| 1. |
- Rochman, D., et al.
(författare)
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A Bayesian Monte Carlo method for fission yield covariance information
- 2016
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Ingår i: Annals of Nuclear Energy. - : Elsevier BV. - 0306-4549 .- 1873-2100. ; 95, s. 125-134
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Tidskriftsartikel (refereegranskat)abstract
- The present work proposes a Bayesian method to combine theoretical fission yields with a set of reference data. These two sources of information are merged using a Monte Carlo process, and leads to a so-called Bayesian Monte Carlo update. Examples are presented for the independent fission yields of four major actinides, using the GEF code as a source of theoretical calculations and an evaluated library of fission yields for the reference data. The impact of the updated fission yields and their covariances is shown for two distinct applications: a UO2 pincell with burn-up up to 40 GWD/tHM and decay heat calculations of a thermal neutron pulse on U-235 and Pu-239.
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| 2. |
- Rochman, D., et al.
(författare)
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A Statistical Analysis of Evaluated Neutron Resonances with TARES for JEFF-3.3, JENDL-4.0, ENDF/B-VIII.0 and TENDL-2019
- 2020
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Ingår i: Nuclear Data Sheets. - : ACADEMIC PRESS INC ELSEVIER SCIENCE. - 0090-3752 .- 1095-9904. ; 163, s. 163-190
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Tidskriftsartikel (refereegranskat)abstract
- In this paper, a statistical analysis of resonance evaluations from the most recent nuclear data libraries is performed with the code TARES. A description of the TARES framework is provided, but also for its use in the production of resonance parameters for the entire TENDL library. Various observables are calculated (e.g., thermal cross sections, capture integrals but also D-0 and other average resonance quantities) for different libraries and compared to experimental values when available. Finally, for isotopes with no or little information, details are provided for the production of statistical resonances, based on a variety of compound nucleus reaction models.
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| 3. |
- Alhassan, Erwin, et al.
(författare)
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Benchmark selection methodology for reactor calculations and nuclear data uncertainty reduction
- 2015
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Ingår i: Annals of Nuclear Energy. - 0306-4549 .- 1873-2100.
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Tidskriftsartikel (refereegranskat)abstract
- Criticality, reactor physics and shielding benchmarks are expected to play important roles in GEN-IV design, safety analysis and in the validation of analytical tools used to design these reactors. For existing reactor technology, benchmarks are used for validating computer codes and for testing nuclear data libraries. Given the large number of benchmarks available, selecting these benchmarks for specic applications can be rather tedious and difficult. Until recently, the selection process has been based usually on expert judgement which is dependent on the expertise and the experience of the user and there by introducing a user bias into the process. This approach is also not suitable for the Total Monte Carlo methodology which lays strong emphasis on automation, reproducibility and quality assurance. In this paper a method for selecting these benchmarks for reactor calculation and for nuclear data uncertainty reduction based on the Total Monte Carlo (TMC) method is presented. For reactor code validation purposes, similarities between a real reactor application and one or several benchmarks are quantied using a similarity index while the Pearson correlation coecient is used to select benchmarks for nuclear data uncertainty reduction. Also, a correlation based sensitivity method is used to identify the sensitivity of benchmarks to particular nuclear reactions. Based on the benchmark selection methodology, two approaches are presented for reducing nuclear data uncertainty using integral benchmark experiments as an additional constraint in the TMC method: a binary accept/reject and a method of assigning file weights using the likelihood function. Finally, the methods are applied to a full lead-cooled fast reactor core and a set of criticality benchmarks. Signicant reductions in Pu-239 and Pb-208 nuclear data uncertainties were obtained after implementing the two methods with some benchmarks.
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| 4. |
- Alhassan, Erwin, et al.
(författare)
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Iterative Bayesian Monte Carlo for nuclear data evaluation
- 2022
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Ingår i: NUCLEAR SCIENCE AND TECHNIQUES. - : Springer Nature. - 1001-8042 .- 2210-3147. ; 33:4
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Tidskriftsartikel (refereegranskat)abstract
- In this work, we explore the use of an iterative Bayesian Monte Carlo (iBMC) method for nuclear data evaluation within a TALYS Evaluated Nuclear Data Library (TENDL) framework. The goal is to probe the model and parameter space of the TALYS code system to find the optimal model and parameter sets that reproduces selected experimental data. The method involves the simultaneous variation of many nuclear reaction models as well as their parameters included in the TALYS code. The `best' model set with its parameter set was obtained by comparing model calculations with selected experimental data. Three experimental data types were used: (1) reaction cross sections, (2) residual production cross sections, and (3) the elastic angular distributions. To improve our fit to experimental data, we update our 'best' parameter set-the file that maximizes the likelihood function-in an iterative fashion. Convergence was determined by monitoring the evolution of the maximum likelihood estimate (MLE) values and was considered reached when the relative change in the MLE for the last two iterations was within 5%. Once the final 'best' file is identified, we infer parameter uncertainties and covariance information to this file by varying model parameters around this file. In this way, we ensured that the parameter distributions are centered on our evaluation. The proposed method was applied to the evaluation of p+ Co-59 between 1 and 100 MeV. Finally, the adjusted files were compared with experimental data from the EXFOR database as well as with evaluations from the TENDL-2019, JENDL/He-2007 and JENDL-4.0/HE nuclear data libraries.
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| 5. |
- Alhassan, Erwin, et al.
(författare)
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On the use of integral experiments for uncertainty reduction of reactor macroscopic parameters within the TMC methodology
- 2016
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Ingår i: Progress in nuclear energy (New series). - : Elsevier BV. - 0149-1970 .- 1878-4224. ; 88, s. 43-52
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Tidskriftsartikel (refereegranskat)abstract
- The current nuclear data uncertainties observed in reactor safety parameters for some nuclides call for safety concerns especially with respect to the design of GEN-IV reactors and must therefore be reduced significantly. In this work, uncertainty reduction using criticality benchmark experiments within the Total Monte Carlo methodology is presented. Random nuclear data libraries generated are processed and used to analyze a set of criticality benchmarks. Since the calculated results for each random nuclear data used are different, an algorithm was used to select (or assign weights to) the libraries which give a good description of experimental data for the analyses of the benchmarks. The selected or weighted libraries were then used to analyze the ELECTRA reactor. By using random nuclear data libraries constrained with only differential experimental data as our prior, the uncertainties observed were further reduced by constraining the files with integral experimental data to obtain a posteriori uncertainties on the k(eff). Two approaches are presented and compared: a binary accept/reject and a method of assigning file weights based on the likelihood function. Significant reductions in (PU)-P-239 and Pb-208 nuclear data uncertainties in the k(eff) were observed after implementing the two methods with some criticality benchmarks for the ELELIRA reactor. (C) 2015 Elsevier Ltd. All rights reserved.
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| 6. |
- Alhassan, Erwin, et al.
(författare)
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Selecting benchmarks for reactor simulations : an application to a Lead Fast Reactor
- 2016
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Ingår i: Annals of Nuclear Energy. - : Elsevier BV. - 0306-4549 .- 1873-2100. ; 96, s. 158-169
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Tidskriftsartikel (refereegranskat)abstract
- For several decades reactor design has been supported by computer codes for the investigation of reactor behavior under both steady state and transient conditions. The use of computer codes to simulate reactor behavior enables the investigation of various safety scenarios saving time and cost. There has been an increase in the development of in-house (local) codes by various research groups in recent times for preliminary design of specific or targeted nuclear reactor applications. These codes must be validated and calibrated against experimental benchmark data with their evolution and improvements. Given the large number of benchmarks available, selecting these benchmarks for reactor calculations and validation of simulation codes for specific or target applications can be rather tedious and difficult. In the past, the traditional approach based on expert judgement using information provided in various handbooks, has been used for the selection of these benchmarks. This approach has been criticized because it introduces a user bias into the selection process. This paper presents a method for selecting these benchmarks for reactor calculations for specific reactor applications based on the Total Monte Carlo (TMC) method. First, nuclear model parameters are randomly sampled within a given probability distribution and a large set of random nuclear data files are produced using the TALYS code system. These files are processed and used to analyze a target reactor system and a set of criticality benchmarks. Similarity between the target reactor system and one or several benchmarks is quantified using a similarity index. The method has been applied to the European Lead Cooled Reactor (ELECTRA) and a set of plutonium and lead sensitive criticality benchmarks using the effective multiplication factor (keffkeff). From the study, strong similarity were observed in the keffkeff between ELECTRA and some plutonium and lead sensitive criticality benchmarks. Also, for validation purposes, simulation results for a list of selected criticality benchmarks simulated with the MCNPX and SERPENT codes using different nuclear data libraries have been compared with experimentally measured benchmark keff values.
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| 7. |
- Alhassan, Erwin, et al.
(författare)
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Uncertainty and correlation analysis of lead nuclear data on reactor parameters for the European Lead Cooled Training Reactor
- 2015
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Ingår i: Annals of Nuclear Energy. - : Elsevier BV. - 0306-4549 .- 1873-2100. ; 75, s. 26-37
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Tidskriftsartikel (refereegranskat)abstract
- The Total Monte Carlo (TMC) method was used in this study to assess the impact of Pb-204, 206, 207, 208 nuclear data uncertainties on reactor safety parameters for the ELECTRA reactor. Relatively large uncertainties were observed in the k-eff and the coolant void worth (CVW) for all isotopes except for Pb-204 with signicant contribution coming from Pb-208 nuclear data; the dominant eectcame from uncertainties in the resonance parameters; however, elastic scattering cross section and the angular distributions also had signicant impact. It was also observed that the k-eff distribution for Pb-206, 207, 208 deviates from a Gaussian distribution with tails in the high k-eff region. An uncertainty of 0.9% on the k-eff and 3.3% for the CVW due to lead nuclear data were obtained. As part of the work, cross section-reactor parameter correlations were also studied using a Monte Carlo sensitivity method. Strong correlations were observed between the k-eff and (n,el) cross section for all the lead isotopes. The correlation between the (n,inl) and the k-eff was also found to be signicant.
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| 8. |
- Helgesson, Petter, 1986-, et al.
(författare)
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Combining Total Monte Carlo and Unified Monte Carlo : Bayesian nuclear data uncertainty quantification from auto-generated experimental covariances
- 2017
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Ingår i: Progress in nuclear energy (New series). - : Elsevier. - 0149-1970 .- 1878-4224. ; 96, s. 76-96
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Tidskriftsartikel (refereegranskat)abstract
- The Total Monte Carlo methodology (TMC) for nuclear data (ND) uncertainty propagation has been subject to some critique because the nuclear reaction parameters are sampled from distributions which have not been rigorously determined from experimental data. In this study, it is thoroughly explained how TMC and Unified Monte Carlo-B (UMC-B) are combined to include experimental data in TMC. Random ND files are weighted with likelihood function values computed by comparing the ND files to experimental data, using experimental covariance matrices generated from information in the experimental database EXFOR and a set of simple rules. A proof that such weights give a consistent implementation of Bayes' theorem is provided. The impact of the weights is mainly studied for a set of integral systems/applications, e.g., a set of shielding fuel assemblies which shall prevent aging of the pressure vessels of the Swedish nuclear reactors Ringhals 3 and 4.In this implementation, the impact from the weighting is small for many of the applications. In some cases, this can be explained by the fact that the distributions used as priors are too narrow to be valid as such. Another possible explanation is that the integral systems are highly sensitive to resonance parameters, which effectively are not treated in this work. In other cases, only a very small number of files get significantly large weights, i.e., the region of interest is poorly resolved. This convergence issue can be due to the parameter distributions used as priors or model defects, for example.Further, some parameters used in the rules for the EXFOR interpretation have been varied. The observed impact from varying one parameter at a time is not very strong. This can partially be due to the general insensitivity to the weights seen for many applications, and there can be strong interaction effects. The automatic treatment of outliers has a quite large impact, however.To approach more justified ND uncertainties, the rules for the EXFOR interpretation shall be further discussed and developed, in particular the rules for rejecting outliers, and random ND files that are intended to describe prior distributions shall be generated. Further, model defects need to be treated.
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| 9. |
- Helgesson, Petter, 1986-, et al.
(författare)
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Sampling of systematic errors to estimate likelihood weights in nuclear data uncertainty propagation
- 2016
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Ingår i: Nuclear Instruments and Methods in Physics Research Section A. - : Elsevier. - 0168-9002 .- 1872-9576. ; 807, s. 137-149
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Tidskriftsartikel (refereegranskat)abstract
- In methodologies for nuclear data (ND) uncertainty assessment and propagation based on random sampling, likelihood weights can be used to infer experimental information into the distributions for the ND. As the included number of correlated experimental points grows large, the computational time for the matrix inversion involved in obtaining the likelihood can become a practical problem. There are also other problems related to the conventional computation of the likelihood, e.g., the assumption that all experimental uncertainties are Gaussian. In this study, a way to estimate the likelihood which avoids matrix inversion is investigated; instead, the experimental correlations are included by sampling of systematic errors. It is shown that the model underlying the sampling methodology (using univariate normal distributions for random and systematic errors) implies a multivariate Gaussian for the experimental points (i.e., the conventional model). It is also shown that the likelihood estimates obtained through sampling of systematic errors approach the likelihood obtained with matrix inversion as the sample size for the systematic errors grows large. In studied practical cases, it is seen that the estimates for the likelihood weights converge impractically slowly with the sample size, compared to matrix inversion. The computational time is estimated to be greater than for matrix inversion in cases with more experimental points, too. Hence, the sampling of systematic errors has little potential to compete with matrix inversion in cases where the latter is applicable. Nevertheless, the underlying model and the likelihood estimates can be easier to intuitively interpret than the conventional model and the likelihood function involving the inverted covariance matrix. Therefore, this work can both have pedagogical value and be used to help motivating the conventional assumption of a multivariate Gaussian for experimental data. The sampling of systematic errors could also be used in cases where the experimental uncertainties are not Gaussian, and for other purposes than to compute the likelihood, e.g., to produce random experimental data sets for a more direct use in ND evaluation.
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| 10. |
- Koning, Arjan, et al.
(författare)
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TENDL : Complete Nuclear Data Library for Innovative Nuclear Science and Technology
- 2019
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Ingår i: Nuclear Data Sheets. - : ACADEMIC PRESS INC ELSEVIER SCIENCE. - 0090-3752 .- 1095-9904. ; 155, s. 1-55
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Tidskriftsartikel (refereegranskat)abstract
- The TENDL library is now established as one of the major nuclear data libraries in the world, striving for completeness and quality of nuclear data files for all isotopes, evaluation methods, processing and applied performance. To reach this status, some basic principles have been applied which sets it apart from other libraries: reproducible dedicated evaluations when differential data are available, through determination of nuclear models implemented in TALYS and their parameters, completeness (with or without experimental data), format and processing standardization, automation of production and reproducibility. In this paper, we will outline how such an approach has become a reality, and recall some of the past successes since the first TENDL release in 2008. Next, we will demonstrate the performance of the latest TENDL releases for different application fields, as well as new approaches for uncertainty quantification based on Bayesian inference methods and possible differential and integral adjustments. Also, current limitations of the library performances due to modelling and needs for new and more precise experimental data will be outlined.
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