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Helgesson, Petter
Publications (10 of 40) Show all publications
Sublet, J.-C. -., Bondarenko, I. P., Bonny, G., Conlin, J. L., Gilbert, M. R., Greenwood, L. R., . . . Vila, R. (2019). Neutron-induced damage simulations: Beyond defect production cross-section, displacement per atom and iron-based metrics. The European Physical Journal Plus, 134(7), Article ID 350.
Open this publication in new window or tab >>Neutron-induced damage simulations: Beyond defect production cross-section, displacement per atom and iron-based metrics
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2019 (English)In: The European Physical Journal Plus, ISSN 2190-5444, E-ISSN 2190-5444, Vol. 134, no 7, article id 350Article, review/survey (Refereed) Published
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.

Place, publisher, year, edition, pages
Springer Berlin/Heidelberg, 2019
National Category
Subatomic Physics
Identifiers
urn:nbn:se:uu:diva-391432 (URN)10.1140/epjp/i2019-12758-y (DOI)000476545900001 ()
Available from: 2019-10-03 Created: 2019-10-03 Last updated: 2019-10-03Bibliographically approved
Helgesson, P. (2018). Approaching well-founded comprehensive nuclear data uncertainties: Fitting imperfect models to imperfect data. (Doctoral dissertation). Uppsala: Acta Universitatis Upsaliensis
Open this publication in new window or tab >>Approaching well-founded comprehensive nuclear data uncertainties: Fitting imperfect models to imperfect data
2018 (English)Doctoral 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.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2018. p. 119
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, ISSN 1651-6214 ; 1669
Keywords
Evaluated nuclear data, uncertainty propagation, uncertainty quantification, model defects, Gaussian processes, TALYS, TENDL, covariances.
National Category
Subatomic Physics
Research subject
Physics with specialization in Applied Nuclear Physics
Identifiers
urn:nbn:se:uu:diva-348553 (URN)978-91-513-0334-5 (ISBN)
Public defence
2018-06-08, Häggsalen, Ångströmslaboratoriet, Lägerhyddsv. 1, Uppsala, 09:15 (English)
Opponent
Supervisors
Available from: 2018-05-17 Created: 2018-04-16 Last updated: 2018-10-08
Helgesson, P., Neudecker, D., Sjöstrand, H., Grosskopf, M., Smith, D. L. & Capote, R. (2018). Assessment of Novel Techniques for Nuclear Data Evaluation. In: Reactor Dosimetry: 16th International Symposium. Paper presented at 16th International Symposium on Reactor Dosimetry, MAY 07-12, 2017, Santa Fe, NM (pp. 105-116). ASTM International
Open this publication in new window or tab >>Assessment of Novel Techniques for Nuclear Data Evaluation
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2018 (English)In: Reactor Dosimetry: 16th International Symposium, ASTM International, 2018, p. 105-116Conference paper, Published 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.

Place, publisher, year, edition, pages
ASTM International, 2018
Series
American Society for Testing and Materials Selected Technical Papers, ISSN 0066-0558
National Category
Subatomic Physics
Identifiers
urn:nbn:se:uu:diva-322558 (URN)10.1520/STP160820170087 (DOI)000474939900010 ()978-0-8031-7661-4 (ISBN)
Conference
16th International Symposium on Reactor Dosimetry, MAY 07-12, 2017, Santa Fe, NM
Available from: 2017-05-25 Created: 2017-05-25 Last updated: 2019-08-07Bibliographically approved
Sjöstrand, H., Asquith, N., Helgesson, P., Rochman, D. & van der Marck, S. (2018). Efficient use of Monte Carlo: The Fast Correlation Coefficient. Paper presented at 4th edition of the International Workshop on Nuclear Data Covariances, October 2-6 2017, Aix en Provence, France.. EPJ N - Nuclear Sciences and Technologies, 4, Article ID 15.
Open this publication in new window or tab >>Efficient use of Monte Carlo: The Fast Correlation Coefficient
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2018 (English)In: EPJ N - Nuclear Sciences and Technologies, E-ISSN 2491-9292, Vol. 4, article id 15Article in journal (Refereed) Published
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.

National Category
Subatomic Physics
Identifiers
urn:nbn:se:uu:diva-339229 (URN)10.1051/epjn/2018019 (DOI)000438573400002 ()
Conference
4th edition of the International Workshop on Nuclear Data Covariances, October 2-6 2017, Aix en Provence, France.
Available from: 2018-01-17 Created: 2018-01-17 Last updated: 2018-10-08Bibliographically approved
Sjöstrand, H. & Helgesson, P. (2018). Justified co-variance data by treating model defects; fitting optimized energy-dependent model parameters in Fe-56 nuclear data evaluation. In: : . Paper presented at 30th WPEC Meetings SG44 meeting Paris May 2018.
Open this publication in new window or tab >>Justified co-variance data by treating model defects; fitting optimized energy-dependent model parameters in Fe-56 nuclear data evaluation
2018 (English)Conference paper, Oral presentation only (Other academic)
National Category
Subatomic Physics
Identifiers
urn:nbn:se:uu:diva-356845 (URN)
Conference
30th WPEC Meetings SG44 meeting Paris May 2018
Available from: 2018-08-08 Created: 2018-08-08 Last updated: 2018-08-13Bibliographically approved
Sjöstrand, H. & Helgesson, P. (2018). Model defect treatment for 56Fe. In: : . Paper presented at JEFF April 2018 Meetings.
Open this publication in new window or tab >>Model defect treatment for 56Fe
2018 (English)Conference paper, Oral presentation only (Other academic)
National Category
Physical Sciences
Identifiers
urn:nbn:se:uu:diva-349852 (URN)
Conference
JEFF April 2018 Meetings
Available from: 2018-05-02 Created: 2018-05-02 Last updated: 2018-05-07Bibliographically approved
Sjöstrand, H., Schnabel, G. & Helgesson, P. (2018). Monte Carlo integral adjustment of nuclear data libraries – experimental covariances and inconsistent data. In: Book of Abstracts: 5th edition of the International Workshop On Nuclear Data Evaluation for Reactor Applications. Paper presented at WONDER-2018: 5th edition of the International Workshop On Nuclear Data Evaluation for Reactor Applications,Aix-en-Provence, France, 8-12 October 2018 (pp. 105).
Open this publication in new window or tab >>Monte Carlo integral adjustment of nuclear data libraries – experimental covariances and inconsistent data
2018 (English)In: Book of Abstracts: 5th edition of the International Workshop On Nuclear Data Evaluation for Reactor Applications, 2018, p. 105-Conference paper, Oral presentation with published abstract (Other academic)
National Category
Subatomic Physics
Identifiers
urn:nbn:se:uu:diva-380377 (URN)
Conference
WONDER-2018: 5th edition of the International Workshop On Nuclear Data Evaluation for Reactor Applications,Aix-en-Provence, France, 8-12 October 2018
Available from: 2019-03-27 Created: 2019-03-27 Last updated: 2019-03-28Bibliographically approved
Sjöstrand, H., Helgesson, P. & Alhassan, E. (2018). TMC adjustment of nuclear data libraries using integral benchmarks. In: : . Paper presented at WPEC meeting SG46 May 2018.
Open this publication in new window or tab >>TMC adjustment of nuclear data libraries using integral benchmarks
2018 (English)Conference paper, Oral presentation only (Other academic)
National Category
Subatomic Physics
Identifiers
urn:nbn:se:uu:diva-356844 (URN)
Conference
WPEC meeting SG46 May 2018
Available from: 2018-08-08 Created: 2018-08-08 Last updated: 2018-08-14Bibliographically approved
Helgesson, P. (2018). TMC, adjustment to data, and model defects.
Open this publication in new window or tab >>TMC, adjustment to data, and model defects
2018 (English)Other (Other (popular science, discussion, etc.))
Publisher
p. 75
National Category
Physical Sciences
Identifiers
urn:nbn:se:uu:diva-344011 (URN)
Note

Presentation 6 month before disseration for local review. 

Available from: 2018-03-05 Created: 2018-03-05 Last updated: 2018-03-08Bibliographically approved
Helgesson, P. & Sjöstrand, H. (2018). Treating model defects by fitting smoothly varying model parameters: Energy dependence in nuclear data evaluation. Annals of Nuclear Energy, 120, 35-47
Open this publication in new window or tab >>Treating model defects by fitting smoothly varying model parameters: Energy dependence in nuclear data evaluation
2018 (English)In: Annals of Nuclear Energy, ISSN 0306-4549, E-ISSN 1873-2100, Vol. 120, p. 35-47Article in journal (Refereed) Published
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.

National Category
Subatomic Physics
Identifiers
urn:nbn:se:uu:diva-348552 (URN)10.1016/j.anucene.2018.05.026 (DOI)000441485700004 ()
Available from: 2018-04-16 Created: 2018-04-16 Last updated: 2018-10-03Bibliographically approved
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