Role of Modeling and Simulation in Used Fuel Recycling

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


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


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 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



q 3Pu, FP

q 3U, Tc

( g )q 3Pu, FP

q 3U, Tc

( g )


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


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


Traditional Accounting Instrumentation (Baseline)

KMP 1

KMP 2

KMP 3

KMP 4

KMP 5KMP 5

KMP 6KMP 6


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


Pu Inventory Difference

(Baseline) (Advanced)


Diversion Scenario

(Baseline) (Advanced)


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





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


modeling

Design criteria Thermodynamic studies,



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


modeling

gcriteria Thermodynamic studies,



B.Hay, ORNL

Outline





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





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


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


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


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


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


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







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







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.

Documents

Role of Modeling and Simulation in Used Fuel Recycling