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Träfflista för sökning "WFRF:(Rochman Dimitri) ;pers:(Sjöstrand Henrik 1978)"

Sökning: WFRF:(Rochman Dimitri) > Sjöstrand Henrik 1978

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1.
  • Alhassan, Erwin, et al. (författare)
  • Reducing A Priori 239Pu Nuclear Data Uncertainty In The Keff Using A Set Of Criticality Benchmarks With Different Nuclear Data Libraries
  • 2015
  • Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
    • 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).
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2.
  • Fischer, Ulrich, et al. (författare)
  • Nuclear data activities of the EUROfusion consortium
  • 2020
  • Ingår i: ND 2019. - : EDP Sciences. - 9782759891061
  • Konferensbidrag (refereegranskat)abstract
    • The activities of the EUROfusion consortiums on the development of high quality nuclear data for fusion applications are presented. The activities, implemented in the Power Plant Physics and Technology (PPPT) programme of EUROfusion, include nuclear data evaluations for neutron and deuteron induced reactions and the production of related data libraries which satisfy the needs for nuclear analyses of the DEMO fusion power plant and the IFMIF-DONES neutron source. The activities are closely linked to the JEFF initiative of the NEA Data Bank. The evaluation work is complemented by extensive benchmark, sensitivity and uncertainty analyses to check the performance of the evaluated cross-section data and libraries against integral experiments.
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3.
  • Helgesson, Petter, 1986-, et al. (författare)
  • Combining Total Monte Carlo and Unified Monte Carlo : Bayesian nuclear data uncertainty quantification from auto-generated experimental covariances
  • 2017
  • Ingår i: Progress in nuclear energy (New series). - : Elsevier. - 0149-1970 .- 1878-4224. ; 96, s. 76-96
  • 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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4.
  • Helgesson, Petter, 1986-, et al. (författare)
  • New 59Ni data including uncertainties and consequences for gas production in steel in LWR spectraNew 59Ni data including uncertainties and consequences for gas production in steel in LWR spectra
  • 2015
  • Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
    • Abstract: With ageing reactor fleets, the importance of estimating material damage parameters in structural materials is increasing. 59Ni is not naturally abundant, but as noted in, e.g., Ref. [1], the two-step reaction 58Ni(n,γ)59Ni(n,α)56Fe gives a very important contribution to the helium production and damage energy in stainless steel in thermal spectra, because of the extraordinarily large thermal (n,α) cross section for 59Ni (for most other nuclides, the (n,α) reaction has a threshold). None of the evaluated data libraries contain uncertainty information for (n,α) and (n,p) for 59Ni for thermal energies and the resonance region. Therefore, new such data is produced in this work, including random data to be used with the Total Monte Carlo methodology [2] for nuclear data uncertainty propagation.                  The limited R-matrix format (“LRF = 7”) of ENDF-6 is used, with the Reich-Moore approximation (“LRF = 3” is just a subset of Reich-Moore). The neutron and gamma widths are obtained from TARES [2], with uncertainties, and are translated into LRF = 7. The α and proton widths are obtained from the little information available in EXFOR [3] (assuming large uncertainties because of lacking documentation) or from sampling from unresolved resonance parameters from TALYS [2], and they are split into different channels (different excited states of the recoiling nuclide, etc.). Finally, the cross sections are adjusted to match the experiments at thermal energies, with uncertainties.                  The data is used to estimate the gas production rates for different systems, including the propagated nuclear data uncertainty. Preliminary results for SS304 in a typical thermal spectrum, show that including 59Ni at its peak concentration increases the helium production rate by a factor of 4.93 ± 0.28 including a 5.7 ± 0.2 % uncertainty due to the 59Ni data. It is however likely that the uncertainty will increase substantially from including the uncertainty of other nuclides and from re-evaluating the experimental thermal cross sections.
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5.
  • Helgesson, Petter, 1986-, et al. (författare)
  • Towards Transparent, Reproducible And Justified Nuclear Data Uncertainty Propagation For Lwr Applications
  • 2015
  • Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
    • 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.
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6.
  • Helgesson, Petter, 1986-, et al. (författare)
  • Uncertainty driven nuclear data evaluation including thermal (n,alpha) applied to Ni-59
  • 2017
  • Ingår i: Nuclear Data Sheets. - : Elsevier BV. - 0090-3752 .- 1095-9904. ; 145, s. 1-24
  • Tidskriftsartikel (refereegranskat)abstract
    • This paper presents a novel approach to the evaluation of nuclear data (ND), combining experimental data for thermalcross sections with resonance parameters and nuclear reaction modeling. The method involves sampling of variousuncertain parameters, in particular uncertain components in experimental setups, and provides extensive covarianceinformation, including consistent cross-channel correlations over the whole energy spectrum. The method is developed for, and applied to, Ni-59, but may be used as a whole, or in part, for other nuclides. Ni-59 is particularly interesting since a substantial amount of Ni-59 is produced in thermal nuclear reactors by neutron capture in Ni-58 and since it has a non-threshold (n,α) cross section. Therefore, Ni-59 gives a very important contribution to the helium production in stainless steel in a thermal reactor. However, current evaluated ND libraries contain old information for Ni-59, without any uncertainty information. The work includes a study of thermal cross section experiments and a novel combination of this experimental information, giving the full multivariate distribution of the thermal cross sections. In particular, the thermal (n,α) cross section is found to be (12.7 ± .7) b. This is consistent with, but yet different from, current established values. Further, the distribution of thermal cross sections is combined with reported resonance parameters, and with TENDL-2015 data, to provide full random ENDF files; all this is done in a novel way, keeping uncertainties and correlations in mind. The random files are also condensed into one single ENDF file with covariance information, which is now part ofa beta version of JEFF 3.3.Finally, the random ENDF files have been processed and used in an MCNP model to study the helium productionin stainless steel. The increase in the (n,α) rate due to Ni-59 compared to fresh stainless steel is found to be a factor of 5.2 at a certain time in the reactor vessel, with a relative uncertainty due to the Ni-59 data of 5.4 %.
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7.
  • Rochman, Dimitri, et al. (författare)
  • Efficient use of Monte Carlo : Uncertainty Propagation
  • 2014
  • Ingår i: Nuclear science and engineering. - 0029-5639 .- 1943-748X. ; 177:3, s. 337-349
  • Tidskriftsartikel (refereegranskat)abstract
    • A new and faster Total Monte Carlo method for the propagation of nuclear data uncertaintiesin Monte Carlo nuclear simulations is presented (the fast TMC method).It is addressing the main drawback of the original Total Monte Carlo method(TMC), namely the necessary large time multiplication factor compared to a singlecalculation. With this new method, Monte Carlo simulations can now be accompaniedwith uncertainty propagation (other than statistical), with small additionalcalculation time. The fast TMC method is presented and compared with the TMCand fast GRS methods for criticality and shielding benchmarks and burn-up calculations.Finally, to demonstrate the efficiency of the method, uncertainties on localdeposited power in 12.7 millions cells are calculated for a full size reactor core,
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8.
  • Rochman, Dimitri, et al. (författare)
  • The TENDL library : Hope, reality and future
  • 2017
  • Ingår i: Nd 2016 Bruges. - Les Ulis : EDP Sciences. - 9782759890200
  • Konferensbidrag (refereegranskat)abstract
    • The TALYS Evaluated Nuclear Data Library (TENDL) has now 8 releases since 2008. Considerable experience has been acquired for the production of such general-purpose nuclear data library based on the feedback from users, evaluators and processing experts. The backbone of this achievement is simple and robust: completeness, quality and reproducibility. If TENDL is extensively used in many fields of applications, it is necessary to understand its strong points and remaining weaknesses. Alternatively, the essential knowledge is not the TENDL library itself, but rather the necessary method and tools, making the library a side product and focusing the efforts on the evaluation knowledge. The future of such approach will be discussed with the hope of nearby greater success.
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9.
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10.
  • Siefman, Daniel, et al. (författare)
  • Data assimilation of post-irradiation examination data for fission yields from GEF
  • 2020
  • Ingår i: EPJ NUCLEAR SCIENCES & TECHNOLOGIES. - : EDP SCIENCES S A. - 2491-9292. ; 6
  • Tidskriftsartikel (refereegranskat)abstract
    • Nuclear data, especially fission yields, create uncertainties in the predicted concentrations of fission products in spent fuel which can exceed engineering target accuracies. Herein, we present a new framework that extends data assimilation methods to burnup simulations by using post-irradiation examination experiments. The adjusted fission yields lowered the bias and reduced the uncertainty of the simulations. Our approach adjusts the model parameters of the code GEF. We compare the BFMC and MOCABA approaches to data assimilation, focusing especially on the effects of the non-normality of GEF's fission yields. In the application that we present, the best data assimilation framework decreased the average bias of the simulations from 26% to 14%. The average relative standard deviation decreased from 21% to 14%. The GEF fission yields after data assimilation agreed better with those in JEFF3.3. For Pu-239 thermal fission, the average relative difference from JEFF3.3 was 16% before data assimilation and after it was 12%. For the standard deviations of the fission yields, GEF's were 100% larger than JEFF3.3's before data assimilation and after were only 4% larger. The inconsistency of the integral data had an important effect on MOCABA, as shown with the Marginal Likelihood Optimization method. When the method was not applied, MOCABA's adjusted fission yields worsened the bias of the simulations by 30%. BFMC showed that it inherently accounted for this inconsistency. Applying Marginal Likelihood Optimization with BFMC gave a 2% lower bias compared to not applying it, but the results were more poorly converged.
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