EGU General Assembly 2007 Neptune and Company, Inc. Los Alamos, NM, USA A Systems Modeling Approach for Performance Assessment of the Mochovce National Radioactive Waste Repository, Slovak Republic John Tauxe, PhD, PE Paul Black, PhD Václav Hanušík VÚJE, Inc. Trnava, Slovakia

EGU General Assembly 2007 Presentation Outline physical system modeling introduction to the facility conceptual system model mathematical model computer model future work

EGU General Assembly 2007 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

EGU General Assembly 2007 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.

EGU General Assembly 2007 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

EGU General Assembly 2007 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.

EGU General Assembly 2007 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?

EGU General Assembly 2007 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

EGU General Assembly 2007 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:

EGU General Assembly 2007 Location Map for Mochovce, Slovakia Wein (Vienna) Bratislava Mochovce

EGU General Assembly 2007 Repository in a Small Watershed Wein (Vienna) Trnava Bratislava Mochovce

EGU General Assembly 2007 Inside a Vault Structure

EGU General Assembly 2007 The Mochovce GoldSim Model

EGU General Assembly 2007 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

EGU General Assembly 2007 Engineering Design Near Field

EGU General Assembly 2007 Near Field Calculations

EGU General Assembly 2007 Repository Far Field Environment repository stream to lake Mochovce NPP

EGU General Assembly 2007 Far Field Calculations

EGU General Assembly 2007 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.

EGU General Assembly 2007 Managing 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

EGU General Assembly 2007 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.

EGU General Assembly 2007 Probabilistic 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

EGU General Assembly 2007 Future 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

EGU General Assembly 2007 Conclusions 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).

EGU General Assembly 2007 Mochovce, Slovakia repository This presentation can be found here: