The validation of advanced simulation tools will still play a very significant role in several areas of reactor system analysis. This is the case of reactor physics and neutronics, where nuclear data uncertainties still play a crucial role for many core and fuel cycle parameters. The present paper gives a summary of validation motivations, objectives and approach. A validation effort is in particular necessary in the frame of advanced (e.g. Generation-IV or GNEP) reactors and associated fuel cycles assessment and design

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-07-13351PREPRINTValidation of Simulation Codes for Future Systems:  Motivations, Approach, and the Role of Nuclear Data 4th Workshop on Neutron Measurements, Evaluations and Applications -- Nuclear Data Needs for Generation IV and Accelerator-Driven SystemsG. Palmiotti M. Salvatores G. Aliberti October 2007 Validation of Simulation Codes for Future Systems: Motivations, Approach and the Role of Nuclear Data G. Palmiotti1), M. Salvatores1,2,3), G. Aliberti2) 1) Idaho National Laboratory, NSE Division, 2525 Fremont Ave. P.O. Box 1625, Idaho Falls, ID 83415-3855 (USA) 2)  Argonne National Laboratory, NE Division, Argonne, IL 60439 (USA) 3) CEA-Cadarache, 13108 St-Paul-Lez-Durance, France massimo.salvatores@cea.frAbstract: The validation of advanced simulation tools will still play a very significant role in several areas of reactor system analysis. This is the case of reactor physics and neutronics, where nuclear data uncertainties still play a crucial role for many core and fuel cycle parameters. The present paper gives a summary of validation motivations, objectives and approach. A validation effort is in particular necessary in the frame of advanced (e.g. Generation-IV or GNEP) reactors and associated fuel cycles assessment and design. Validation and verification Validation of simulation codes is complementary to the “verification” process. In fact, “verification” addresses the question “are we solving the equations correctly” while validation addresses the question “are we solving the correct equations with the correct parameters”. Verification implies comparisons with “reference” equation solutions or with analytical solutions, when they exist. Most of what is called “numerical validation” falls in this category.  Validation strategies differ according to the relative weight of the methods and of the parameters that enter into the simulation tools. Most validation is based on experiments, and the field of neutronics where a “robust” physics description model exists and which is function of “input” parameters not fully known, will be the focus of this paper. In fact, in the case of reactor core, shielding and fuel cycle physics the model (theory) is well established (the Boltzmann and Bateman equations) and the parameters are the nuclear cross-sections, decay data etc. Two types of validation approaches can and have been used: a) Mock-up experiments (“global” validation): need for a very close experimental simulation of a reference configuration. Bias factors cannot be extrapolated beyond reference configuration; b) Use of “clean”, “representative” integral experiments (“bias factor and adjustment” method). Allows to define bias factors, uncertainties and can be used for a wide range of applications. It also allows to define “adjusted” application libraries or even “adjusted” data files. The use of this last approach has been particularly successful in the design of SUPERPHENIX. In fact the prediction of the critical mass has been remarkably close to the experimental value observed at reactor start up (discrepancy of ~3 out of ~400 core sub-assemblies). Validation: motivation and objectives The recent extensive sensitivity/uncertainty studies, have allowed to preliminary quantify the impact of current nuclear data uncertainties on design parameters of the major Gen-IV  and transmutation systems, and in particular on fast reactors with different coolants, with different fuels (oxide, metal, carbide, nitride), fuel composition (e.g. different Pu/TRU ratios), MA content  and different conversion ratios. In general, innovative characteristics of future reactor cores will in fact imply new core architectures (e.g. without fertile blankets), reduced void reactivity coefficients, wide range of possible Pu vectors, significant presence of minor actinides in innovative fuels (metal, oxide, carbide, nitride) in burner or breeder core configurations. These studies [1 to 4] have pointed out that present uncertainties on the nuclear data should be significantly reduced, in order to get full benefit from the advanced modeling and simulation initiatives. Only a parallel effort in advanced simulation and in nuclear data improvement will enable to provide designers with more general and well validated calculation tools, that would be able to meet design target accuracies.  This point can be illustrated by the inspection of the review of current and targeted uncertainties for  some SFR design parameters, as indicated in Tables 1-3 (consistent with the requirements of [5]): Table 1. Neutronics: Core Current uncertainty (SFR)Current uncertainty (SFR) Parameter Input Data Origin (A Priori) Modeling OriginTargeted Uncertainty Parameter Input Data Origin (A Priori) Modeling OriginTargeted UncertaintyMultiplication Factor, keff (ǻk/k) 1% 0.5% 0.3%  Reactivity Coefficients: Total 7% 15% 7% Power Peak 1% 3% 2%  Reactivity Coefficients: Component 20% 20% 10% Power Distribution 1% 6% 3%  Fast Flux for Damage 7% 3% 3% Conversion Ratio (Absolute Value) 5% 2% 2%  Kinetics Parameters 10% 5% 5% Control Rod Worth: Element 5% 6% 5%  Local Nuclide Densities: Major 5% 3% 2% Control Rod Worth: Total 5% 4% 2%  Local Nuclide Densities: Minor 30% 10% 10% Burnup Reactivity Swing (ǻk/k) 0.7% 0.5% 0.3%  Fuel Decay Heat at Shutdown 10% 3% 5% Table 2. Neutronics: Shielding Table 3. Neutronics: Fuel CycleCurrent uncertainty (SFR)Current uncertainty (SFR) Parameter Input Data Origin (A Priori) Modeling OriginTargeted Uncertainty Parameter Input Data Origin (A Priori) Modeling OriginTargeted UncertaintyOut of Core Coolant Activation 70% 70% 50%  Neutron Dose at Fuel Fabrication 15% 15% 10% Shield Dimensioning (Total Flux) 70% 30% 20%  Decay Heat of Spent Fuel at Repository 50% 15% 20% Structural Damage Out of Core (Total Flux) 40% 30% 20%  Radiotoxicity  at Repository 50% 15% 20% These tight design target accuracies, which justified in a consolidated phase of design, in order to comply with safety and optimization requirements and objectives, can only be met if very accurate nuclear data are used for a large number of isotopes, reaction types and energy ranges. The required accuracies on the nuclear data are such that it is difficult to meet them using only differential experiments, even if innovative experimental techniques are used. The use of integral experiments has been essential in the past to insure enhanced predictions for power fast reactor cores. In some cases, these integral experiments have been documented in an effective manner and associated uncertainties are well understood.    A combined use of scientifically based covariance data and of integral experiments can be made using advanced statistical adjustment techniques (see, e.g., [6]). These techniques can provide in a first step adjusted nuclear data for a wide range of applications, together with new, improved covariance data and bias factors (with reduced uncertainties) for the required design parameters, in order to meet design target accuracies. The method can be further improved to “adjust” physical parameters and to obtain in a second phase, a fully “adjusted” data file.  Uncertainty reduction needed to meet integral parameter target accuracies for allsystems As an example of the accuracy requirements to meet design target accuracies for innovative fast reactors, in [7] a study has been performed in order to quantify the requirements to meet simultaneously target accuracies such those indicated in Table 1 for a wide range of fast reactors with different coolants, fuel type, MA content in the fuel, being iso-generators or burners and critical or sub-critical. In practice the following systems have been considered: ABTR: Na-cooled Pu burner, with Conversion Ratio CR~0.5; SFR: Na-cooled TRU burner with CR~0.25; EFR: Na-cooled FR for homogeneous TRU recycle and CR~1; GFR: Gas-cooled FR for homogeneous TRU recycle and CR~1; LFR:  Lead-cooled FR for homogeneous TRU recycle and CR~0.8; ADMAB: Lead-cooled ADS with U-free fuel and Pu/MA~1/2. A summary of the results is given in the following table: Table 4. ABTR, SFR, EFR, GFR, LFR, ADMAB: Uncertainty Reduction Requirements to Meet Integral Parameter Target Accuracies Uncertainty (%)Uncertainty (%)Uncertainty (%)Isotope Cross- Section Energy RangeInitial Target IsotopeCross- Section Energy 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 B10 ıcapt9.12 - 2.03 keV 8.0 6.0 Pu240 ıfiss2.03 - 0.454 keV 21.6 11.8 U238 ıinel183 - 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 Si28 ıcapt19.6 - 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 Si28 ıinel 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 Pb ıinel6.07 - 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 Bi209 ıinel 1.35 - 0.498 MeV 41.8 4.3 U238 ıcapt 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 N15 ıel183 - 67.4 keV 5.0 2.3    Na23 ıinel1.35 - 0.498 MeV 28.0 10.5        Zr90 ıinel6.07 - 2.23 MeV 18.0 3.3 These results confirm the very significant uncertainty reduction needed to meet target accuracies on important design parameters. In some cases (e.g. inelastic of U-238), the required reduction seems hard to be met with differential measurements only. The data statistical adjustment and bias factor method The adjustment and bias factor method [6] makes use of: • “a priori” nuclear data covariance information,  • integral experiments analysis to define C/E values, • integral experiment uncertainties, in order to evaluate “a priori” uncertainties on reference design performance parameters, to reduce these uncertainties using integral experiments (“a posteriori” uncertainties on performance parameters) and to define “adjusted“ nuclear data and associated “a posteriori” covariance data. A crucial step is the selection of a set of relevant experiments. This task can be performed using sensitivity analysis of selected configurations including reference design configurations for a wide range of integral parameters related to the core performances (critical mass, reactivity coefficients, control rod worth, power distributions etc), and fuel cycle parameters (reactivity loss/cycle, decay heat, transmutation rates, neutron sources and doses of spent fuel etc). A second crucial step is the selection of science based covariance data for uncertainty evaluation and target accuracy assessment. Finally, the analysis of experiments should be performed using the best methods available, with some redundancy to avoid systematic errors. Finally the adjustment procedure allows to use calculation/experiment discrepancies (and associated uncertainties) in a statistical adjustment. The “adjustment” procedure can be generalized and applied to the physical parameters that enter into the model description of a specific cross-section type. This generalized method is called “consistent method” [8], and is shortly described below.  If the cross-section is schematically described as:    ip,EfE  Vthe sensitivity coefficients of the cross-section to the variations of the parameters pi can be obtained from the model codes as:   f/pp/fS iipi GG These sensitivity coefficients can then be folded with standard sensitivity coefficients of integral parameters R to cross-section ı variations:    piii SSR/pp/R V GGA correlation can now be established among integral parameters and basic physics parameters, and the adjustment procedure outlined previously, can be applied to the pi parameters, if uncertainties (covariance data) are provided for the parameters.  Finally, if these parameters are part of the data file (e.g. the temperature values associated to the evaporation spectrum describing the secondary neutron distribution in inelastic scattering) the file itself can be in principle “adjusted”. Integral experiments Integral experiments have been performed in large number in the past. Future experiments can be foreseen only on a few installations and at a later date (this is the case of the MASURCA critical facility at CEA-Cadarache). Some of the most representative (and “clean”) integral experiments are being collected within the NEA-NSC project IRPHEP [9]. In this respect, it should be stressed that documentation is essential: experimental conditions and environment, “credible” uncertainties, correlations among experiments.  Another very crucial point is the availability and share of power reactor experiments, e.g. ¾ Physics experiments at reactor start-up (e.g. SUPERPHENIX); ¾ Operation experiments (e.g. EBR-II, FFTF, PHENIX, JOYO); ¾ New experiments (e.g. at the future MONJU start-up); ¾ Irradiation experiments (e.g. PROFIL and TRAPU experiments in PHENIX). Issues and perspectives Innovative reactor system design and requirements for improved economy and safety, will require significant improvements beyond current simulation tools, associated to significant improvements in their validation, in particular in order to cope with very tight requirements on nuclear data uncertainties. In this respect, a robust validation approach can be used in the reactor core and fuel cycle physics field. Powerful and flexible sensitivity analysis methods and tools are available (see [10]), and a large effort is underway to assess nuclear covariance data in very comprehensive way [11].  However, a choice of appropriate integral experiments has to be carefully made. There is the need for integral experiments with well documented uncertainty values and possible correlations among different experiment types. There is also the need for an increased role of power reactor integral experiments.  The experiment analysis should be performed with more than one reference methods, as far as possible independent from each other, in order to reduce or eliminate the risks of systematic method errors. Since the result of the validation will provide bias factors and reduced uncertainties on most design parameters, together with statistically “adjusted” cross-sections with new associated covariance data, it will be needed to define the protocols for using them as application libraries. However, and more important, the “adjustments” will have to be interpreted as “trends” to be used by nuclear data evaluators, in order to improve current data files (such as ENDF/B-VII). We have also described in the present paper, how the statistical adjustment procedure can be generalized to provide “adjustments” of physics parameters that enter into the models which describe the different cross-sections.  Finally, it should be stressed that the present data bases of integral experiments are relatively wide, even if not always documented in a satisfactory way. Future design studies, new core and fuel types, new core configurations, could require selected, high accuracy integral experiments, to be performed in the few adapted critical facilities, still available world-wide. 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] G. Aliberti et al., “Impact of Nuclear Data Uncertainties on Transmutation of Actinides in Accelerator-Driven Assemblies,” Nucl. Sci. Eng., 46, 13-50 (2004). [5] G. Palmiotti, J. Cahalan, P. Pfeiffer, T. Sofu, T. Taiwo, T. Wei, A. Yacout, W. Yang, A. Siegel, Z. Insepov, M. Anitescu, P. Hovland, C. Pereira, M. Regalbuto, J. Copple, M. Williamson, “Requirements for Advanced Simulation of Nuclear Reactor and Chemical Separation Plants”, ANL-AFCI-168, May 2006. [6] M. Salvatores, G. Aliberti, G. Palmiotti, “Nuclear Data Validation and Fast Reactor Design Performances Uncertainty Reduction”, ANS, Boston, June 2007. [7] G. Aliberti, G. Palmiotti and M. Salvatores “Simultaneous Nuclear Data Target Accuracy Study for Innovative Fast Reactors”. This Workshop. [8] D’Angelo, A., Oliva, A., Palmiotti, G., Salvatores, M., Zero, S., “ Consistent Utilization of Shielding Benchmark Experiments”. Nuclear Science Engineering, 65, 477, 1978. [9] “International Handbook of Evaluated Reactor Physics Benchmark Experiments”. NEA/NSC/DOC(2006). [10] G.Palmiotti, M.Salvatores, G.Aliberti, “Methods in Use for Sensitivity Analysis, Uncertainty Evaluation, and Target Accuracy Assessment”. This Workshop. [11] P.Oblozinsky. This Workshop. 

Validation of Simulation Codes for Future Systems: Motivations, Approach and the Role of Nuclear Data

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Validation of Simulation Codes for Future Systems: Motivations, Approach and the Role of Nuclear Data

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