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Summary of UK PIE data Richard Moore

Summary of UK PIE data Richard Moore. Presented by : Jim Gulliford. Overview. Sellafield Dataset Other Data CERES, UK BUC validation measurements Consistency, Completeness, Uncertainties Need for more data Classification – suitability for benchmarking Lessons Learned

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Summary of UK PIE data Richard Moore

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  1. Summary of UK PIE dataRichard Moore Presented by : Jim Gulliford

  2. Overview • Sellafield Dataset • Other Data • CERES, UK BUC validation measurements • Consistency, Completeness, Uncertainties • Need for more data • Classification – suitability for benchmarking • Lessons Learned • UK participation in OECD-NEA PIE Experts Group

  3. Sellafield Dataset • PIE data used to validate spent fuel inventory calculations to support UK nuclear operations • Database of PIE measurements from around the world • Database includes the results of calculations performed • WIMS – TRAIL – FISPIN • Classification of validation data

  4. Sellafield database • Includes: • Experimental result • Calculated result • C/E • Cooling • Enrichment • Assembly and sample irradiation • Laboratory • Classification • Calculation code and details

  5. LWR PIE Summary

  6. LWR & MOX Data ‘pulled-pin’ samples – not used for benchmarking

  7. Other PIE Data • Burnup Credit Programmes at Winfrith • Used to valid WIMS & MONK • CERES • Reactivity and PIE measurements on PWR & BWR samples from France & USA • PIE included analysis of 15 major BUC fission products • Pre-CERES • Reactivity and PIE on HEU research reactor fuel, AGR & PWR (Zorita & Besnau) samples • UK ready to make data available (need to get agreement from US and French partners for CERES data)

  8. CERES Reactivity Measurements – fuel samples

  9. CERES Reactivity Measurements – FP samples

  10. CERES PIE Analysis • Result for Sm149 appears to be due to problem with measurement. Later PIE work gave much better agreement

  11. Inter lab agreement Sometimes excellent, sometimes not…

  12. Inter-lab agreement For measurements made in two laboratories: • Some studies show up to 77% of results agree to within 2-sigma errors • Statistically it should be 95% • 77% is good when compared with other studies: • 36% for fission products • 22% for actinides Demonstrates a problem with measurements or uncertainty estimation – need some other means to assess the reliability of validation data.

  13. Data classifications • Class A [Most consistent and reliable data; laboratory cross checks performed and consistent] • Class B [Multiple laboratory measurement on dissolved sample and results consistent] • Class C [Single laboratory measurement on multiple similar samples and results consistent] • Class D [Reliable data as assessed by experts, without laboratory cross check] • Class F [Results unsuitable for validation]

  14. Classification overview Actinides Fission products

  15. Lessons Learned • Chemical separation process is very delicate – (particularly for Fission Products) good idea to get independent verification • ‘Pulled-pin’ irradiations difficult to analyse – try to avoid if possible • Rh chemistry difficult – we have experienced problems with sample manufacture and PIE • Need to do thorough check on completeness of description of irradiation history & environment • Inconsistent results - measurement uncertainty analysis appears incomplete in some cases (i.e. uncertainties in chemical separation)

  16. UK Participation in PIE Experts Group • Donation of data as/when available • Review of other’s contributions • Seek to identify remaining UK expertise in chemical separation to add to ‘lesson learned’ • Build consensus on reliable experimental techniques • Benchmark new data and present summary of new and old benchmark results • Identify gaps in database • Highlight problem areas in calculations

  17. Finally • Currently investigating whether we can give all this information for use in SFCOMPO • Hopefully we will be able to provide the data soon • Suggest inclusion of similar procedure to ICSBEP where evaluation includes results of ‘indicative’ calculation results • Provides test of completeness of data and gives early indication of gross errors

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