A Systems Modeling Approach for Performance Assessment of the Mochovce National Radioactive Waste Repository, Slovak Republic Neptune and Company, Inc. Los Alamos, NM, USA John Tauxe, PhD, PE Paul Black, PhD http://www.neptuneandco.com/~jtauxe/egu07 Václav Hanu š ík VÚJE, Inc. Trnava, Slovakia
physical system modeling
introduction to the facility
conceptual system model
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?
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 abstract 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.” ¹
We 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 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 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 Modeled 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 solubility atmospheric resuspension
System Modeling model input parameters modeled processes modeling results average annual precipitation = N( =55 cm, =35 cm ) examples: time dose water movement follows Darcy’s Law:
Location Map for Mochovce, Slovakia Wein (Vienna) Bratislava Mochovce SLOVAK REPUBLIC
Repository in a Small Watershed Wein (Vienna) Trnava Bratislava Mochovce
Inside a Vault Structure
The Mochovce GoldSim Model
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 Field
Near Field Calculations
Repository Far Field Environment repository Mochovce NPP stream to lake
Far Field Calculations
Typical Results Any state or condition of the model can be tracked and graphed through time ( e.g. concentrations, flow rates, doses). This could be concentration or dose.
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?
UA allows a more honest answer, based on our state of knowledge.
SA provides insight into which modeling aspects (parameters and processes) are important.
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 • Extensions
Performance assessment modeling can be extended to help with
optimization of operations
development of waste acceptance criteria
efficient use of monetary resources
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, Slovakia repository This presentation can be found here: http://www.neptuneandco.com/~jtauxe/egu07