| 1. |
- Alhassan, Erwin, et al.
(författare)
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Combining Total Monte Carlo and Benchmarks for Nuclear Data Uncertainty Propagation on a Lead Fast Reactor's Safety Parameters
- 2014
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Ingår i: Nuclear Data Sheets. - : Elsevier BV. - 0090-3752 .- 1095-9904. ; 118, s. 542-544
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Tidskriftsartikel (refereegranskat)abstract
- Analyses are carried out to assess the impact of nuclear data uncertainties on some reactor safety parameters for the European Lead Cooled Training Reactor (ELECTRA) using the Total Monte Carlo method. A large number of Pu-239 random ENDF-format libraries, generated using the TALYS based system were processed into ACE format with NJOY99.336 code and used as input into the Serpent Monte Carlo code to obtain distribution in reactor safety parameters. The distribution in keff obtained was compared with the latest major nuclear data libraries – JEFF-3.1.2, ENDF/B-VII.1 and JENDL-4.0. A method is proposed for the selection of benchmarks for specific applications using the Total Monte Carlo approach based on a correlation observed between the keff of a given system and the benchmark. Finally, an accept/reject criteria was investigated based on chi squared values obtained using the Pu-239 Jezebel criticality benchmark. It was observed that nuclear data uncertainties were reduced considerably from 748 to 443 pcm.
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| 2. |
- Alhassan, Erwin
(författare)
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Nuclear data uncertainty propagation for a lead-cooled fast reactor: Combining TMC with criticality benchmarks for improved accuracy
- 2014
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- For the successful deployment of advanced nuclear systems and for optimization of current reactor designs, high quality and accurate nuclear data are required. Before nuclear data can be used in applications, they are first evaluated, benchmarked against integral experiments and then converted into formats usable for applications. The evaluation process in the past was usually done by using differential experimental data which was then complimented with nuclear model calculations. This trend is fast changing because of increase in computational power and tremendous improvements in nuclear reaction theories over the last decade. Since these model codes are not perfect, they are usually validated against a large set of experimental data. However, since these experiments are themselves not exact, the calculated quantities of model codes such as cross sections, angular distributions etc., contain uncertainties. A major source of uncertainty being the input parameters to these model codes. Since nuclear data are used in reactor transport codes asinput for simulations, the output of transport codes ultimately contain uncertainties due to these data. Quantifying these uncertainties is therefore important for reactor safety assessment and also for deciding where additional efforts could be taken to reduce further, these uncertainties.Until recently, these uncertainties were mostly propagated using the generalized perturbation theory. With the increase in computational power however, more exact methods based on Monte Carlo are now possible. In the Nuclear Research and Consultancy Group (NRG), Petten, the Netherlands, a new method called ’Total Monte carlo (TMC)’ has been developed for nuclear data evaluation and uncertainty propagation. An advantage of this approach is that, it eliminates the use of covariances and the assumption of linearity that is used in the perturbation approach.In this work, we have applied the TMC methodology for assessing the impact of nuclear data uncertainties on reactor macroscopic parameters of the European Lead Cooled Training Reactor (ELECTRA). ELECTRA has been proposed within the GEN-IV initiative within Sweden. As part of the work, the uncertainties of plutonium isotopes and americium within the fuel, uncertainties of the lead isotopes within the coolant and some structural materials of importance have been investigated at the beginning of life. For the actinides, large uncertainties were observed in the k-eff due to Pu-238, 239, 240 nuclear data while for the lead coolant, the uncertainty in the k-eff for all the lead isotopes except for Pb-204 were large with significant contribution coming from Pb-208. The dominant contributions to the uncertainty in the k-eff came from uncertainties in the resonance parameters for Pb-208.Also, before the final product of an evaluation is released, evaluated data are tested against a large set of integral benchmark experiments. Since these benchmarks differ in geometry, type, material composition and neutron spectrum, their selection for specific applications is normally tedious and not straight forward. As a further objective in this thesis, methodologies for benchmark selection based the TMC method have been developed. This method has also been applied for nuclear data uncertainty reduction using integral benchmarks. From the results obtained, it was observed that by including criticality benchmark experiment information using a binary accept/reject method, a 40% and 20% reduction in nuclear data uncertainty in the k-eff was achieved for Pu-239 and Pu-240 respectively for ELECTRA.
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| 3. |
- Alhassan, Erwin, et al.
(författare)
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Selecting benchmarks for reactor calculations
- 2014
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Ingår i: PHYSOR 2014 - The Role of Reactor Physics toward a Sustainable Future.
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Konferensbidrag (refereegranskat)abstract
- 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.
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| 4. |
- Duan, Junfeng, 1976-, et al.
(författare)
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Uncertainty Study of Nuclear Model Parameters for the n+Fe-56 Reactions in the Fast Neutron Region below 20 MeV
- 2014
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Ingår i: Nuclear Data Sheets. - : Elsevier BV. - 0090-3752 .- 1095-9904. ; 118, s. 346-348
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Tidskriftsartikel (refereegranskat)abstract
- In this work we study the uncertainty of nuclear model parameters for neutron induced Fe-56 reactions in the fast neutron region by using the Total Monte Carlo method. We perform a large number of TALYS runs and compare the calculated results with the experimental data of the cross sections to obtain the uncertainties of the model parameters. Based on the derived uncertainties another 1000 TALYS runs have been performed to create random cross section files. For comparison with the experimental data we calculate a weighted chi(2) value for each random file as well as the ENDF/B-VII. 1, JEFF-3.1, JENDL-4.0 and CENDL-3.1 data libraries. Furthermore, we investigate the optical model parameters correlation obtained by way of this procedure.
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| 5. |
- Helgesson, Petter, 1986-, et al.
(författare)
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UO-2 Versus MOX: Propagated Nuclear Data Uncertainty for k-eff, with Burnup
- 2014
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Ingår i: Nuclear science and engineering. - 0029-5639 .- 1943-748X. ; 177:3, s. 321-336
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 6. |
- Rochman, Dimitri, et al.
(författare)
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Efficient use of Monte Carlo : Uncertainty Propagation
- 2014
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Ingår i: Nuclear science and engineering. - 0029-5639 .- 1943-748X. ; 177:3, s. 337-349
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Tidskriftsartikel (refereegranskat)abstract
- A new and faster Total Monte Carlo method for the propagation of nuclear data uncertaintiesin Monte Carlo nuclear simulations is presented (the fast TMC method).It is addressing the main drawback of the original Total Monte Carlo method(TMC), namely the necessary large time multiplication factor compared to a singlecalculation. With this new method, Monte Carlo simulations can now be accompaniedwith uncertainty propagation (other than statistical), with small additionalcalculation time. The fast TMC method is presented and compared with the TMCand fast GRS methods for criticality and shielding benchmarks and burn-up calculations.Finally, to demonstrate the efficiency of the method, uncertainties on localdeposited power in 12.7 millions cells are calculated for a full size reactor core,
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| 7. |
- Sjöstrand, Henrik, et al.
(författare)
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Propagation of nuclear data uncertainties for ELECTRA burn-up calculations
- 2014
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Ingår i: Nuclear Data Sheets. - : Elsevier BV. - 0090-3752 .- 1095-9904. ; 118, s. 527-530
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Tidskriftsartikel (refereegranskat)abstract
- The European Lead-Cooled Training Reactor (ELECTRA) has been proposed as a training reactor for fast systems within the Swedish nuclear program. It is a low -power fast reactor cooled by pure liquid lead. In this work, we propagate the uncertainties in 239Pu transport data to uncertainties in the fuel inventory of ELECTRA during the reactor life using the Total Monte Carlo approach(TMC). Within the TENDL project the nuclear models input parameters were randomized within their uncertainties and 740 239Pu nuclear data libraries were generated. These libraries are used as inputs to reactor codes, in our case SERPENT, to perform uncertainty analysis of nuclear reactor inventory during burn-up. The uncertainty in the inventory determines uncertainties in: the long term radio-toxicity, the decay heat, the evolution of reactivity parameters, gas pressure and volatile fission product content. In this work, a methodology called fast TMC is utilized, which reduces the overall calculation time. The uncertainty in the long-term radiotoxicity, decay heat, gas pressureand volatile fission products were found to be insignificant. However, the uncertainty of some minor actinides were observed to be rather large and therefore their impact on multiple recycling should be investigated further. It was also found that, criticality benchmarks can be used to reduce inventory uncertainties due to nuclear data. Further studies are needed to include fission yield uncertainties, more isotopes, and a larger set of benchmarks.
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| 8. |
- Sjöstrand, Henrik, et al.
(författare)
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Total Monte Carlo evaluation for dose calculations
- 2014
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Ingår i: Radiation Protection Dosimetry. - : Oxford University Press (OUP). - 0144-8420 .- 1742-3406. ; 161:1-4, s. 312-315
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Tidskriftsartikel (refereegranskat)abstract
- Total Monte Carlo (TMC) is a method to propagate nuclear data (ND) uncertainties in transport codes, by using a large set of ND files, which covers the ND uncertainty. The transport code is run multiple times, each time with a unique ND file, and the result is a distribution of the investigated parameter, e.g. dose, where the width of the distribution is interpreted as the uncertainty due to ND. Until recently, this was computer intensive, but with a new development, fast TMC, more applications are accessible. The aim of this work is to test the fast TMC methodology on a dosimetry application and to propagate the 56Fe uncertainties on the predictions of the dose outside a proposed 14-MeV neutron facility. The uncertainty was found to be 4.2 %. This can be considered small; however, this cannot be generalised to all dosimetry applications and so ND uncertainties should routinely be included in most dosimetry modelling.
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