18.5 ms (100 frames)• Elapsed time: 37 ms (200 frames)
Identified significant air entrainment in realistic operation for most f th di fl t ti
Organic-rich flow regimes possess greater air entrainment
of the corresponding flow rate ratiosThese videos provide more than insight for modeling . . .
V. de Almeida, ORNL
Computer-Aided Image Analysis for DataLarge data sets are obtained (8 GB for 1-s elapsed time)g ( p )
Can utilize powerful tools of computer image analysis (machine vision)• Algorithms (filters) and open-source code
targetdata measure
300 μm Reduce noise and preserve edges
+ +
Computing intensive• Need to inspect every pixel and its• Need to inspect every pixel and its
neighborhood• Repeat for every target size for every
frame
70 μmair bubble Parallel computing can process large data
sets on modest clustersV. de Almeida, ORNL
bubbles
Generate data
5 m
m
Generate data useful for model development
3.5 noise
dropsArea (pixels)
μm
Drop and bubble size distributionVelocity trackingC lib ti f h
300 μ Calibration of phase
mixture turbulent momentum balance
V. de Almeida, ORNL
Outline
• Introduction• Some applications to datepp
– Solvent extraction– Plant-level modeling– Agent designAgent design
• Key research needs• Advancing to the future• Advancing to the future
– NEAMS vision– Separations M&S development
Solvent extraction• Solvent extraction– Continuum– Molecular-level
• Visualization with interactive models
Chemical TransportImportant chemical reactions occur at the interface
Challenges
Important chemical reactions occur at the interface
Lack of basic understanding of how species moveLack of basic understanding of how species move across interfacial “region”
Strongly coupled physical and chemical processes H+
NO3–
H2O
TBPAn approach to understand l t t ti
?HNO3•2TBP
UO2(NO3)2 • 2 TBP
HNO3•TBPUO22+
solvent extraction
Molecular dynamics simulation?
10 ± 1 Å
C12H26
Calibration from experimental data
I i ht f l l t h i t l l ti h
10 nm ± 1 Å
Insight from molecular quantum chemistry calculations when experimental data are not available
Interfacial Transport “Visualization” by Molecular Dynamics Simulation
Insight on uranyl nitrate extraction into tributyl phosphate diluted in dodecane
species # it
# computational atoms/ nit 298 15 Kspecies units atoms/unit
tributyl phosphate 230 17
dodecane 350 12C12H26
(B uO)3P O
Å
298.15 K
uranyl 18 3nitrate ion 36 3
water 3,490 4H2O
50
NO3–
UO22+
total 4,124 18,778 140 Åaqueous organic
Uranyl Nitrate Ion Water TBP Dodecane
All molecular models are of flexible type; no bond constraints V. de Almeida, ORNL
Status of MD Modeling and Simulation
No published MD work on this TBP system in the open literature• There are similar systems that have different diluents with smaller
molecules (e.g. SC CO2 and CHCl3)D d h diff t i t ti ith TBP th l l i l• Dodecane has a different interaction with TBP; the molecule is larger therefore less packing
Force field for TBP in the literature only from G. Wipff’s group; tested with only a few systems• Charges assigned to atoms from QM electrostatic potential obtained
on an isolated molecule• A systematic experimental calibration of the MD model is still lacking
Simulations performed so far (mostly, if not only, by G. Wipff’s group) are still quite limited in the number of atoms (~4000 atoms)
V. de Almeida, ORNL
t = 0 ns t = 10 nsAqueous and organic: TBP butyl tails hidden
H2O hiddenAll uranyl adsorbed on interfaceSome nitrate ion also adsorbed
Species have crossed the TBP surfactant layer
Onset of extraction of UO22+ , NO3
– , H2O
V. de Almeida, ORNL
Equatorial coordination of uranyl after crossing the interfacetop view
TBP phosphoryl groupNO3
–
UO22+ (NO3
–)(H2O)• 3TBP
t = 9:8 ns monodentate
group3
UO22+H2O
front-top view
“TPB interface thickness” ~10 Å10 Å
Bridging mechanism for transporting into
UO22+ • 5 TBP
the bulk region of the organic phase?
V. de Almeida, ORNL
Outline
• Introduction
• Some applications to date– Solvent extraction– Plant-level modeling– Agent design– Agent design
• Key research needs
Ad i t th f t• Advancing to the future– NEAMS vision– Separations M&S developmentp p
• Solvent extraction– Continuum– Molecular-level
Vi li ti ith i t ti d l• Visualization with interactive models
Photorealistic and Physics-Realistic Interactive Models for Test Evaluation and Analysisfor Test, Evaluation and Analysis
K. Michel, LANL
Mixer/Settler Equipment Computer Graphics 3-D Model
• Model built and textured from scratch in 1 5 work days by the Los Alamos National Laboratory VISIBLE
K. Michel, LANL
• Model built and textured from scratch in 1.5 work days by the Los Alamos National Laboratory VISIBLE development team, using only photos of the original equipment.
• Each part of the modeled equipment can be manipulated and custom programmed for behavior.
Screenshot from an immersive virtual model
K. Michel, LANL
Visualization in Safeguards• 3-D models already employed in safeguards
– Experiments, micro scale - High Performance Computing– Power wallsPower walls
– IAEA Training – Mock Inspection exercises
Vi l d l ld id ff i f d• Visual models could provide a cost-effective safeguards design and test of a facility design– Drop-in toolkit for safeguards implementsp g p– Integrate numeric models that characterize materials, chemical
processes, instruments, detectors– Challenge safeguards in virtual environmentChallenge safeguards in virtual environment
– Where are the planned safeguards weak or effective? – Where should the safeguards be improved?
Multi player engagement in an integrated virtual computer locale– Multi-player engagement in an integrated virtual computer locale
K. Michel, LANL
Broader Application
• Process design– Integrated process simulation with imagery– Utilize existing process simulation codes from across DOE
complex in a virtual modeled framework
• TrainingTraining
K. Michel, LANL
Real-world vs. Virtual World• A virtual
model can have as much,have as much, or as little detail as neededneeded.
K. Michel, LANL
Future Safeguards Data Review Interface: Safeguards Data Shown in Context for Evaluation and Analysis of Events
12:05:34
12:05:36
12:05:40
12:05:34
Summary
• Modeling and simulation have provided useful input to the development of fuel cycle p p yseparations over the past several decades
• With significant scientific advancements and• With significant scientific advancements and vast increases in computational power, modeling and simulation can play an i i l i l i th lincreasing role in solving the complex challenges to be overcome in developing advanced nuclear energy systems.advanced nuclear energy systems.
AcknowledgmentsB Ci iti K ll Mi h lBen Cipiti
Valmor de AlmeidaKelly MichelCandido Pereira
Ben Hay
Bob Jubin
Monica RegalbutoKent Wardle
Alex Larzelere
Prepared by Oak Ridge National Laboratory, P.O. Box 2008, Oak Ridge, Tennessee 37831-6285, managed by UT-Battelle, LLC, for the U.S. Department of Energy under contract DE-AC05-00OR22725.
The submitted manuscript has been authored by a contractor of the U.S. Government under contract DE-AC05-00OR22725. Accordingly, the U.S. Government retains a nonexclusive, royalty-free license to publish or reproduce the published form of this contribution, or allow others to do so, for U.S. Government purposes.p , , p p
This report was prepared as an account of work sponsored by an agency of the United States government. Neither the United States government nor any agency thereof, nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product process or service by trade name trademark manufacturer or otherwise does notspecific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise, does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States government or any agency thereof. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States government or any agency thereof.