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

1. A Systems Modeling Approach for Performance Assessment of the Mochovce National RadioactiveWaste Repository, Slovak Republic John Tauxe, PhD, PE Paul Black, PhD Václav Hanušík Neptune and Company, Inc. Los Alamos, NM, USA VÚJE, Inc. Trnava, Slovakia http://www.neptuneandco.com/~jtauxe/egu07

2. Presentation Outline • physical system modeling • introduction to the facility • conceptual system model • mathematical model • computer model • future work

3. 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

4. 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.

5. Are models too abstractto 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

6. Physical System Modeling Overview 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. Near field: Radiological materials leak out of stacked concrete vaults. Physiological exposure: Human or ecological receptors are exposed by several pathways. a radioactive waste disposal facility in Tennessee USA

7. Physical System Processes The processes involved in this exposure modeling are radiological, physical, chemical, geological, and biological. Near field: • decay / ingrowth • advection / dispersion • diffusion • dissolution • precipitation • containment degradation 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?

8. Mathematical Coupling of Modeled Processes Physical processes are modeled as coupled partial differential equations: radioactive decay and ingrowth air/water partitioning gaseous diffusion soil/water chemical partitioning aqueous diffusion aqueous advection atmospheric resuspension chemical solubility

9. water movement follows Darcy’s Law: dose time System Modeling examples: model input parameters average annual precipitation = N( m=55 cm, s=35 cm ) modeled processes modeling results

10. Location Map for Mochovce, Slovakia SLOVAK REPUBLIC Mochovce Bratislava Wein (Vienna)

11. Repository in a Small Watershed Mochovce Trnava Bratislava Wein (Vienna)

12. Inside a Vault Structure

13. The Mochovce GoldSim Model

14. 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

15. Engineering Design • Near Field

16. Near Field Calculations

17. stream to lake Repository Far Field Environment Mochovce NPP repository

18. Far Field Calculations

19. 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.

20. dose dose time time 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. a collection of answers reflects our knowledge no single answer is correct

21. 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.

22. 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

23. 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

24. 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).

25. Mochovce, Slovakia repository This presentation can be found here: http://www.neptuneandco.com/~jtauxe/egu07