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  • 1. Adoo, N.A.
    et al.
    Nyarko, B.J.B.
    Akaho, E.H.K.
    Alhassan, Erwin
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
    Agbodemegbe, V.Y.
    Bansah, C.Y.
    Della, R.
    Determination of thermal hydraulic data of GHARR-1 under reactivity insertion transients using the PARET/ANL code2011In: Nuclear Engineering and Design, ISSN 0029-5493, E-ISSN 1872-759X, Vol. 241, p. 5303-5210Article in journal (Refereed)
    Abstract [en]

    The PARET/ANL code has been adapted by the IAEA for testing transient behaviour in research reactors since it provides a coupled thermal hydrodynamic and point kinetics capability for estimating thermalhydraulic margins. A two-channel power peaking profile of the Ghana Research Reactor-1 (GHARR-1) has been developed for the PARET/ANL (Version 7.3; 2007) using the Monte Carlo N-Particle code (MCNP) to determine the thermal hydraulic data for reactivity insertion transients in the range of 2.0×10^−3k/k to 5.5×10^−3k/k. Peak clad and coolant temperatures ranged from 59.18 ◦C to 112.36 ◦C and 42.95 ◦C to 79.42 ◦C respectively. Calculated safety margins (DNBR) satisfied the MNSR thermal hydraulic design criteria for which no boiling occurs in the reactor core. The generated thermal hydraulic data demonstrated a high inherent safety feature of GHARR-1 for which the high negative reactivity feedback of the moderator limits power excursion and consequently the escalation of the clad temperature.

  • 2.
    Al-Adili, Ali
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
    Alhassan, Erwin
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Neutron Research, Applied Nuclear Physics.
    Gustavsson, Cecilia
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
    Helgesson, Petter
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
    Jansson, Kaj
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
    Koning, Arjan
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics. Nucl Res & Consultancy Grp NRG, Petten, Netherlands.
    Lantz, Mattias
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
    Mattera, Andrea
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
    Prokofiev, Alexander V.
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics. Uppsala University, The Svedberg Laboratory.
    Rakopoulos, Vasileios
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
    Sjöstrand, Henrik
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
    Solders, Andreas
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
    Tarrío, Diego
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
    Österlund, Michael
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
    Pomp, Stephan
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
    Fission Activities of the Nuclear Reactions Group in Uppsala2015In: Scientific Workshop on Nuclear Fission Dynamics and the Emission of Prompt Neutrons and Gamma Rays, THEORY-3 / [ed] Franz-Josef Hambsch and Nicolae Carjan, 2015, p. 145-149Conference paper (Refereed)
    Abstract [en]

    This paper highlights some of the main activities related to fission of the nuclear reactions group at Uppsala University. The group is involved for instance in fission yield experiments at the IGISOL facility, cross-section measurements at the NFS facility, as well as fission dynamics studies at the IRMM JRC-EC. Moreover, work is ongoing on the Total Monte Carlo (TMC) methodology and on including the GEF fission code into the TALYS nuclear reaction code. Selected results from these projects are discussed.

  • 3.
    Alhassan, Erwin
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
    Nuclear data uncertainty quantification and data assimilation for a lead-cooled fast reactor: Using integral experiments for improved accuracy2015Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    For the successful deployment of advanced nuclear systems and optimization of current reactor designs, high quality nuclear data are required. Before nuclear data can be used in applications they must first be evaluated, tested and validated against a set of integral experiments, and then converted into formats usable for applications. The evaluation process in the past was usually done by using differential experimental data which was then complemented with nuclear model calculations. This trend is fast changing due to the increase in computational power and tremendous improvements in nuclear reaction models over the last decade. Since these models have uncertain inputs, they are normally calibrated using experimental data. However, these experiments are themselves not exact. Therefore, the calculated quantities of model codes such as cross sections and angular distributions contain uncertainties. Since nuclear data are used in reactor transport codes as input for simulations, the output of transport codes contain uncertainties due to these data as well. Quantifying these uncertainties is important for setting safety margins; for providing confidence in the interpretation of results; and for deciding where additional efforts are needed to reduce these uncertainties. Also, regulatory bodies are now moving away from conservative evaluations to best estimate calculations that are accompanied by uncertainty evaluations.

    In this work, the Total Monte Carlo (TMC) method was applied to study the impact of nuclear data uncertainties from basic physics to macroscopic reactor parameters for the European Lead Cooled Training Reactor (ELECTRA). As part of the work, nuclear data uncertainties of actinides in the fuel, lead isotopes within the coolant, and some structural materials have been investigated. In the case of the lead coolant it was observed that the uncertainty in the keff and the coolant void worth (except in the case of 204Pb), were large, with the most significant contribution coming from 208Pb. New 208Pb and 206Pb random nuclear data libraries with realistic central values have been produced as part of this work. Also, a correlation based sensitivity method was used in this work, to determine parameter - cross section correlations for different isotopes and energy groups.

    Furthermore, an accept/reject method and a method of assigning file weights based on the likelihood function are proposed for uncertainty reduction using criticality benchmark experiments within the TMC method. It was observed from the study that a significant reduction in nuclear data uncertainty was obtained for some isotopes for ELECTRA after incorporating integral benchmark information. As a further objective of this thesis, a method for selecting benchmark for code validation for specific reactor applications was developed and applied to the ELECTRA reactor. Finally, a method for combining differential experiments and integral benchmark data for nuclear data adjustments is proposed and applied for the adjustment of neutron induced 208Pb nuclear data in the fast energy region.

  • 4.
    Alhassan, Erwin
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
    Sjöstrand, Henrik
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
    Duan, Junfeng
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
    Gustavsson, Cecilia
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
    Koning, Arjan
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
    Pomp, Stephan
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
    Rochman, Dimitri
    Nuclear Research and Consultancy Group.
    Österlund, Michael
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
    Combining Total Monte Carlo and Benchmarks for Nuclear Data Uncertainty Propagation on a Lead Fast Reactor's Safety Parameters2014In: Nuclear Data Sheets, ISSN 0090-3752, E-ISSN 1095-9904, Vol. 118, p. 542-544Article in journal (Refereed)
    Abstract [en]

    Analyses are carried out to assess the impact of nuclear data uncertainties on some reactor safety parameters for the European Lead Cooled Training Reactor (ELECTRA) using the Total Monte Carlo method. A large number of Pu-239 random ENDF-format libraries, generated using the TALYS based system were processed into ACE format with NJOY99.336 code and used as input into the Serpent Monte Carlo code to obtain distribution in reactor safety parameters. The distribution in keff obtained was compared with the latest major nuclear data libraries – JEFF-3.1.2, ENDF/B-VII.1 and JENDL-4.0. A method is proposed for the selection of benchmarks for specific applications using the Total Monte Carlo approach based on a correlation observed between the keff of a given system and the benchmark. Finally, an accept/reject criteria was investigated based on chi squared values obtained using the Pu-239 Jezebel criticality benchmark. It was observed that nuclear data uncertainties were reduced considerably from 748 to 443 pcm.

  • 5.
    Alhassan, Erwin
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
    Sjöstrand, Henrik
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
    Duan, Junfeng
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
    Gustavsson, Cecilia
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
    Pomp, Stephan
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
    Österlund, Michael
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
    Rochman, Dimitri
    Nuclear Research and Consultancy Group.
    Koning, Arjan J.
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
    Uncertainty analysis of Lead cross sections on reactor safety for ELECTRA2016In: SNA + MC 2013 - Joint International Conference on Supercomputing in Nuclear Applications + Monte Carlo, 2016, article id 02401Conference paper (Refereed)
    Abstract [en]

    The Total Monte Carlo (TMC) method was used in this study to assess the impact of Pb-206, 207 and 208 nucleardata uncertainties on k-eff , beta-eff, coolant temperature coefficient, the coolant void worth for the ELECTRA reactor. Relatively large uncertainties were observed in the k-eff and the coolant void worth for all the isotopes with significant contribution coming from Pb-208 nuclear data. The large Pb-208 nuclear data uncertainty observed was further investigated by studying the impact of partial channels on the k-eff and beta-eff. Various sections of ENDF file: elasticscattering (n,el), inelastic scattering (n,inl), neutron capture (n,gamma), (n,2n), resonance parameters and the angular distribution were varied randomly and distributions in k-eff and beta-eff obtained. The dominant contributions to the uncertainty in the k-eff from Pb-208 came from uncertainties in the resonance parameters; however, elastic scattering cross section and the angular distribution also had significant impact. The impact of nuclear data uncertainties on the beta-eff was observed to be small.

  • 6.
    Alhassan, Erwin
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
    Sjöstrand, Henrik
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
    Duan, Junfeng
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
    Helgesson, Petter
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
    Pomp, Stephan
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
    Österlund, Michael
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
    Rochman, Dimitri
    Nuclear Research and Consultancy Group.
    Koning, Arjan J.
    Nuclear Research and Consultancy Group.
    Selecting benchmarks for reactor calculations2014In: PHYSOR 2014 - The Role of Reactor Physics toward a Sustainable Future, 2014Conference paper (Refereed)
    Abstract [en]

    Criticality, reactor physics, fusion and shielding benchmarks are expected to play important roles in GENIV design, safety analysis and in the validation of analytical tools used to design these reactors. For existing reactor technology, benchmarks are used to validate computer codes and test nuclear data libraries. However the selection of these benchmarks are usually done by visual inspection which is dependent on the expertise and the experience of the user and there by resulting in a user bias in the process. In this paper we present a method for the selection of these benchmarks for reactor applications based on Total Monte Carlo (TMC). Similarities betweenan application case and one or several benchmarks are quantified using the correlation coefficient. Based on the method, we also propose an approach for reducing nuclear data uncertainty using integral benchmark experiments as an additional constrain on nuclear reaction models: a binary accept/reject criterion. Finally, the method was applied to a full Lead Fast Reactor core and a set of criticality benchmarks.

  • 7.
    Alhassan, Erwin
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
    Sjöstrand, Henrik
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
    Helgesson, Petter
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
    Arjan, J. Koning
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
    Österlund, Michael
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
    Pomp, Stephan
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
    Dimitri, Rochman
    Nuclear Research and Consultancy Group.
    Uncertainty and correlation analysis of lead nuclear data on reactor parameters for the European Lead Cooled Training Reactor2015In: Annals of Nuclear Energy, ISSN 0306-4549, E-ISSN 1873-2100, Vol. 75, p. 26-37Article in journal (Refereed)
    Abstract [en]

    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.

  • 8.
    Alhassan, Erwin
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
    Sjöstrand, Henrik
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
    Helgesson, Petter
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
    Österlund, Michael
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
    Pomp, Stephan
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
    Arjan, J. Koning
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics. Int Atom Energy Commiss, Nucl Data Sect, Vienna, Austria.
    Rochman, Dimitri
    Paul Scherrer Inst, Reactor Phys & Syst Behav Lab, CH-5232 Villigen, Switzerland.
    Selecting benchmarks for reactor simulations: an application to a Lead Fast Reactor2016In: Annals of Nuclear Energy, ISSN 0306-4549, E-ISSN 1873-2100, Vol. 96, p. 158-169Article in journal (Refereed)
    Abstract [en]

    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.

  • 9.
    Alhassan, Erwin
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
    Sjöstrand, Henrik
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
    Helgesson, Petter
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
    Österlund, Michael
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
    Pomp, Stephan
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
    Koning, Arjan J.
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics. Nuclear Research and Consultancy Group, Petten, The Netherlands.
    Rochman, D.
    Laboratory for Reactor Physics Systems Behaviour, Paul Scherrer Institut, Villigen, Switzerland.
    Benchmark selection methodology for reactor calculations and nuclear data uncertainty reduction2015In: Annals of Nuclear Energy, ISSN 0306-4549, E-ISSN 1873-2100Article in journal (Refereed)
    Abstract [en]

    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.

  • 10.
    Alhassan, Erwin
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
    Sjöstrand, Henrik
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
    Helgesson, Petter
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
    Österlund, Michael
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
    Pomp, Stephan
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
    Koning, Arjan J.
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics. Int Atom Energy Commiss IAEA, Nucl Data Sect, Vienna, Austria.
    Rochman, Dmitri
    Paul Scherrer Inst, CH-5232 Villigen, Switzerland.
    On the use of integral experiments for uncertainty reduction of reactor macroscopic parameters within the TMC methodology2016In: Progress in nuclear energy (New series), ISSN 0149-1970, E-ISSN 1878-4224, Vol. 88, p. 43-52Article in journal (Refereed)
    Abstract [en]

    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.

  • 11.
    Alhassan, Erwin
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
    Sjöstrand, Henrik
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
    Rochman, Dimitri
    Helgesson, Petter
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
    J. Koning, Arjan
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics. IAEA.
    Österlund, Michael
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
    Pomp, Stephan
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
    Reducing A Priori 239Pu Nuclear Data Uncertainty In The Keff Using A Set Of Criticality Benchmarks With Different Nuclear Data Libraries2015Conference paper (Other academic)
    Abstract [en]

    In the Total Monte Carlo (TMC) method [1] developed at the Nuclear Research and Consultancy Group for nuclear data uncertainty propagation, model calculations are compared with differential experimental data and a specific a priori uncertainty is assigned to each model parameter. By varying the model parameters all together within model parameter uncertainties, a full covariance matrix is obtained with its off diagonal elements if desired [1]. In this way, differential experimental data serve as a constraint for the model parameters used in the TALYS nuclear reactions code for the production of random nuclear data files. These files are processed into usable formats and used in transport codes for reactor calculations and for uncertainty propagation to reactor macroscopic parameters of interest.

     

    Even though differential experimental data together with their uncertainties are included (implicitly) in the production of these random nuclear data files in the TMC method, wide spreads in parameter distributions have been observed, leading to large uncertainties in reactor parameters for some nuclides for the European Lead cooled Training Reactor [2]. Due to safety concerns and the development of GEN-IV reactors with their challenging technological goals, the present uncertainties should be reduced significantly if the benefits from advances in modelling and simulations are to be utilized fully [3]. In Ref.[4], a binary accept/reject approach and a more rigorous method of assigning file weights based on the likelihood function were proposed and presented for reducing nuclear data uncertainties using a set of integral benchmarks obtained from the International Handbook of Evaluated Criticality Safety Benchmark Experiments (ICSBEP). These methods are depended on the reference nuclear data library used, the combined benchmark uncertainty and the relevance of each benchmark for reducing nuclear data uncertainties for a particular reactor system. Since each nuclear data library normally comes with its own nominal values and covariance matrices, reactor calculations and uncertainties computed with these libraries differ from library to library.

     

    In this work, we apply the binary accept/reject approach and the method of assigning file weights based on the likelihood function for reducing a priori 239Pu nuclear data uncertainties for the European Lead Cooled Training Reactor (ELECTRA) using a set of criticality benchmarks. Prior and posterior uncertainties computed for ELECTRA using ENDF/B-VII.1, JEFF-3.2 and JENDL-4.0 are compared after including experimental information from over 10 benchmarks.

    [1] A.J. Koning and D. Rochman, Modern Nuclear Data Evaluation with the TALYS Code System. Nuclear Data Sheets 113 (2012) 2841-2934.

     

    [2] E. Alhassan, H. Sjöstrand, P. Helgesson, A. J. Koning, M. Österlund, S. Pomp, D. Rochman, Uncertainty and correlation analysis of lead nuclear data on reactor parameters for the European Lead Cooled Training reactor (ELECTRA). Annals of Nuclear Energy 75 (2015) 26-37.

     

    [3] G. Palmiotti, M. Salvatores, G. Aliberti, H. Hiruta, R. McKnight, P. Oblozinsky, W. Yang, A global approach to the physics validation of simulation codes for future nuclear systems, Annals of Nuclear Energy 36 (3) (2009) 355-361.

     

    [4] E. Alhassan, H. Sjöstrand, J. Duan, P. Helgesson, S. Pomp, M. Österlund, D. Rochman, A.J. Koning, Selecting benchmarks for reactor calculations: In proc. PHYSOR 2014 - The Role of Reactor Physics toward a Sustainable Future, kyoto, Japan, Sep. 28 - 3 Oct. (2014).

  • 12. Bansah, C. Y.
    et al.
    Akaho, E. H. K.
    Ayensu, A.
    Adoo, N. A.
    Agbodemegbe, V. Y.
    Alhassan, Erwin
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
    Della, R.
    Theoretical model for predicting the relative timings of potential failures in steam generator tubes of a PWR during a severe accident2013In: Annals of Nuclear Energy, ISSN 0306-4549, E-ISSN 1873-2100, Vol. 59, p. 10-15Article, review/survey (Refereed)
    Abstract [en]

    During certain severe reactor accidents such as station-blackout accidents, countercurrent natural circulation flow could develop within the reactor coolant system. Natural circulation flow is very important because of transfer of decay energy from the core to other parts of the reactor coolant system. The associated heat-ups of the reactor coolant system structures can lead to pressure boundary failures with notable vulnerabilities in the pressurizer surge line, the hot leg nozzles and the steam generator tubes. The potential for a steam generator tube failure has been of particular concern because fission products could be released to the environment through such a failure. To solve the problem of steam generator tube failure, a computer code - Steam Generator Mitigation Program (SGMP), written in FORTRAN 95 computes the recirculation ratio (RR) and the mixing fraction (MF) which are the main parameters used in characterizing natural circulation. In the flow analysis, the RR and MF were respectively found to be 2.4 +/- 0.3 and 0.8 +/- 0.17. The results obtained showed that the natural circulation would delay the failure time of the steam generator tubes and is in good qualitative agreement with results from literature. 

  • 13.
    Boafo, E.K.
    et al.
    National Nuclear Research Institute, Ghana Atomic Energy Commission.
    Alhassan, Erwin
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
    Akaho, E.H.K.
    National Nuclear Research Institute, Ghana Atomic Energy Commission.
    Utilizing the burnup capability in MCNPX to perform depletion analysisof an MNSR fuel2014In: Annals of Nuclear Energy, ISSN 0306-4549, E-ISSN 1873-2100, Vol. 73, p. 478-483Article in journal (Refereed)
    Abstract [en]

    In this work, we present results of fuel depletion analyses performed for a potential LEU core of Ghana’s Miniature Neutron Source Reactor (GHARR-1) using the Monte Carlo N-particle extended (MCNPX) neutron transport code. Depletion calculation was carried out for the reactor core from the Beginning of Life (BOL) to the End of Life (EOL) which corresponds to 10 years of reactor operation. The amounts of uranium and plutonium actinides were estimated at BOL and EOL of the core. Decay heat removal rate for the MNSR after reactor shut down was investigated due to its significance to reactor safety. Inventory of fission products produced as a result of burnup was also calculated. The results show that a maximum discharge burnup equivalent to 0.568% of U-235 was consumed at EOL equivalent to operating the reactor for 200 Effective Full Power Days (EFPD), while the amount of Pu-239 produced was not significant.Also, the decay heat decreased exponentially after reactor shutdown confirming that decay heat will be removed in the system by natural circulation after shutdown and thus guaranteeing the safety of the reactor.

  • 14.
    Della, Richard
    et al.
    Ghana Atomic Energy Commission.
    Alhassan, Erwin
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy.
    Adoo, Nana Ansah
    Ghana Atomic Energy Commission.
    Bansah, Yaw Christopher
    Ghana Atomic Energy Commission.
    Nyarko, Benjamin J.B
    Ghana Atomic Energy Commission.
    Akaho, Edward H.K.
    Ghana Atomic Energy Commission.
    Stability analysis of the Ghana Research Reactor-1 (GHARR-1)2013In: Energy Conversion and Management, ISSN 0196-8904, E-ISSN 1879-2227, Vol. 74, p. 587-593Article in journal (Refereed)
    Abstract [en]

    A theoretical model has been developed to study the stability of the Ghana Research Reactor one(GHARR-1). The closed-loop transfer function of GHARR-1 was established based on the model, which involved the neutronics and the thermal hydraulics transfer functions. The reactor kinetics was described by the point kinetics model for a single group of delayed neutrons, whilst the thermal hydraulics transfer function was based on the modified lumped parameter concept. The inherent internal feedback effect due to the fuel and the coolant was represented by the fuel temperature coefficient and the moderator temperature coefficient respectively. A computer code, RESA (REactor Stability Analysis), entirely in Java was developed based on the model for systems analysis. Stability analysis of the open-loop transfer function of GHARR-1 based on the Nyquist criterion and Bode diagrams using RESA, has shown that the closed loop transfer function was marginally stable for variable operating power levels. The relative stability margins of GHARR-1 were also identified.

  • 15.
    Duan, Junfeng
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
    Pomp, Stephan
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
    Sjöstrand, Henrik
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
    Alhassan, Erwin
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
    Gustavsson, Cecillia
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
    Österlund, Michael
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
    Koning, Arjan
    Rochman, Dimitri
    Uncertainty Study of Nuclear Model Parameters for the n+Fe-56 Reactions in the Fast Neutron Region below 20 MeV2014In: Nuclear Data Sheets, ISSN 0090-3752, E-ISSN 1095-9904, Vol. 118, p. 346-348Article in journal (Refereed)
    Abstract [en]

    In this work we study the uncertainty of nuclear model parameters for neutron induced Fe-56 reactions in the fast neutron region by using the Total Monte Carlo method. We perform a large number of TALYS runs and compare the calculated results with the experimental data of the cross sections to obtain the uncertainties of the model parameters. Based on the derived uncertainties another 1000 TALYS runs have been performed to create random cross section files. For comparison with the experimental data we calculate a weighted chi(2) value for each random file as well as the ENDF/B-VII. 1, JEFF-3.1, JENDL-4.0 and CENDL-3.1 data libraries. Furthermore, we investigate the optical model parameters correlation obtained by way of this procedure.

  • 16.
    Helgesson, Petter
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
    Rochman, Dimitri
    Nuclear Research and Consultancy Group NRG, Petten, The Netherlands.
    Sjöstrand, Henrik
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
    Alhassan, Erwin
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
    Koning, Arjan
    Nuclear Research and Consultancy Group NRG, Petten, The Netherlands.
    UO-2 Versus MOX: Propagated Nuclear Data Uncertainty for k-eff, with Burnup2014In: Nuclear science and engineering, ISSN 0029-5639, E-ISSN 1943-748X, Vol. 177, no 3, p. 321-336Article in journal (Refereed)
    Abstract [en]

    Precise assessment of propagated nuclear data uncertainties in integral reactor quantities is necessary for the development of new reactors as well as for modified use, e.g. when replacing UO-2 fuel by MOX fuel in conventional thermal reactors.

    This paper compares UO-2 fuel to two types of MOX fuel with respect to propagated nuclear data uncertainty, primarily in k-eff, by applying the Fast Total Monte Carlo method (Fast TMC) to a typical PWR pin cell model in Serpent, including burnup. An extensive amount of nuclear data is taken into account, including transport and activation data for 105 isotopes, fission yields for 13 actinides and thermal scattering data for H in H2O.

    There is indeed a significant difference in propagated nuclear data uncertainty in k-eff; at 0 burnup the uncertainty is 0.6 % for UO-2 and about 1 % for the MOX fuels. The difference decreases with burnup. Uncertainties in fissile fuel isotopes and thermal scattering are the most important for the difference and the reasons for this are understood and explained.

    This work thus suggests that there can be an important difference between UO-2 and MOX for the determination of uncertainty margins. However, the effects of the simplified model are difficult to overview; uncertainties should be propagated in more complicated models of any considered system. Fast TMC however allows for this without adding much computational time.

  • 17.
    Helgesson, Petter
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics. Nucl Res & Consultancy Grp NRG, Petten, Netherlands.
    Sjöstrand, Henrik
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
    Arjan, J. Koning
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics. Nucl Res & Consultancy Grp NRG, Petten, Netherlands.
    Rydén, Jesper
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Mathematics, Applied Mathematics and Statistics.
    Rochman, Dimitri
    PSI, Villigen, Switzerland.
    Alhassan, Erwin
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
    Pomp, Stephan
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
    Combining Total Monte Carlo and Unified Monte Carlo: Bayesian nuclear data uncertainty quantification from auto-generated experimental covariances2017In: Progress in nuclear energy (New series), ISSN 0149-1970, E-ISSN 1878-4224, Vol. 96, p. 76-96Article in journal (Refereed)
    Abstract [en]

    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.

  • 18.
    Helgesson, Petter
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
    Sjöstrand, Henrik
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
    J. Koning, Arjan
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics. IAEA.
    Rochman, Dimitri
    Alhassan, Erwin
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
    Pomp, Stephan
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
    Towards Transparent, Reproducible And Justified Nuclear Data Uncertainty Propagation For Lwr Applications2015Conference paper (Other academic)
    Abstract [en]

    Any calculated quantity is practically meaningless without estimates on the uncertainty of theobtained results, not the least when it comes to, e.g., safety parameters in a nuclear reactor. Oneof the sources of uncertainty in reactor physics computations or simulations are the uncertaintiesof the so called nuclear data, i.e., cross sections, angular distributions, fission yields, etc. Thecurrently dominating method for propagating nuclear data uncertainties (using covariance dataand sensitivity analysis) suffers from several limitations, not the least in how the the covariancedata is produced – the production relies to a large extent on personal judgment of nuclear dataevaluators, leading to results which are difficult to reproduce from fundamental principles.Further, such a method assumes linearity, it in practice limits both input and output to bemodeled as Gaussian distributions, and the covariance data in the established nuclear datalibraries is incomplete.“Total Monte Carlo” (TMC) is a nuclear data uncertainty propagation method based on randomsampling of nuclear reaction model parameters which aims to resolve these issues. The methodhas been applied to various applications, ranging from pin cells and criticality safety benchmarksto full core neutronics as well as models including thermo-hydraulics and transients. However,TMC has been subject to some critique since the distributions of the nuclear model parameters,and hence of the nuclear data, has not been deduced from really rigorous statistical theory. Thispresentation briefly discusses the ongoing work on how to use experimental data to approachjustified results from TMC, including the effects of correlations between experimental datapoints and the assessment of such correlations. In this study, the random nuclear data libraries areprovided with likelihood weights based on their agreement to the experimental data, as a meansto implement Bayes' theorem.Further, it is presented how TMC is applied to an MCNP-6 model of shielding fuel assemblies(SFA) at Ringhals 3 and 4. Since the damage from the fast neutron flux may limit the lifetimes ofthese reactors, parts of the fuel adjacent to the pressure vessel is replaced by steel (the SFA) toprotect the vessel, in particular the four points along the belt-line weld which have been exposedto the largest fluence over time. The 56Fe data uncertainties are considered, and the estimatedrelative uncertainty at a quarter of the pressure vessel is viewed in Figure 1 (right) as well as theflux pattern itself (left). The uncertainty in the flux reduction at a selected sensitive point is 2.5± 0.2 % (one standard deviation). Applying the likelihood weights does not have muchimpact for this case, which could indicate that the prior distribution for the 56Fe data is too“narrow” (the used libraries are not really intended to describe a prior distribution), and that thetrue uncertainty is substantially greater. Another explanation could be that the dominating sourceof uncertainty is the high-energy resonances which are treated inefficiently by such weights.In either case, the efforts to approach justified, transparent, reproducible and highly automatizednuclear data uncertainties shall continue. On top of using libraries that are intended to describeprior distributions and treating the resonance region appropriately, the experimental correlationsshould be better motivated and the treatment of outliers shall be improved. Finally, it is probablynecessary to use experimental data in a more direct sense where a lot of experimental data isavailable, since the nuclear models are imperfect.Figure 1. The high energy neutron flux at the reactor pressure vessel in the SFA model, and thecorresponding propagated 56Fe data uncertainty.

  • 19.
    Helgesson, Petter
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
    Sjöstrand, Henrik
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
    J. Koning, Arjan
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics. IAEA.
    Rydén, Jesper
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Mathematics, Applied Mathematics and Statistics.
    Rochman, Dimitri
    Paul Scherrer Institute PSI, Villigen, Switzerland.
    Alhassan, Erwin
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
    Pomp, Stephan
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
    Sampling of systematic errors to estimate likelihood weights in nuclear data uncertainty propagation2016In: Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, ISSN 0168-9002, E-ISSN 1872-9576, Vol. 807, p. 137-149Article in journal (Refereed)
    Abstract [en]

    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.

  • 20.
    Helgesson, Petter
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
    Sjöstrand, Henrik
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
    Koning, Arjan
    Nuclear Research and Consultancy Group NRG, Petten, The Netherlands.
    Rochman, Dimitri
    Nuclear Research and Consultancy Group NRG, Petten, The Netherlands.
    Alhassan, Erwin
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
    Pomp, Stephan
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
    Incorporating Experimental Information in the Total Monte Carlo Methodology Using File Weights2015In: Nuclear Data Sheets, ISSN 0090-3752, E-ISSN 1095-9904, Vol. 123, no SI, p. 214-219Article in journal (Refereed)
    Abstract [en]

    Some criticism has been directed towards the Total Monte Carlo method because experimental information has not been taken into account in a statistically well-founded manner. In this work, a Bayesian calibration method is implemented by assigning weights to the random nuclear data files and the method is illustratively applied to a few applications. In some considered cases, the estimated nuclear data uncertainties are significantly reduced and the central values are significantly shifted. The study suggests that the method can be applied both to estimate uncertainties in a more justified way and in the search for better central values. Some improvements are however necessary; for example, the treatment of outliers and cross-experimental correlations should be more rigorous and random files that are intended to be prior files should be generated.

  • 21.
    Helgesson, Petter
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
    Sjöstrand, Henrik
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
    Koning, Arjan
    Nuclear Research and Consultancy Group NRG, Petten, The Netherlands.
    Rydén, Jesper
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Mathematics.
    Rochman, Dimitri
    Paul Scherrer Institute PSI, Villigen, Switzerland.
    Alhassan, Erwin
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
    Pomp, Stephan
    Including experimental information in TMC using file weights from automatically generated experimental covariance matricesManuscript (preprint) (Other academic)
    Abstract [en]

    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 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.For many applications, the weighting does not have much impact, something which can be explained by too narrow prior distributions. 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, which can be due to the prior parameter distributions or model defects.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.

  • 22.
    Hernandez Solis, Augusto
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
    Sjostrand, Henrik
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
    Alhassan, Erwin
    Helgesson, Petter
    DRAG-MOC: A tool for the study of uncertainty analysis through OpenMOC2015Conference paper (Refereed)
  • 23.
    Hernandez Solis, Augusto
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
    Sjöstrand, Henrik
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
    Alhassan, Erwin
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
    Helgesson, Petter
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
    Development Of Drag-MOC: A tool for the study of uncertainty analysis through the deterministic OpenMOC transport code2016Conference paper (Refereed)
  • 24.
    Pomp, Stephan
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
    Al-Adili, Ali
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
    Alhassan, Erwin
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
    Gustavsson, Cecilia
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
    Helgesson, Petter
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
    Hellesen, Carl
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
    Koning, Arjan J.
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
    Lantz, Mattias
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
    Österlund, Michael
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
    Rochman, D.
    Simutkin, V.
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
    Sjöstrand, Henrik
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
    Solders, Andreas
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
    Experiments and Theoretical Data for Studying the Impact of Fission Yield Uncertainties on the Nuclear Fuel Cycle with TALYS/GEF and the Total Monte Carlo Method2015In: Nuclear Data Sheets, ISSN 0090-3752, E-ISSN 1095-9904, Vol. 123, no SI, p. 220-224Article in journal (Refereed)
    Abstract [en]

    We describe the research program of the nuclear reactions research group at Uppsala University concerning experimental and theoretical efforts to quantify and reduce nuclear data uncertainties relevant for the nuclear fuel cycle. We briefly describe the Total Monte Carlo (TMC) methodology and how it can be used to study fuel cycle and accident scenarios, and summarize our relevant experimental activities. Input from the latter is to be used to guide the nuclear models and constrain parameter space for TMC. The TMC method relies on the availability of good nuclear models. For this we use the TALYS code which is currently being extended to include the GEF model for the fission channel. We present results from TALYS-1.6 using different versions of GEF with both default and randomized input parameters and compare calculations with experimental data for U-234(n,f) in the fast energy range. These preliminary studies reveal some systematic differences between experimental data and calculations but give overall good and promising results.

  • 25.
    Sjöstrand, Henrik
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
    Alhassan, Erwin
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
    Conroy, Sean
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
    Duan, Junfeng
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
    Hellesen, Carl
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
    Pomp, Stephan
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
    Österlund, Michael
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
    Arjan, Koning J.
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
    Dimitri, Rochman
    Nuclear Research and Consultancy Group.
    Total Monte Carlo evaluation for dose calculations2014In: Radiation Protection Dosimetry, ISSN 0144-8420, E-ISSN 1742-3406, Vol. 161, no 1-4, p. 312-315Article in journal (Refereed)
    Abstract [en]

    Total Monte Carlo (TMC) is a method to propagate nuclear data (ND) uncertainties in transport codes, by using a large set of ND files, which covers the ND uncertainty. The transport code is run multiple times, each time with a unique ND file, and the result is a distribution of the investigated parameter, e.g. dose, where the width of the distribution is interpreted as the uncertainty due to ND. Until recently, this was computer intensive, but with a new development, fast TMC, more applications are accessible. The aim of this work is to test the fast TMC methodology on a dosimetry application and to propagate the 56Fe uncertainties on the predictions of the dose outside a proposed 14-MeV neutron facility. The uncertainty was found to be 4.2 %. This can be considered small; however, this cannot be generalised to all dosimetry applications and so ND uncertainties should routinely be included in most dosimetry modelling.

  • 26.
    Sjöstrand, Henrik
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
    Alhassan, Erwin
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
    Duan, Junfeng
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
    Gustavsson, Cecilia
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
    Koning, Arjan J.
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
    Pomp, Stephan
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
    Rochman, Dimitri
    Nuclear Research and Consultancy Group.
    Österlund, Michael
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
    Propagation of nuclear data uncertainties for ELECTRA burn-up calculations2014In: Nuclear Data Sheets, ISSN 0090-3752, E-ISSN 1095-9904, Vol. 118, p. 527-530Article in journal (Refereed)
    Abstract [en]

    The European Lead-Cooled Training Reactor (ELECTRA) has been proposed as a training reactor for fast systems within the Swedish nuclear program. It is a low -power fast reactor cooled by pure liquid lead. In this work, we propagate the uncertainties in 239Pu transport data to uncertainties in the fuel inventory of ELECTRA during the reactor life using the Total Monte Carlo approach(TMC). Within the TENDL project the nuclear models input parameters were randomized within their uncertainties and 740 239Pu nuclear data libraries were generated. These libraries are used as inputs to reactor codes, in our case SERPENT, to perform uncertainty analysis of nuclear reactor inventory during burn-up. The uncertainty in the inventory determines uncertainties in: the long term radio-toxicity, the decay heat, the evolution of reactivity parameters, gas pressure and volatile fission product content. In this work, a methodology called fast TMC is utilized, which reduces the overall calculation time. The uncertainty in the long-term radiotoxicity, decay heat, gas pressureand volatile fission products were found to be insignificant. However, the uncertainty of some minor actinides were observed to be rather large and therefore their impact on multiple recycling should be investigated further. It was also found that, criticality benchmarks can be used to reduce inventory uncertainties due to nuclear data. Further studies are needed to include fission yield uncertainties, more isotopes, and a larger set of benchmarks.

  • 27.
    Sjöstrand, Henrik
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
    Asquith, Nicola
    Helgesson, Petter
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
    Alhassan, Erwin
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
    Rochman, Dimitri
    van der Marck, S.C.
    NRG.
    Efficient use of Monte Carlo: The Fast Correlation Coefficient2017Conference paper (Other academic)
    Abstract [en]

    Monte Carlo methods are increasingly used for Nuclear Data evaluation and propagation. In particular, the Total Monte Carlo (TMC) Method has proved to be an efficient tool. A disadvantage of MC is that statistical uncertainties are also introduced. For evaluating the propagated nuclear data uncertainty, this was addressed with the so-called Fast-TMC method, which has become the standard route for TMC uncertainty propagation.

  • 28.
    Sjöstrand, Henrik
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
    Helgesson, Petter
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
    Alhassan, Erwin
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
    Adjustment of nuclear data libraries using integral benchmarks2017Conference paper (Other academic)
    Abstract [en]

    Integral experiments can be used to adjust ND-libraries and consequently the uncertainty response in important applications. . In this work we show how we can use integral experiments in a consistent way to adjust the TENDL library.  A Bayesian method based on assigning weights to the different random files using a maximum likelihood function [1] is used. Emphasis is put on the problems that arise from multiple isotopes being present in a benchmark [2].  The challenges in using multiple integral experiments are also addressed, including the correlation between the different integral experiments.

    Methods on how to use the Total Monte Carlo method to select benchmarks for reactor application will further be discussed. In particular in respect to the so-called fast correlation coefficient and the fast-TMC method [3]

  • 29.
    Sjöstrand, Henrik
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
    Helgesson, Petter
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
    Alhassan, Erwin
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
    TENDL adjustments using integral benchmarks2017Conference paper (Other academic)
    Abstract [en]

    Integral experiments can be used to adjust ND-libraries. In this work we show how we can use integral experiments in a consistent way to adjust the TENDL library.  A Bayesian method based on assigning weights to the different random files using a maximum likelihood function [1] is used. Emphasis is put on the problems that arise from multiple isotopes being present in a benchmark [2].    The challenges in using multiple integral experiments are also addressed, including the correlation between the different integral experiments.

     

    [1] P. Helgesson, H. Sjöstrand, A.J. Koning, J. Rydén, D. Rochman, E. Alhassan, S. Pomp, Combining Total Monte Carlo and Unified Monte Carlo: Bayesian nuclear data uncertainty quantification from auto-generated experimental covariances, Progress in Nuclear Energy, Volume 96, 2017, Pages 76-96, ISSN 0149-1970, http://dx.doi.org/10.1016/j.pnucene.2016.11.006.

    [2]E. Alhassan, H. Sjöstrand, P. Helgesson, M. Österlund, S. Pomp, A.J. Koning, D. Rochman, On the use of integral experiments for uncertainty reduction of reactor macroscopic parameters within the TMC methodology, Progress in Nuclear Energy, Volume 88, 2016, Pages 43-52, ISSN 0149-1970, http://dx.doi.org/10.1016/j.pnucene.2015.11.015.

  • 30.
    Sjöstrand, Henrik
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
    Helgesson, Petter
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
    Alhassan, Erwin
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
    TMC adjustment of nuclear data libraries using integral benchmarks2018Conference paper (Other academic)
  • 31.
    Sjöstrand, Henrik
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
    Helgesson, Petter
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
    Alhassan, Erwin
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
    Pomp, Stephan
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
    Nuclear Data Uncertainty Quantification for the Nuclear Fuel Cycle Using the TMC Method2015Conference paper (Other academic)
    Abstract [en]

    Reactor, criticality and transport simulations are widely used in the nuclear community to e.g. determine safety parameters, evaluate transients, and calculate the fuel inventory. These simulations use nuclear data (ND) as one of their most important inputs. ND is obtained by performing experiments and using theoretical nuclear models. Both experiments and theory are associated with uncertainties and consequently all ND are associated with uncertainties. There are also strong correlations in the uncertainties between different energies, reaction channels and isotopes, in both experiments and modelling. Many ND libraries (e.g. JEFF-3.2 and ENDF/B-VII.1) contain information on the ND uncertainties and their correlations in covariance files. However, this information relies on assumptions of normal distributions and is not complete.  Furthermore, many reactor codes do not use ND uncertainties as input, and when they do, they rely on different assumptions, which tend to underestimate the propagated uncertainty. In order to address these issues the Total Monte Carlo (TMC) methodology has been developed [1]. The basic principles of the TMC method are illustrated in Figure 1.     

     

    Figure 1. The TMC uncertainty propagation and TENDL production. In the TMC uncertainty propagation the final result is the spread in a macroscopic parameter. This spread is the systematic uncertainty in the calculation due to ND in the investigated parameter. (CS = cross section, FY = fission yield)

     

    In the TMC method a large set of random files are derived by sampling nuclear model parameters in the nuclear model codes TALYS. The random files are subsequently compared to experimental values [2]. Consequently, each random file is a complete nuclear data library containing one possible representation of the nuclear data given the uncertainties from theory and experiments.

     

    By running a reactor simulation multiple times, each time with a new random file as input, distributions of the different nuclear engineering parameters (e.g. keff, temperature coefficient, inventory, fuel temperature) are derived. These distributions are interpreted as the uncertainties in the engineering parameters due to ND. The TMC method can also be used to produce nuclear data for all open reaction channels including covariances; the “TALYS Evaluated Nuclear Data Library” (TENDL) is an example [3]. In order to select the best file to be included in the TENDL library, the random nuclear data files produced are compared against differential experimental data taken from the EXFOR database and a large set of integral measurements.

     

    The TMC method can be used for any neutronic-system and in this contribution we present results from both thermal and fast neutron systems. The importance to include nuclear data uncertainty from angular distribution and thermal scattering are highlighted.

     

    [1] A.J.Koning, and D.Rochman,  Modern Nuclear Data Evaluation with the TALYS Code System. Nuclear Data Sheets 113 12 2841-2934 (2012)

    [2] P. Helgesson, H.Sjöstrand,  A.J.Koning,  D.Rochman,  E.Alhassan,  S.Pomp

    Nuclear Data Sheets, Volume 123, Pages 214-219, 2015.

    [3] "TENDL-2014: TALYS-based evaluated nuclear data library", A.J. Koning, D. Rochman, S. van der Marck, J. Kopecky, J. Ch. Sublet, S. Pomp, H. Sjostrand, R. Forrest, E. Bauge, H. Henriksson, O. Cabellos, S. Goriely J. Leppanen, H. Leeb, A. Plompen and R. Mills, www.talys.eu/tendl-2014.html

1 - 31 of 31
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