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  • 1.
    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.

  • 2.
    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.

  • 3.
    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.

  • 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.
    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.

  • 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.
    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).

  • 6.
    Helgesson, Petter
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
    Approaching well-founded comprehensive nuclear data uncertainties: Fitting imperfect models to imperfect data2018Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Nuclear physics has a wide range of applications; e.g., low-carbon energy production, medical treatments, and non-proliferation of nuclear weapons. Nuclear data (ND) constitute necessary input to computations needed within all these applications.

    This thesis considers uncertainties in ND and their propagation to applications such as ma- terial damage in nuclear reactors. TENDL is today the most comprehensive library of evaluated ND (a combination of experimental ND and physical models), and it contains uncertainty estimates for all nuclides it contains; however, TENDL relies on an automatized process which, so far, includes a few practical remedies which are not statistically well-founded. A longterm goal of the thesis is to provide methods which make these comprehensive uncertainties well-founded. One of the main topics of the thesis is an automatic construction of experimental covariances; at first by attempting to complete the available uncertainty information using a set of simple rules. The thesis also investigates using the distribution of the data; this yields promising results, and the two approaches may be combined in future work.

    In one of the papers underlying the thesis, there are also manual analyses of experiments, for the thermal cross sections of Ni-59 (important for material damage). Based on this, uncertainty components in the experiments are sampled, resulting in a distribution of thermal cross sections. After being combined with other types of ND in a novel way, the distribution is propagated both to an application, and to an evaluated ND file, now part of the ND library JEFF 3.3.

    The thesis also compares a set of different techniques used to fit models in ND evaluation. For example, it is quantified how sensitive different techniques are to a model defect, i.e., the inability of the model to reproduce the truth underlying the data. All techniques are affected, but techniques fitting model parameters directly (such as the primary method used for TENDL) are more sensitive to model defects. There are also advantages with these methods, such as physical consistency and the possibility to build up a framework such as that of TENDL.

    The treatment of these model defects is another main topic of the thesis. To this end, two ways of using Gaussian processes (GPs) are studied, applied to quite different situations. First, the addition of a GP to the model is used to enable the fitting of arbitrarily shaped peaks in a histogram of data. This is shown to give a substantial improvement compared to if the peaks are assumed to be Gaussian (when they are not), both using synthetic and authentic data.

    The other approach uses GPs to fit smoothly energy-dependent model parameters in an ND evaluation context. Such an approach would be relatively easy to incorporate into the TENDL framework, and ensures a certain level of physical consistency. It is used on a TALYS-like model with synthetic data, and clearly outperforms fits without the energy-dependent model parameters, showing that the method can provide a viable route to improved ND evaluation. As a proof of concept, it is also used with authentic TALYS, and with authentic data.

    To conclude, the thesis takes significant steps towards well-founded comprehensive ND un- certainties.

  • 7.
    Helgesson, Petter
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
    Experimental data and Total Monte Carlo: Towards justified, transparent and complete nuclear data uncertainties2015Licentiate thesis, comprehensive summary (Other academic)
    Abstract [en]

    The applications of nuclear physics are many with one important being nuclear power, which can help decelerating the climate change. In any of these applications, so-called nuclear data (ND, numerical representations of nuclear physics) is used in computations and simulations which are necessary for, e.g., design and maintenance. The ND is not perfectly known - there are uncertainties associated with it - and this thesis concerns the quantification and propagation of these uncertainties. In particular, methods are developed to include experimental data in the Total Monte Carlo methodology (TMC). The work goes in two directions. One is to include the experimental data by giving weights to the different "random files" used in TMC. This methodology is applied to practical cases using an automatic interpretation of an experimental database, including uncertainties and correlations. The weights are shown to give a consistent implementation of Bayes' theorem, such that the obtained uncertainty estimates in theory can be correct, given the experimental data. In the practical implementation, it is more complicated. This is much due to the interpretation of experimental data, but also because of model defects - the methodology assumes that there are parameter choices such that the model of the physics reproduces reality perfectly. This assumption is not valid, and in future work, model defects should be taken into account. Experimental data should also be used to give feedback to the distribution of the parameters, and not only to provide weights at a later stage.The other direction is based on the simulation of the experimental setup as a means to analyze the experiments in a structured way, and to obtain the full joint distribution of several different data points. In practice, this methodology has been applied to the thermal (n,α), (n,p), (n,γ) and (n,tot) cross sections of 59Ni. For example, the estimated expected value and standard deviation for the (n,α) cross section is (12.87 ± 0.72) b, which can be compared to the established value of (12.3 ± 0.6) b given in the work of Mughabghab. Note that also the correlations to the other thermal cross sections as well as other aspects of the distribution are obtained in this work - and this can be important when propagating the uncertainties. The careful evaluation of the thermal cross sections is complemented by a coarse analysis of the cross sections of 59Ni at other energies. The resulting nuclear data is used to study the propagation of the uncertainties through a model describing stainless steel in the spectrum of a thermal reactor. In particular, the helium production is studied. The distribution has a large uncertainty (a standard deviation of (17 ± 3) \%), and it shows a strong asymmetry. Much of the uncertainty and its shape can be attributed to the more coarse part of the uncertainty analysis, which, therefore, shall be refined in the future.

  • 8.
    Helgesson, Petter
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
    TMC, adjustment to data, and model defects2018Other (Other (popular science, discussion, etc.))
  • 9.
    Helgesson, Petter
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
    Neudecker, Denise
    XCP Division, Los Alamos National Lab, NM, USA.
    Sjöstrand, Henrik
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
    Grosskopf, Michael
    Department of Statistics and Actuarial Science, Simon Fraser University, Canada.
    Smith, Donald L.
    Argonne Associate of Seville, Argonne National Laboratory, CA, USA.
    Capote, Roberto
    NAPC-Nuclear Data Section, International Atomic Energy Agency, Austria.
    Assessment of Novel Techniques for Nuclear Data Evaluation2018In: Reactor Dosimetry: 16th International Symposium, ASTM International, 2018, p. 105-116Conference paper (Refereed)
    Abstract [en]

    The quality of evaluated nuclear data can be impacted by, e.g., the choice of the evaluation algorithm. The objective of this work is to compare the performance of the evaluation techniques GLS, GLS-P, UMC-G, and, UMC-B, by using synthetic data. In particular, the effects of model defects are investigated. For small model defects, UMC-B and GLS-P are found to perform best, while these techniques yield the worst results for a significantly defective model; in particular, they seriously underestimate the uncertainties. If UMC-B is augmented with Gaussian processes,it performs distinctly better for a defective model but is more susceptible to an inadequate experimental covariance estimate.

  • 10.
    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.

  • 11.
    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.
    Fitting a defect non-linear model with or without prior, distinguishing nuclear reaction products as an example2017In: Review of Scientific Instruments, ISSN 0034-6748, E-ISSN 1089-7623, Vol. 88, article id 115114Article in journal (Refereed)
    Abstract [en]

    Fitting parametrized functions to data is important for many researchers and scientists. If the model is non-linear and/or defect, it is not trivial to do correctly and to include an adequate uncertainty analysis. This work presents how the Levenberg-Marquardt algorithm for non-linear generalized least squares fitting can be used with a prior distribution for the parameters, and how it can be combined with Gaussian processes to treat model defects. An example, where three peaks in a histogram are to be distinguished, is carefully studied. In particular, the probability r1 for a nuclear reaction to end up in one out of two overlapping peaks is studied. Synthetic data is used to investigate effects of linearizations and other assumptions. For perfect Gaussian peaks, it is seen that the estimated parameters are distributed close to the truth with good covariance estimates. This assumes that the method is applied correctly; for example, prior knowledge should be implemented using a prior distribution, and not by assuming that some parameters are perfectly known (if they are not). It is also important to update the data covariance matrix using the fit if the uncertainties depend on the expected value of the data (e.g., for Poisson counting statistics or relative uncertainties). If a model defect is added to the peaks, such that their shape is unknown, a fit which assumes perfect Gaussian peaks becomes unable to reproduce the data, and the results for r1 become biased. It is, however, seen that it is possible to treat the model defect with a Gaussian process with a covariance function tailored for the situation, with hyper-parameters determined by leave-one-out cross validation. The resulting estimates for r1 are virtually unbiased, and the uncertainty estimates agree very well with the underlying uncertainty.

  • 12.
    Helgesson, Petter
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics. 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.
    Justified and complete gas-production cross sections with uncertainties for Ni-59 and consequences for stainless steel in LWR spectra2016Other (Other academic)
  • 13.
    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.
    Treating defects in nuclear reaction models to improve material damage parameters and their uncertainties2017Conference paper (Other academic)
  • 14.
    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.
    Treating model defects by fitting smoothly varying model parameters: Energy dependence in nuclear data evaluation2018In: Annals of Nuclear Energy, ISSN 0306-4549, E-ISSN 1873-2100, Vol. 120, p. 35-47Article in journal (Refereed)
    Abstract [en]

    The fitting of models to data is essential in nuclear data evaluation, as in many other fields of science. The models maybe necessary for interpolation or extrapolation, but they are seldom perfect; there are model defects present which can result in severe biases and underestimated uncertainties. This work presents and investigates the idea to treat this problem by letting the model parameters vary smoothly with an input parameter. To be specific, the model parameters for neutron cross sections are allowed to vary with neutron energy. The parameter variation is limited by Gaussian processes, but the method should not be confused with adding a Gaussian process to the model. The performance of the method is studied using a large number of synthetic data sets, such that it is possible to quantitatively study the distribution of results compared to the underlying truth. There are imperfections in the results, but the method is seen to readily outperform fits without the energy dependent parameters. In particular, the estimates of uncertainty and correlations are much better. Hence, the method seems to offer a promising route for future treatment of model defects, both for nuclear data and elsewhere.

  • 15.
    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.
    Treating model defects with a Gaussian Process prior for the parameters2017Conference paper (Other academic)
    Abstract [en]

    The covariance information in TENDL is obtained by propagating uncertainties of, e.g., TALYSparameters to the observables, and by attempting to match the parameter uncertainties to the experimental data. This results in model-driven covariances with strong energy‐energy correlations, which can lead to erroneously estimated uncertainties for both differential and integral observables.Further, the model driven approach is sensitive to model defects, which can introduce biases and underestimated uncertainties.To resolve the issue of model defects in nuclear data (ND) evaluation, models the defect with a Gaussian process. This can reduce biases and give more correct covariances, including weakerenergy‐energy correlations. In the presented work, we continue the development of using Gaussian processes to treat model defects in ND evaluation, within a TENDL framework. The Gaussian processes are combined with the Levenberg‐Marquardt algorithm for non‐linear fitting, which reduces the need for a prior distribution. Further, it facilitates the transfer of knowledge to other nuclides by working in the parameter domain. First, synthetic data is used to validate the quality of both mean values and covariances provided by the method. After this, we fit TALYS parameters and a model defect correction to the 56Fe data in EXFOR.

  • 16.
    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.

  • 17.
    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
    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 spectra2015Conference paper (Other academic)
    Abstract [en]

    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.

  • 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. 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.
    Rochman, Dimitri
    Paul Scherrer Institute PSI, Villigen, Switzerland.
    Uncertainty driven nuclear data evaluation including thermal (n,alpha) applied to Ni-592017In: Nuclear Data Sheets, ISSN 0090-3752, E-ISSN 1095-9904, Vol. 145, p. 1-24Article in journal (Refereed)
    Abstract [en]

    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 %.

  • 22.
    Helgesson, Petter
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics. 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.
    Rochman, Dimitri
    Paul Scherrer Institute PSI, Villigen, Switzerland.
    Koning, Arjan J.
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics. IAEA Nuclear Data Section, Vienna, Austria.
    Evaluation of the Ni-59 cross sections including thermal (n,alpha), (n,p) and complete uncertainty information2016Other (Other academic)
  • 23.
    Helgesson, Petter
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics. 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.
    Rochman, Dimitri
    Paul Scherrer Institute PSI, Villigen, Switzerland.
    Koning, Arjan J.
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics. IAEA Nuclear Data Section, Vienna, Austria.
    Ni-59 cross section evaluation: covariance focus2016Other (Other academic)
  • 24.
    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)
  • 25.
    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.
    Helgesson, Petter
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
    Propagation of neutron-reaction uncertainties through multi-physics models of novel LWR's2017In: ND 2016: INTERNATIONAL CONFERENCE ON NUCLEAR DATA FOR SCIENCE AND TECHNOLOGY / [ed] Plompen, A; Hambsch, FJ; Schillebeeckx, P; Mondelaers, W; Heyse, J; Kopecky, S; Siegler, P; Oberstedt, S, Les Ulis: EDP Sciences, 2017, Vol. 146, article id 02035Conference paper (Refereed)
    Abstract [en]

    The novel design of the renewable boiling water reactor (RBWR) allows a breeding ratio greater than unity and thus, it aims at providing for a self-sustained fuel cycle. The neutron reactions that compose the different microscopic cross-sections and angular distributions are uncertain, so when they are employed in the determination of the spatial distribution of the neutron flux in a nuclear reactor, a methodology should be employed to account for these associated uncertainties. In this work, the Total Monte Carlo (TMC) method is used to propagate the different neutron-reactions (as well as angular distributions) covariances that are part of the TENDL-2014 nuclear data (ND) library. The main objective is to propagate them through coupled neutronic and thermal-hydraulic models in order to assess the uncertainty of important safety parameters related to multi-physics, such as peak cladding temperature along the axial direction of an RBWR fuel assembly. The objective of this study is to quantify the impact that ND covariances of important nuclides such as U-235, U-238, Pu-239 and the thermal scattering of hydrogen in H2O have in the deterministic safety analysis of novel nuclear reactors designs.

  • 26.
    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.

  • 27.
    Rochman, Dimitri
    et al.
    NRG.
    Koning, Arjan
    NRG.
    van der Marck, S.C.
    NRG.
    Zwermann, Winfried
    bGesellschaft f¨ur Anlagen- und Reaktorsicherheit (GRS) mbH, Garching, Germany.
    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.
    Krzykacz-Hausmann, B.
    bGesellschaft f¨ur Anlagen- und Reaktorsicherheit (GRS) mbH, Garching, Germany.
    Efficient use of Monte Carlo: Uncertainty Propagation2014In: Nuclear science and engineering, ISSN 0029-5639, E-ISSN 1943-748X, Vol. 177, no 3, p. 337-349Article in journal (Refereed)
    Abstract [en]

    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,

  • 28.
    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.

  • 29.
    Sjöstrand, Henrik
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
    Asquith, Nicola
    Nuclear Research and Consultancy Group NRG, Petten, The Netherlands.
    Helgesson, Petter
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics. Nuclear Research and Consultancy Group NRG, Petten, The Netherlands.
    Rochman, Dimitri
    Reactor Physics and Thermal Hydraulic Laboratory, Paul Scherrer Institut, Villigen, Switzerland.
    van der Marck, Steven
    Nuclear Research and Consultancy Group NRG, Petten, The Netherlands.
    Efficient use of Monte Carlo: The Fast Correlation Coefficient2018In: EPJ N - Nuclear Sciences and Technologies, E-ISSN 2491-9292, Vol. 4, article id 15Article in journal (Refereed)
    Abstract [en]

    Random sampling methods are used for nuclear data (ND) uncertainty propagation, often in combination with the use of Monte Carlo codes (e.g., MCNP). One example is the Total Monte Carlo (TMC) method. The standard way to visualize and interpret ND covariances is by the use of the Pearson correlation coefficient, rho = cov(x, y)/sigma(x) x sigma(y), where x or y can be any parameter dependent on ND. The spread in the output, sigma, has both an ND component, sigma(ND), and a statistical component, sigma(stat). The contribution from sigma(stat) decreases the value of rho, and hence it underestimates the impact of the correlation. One way to address this is to minimize sigma(stat) by using longer simulation run-times. Alternatively, as proposed here, a so-called fast correlation coefficient is used, rho(fast) = cov (x, y)-cov (x(stat), y(stat))/root sigma(2)(x)-sigma(2)(x,stat).root sigma(2)(y)-sigma(2)(y,stat) .

    In many cases, cov (x(stat), y(stat)) can be assumed to be zero. The paper explores three examples, a synthetic data study, correlations in the NRG High Flux Reactor spectrum, and the correlations between integral criticality experiments. It is concluded that the use of rho underestimates the correlation. The impact of the use of rho(fast) is quantified, and the implication of the results is discussed.

  • 30.
    Sjöstrand, Henrik
    et al.
    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.
    Helgesson, Petter
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
    Hernandez Solis, Augusto
    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. Nuclear Data Section, IAEA, Vienna, Austria.
    Pomp, Stephan
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
    Rochman, Dimitri
    Reactor Physics and Systems Behaviour Laboratory, Paul Scherrer Institut, Villigen, Switzerland.
    Propagation of nuclear data uncertainties for fusion power measurements2017In: ND 2016: International Conference On Nuclear Data For Science And Technology / [ed] Plompen, A.; Hambsch, FJ.; Schillebeeckx, P.; Mondelaers, W.; Heyse, J.; Kopecky, S.; Siegler, P.; Oberstedt, S., Les Ulis: EDP Sciences, 2017, Vol. 146, no 02034, article id 02034Conference paper (Refereed)
    Abstract [en]

    Neutron measurements using neutron activation systems are an essential part of the diagnostic system at large fusion machines such as JET and ITER. Nuclear data is used to infer the neutron yield. Consequently, high-quality nuclear data is essential for the proper determination of the neutron yield and fusion power. However, uncertainties due to nuclear data are not fully taken into account in uncertainty analysis for neutron yield calibrations using activation foils. This paper investigates the neutron yield uncertainty due to nuclear data using the so-called Total Monte Carlo Method. The work is performed using a detailed MCNP model of the JET fusion machine; the uncertainties due to the cross-sections and angular distributions in JET structural materials, as well as the activation cross-sections in the activation foils, are analysed. It is found that a significant contribution to the neutron yield uncertainty can come from uncertainties in the nuclear data.

  • 31.
    Sjöstrand, Henrik
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics. Nuclear Research and Consultancy Group NRG.
    Helgesson, Petter
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics. Nuclear Research and Consultancy Group NRG.
    Choosing Nuclear Data evaluation techniques to obtain complete and motivated covariances2017Conference paper (Other academic)
    Abstract [en]

    The quality of evaluated nuclear data and its covariances is affected by the choice of the evaluation algorithm. The evaluator can choose to evaluate in the observable domain or the parameter domain and choose to use a Monte Carlo- or deterministic techniques[1]. The evaluator can also choose to model potential model-defects using, e.g., Gaussian Processes [2].  In this contribution, the performance of different evaluation techniques is investigated by using synthetic data.  Different options for how to model the model-defects are also discussed.

    In addition, the example of a new Ni-59 is presented where different co-variance driven evaluation techniques are combined to create a final file for JEFF-3.3 [3].

     

    Keywords: Total Monte Carlo, Nuclear data evaluation

    AMS subject classifications.  62P35; 81V35; 62-07;

     

    References

    [1] P.Helgesson, D.Neudecker, H.Sjöstrand, M.Grosskopf, D.Smith, R.Capote; Assessment of Novel Techniques for Nuclear Data Evaluation, 16th International Symposium of Reactor Dosimetry (ISRD16) (2017)

    [2] G. Schnabel, Large Scale Bayesian Nuclear Data Evaluation with Consistent Model Defects, Ph.D. thesis, Technishe Universitätt Wien (2015)

    [3] P.Helgesson, H.Sjöstrand, D.Rochman; Uncertainty driven nuclear data evaluation including thermal (n,alpha): applied to Ni-59; Nuclear Data Sheets 145 (2017) 1–24

     

  • 32.
    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.
    Justified co-variance data by treating model defects; fitting optimized energy-dependent model parameters in Fe-56 nuclear data evaluation2018Conference paper (Other academic)
  • 33.
    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.
    Model defect treatment for 56Fe2018Conference paper (Other academic)
  • 34.
    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]

  • 35.
    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.

  • 36.
    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)
  • 37.
    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

  • 38.
    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.
    Rochman, Dimitri
    Paul Scherrer Institute PSI, Villigen, Switzerland.
    Covariance in Ni-59 for JEFF 3.3: A TMC based uncertainty approach2017Conference paper (Other academic)
  • 39.
    Sjöstrand, Henrik
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
    Schnabel, Georg
    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.
    Monte Carlo integral adjustment of nuclear data libraries – experimental covariances and inconsistent data2018In: Book of Abstracts: 5th edition of the International Workshop On Nuclear Data Evaluation for Reactor Applications, 2018, p. 105-Conference paper (Other academic)
  • 40.
    Sublet, J. -Ch.
    et al.
    IAEA, Wagramerstr 5, A-1400 Vienna, Austria.
    Bondarenko, I. P.
    State Atom Energy Corp ROSATOM, Inst Phys & Power Engn, Obninsk, Russia.
    Bonny, G.
    SCK CEN, Nucl Mat Sci Inst, Boeretang 200, B-2400 Mol, Belgium.
    Conlin, J. L.
    Los Alamos Natl Lab, Los Alamos, NM USA.
    Gilbert, M. R.
    Culham Sci Ctr, United Kingdom Atom Energy Author, Abingdon OX14 3DB, Oxon, England.
    Greenwood, L. R.
    Pacific Northwest Natl Lab, Richland, WA 99352 USA.
    Griffin, P. J.
    Sandia Natl Labs, Radiat & Elect Sci Ctr, POB 5800, Albuquerque, NM 87185 USA.
    Helgesson, Petter
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
    Iwamoto, Y.
    Japan Atom Energy Agcy, Nucl Sci & Engn Ctr, Tokai, Ibaraki 3191195, Japan.
    Khryachkov, V. A.
    State Atom Energy Corp ROSATOM, Inst Phys & Power Engn, Obninsk, Russia.
    Khromyleva, T. A.
    State Atom Energy Corp ROSATOM, Inst Phys & Power Engn, Obninsk, Russia.
    Konobeyev, A. Yu.
    Karlsruhe Inst Technol, Inst Neutron Phys & Reactor Technol, D-76021 Karlsruhe, Germany.
    Lazarev, N.
    NSC Kharkiv Inst Phys & Technol, UA-61108 Kharkov, Ukraine.
    Luneville, L.
    Univ Paris Saclay, CEA, F-91191 Gif Sur Yvette, France.
    Mota, F.
    CIEMAT, Lab Nacl Fus Confinamiento Magnet, E-28040 Madrid, Spain.
    Ortiz, C. J.
    CIEMAT, Lab Nacl Fus Confinamiento Magnet, E-28040 Madrid, Spain.
    Rochman, D.
    Paul Scherrer Inst, CH-5232 Villigen, Switzerland.
    Simakov, S. P.
    Karlsruhe Inst Technol, Inst Neutron Phys & Reactor Technol, D-76021 Karlsruhe, Germany.
    Simeone, D.
    Univ Paris Saclay, CEA, F-91191 Gif Sur Yvette, France.
    Sjöstrand, Henrik
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
    Terentyev, D.
    SCK CEN, Nucl Mat Sci Inst, Boeretang 200, B-2400 Mol, Belgium.
    Vila, R.
    CIEMAT, Lab Nacl Fus Confinamiento Magnet, E-28040 Madrid, Spain.
    Neutron-induced damage simulations: Beyond defect production cross-section, displacement per atom and iron-based metrics2019In: The European Physical Journal Plus, ISSN 2190-5444, E-ISSN 2190-5444, Vol. 134, no 7, article id 350Article, review/survey (Refereed)
    Abstract [en]

    Nuclear interactions can be the source of atomic displacement and post-short-term cascade annealing defects in irradiated structural materials. Such quantities are derived from, or can be correlated to, nuclear kinematic simulations of primary atomic energy distributions spectra and the quantification of the numbers of secondary defects produced per primary as a function of the available recoils, residual and emitted, energies. Recoils kinematics of neutral, residual, charged and multi-particle emissions are now more rigorously treated based on modern, complete and enhanced nuclear data parsed in state of the art processing tools. Defect production metrics are the starting point in this complex problem of correlating and simulating the behaviour of materials under irradiation, as direct measurements are extremely improbable. The multi-scale dimensions (nuclear-atomic-molecular-material) of the simulation process is tackled from the Fermi gradation to provide the atomic- and meso-scale dimensions with better metrics relying upon a deeper understanding and modelling capabilities of the nuclear level. Detailed, segregated primary knock-on-atom metrics are now available as the starting point of further simulation processes of isolated and clustered defects in material lattices. This allows more materials, incident energy ranges and particles, and irradiations conditions to be explored, with sufficient data to adequately cover both standard applications and novel ones, such as advanced-fission, accelerators, nuclear medicine, space and fusion. This paper reviews the theory, describes the latest methodologies and metrics, and provides recommendations for standard and novel approaches.

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