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Nuclear emergency management: taking the right decisions with uncertain models

Nuclear emergency management: taking the right decisions with uncertain models. Catrinel Turcanu, Johan Camps & Benny Carlé cturcanu@sckcen.be / jcamps@sckcen.be Society and Policy Support Institute Environment, Health and Safety Belgian Nuclear Research Centre.

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Nuclear emergency management: taking the right decisions with uncertain models

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  1. Nuclear emergency management: taking the right decisions with uncertain models Catrinel Turcanu, Johan Camps & Benny Carlé cturcanu@sckcen.be / jcamps@sckcen.be Society and Policy Support Institute Environment, Health and Safety Belgian Nuclear Research Centre Workshop “All models are wrong…”, Groningen, 14-16/03/2011

  2. Outline • Context • Nuclear emergency model uncertainties • Conclusions Model forevaluation of nuclearemergencies for the Doel NPP site

  3. Nuclear emergencies & models Useof models for the protection of people in emergencysituations & preparedness phase: • Atmospheric transport anddispersion models (concentrations, deposition) • Dose models (doseadults, children, thyroid, .) • Food models (concentrations, dose) • …

  4. The modelling problem: Inversion layer Height-dependent wind velocity Atmospheric turbulence rain Dry deposition Wash-out Plume rise Irradiation Inhalation Ingestion Irradiation shielding

  5. How are decisions taken?Legislation • Reference band of dose values – calculated based on model predictions (or measurements or both) • Action levels on specific actions (Belgian levels) • New recommendations: 20-100 mSv/y, all pathways • Range ≠ uncertainty !

  6. Uncertainties • Modelling assumptions • Simplifications of reality • Parameter uncertainty • Calibration of model parameters • Input data • Meteorology • Source term

  7. Source models: Torben Mikkelsen, Risø Uncertainties from modelling assumptions Average plume simple Meandering plume Conservativecalculation Best estimate Fluctuating plume complex

  8. Model intercomparison Standard conditions Experimentally validated: factor 2-3 within experiments

  9. Model intercomparisonVeryspecificconditions

  10. Model intercomparisonrealistic scenario Noodplan Doel JRODOS Rimpuff (ARGOS) TIC [Bq s/m3] same color scale

  11. Potential problems related to the resolution of the calculation grid For the same scenario: Inner grid cell: 1 km Inner grid cell: 100m  no sheltering  sheltering >1km

  12. Source: Florian Gering days after deposition Parameter uncertainty:Cs-137 in milk

  13. Model uncertainties in radiological assessments Malcolm Crick, IAEA Source: Malcolm Crick

  14. Uncertainties in input data (1): meteorology Source: Marc de Cort

  15. Uncertainties in the input data (2) with on-site single rain gauge data with multiple rain gauge data With rain-radar data Example: Input data precipitation

  16. Effect of conservative approach for treatment of rain

  17. Example: conservative approach Tihange, coremelt, ruptureprimary circuit Standard weatherconditions

  18. Example: conservative approach Tihange, partialcoremelt, ruptureprimary circuit Standard weatherconditions

  19. Example: conservative approach Tihange, coremelt, ruptureprimary circuit unstableweatherconditions

  20. Conclusions • Complex problem • Often models extended beyond validated range • Difficult to obtain realistic uncertainties on calculations • Even more difficult to communicate these uncertainties to decision-makers • Best estimate often replaced by conservative approach • But … conservative estimates may lead to unfeasible countermeasures

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