The present paper summarizes the major outcomes of a study conducted within a Nuclear Energy Agency Working Party on Evaluation Cooperation (NEA WPEC) initiative aiming to investigate data needs for future innovative nuclear systems, to quantify them and to propose a strategy to meet the

Aliberti, G.

Palmiotti, G.

Salvatores, M.

UNT Digital Library

This is a preprint of a paper intended for publication in a journal or proceedings. Since changes may be made before publication, this preprint should not be cited or reproduced without permission of the author. This document was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government nor any agency thereof, or any of their employees, makes any warranty, expressed or implied, or assumes any legal liability or responsibility for any third party’s use, or the results of such use, of any information, apparatus, product or process disclosed in this report, or represents that its use by such third party would not infringe privately owned rights. The views expressed in this paper are not necessarily those of the United States Government or the sponsoring agency. INL/CON-07-13355PREPRINTSimultaneous Nuclear Data Target Accuracy Study for Innovative Fast Reactors4th Workshop on Neutron Measurements, Evaluations and Applications -- Nuclear Data Needs for Generation IV and Accelerator-Driven SystemsG. Aliberti G. Palmiotti M. Salvatores October 2007 Simultaneous Nuclear Data Target Accuracy Study for Innovative Fast Reactors G. Aliberti1), G. Palmiotti2), M. Salvatores1,2,3)1)  Argonne National Laboratory, NE Division, Argonne, IL 60439 (USA) 2) Idaho National Laboratory, NSE Division, 2525 Fremont Ave. P.O. Box 1625, Idaho Falls, ID 83415-3855 (USA) 3) CEA-Cadarache, 13108 St-Paul-Lez-Durance, France aliberti@anl.gov Abstract: The present paper summarizes the major outcomes of a study conducted within a Nuclear Energy Agency Working Party on Evaluation Cooperation (NEA WPEC) initiative aiming to investigate data needs for future innovative nuclear systems, to quantify them and to propose a strategy to meet them IntroductionWithin the NEA WPEC Subgroup 26 an uncertainty assessment has been carried out [1] using covariance data recently processed by joint efforts of several US and European Labs. In general, the uncertainty analysis shows that for the wide selection of fast reactor concepts considered, the present integral parameters uncertainties resulting from the assumed uncertainties on nuclear data are probably acceptable in the early phases of design feasibility studies. However, in the successive phase of preliminary conceptual designs and in later design phases of selected reactor and fuel cycle concepts, there will be the need for improved data and methods, in order to reduce margins, both for economic and safety reasons. It is then important to define as soon as possible priority issues, i.e. which are the nuclear data (isotope, reaction type, energy range) that need improvement, in order to quantify target accuracies and to select a strategy to meet the requirements needed (e.g. by some selected new differential measurements and by the use of integral experiments). In this context one should account for the wide range of high accuracy integral experiments already performed and available in national or, better, international data basis, in order to indicate new integral experiments that will be needed to account for new requirements due to innovative design features, and to provide the necessary full integral data base to be used for validation of the design simulation tools. In previous studies [2,3], a target accuracy assessment was performed separately for selected Gen-IV systems. In the present study, a simultaneous target accuracy study has been performed over an ensemble of fast neutron systems, with different coolants (sodium (Na), gas, lead, lead bismuth eutectic), different fuel types (oxides, metals, carbides, nitrides) and different Pu/TRU compositions, in different core volumes. These systems (ABTR, SFR, EFR, GFR, LFR and ADS), have been defined in [2,4,5,6] and their characteristics are summarized in Table 1. Table 1. Features of the Investigated SystemsSystem Fuel Coolant TRU/(U+TRU) MA(a)/(U+TRU) Power (MWth)ABTR Metal Na 0.162 ~0 250 SFR Metal Na 0.605 0.106 840 EFR MOX Na 0.237 0.012 3600 GFR Carbide He 0.217 0.050 2400 LFR Metal Pb 0.233 0.024 900 ADS Nitride Pb-Bi 1.0 0.680 380 (a) Minor Actinides Data target accuracies To be consistent with the target accuracy study presented in [3], the guidelines that will be provided in the present paper for data improvements will refer to the analysis of the following parameters: multiplication factor, power peak, Doppler and coolant void reactivity coefficient, burnup ǻk/k, and nuclide density at end of cycle. Within the Subgroup 26, a preliminary list of design target accuracies for fast reactor systems (at first, independently of the coolant and fuel type) has been established as presented in Table 2. These target accuracies reflect the perceived state of the art, even if they are not yet the result of a systematic analysis, which should necessarily involve industrial partners. The target accuracy requirements presented in Table 2 have also been extended to the ADS system. Table 2. Fast Burner Reactor and ADS Target Accuracies (1ı)Multiplication factor (BOL) 300 pcm Reactivity coefficients (Coolant void and Doppler ) 7% Power peak (BOL) 2% Major nuclide(a) density at end of irradiation cycle 2% Burnup reactivity swing 300 pcm Other nuclide density at end of irradiation cycle 10%  (a) U-235, U-238, Pu-238, Pu-239, Pu-240, Pu-241, Pu-242 Moreover, the same covariance data have been used as in [3]. These data have been produced by a major joint effort within Subgroup 26 by BNL, LANL, ORNL and NRG [7 to 16]Theoretical approach and reference calculations As already discussed in the introduction, in addition to the selected fast systems analyzed in [2,4], the ADS system investigated in [5,6] has been also considered. Sensitivity and uncertainty coefficients are consistent with the results presented in [1,6] and calculated at ANL with the ERANOS code system [17]. As reminder, once the sensitivity coefficient matrix SR for each integral parameter R and the covariance matrix D are available, the uncertainty on the integral parameter can be evaluated as: RR20 DSSR ' .A successive step is the assessment of target accuracy requirements. To establish priorities and target accuracies on data uncertainty reduction, a formal approach can be adopted by defining target accuracy on design parameters and finding out the required accuracy on the nuclear data ıi. In fact, the unknown uncertainty data requirements di can be obtained (e.g. for parameters i not correlated among themselves), by solving the minimization problem: mind/ 2iii O¦ , (i = 1…I, I: total number of parameters), with the following constraints:  2Tn2i2 iRiRdSn¦  (n = 1…N, N is the total number of integral design parameters), where iRnS  are the sensitivity coefficients for the integral parameter Rn, and TnR  are the target accuracies on the N integral parameters; Ȝi are “cost” parameters related to each ıi and should give a relative figure of merit of the difficulty of improving that parameter (e.g., reducing uncertainties with an appropriate experiment). The cross-sections uncertainties required for satisfying the target accuracies have been calculated by a minimization process that satisfies the nonlinear constraints with bounded parameters. The SNOPT code [18] has been used for this purpose. To avoid the introduction of meaningless parameters, as unknown “d” parameters (i.e., as cross-sections for which target accuracies are required), only those which globally account at least for 98% of the overall uncertainty for each integral parameter have been chosen. Concerning the cost parameters, as already done in previous work [2,3], a constant value of one for all Ȝi is initially taken. Additionally, at the first stage it was decided not to account for correlations between data. This assumption is of course rather arbitrary, but it is consistent with standard requirements for reactor designs in early phases of development. Uncertainty results The uncertainties on the major integral parameters due to diagonal values of the BOLNA covariance matrix are provided in Table 3 (see values only associated to the label “With initial uncertainties”). For the ADS, the Doppler reactivity coefficient has not been considered due to its small calculated value. In Table 3, in italic font are the initial parameter uncertainties larger than the required accuracies summarized in Table 2. In general, it can be observed that the power peak, the Doppler and void reactivity coefficients, meet the accuracy requirements in all cases with the only exception of the ADS for the three parameters and of the SFR for the void coefficient. The worst situation is represented by the ADS, where all integral parameter uncertainties (with the only exception of the nuclide densities at end of irradiation, due to the short burn up) do not meet the accuracy requirements. As for the nuclei densities at the end of irradiation, most of the target accuracies are already met. Table 3. Integral Parameter Uncertainties (%) with Initial and Required Cross-Section Uncertainties  ABTR SFR EFR GFR LFR ADS With initial uncertainties 643 1108 877 1270 890 1882 keff BOC [pcm] With required uncertainties 291 348 322 326 320 279 With initial uncertainties 0.3 0.3 0.8 1.2 0.4 14.2 Power Peak BOC With required uncertainties 0.2 0.1 0.3 0.3 0.2 2.2 With initial uncertainties 2.9 3.6 2.5 3.6 2.8 - Doppler BOC With required uncertainties 1.4 1.7 1.1 1.4 1.4 - With initial uncertainties 5.1 15.7 6.7 5.5 5.0 13.1 Void With required uncertainties 2.8 6.0 3.3 3.1 1.9 3.5 With initial uncertainties -37 -152 -584 254 -128 -603 Burnup[pcm] With required uncertainties -14 -45 -201 92 -45 -207 Target accuracy resultsTables 4 and 5 show the relevant target accuracy results for the ensemble of only Na-cooled and all fast systems respectively. The required nuclear data accuracies, obtained from the optimization procedures, are such that the design target accuracies are fulfilled in most cases. Besides the initial integral parameter uncertainties, Table 3 shows the calculated residual uncertainties on the major integral parameters when one uses the required cross-section uncertainties, as obtained with the minimization procedure applied to all fast systems. Note that the required parameter accuracies are not exactly met because of the cross-sections not accounted in the minimization procedures which give as consequence a residual uncertainty going to be added to the specified accuracy. In the two cases (i.e. only Na-cooled or all fast reactor types), the major requirements are related to the same type of data (Pu-241 fission, U-238 inelastic and capture, Pu-240 fission) and to approximately the same level of accuracy. Specific requirements can show up in the two cases according to the cooling type or to the specific structural materials. Minor actinide data needs become more evident if the ADS case (i.e. with MA dominated fuel) is considered. There are however some general requirements, whatever is the type of system, as e.g. for Cm-244 and Am-242m fission data. Table 4. ABTR, SFR, EFR: Uncertainty Reduction Requirements to Meet Integral Parameter Target Accuracies Uncertainty (%)Uncertainty (%)Uncertainty (%)IsotopeCross-SectionEnergy RangeInitial Target IsotopeCross-SectionEnergy RangeInitial TargetIsotopeCross-SectionEnergy RangeInitial Target19.6 - 6.07 MeV 29.3 20.1 19.6 - 6.07 MeV 13.0 8.9 6.07 - 2.23 MeV 23.4 8.0 6.07 - 2.23 MeV 19.8 4.6 6.07 - 2.23 MeV 7.2 4.1 2.23 - 1.35 MeV 19.7 8.2 2.23 - 1.35 MeV 20.6 4.5 2.23 - 1.35 MeV 25.4 3.3 1.35 - 0.498 MeV 16.5 4.3 U238ıinel1.35 - 0.498 MeV 11.6 5.5 Fe56 ıinel1.35 - 0.498 MeV 16.1 3.2 498 - 183 keV 16.6 3.1 6.07 - 2.23 MeV 14.2 6.5 1.35 - 0.498 MeV 18.2 10.1 183 - 67.4 keV 16.6 3.1 2.23 - 1.35 MeV 21.3 5.8 498 - 183 keV 11.6 6.5 67.4 - 24.8 keV 14.4 4.1 1.35 - 0.498 MeV 16.6 3.4 183 - 67.4 keV 9.0 5.6 24.8 - 9.12 keV 11.8 4.3 498 - 183 keV 13.5 2.6 67.4 - 24.8 keV 10.1 6.3 9.12 - 2.03 keV 12.4 6.5 183 - 67.4 keV 19.9 2.6 24.8 - 9.12 keV 7.4 5.5 Am242mıfiss2.03 - 0.454 keV 12.2 5.2 67.4 - 24.8 keV 8.7 3.3 Pu239 ıcapt9.12 - 2.03 keV 15.5 6.7 19.6 - 6.07 MeV 9.6 8.6 24.8 - 9.12 keV 11.3 3.5 19.6 - 6.07 MeV 100.0 62.3 6.07 - 2.23 MeV 4.8 2.8 9.12 - 2.03 keV 10.4 5.4 O16 ıcapt 6.07 - 2.23 MeV 100.0 39.5 2.23 - 1.35 MeV 5.7 2.6 2.03 - 0.454 keV 12.7 4.4 2.23 - 1.35 MeV 12.6 9.3 1.35 - 0.498 MeV 5.8 1.8 Pu241 ıfiss454 - 22.6 eV 19.4 8.6 Na23ıinel 1.35 - 0.498 MeV 28.0 4.0 498 - 183 keV 3.9 3.9 6.07 - 2.23 MeV 31.3 8.2 Pu240 ıfiss2.03 - 0.454 keV 21.6 12.4 U238ıcapt24.8 - 9.12 keV 9.4 3.8 2.23 - 1.35 MeV 43.8 8.2       1.35 - 0.498 MeV 50.0 5.1        Cm244ıfiss498 - 183 keV 36.5 12.1     Table 5. ABTR, SFR, EFR, GFR, LFR, ADMAB: Uncertainty Reduction Requirements to Meet Integral Parameter Target Accuracies Uncertainty (%)Uncertainty (%)Uncertainty (%)IsotopeCross-SectionEnergy RangeInitial Target IsotopeCross-SectionEnergy RangeInitial TargetIsotopeCross-SectionEnergy RangeInitial Target19.6 - 6.07 MeV 29.3 9.0 498 - 183 keV 15.0 2.9 6.07 - 2.23 MeV 4.8 2.9 6.07 - 2.23 MeV 19.8 2.0 183 - 67.4 keV 10.0 2.7 2.23 - 1.35 MeV 5.7 2.6 2.23 - 1.35 MeV 20.6 2.1 67.4 - 24.8 keV 10.0 3.3 1.35 - 0.498 MeV 5.8 1.6 1.35 - 0.498 MeV 11.6 2.3 24.8 - 9.12 keV 8.0 3.9 498 - 183 keV 3.9 3.7 498 - 183 keV 4.2 3.8 B10ıcapt9.12 - 2.03 keV 8.0 6.0 Pu240 ıfiss2.03 - 0.454 keV 21.6 11.8 U238ıinel183 - 67.4 keV 11.0 4.2 1.35 - 0.498 MeV 18.2 6.6 6.07 - 2.23 MeV 14.2 5.0 498 - 183 keV 11.6 4.4 Si28ıcapt19.6 - 6.07 MeV 52.9 7.2 2.23 - 1.35 MeV 21.3 3.9 183 - 67.4 keV 9.0 4.0 6.07 - 2.23 MeV 13.5 3.9 1.35 - 0.498 MeV 16.6 2.1 67.4 - 24.8 keV 10.1 4.2 Si28ıinel 2.23 - 1.35 MeV 50.0 7.4 498 - 183 keV 13.5 1.7 24.8 - 9.12 keV 7.4 3.8 6.07 - 2.23 MeV 5.5 4.2 183 - 67.4 keV 19.9 1.7 Pu239 ıcapt9.12 - 2.03 keV 15.5 3.2 2.23 - 1.35 MeV 14.2 4.0 67.4 - 24.8 keV 8.7 1.9 19.6 - 6.07 MeV 100.0 37.9 Pb206 ıinel 1.35 - 0.498 MeV 9.2 4.7 24.8 - 9.12 keV 11.3 2.0 O16 ıcapt 6.07 - 2.23 MeV 100.0 37.9 6.07 - 2.23 MeV 5.0 4.9 9.12 - 2.03 keV 10.4 2.1 6.07 - 2.23 MeV 17.9 4.9 2.23 - 1.35 MeV 13.8 6.0 2.03 - 0.454 keV 12.7 2.7 2.23 - 1.35 MeV 35.3 3.9 Pb207 ıinel 1.35 - 0.498 MeV 11.3 3.6 Pu241 ıfiss454 - 22.6 eV 19.4 5.4 1.35 - 0.498 MeV 42.2 2.3 6.07 - 2.23 MeV 31.3 3.0 498 - 183 keV 41.0 3.7 Pbıinel6.07 - 2.23 MeV 5.4 3.0 2.23 - 1.35 MeV 43.8 2.6 183 - 67.4 keV 79.5 3.7 6.07 - 2.23 MeV 11.0 2.3 1.35 - 0.498 MeV 50.0 1.5 Am243 ıinel67.4 - 24.8 keV 80.8 12.4 2.23 - 1.35 MeV 6.0 1.9 498 - 183 keV 36.5 4.0 1.35 - 0.498 MeV 23.4 21.4 Am243ıfiss1.35 - 0.498 MeV 9.2 1.7 Cm244 ıfiss183 - 67.4 keV 47.6 7.3 498 - 183 keV 16.5 6.3 2.23 - 1.35 MeV 34.1 2.8 24.8 - 9.12 keV 9.4 1.8 183 - 67.4 keV 16.6 4.7 Bi209ıinel 1.35 - 0.498 MeV 41.8 4.3 U238ıcapt 9.12 - 2.03 keV 3.1 1.8 67.4 - 24.8 keV 16.6 4.8 2.23 - 1.35 MeV 5.0 3.1 6.07 - 2.23 MeV 7.2 2.6 24.8 - 9.12 keV 14.4 5.6 1.35 - 0.498 MeV 5.0 1.2 2.23 - 1.35 MeV 25.4 1.7 Am242mıfiss2.04 - 0.454 keV 11.8 5.9 498 - 183 keV 5.0 1.9 Fe56 ıinel 1.35 - 0.498 MeV 16.1 1.5 N15ıel183 - 67.4 keV 5.0 2.3    Na23ıinel1.35 - 0.498 MeV 28.0 10.5        Zr90 ıinel6.07 - 2.23 MeV 18.0 3.3 These results should be used with precaution. They indicate trends and general priority needs. In fact, these quantitative values have been obtained considering only diagonal (variance) uncertainty values that represent an underestimation of the real uncertainty. Moreover, and certainly more important, the accuracy requirements and priorities are strongly dependent on the assumed initial uncertainty variance-covariance data, and in particular on the very low initial uncertainty values on the fission cross-section of Pu-239. This work however provides a clear indication for future work: a) Improvement of the present covariance data: b) Selection of a few priority differential measurements, where the expected experimental uncertainties can match the data required uncertainty; c) Definition of a strategy of combined use of high quality integral experiments, sophisticated analysis tools, scientifically based covariance data within a statistical data adjustment, in order to fully validate calculation tools for the design of future innovative systems. This approach is discussed in a companion paper at this workshop [19].   Acknowledgments Argonne National Laboratory's work was supported by the U.S. Department of Energy, Office of Nuclear Energy, under contract DE-AC02-06CH11357. References [1] M. Salvatores et al., “Nuclear Data Needs for Advanced Reactor Systems. A NEA Nuclear Science Committee Initiative,” Proc. Int. Conf. ND-2007, Nice, France (April 2007). [2] G. Aliberti et al., “Nuclear Data Sensitivity, Uncertainty and Target Accuracy Assessment for Future Nuclear Systems”, Annals of Nucl. Energy, 33, 700-733 (2006). [3] M. Salvatores, G. Aliberti and G. Palmiotti, “The Role of Differential and Integral Experiments to Meet Requirements for Improved Nuclear Data”, ND2007, Nice (France), April 2007. [4] Y. I. Chang, P. J. Finck, C. Grandy, “Advanced Burner Reactor Preconceptual Design Report”, ANL-ABR-1 (Argonne National Laboratory, September 2006). [5] G. Aliberti et al., “Impact of Nuclear Data Uncertainties on Transmutation of Actinides in Accelerator-Driven Assemblies,” Nucl. Sci. Eng., 46, 13-50 (2004). [6] G. Aliberti, G. Palmiotti, M. Salvatores, “New Covariance Data and Their Impact on ADS Designs,” in Proc. of AccApp Conference, Pocatello, ID, USA, (July-August 2007). [7] D. Rochman et al., “Preliminary Cross-Section Covariances for WPEC Subgroup 26,” Tech. Rep. BNL-77407-2007-IR, Brookhaven National Laboratory, (2007). [8] D. Rochman et al., “Preliminary nu-bar Covariances for 238; 242Pu and 242; 243; 244; 245Cm,” Tech. Rep. BNL-77407-2007-IR-Suppl.1, Brookhaven National Laboratory, (2007). [9] M. Chadwick et al., “ENDF/B-VII.0: Next Generation Evaluated Nuclear Data Library for Nuclear Science and Technology,” Nuclear Data Sheets, 107, pp. 2931 (December 2006). [10] S. F. Mughabghab, “Atlas of Neutron Resonances: Resonance Parameters and Thermal Cross-Sections,” Amsterdam: Elsevier, (2006). [11] M. Herman et al., “EMPIRE Nuclear Reaction Model Code, Version 2.19 (Lodi),” www.nndc.bnl.gov/empire219/ (March 2005). [12] T. Kawano, Tech. Rep. JAERI-Research, 99-009, JAERI, (1999). [13] N. M. Larson, “Updated Users’ Guide for SAMMY: Multilevel R-Matrix Fits to Neutron Data Using Bayes’ Equations,” ORNL/TM-9179/R7 (2007). [14] L. Leal, H. Derrien, N. Larson, G. Arbanas, and Royce Sayer, “ORNL Methodology for Covariance Generation for Sensitivity/Uncertainty Analysis,” in Proc. of International Conference on Nuclear Criticality Safety, St. Petersburg, Russia, (May 2007). [15] J. Koning, “Generating Covariance Data with Nuclear Models,” in Proceedings of the International Workshop on Nuclear data Needs for Generation IV Nuclear energy systems, Antwerpen, (April 5-7, 2005), ed. P. Rullhusen, World Scientific (2006), p. 153. [16] J. Koning, S. Hilaire and M. C. Duijvestijn, “TALYS: Comprehensive Nuclear Reaction Modeling,” in Proc. of Int. Conf. on Nuclear Data for Science and Technology - ND2004, AIP vol. 769, Santa Fe, NM, USA, (Sep. 26 - Oct. 1, 2004), p. 1154, (2005). [17] G. Rimpault, et al., “The ERANOS Code and Data System for Fast Reactor Neutronic Analyses,” in Proc. of PHYSOR 2002 Conference, Seoul, South Korea, (October 2002).  [18] P.E. Gill et al.,  “SNOPT: An SQP algorithm for Large-Scale constrained programming”, Technical Report SOL 97-3, Systems Optimization Laboratory, Department of Operations Research, Stanford University, Stanford, California 94305-4022, 1997. [19] G.Palmiotti, M.Salvatores, G.Aliberti “Validation of simulation codes for future systems: motivations, approach and the role of nuclear data”, this workshop. 

Simultaneous Nuclear Data Target Accuracy Study for Innovative Fast Reactors

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Simultaneous Nuclear Data Target Accuracy Study for Innovative Fast Reactors

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