egu general assembly 2007 neptune and company, inc. los alamos, nm, usa a systems modeling approach...

EGU General Assembly 2007

Neptune and Company, Inc.Los Alamos, NM, USA

A Systems Modeling Approach for A Systems Modeling Approach for Performance Assessment of the Mochovce Performance Assessment of the Mochovce

National RadioactiveNational RadioactiveWaste Repository, Slovak RepublicWaste Repository, Slovak Republic

John Tauxe, PhD, PEPaul Black, PhD

http://www.neptuneandco.com/~jtauxe/egu07

Václav Hanušík

VÚJE, Inc.Trnava, Slovakia


Presentation OutlinePresentation Outline

• physical system modeling

• introduction to the facility

• conceptual system model

• mathematical model

• computer model

• future work


What is the problem?What is the problem?

• Radioactive wastes exist. Sources: nuclear power, nuclear medicine, industry, and (in some countries) nuclear weapons

• They pose a long-term health hazard.At risk: workers, the general public, the environment

• How should they be managed?Considerations: worker exposure, containment, release to the environment, future harm reduction


Why use modeling?Why use modeling?

• Models provide insight into the problem.Important processes can be identified.The effects of uncertainty can be quantified.

• Models help to evaluate alternatives.Cost/benefit of alternatives can be performed.

Relative effectiveness can be evaluated.

• Models communicate technical issues.Transparent modeling is accessible to the public.

Visualization of processes increases understanding.


Are models too abstractAre models too abstractto be of use?to be of use?

• “Essentially all models are wrong...We know that none of the results are correct per se, though we have defined an envelope of plausible estimates, conditioned on knowledge.

• ...but some are useful.” ¹We gain insight into what is important, and can demonstrate relative effects of mitigation (of doses, for example).

¹ Box, George E. P.; Norman R. Draper (1987). Empirical Model-Building and Response Surfaces, p. 424


Physical System Modeling OverviewPhysical System Modeling Overview

a radioactive waste disposal facility in Tennessee USA

Near field:Radiological materials leak out of stacked concrete vaults.

example: Human and ecological health effects arise from exposure to contaminants transported through an engineered (near field) and natural (far field) environment to a biological (physiological) environment

Far field:Contaminants migrate through geologic materials.

Physiological exposure:Human or ecological receptors are exposed by several pathways.


Physical System ProcessesPhysical System Processes

Near field:• decay / ingrowth• advection / dispersion• diffusion• dissolution • precipitation • containment degradation

The processes involved in this exposure modeling are radiological, physical, chemical, geological, and biological.

Far field:• decay / ingrowth• advection / dispersion• dilution• colloidal transport• chemical transformation• biological uptake and translocation

Physiological exposure:• habitation• drinking water• eating plant and animal foodstuffs• breathing• pharmacokinetics and dose response

These (and more) can be modeled in any degree of detail.

An important question: What degree of detail is appropriate?


Mathematical Coupling of Mathematical Coupling of Modeled ProcessesModeled Processes

Physical processes are modeled as coupled partial differential equations:

radioactive decay and ingrowth

gaseous diffusion

aqueous diffusion

aqueous advection

soil/water chemical partitioning

air/water partitioning

chemical solubilityatmosphericresuspension

i

jjk

jk

t

ii

jeNN

1)0(1121

)(

CDJ sw ~

hnK

vx

CDJ sa ~

soilRatm CfQ solaq CC

aqHair CKC

soilw

bdwater CKC

1


System ModelingSystem Modeling

model input parameters

modeled processes

modeling results

average annual precipitation = N( =55 cm, =35 cm )

examples:

timedo

se

hnKvx

water movement follows Darcy’s Law:


Location Map for Location Map for Mochovce, SlovakiaMochovce, Slovakia

Wein(Vienna)

Bratislava

Mochovce


Repository in a Small WatershedRepository in a Small Watershed

Wein(Vienna)

Trnava

Bratislava

Mochovce


Inside a Vault StructureInside a Vault Structure


The Mochovce GoldSim ModelThe Mochovce GoldSim Model


Computer Modeling in GoldSim*Computer Modeling in GoldSim*

• materials are defined (Water, Soil, etc.)

• compartmentalization of model domain uses Cell and Pipe elements

• connections between compartments define transport pathways

• Source elements contain initial radionuclide inventory (Species)

• contaminants disperse along pathways

• calculations are done through time

• GoldSim is natively probabilistic

*Information about GoldSim™ is available from www.goldsim.com


Engineering Design • Near FieldEngineering Design • Near Field


Near Field CalculationsNear Field Calculations


Repository Far Field EnvironmentRepository Far Field Environment

repository

stream

to lake

Mochovce NPP


Far Field CalculationsFar Field Calculations


Typical ResultsTypical ResultsAny state or condition of the model can be tracked and graphed through time (e.g. concentrations, flow rates, doses).

Thi

s co

uld

be

conc

entr

atio

n or

dos

e.


Managing UncertaintyManaging Uncertainty

• We know that our knowledge is incomplete. Of that we are certain.

• How can we allow and account for imperfect knowledge?

• Each modeling parameter and process has inherent uncertainty and variability, and therefore so must our results.

no single answer is correct

a collection of answers reflects our knowledge

time

dose

time

dose


Why Probabilistic Modeling?Why Probabilistic Modeling?

• Uncertainty Analysis

UA allows a more honest answer, based on our state of knowledge.

• Sensitivity Analysis

SA provides insight into which modeling aspects (parameters and processes) are important.


Probabilistic AnalysisProbabilistic Analysis

• modeling parameters are defined stochastically, capturing uncertainty

• Monte Carlo is handled by GoldSim

• sensitivity analysis performed on results using the open source R software

• sensitive parameters are identified

• value-of-information analysis performed

• revisions through Bayesian updating


Future Work • ExtensionsFuture Work • Extensions

Performance assessment modeling can be extended to help with

• worker safety

• facility design

• optimization of operations

• development of waste acceptance criteria

• efficient use of monetary resources


ConclusionsConclusions

• Thoughtful stochastic physical system modeling can capture our state of knowledge.

• Defensible and transparent decisions can be made using such models.

• A system model can do much more than radiological performance assessment (worker risk, optimization, cost/benefit).


Mochovce, SlovakiaMochovce, Slovakia

repository

This presentation can be found here: http://www.neptuneandco.com/~jtauxe/egu0

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egu general assembly 2007 neptune and company, inc. los alamos, nm, usa a systems modeling approach...

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