79urn:lsid:arphahub.com:pub:D015427E53F6553C92792E51CD684756Nuclear Energy and TechnologyNUCET24523038National Research Nuclear University MEPhI (Moscow Engineering Physics Institute)10.3897/nucet.9.111919111919Research ArticleReactor physicsNuclear data uncertainty on generation IV fast reactors criticality calculations analysis comparisonChereshkovDimitar G.dgchereshkov@mephi.ru1TernovykhMikhail Yu.https://orcid.org/00000002983055011TikhomirovGeorgiy V.https://orcid.org/00000002533272721RyzhkovAlexander A.1MEPhI, 31 Kashirskoe Sh., 115409 Moscow, RussiaMEPhIMoscowRussia
Corresponding author: Dimitar G. Chereshkov (dgchereshkov@mephi.ru)
Academic editor: Yury Korovin
20232010202393157162F36AB721CCCB5837ABF2F6D71575717002511202224042023Dimitar G. Chereshkov, Mikhail Yu. Ternovykh, Georgiy V. Tikhomirov, Alexander A. RyzhkovThis is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
The new calculation code capabilities are applied in the current work as well as important fast reactor criticality parameters uncertainty assessment articles’ results based on different nuclear data libraries and covariance matrices. A comparative analysis of uncertainty estimations related to neutron reactions is presented for leadcooled reactor models and sodiumcooled reactor models. For the models of advanced BN and BR fast reactors with three fuel types (UO_{2}, MOX, MNUP), the multiplication factor uncertainty calculations are performed using 252group covariance matrices based on ENDF/BVII.1 library via the SCALE 6.2.4 code system. The main nuclear data uncertainty contributors in the multiplication factor are determined. Recommendations are formulated for improving the cross sections accuracy for several nuclides in order to provide more reliable results of fast reactor criticality calculations. Leadcooled reactors have no operational history compared to lightwater and sodiumcooled reactors. The experimental data insufficiency calls in the question about reliability of the simulation results and requires a comprehensive initial data uncertainty analysis for the neutron transport simulation. The obtained results support the idea that lead and sodiumcooled reactors have close nuclear data sensitivity using one and the same computation tools, nuclear data libraries and fuel compositions. This makes it possible to use the accumulated data of benchmarks for sodiumcooled reactors in the safety determination of leadcooled reactors.
Fast reactorsGeneration IVcovariance matricessensitivity coefficientnuclear data uncertaintySCALEMNUPMOXCitation
Chereshkov DG, Ternovykh MYu, Tikhomirov GV, Ryzhkov AA (2023) Nuclear data uncertainty on generation IV fast reactors criticality calculations analysis comparison. Nuclear Energy and Technology 9(3): 157–162. https://doi.org/10.3897/nucet.9.111919
Introduction
Generation IV International Forum has identified and selected six nuclear power systems for further investigations and development making world’s future energy demand supply possible. Sodiumcooled fast reactors are one of the most extensively studied and advanced considered commercialsize reactor concepts, greatly supported by industries and research institutions. For largescale twocomponent nuclear power system (fast reactors with a closed nuclear fuel cycle), it is so far theoretically proven and computationally and experimentally attested that such three conceptual requirements as core BR close to unity, lead coolant and highdensity mixed nitride uraniumplutonium (MNUP) fuel allow improving the safety of nuclear reactors notably (Adamov et al. 2022). Increased safety properties of lead (leadbismuth), including relative chemical inertness, capability to retaining hazardous radionuclides, such as iodine and cesium, and high boiling temperature, contributes to the selection of leadcooled reactor as an economically competitive Generation IV reactor. However, leadcooled reactors do not have a comparably considerable operation experience as light water and sodiumcooled reactors do. It is stated in Trottier et al. 2018; Trivedi et al. 2020; Castelluccio et al. 2021; Romojaro et al. 2021, that insufficient experimental data calls in the question about reliability of computational simulation results and requires a comprehensive initial data uncertainty analysis for the neutron transport simulation. Propagating these uncertainties gives a better notion of their influence on the reactor core performance and makes it possible to estimate the design safety limits. Based on the results obtained, the paper supports the statement that the nuclear data sensitivity is close to both lead and sodiumcooled reactors with analogous fuel compositions using one and the same computational tools and nuclear data libraries. This allows us to use the accumulated benchmarks of sodiumcooled reactors to prove the leadcooled reactor safety.
Russia has the world’s most highest level experience in developing and operating sodiumcooled fast reactors. During the initial stage of the BN technology development and adoption process, the use of oxide fuel, due to its maturity in terms of application in thermal reactors, was a reasonable decision. However, highdensity fuel types on the account of their physical properties have obvious advantages in fast reactors. This is the reason why all countries, developing innovative fastneutron reactors, are considering transition from oxide to highdensity fuel types, though Russian and the international experience in using nitride fuel is not enough for predicting reliably the represented fuel elements serviceability working at the BN and BR reactor parameters.
The initial data uncertainties together with the obtained results uncertainties are an integral part of the studies aiming to demonstrate the reactor facilities nuclear safety. Analyzing innovative fast reactor models with mixed uraniumplutonium fuel, the cumulative nuclear data uncertainty contribution to the multiplication factor k_{eff} calculation without taking into account integral experiments is ± 1.5–1.9% [6]. Besides, it is noted in the Manturov et al. 2022 paper that the uncertainty may be decreased to ± 0.4–0.6% as a result of the compensative effect of the uncertainty correlations of evaluated quantities at different neutron energies and reaction channels.
This paper analyzes the influence of nuclear data uncertainties in leadcooled (Table 1) and sodiumcooled (Table 2) reactor models. The listed data have been supplemented with own calculations of BR1200 and BN1200 reactor models with three fuel types (UO2, MOX and MNUP). In the BR and BN calculations, the sensitivity analysis was provided using the TSUNAMI sequence of the SCALE 6.2.4 code (Bostelmann et al. 2022). The k_{eff} uncertainty was estimated and quantitatively investigated using the SAMS module applied in the TSUNAMI sequence. The results were compared to identify the potential needs for updating the nuclear data. Own calculations were undertaken to identify the nuclides and reactions that have the greatest effect on the BR and BN neutronic performance.
Analyzed leadcooled fast reactors
Reactor
Nuclear data library
Reference
SEALER
JEFF3.1, ENDF/BVII.1
Trottier et al. 2018
ALFRED
JEFF3.3, ENDF/BVIII.0
Romojaro and AlvarezVelarde 2020
ENDF/BVII.0, ENDF/BVII.1
Romojaro et al. 2017a
ENDF/BVIII.0
Castelluccio et al. 2021
DLFR
ENDF/BVII.0
Trivedi et al. 2020
MYRRHA
JEFF3.1.2, ENDF/BVII.0, ENDF/BVII.1
Romojaro et al. 2017b
JEFF3.3, ENDF/BVIII.0
Romojaro et al. 2021
Analyzed sodiumcooled fast reactors
Reactor
Nuclear data library
Reference
EBRII
ENDF/BVII.1, ENDF/BVIII.0
Bostelmann et al. 2021
BN600
ENDF/BVII.1, ENDF/BVIII.0
Ma et al. 2021
JOYO
ENDF/BVII.1
Wan et al. 2020
ASTRID
ENDF/BVII.1
Griseri et al. 2017
B & BR
ENDF/BVII.0, ENDF/BVII.1
Vu and Hartanto 2021
ZPPR9
ENDF/BVII.0
Zheng et al. 2018
ESFR
JEFF3.3, ENDF/BVIII.0
Romojaro et al. 2021
Observed fast reactors features
The following four models of leadcooled reactors have been selected for the comparative analysis.
SEALER (Trottier et al. 2018) is a modular reactor with 19.9% enriched UO_{2} fuel. The electric power is 3 to 10 MW. The reactor core life is 10 to 30 years (full power operation without refuelling).
ALFRED (Romojaro et al. 2017a; Romojaro and AlvarezVelarde 2020; Castelluccio et al. 2021) is a smallsize reactor, its core is divided in two zones (internal and external) with different plutonium contents (20.5% in the external zone) for power field flattening. The electric power is 125 MW and the maximum fuel burnup is 100 MW∙day/kg. Each year, 1/5 of the core is unloaded and replaced with fresh fuel.
DLFR (Trivedi et al. 2020) is a mediumsize reactor, its core includes two uranium enrichment zones (17.5% in the external zone). The electric power is 450 MW. The refuelling scenarios are at the development stage.
MYRRHA (Romojaro et al. 2017b; Romojaro et al. 2021) is a smallsize reactor capable to operate both in a subcritical state when using a linear 600 MeV proton accelerator and in a critical mode (as a leadbismuthcooled fastneutron reactor). The electric power is 57 MW. Different fuel compositions are considered.
Seven sodiumcooled reactor models have been taken for the comparative analysis.
EBRII (Bostelmann et al. 2021) is a 20 MW(e) demonstration reactor, consisting of three regions (core, inner and outer shield). Fuel elements are fuelled with enriched uranium metal (67%) and it is chosen stainlesssteel cladding.
BN600 (Ma et al. 2021) is a 600 MW(e) commercial reactor using enriched UO_{2} fuel since the start of the operation. The paper considers the results of a benchmark calculation with MOX fuel with a 20% plutonium content.
JOYO (Wan et al. 2020) is an experimental reactor using MOX fuel which comprises 23% enriched uranium and 17.7% plutonium. The fissionable plutonium isotopes content is 80.4%. The thermal power is 140 MW.
ASTRID (Griseri et al. 2017) is a commercial reactor with core comprising two fuel subzones. It is considered as an MA burner. The fuel consists of about 70% depleted uranium, 20% to 22% plutonium, and about 10% MAs. The electric power is 500 MW.
B & BR (Vu and Hartanto 2021) is a modular reactor with UO_{2} fuel, enriched to 12.32%. The electric power is 400 MW. The reactor core life is up to 50 years (full power operation without refuelling).
ZPPR9 (Zheng et al. 2018) is a zeropower reactor using MOX fuel with a 17.7% plutonium content.
ESFR (Romojaro et al. 2021) is a commercialsize reactor with two subzones core with different fuel section heights in the fuel rods. There is a MOX fuel used with a 14.6% and 17% plutonium content in the subzones. The electric power is 1500 MW.
Codes and methodologies
The sensitivity and uncertainty calculations were performed in the software suite SCALE 6.2.4. In particular, the package was used to test the developed newgeneration codes for fast reactor neutronic calculations (Ternovykh et al. 2017; Ternovykh and Bogdanova 2020; Tikhomirov et al. 2021). System code SCALE involves several control sequences for neutronic calculations and nuclear safety analysis, it has been developed and evolved by Oak Ridge National Laboratory. It combines modules for criticality calculation, radiation shielding and nuclide kinetics, sensitivity and uncertainty analysis and other problems. A Monte Carlo transport code, KENOVI, is used to support calculations in a 3D geometry. The neutron transport simulation can be performed both in a multigroup approximation and with continuous representation of crosssections by energy. TSUNAMI is a sensitivity and uncertainty analysis module. The TSUNAMI module uses the forward and adjoint neutron transport solutions, obtained by a KENOVI calculation, using SAMS to compute sensitivities via first order perturbation theory. Sensitivity calculations together with the ENDF/BVII.1 covariance data can be used to estimate the uncertainty of k_{eff} or other functionals.
Analysis of observed and scale calculated reactor results
Increased reactor safety requirements call for improving the characteristics prediction accuracy of the fast reactors both in operation and under design. One of the key objectives is to refine the available and develop new, more advanced software tools and databases to support neutronic calculations, estimate the existing uncertainties and work out recommendations for reducing them (Manturov et al. 2022).
Sensitivity analysis of observed fast reactors
There is a comparative analysis of the k_{eff} sensitivity coefficients using different nuclear data libraries in the observed fast reactors presented.
Fig. 1 presents the k_{eff} sensitivity coefficients to the seven most important nuclides and reactions for the investigated reactor models with MOX fuel. The k_{eff} sensitivity coefficients for MYRRHA, using two different libraries (JEFF3.3 and ENDF/BVII.0), are close to the k_{eff} sensitivity coefficients for ALFRED. When comparing ZPPR9 and ESFR sodiumcooled reactors with MYRRHA leadcooled reactor, with similar libraries used (ENDF/BVII.0 and JEFF3.3), it is noted that the sensitivity coefficients have close values. The sensitivity coefficient differences are explained mainly by the ESFR design features (fertile blankets) and a smaller plutonium content rather than by different coolants used in the reactors.
Top 7 integrated k_{eff} sensitivity coefficients for reactors with MOX fuel.
https://binary.pensoft.net/fig/923165
A common conclusion for lead and sodiumcooled reactors with MOX fuel is that greater k_{eff} sensitivity is shown to ν_{f} and ^{239}Pu fission. We should note that these reactions with highest sensitivity coefficients usually do lead to the largest uncertainties with different used libraries in the reactor calculations.
Analysis of <italic>k</italic><sub>eff</sub> nuclear data uncertainties
The k_{eff} nuclear data uncertainties introduced by their major contributors in the different libraries used in ALFRED reactor calculations are provided in Table 3. The biggest contributors to the k_{eff} uncertainties are the uncertainties of ^{239}Pu ν_{f} and ^{238}U (n,n′) when ENDF/BVII.0 library is used, the crosssection uncertainties of ^{238}U (n,n′) and ^{239}Pu (n,γ) when ENDF/BVII.1 library is used, the crosssection uncertainties of ^{240}Pu (n,f) and ^{240}Pu (n,γ) when JEFF3.3 library is used, and crosssection uncertainties of ^{238}U (n,f) and ^{239}Pu (n,γ) when ENDF/BVIII.0 library is used. It is to be noted that a reestimation of crosssections in ENDF/BVIII.0 library have led to the k_{eff} uncertainties caused by the crosssection uncertainties of ^{240}Pu (n,f) and ^{240}Pu (n, γ), regarding the crosscorrelations between ^{240}Pu (n,γ) and ^{240}Pu (n,γ), reduced by a factor of 10, and the uncertainties caused by the ^{239}Pu (n,f) and ^{239}Pu (n,γ) reactions increased by a factor of over 1.5 as compared with JEFF3.3 calculations. When ENDF/BVII.0 library is used for the calculations, the uncertainties of ^{239}Pu ν_{f} and ^{238}U (n,n′) are the biggest contributors in contrast to the other libraries where their values are several times smaller. The covariance matrix of ENDF/BVII.1 library have the smallest k_{eff} uncertainty caused by the uncertainty of ^{239}Pu ν_{f}, as compared to the other libraries. The total uncertainty in k_{eff} does not exceed 0.8% when ENDF/BVIII.0 and JEFF3.3 libraries are used.
k_{eff} nuclear data uncertainties in different libraries in ALFRED, %
Covariance
JEFF3.3
ENDF/BVIII.0
ENDF/BVII.1
ENDF/BVII.0
^{240}Pu (n,f)  ^{240}Pu (n,f)
0.52



^{240}Pu (n,f)  ^{240}Pu (n,γ)
0.42



^{239}Pu ν_{f}  ^{239}Pu ν_{f}
0.32
0.19
0.06
0.7
^{239}Pu (n,f)  ^{239}Pu (n,f)
0.3
0.58
0.2
0.2
^{238}U (n,n‘)  ^{238}U (n,n‘)
0.23
0.13
0.54
0.53
^{239}Pu (n, γ)  ^{239}Pu (n, γ)
0.14
0.21
0.25
0.27
Total uncertainty
0.79
0.75


The k_{eff} nuclear data uncertainties introduced by their major contributors in the different libraries used in the MYRRHA reactor calculations are provided in Table 4. The same conclusions can be made as well as for ALFRED reactor due to the close characteristics of the two reactors. When JENDL4.0 m library is used, the major contributors to the k_{eff} nuclear data uncertainty are the crosssection uncertainties of ^{239}Pu (n,f) and ^{239}Pu (n,γ). The smallest uncertainties are found in JENDL4.0 m data, as compared to the other libraries, except the ^{239}Pu (n,f) uncertainty value in ENDF/BVII.0. The total uncertainty is equal to 0.96% when ENDF/BVII.0 library is used, 0.77% when ENDF/BVIII.0 and JEFF3.3 libraries are used, and 0.55% when JENDL4.0 m library is used.
k_{eff} nuclear data uncertainties in different libraries in MYRRHA, %
Covariance
JEFF3.3
ENDF/BVIII.0
JENDL 4.0 m
ENDF/BVII.0
^{240}Pu (n,f)  ^{240}Pu (n,f)
0.54



^{240}Pu (n,f)  ^{240}Pu (n,γ)
0.42



^{239}Pu ν_{f}  ^{239}Pu ν_{f}
0.32
0.19
0.11
0.7
^{239}Pu (n,f)  ^{239}Pu (n,f)
0.3
0.55
0.27
0.19
^{238}U (n,n‘)  ^{238}U (n,n‘)


0.15
0.32
^{239}Pu (n, γ)  ^{239}Pu (n, γ)
0.15
0.23
0.19
0.27
Total uncertainty
0.77
0.77
0.55
0.96
The k_{eff} nuclear data uncertainties introduced by their major contributors in the different libraries used in BN600 and ESFR reactors are provided in Table 5.
k_{eff} nuclear data uncertainties in different libraries in ESFR and BN600, %
Reactor
ESFR
BN600
Covariance
JEFF3.3
ENDF/BVIII.0
ENDF/BVII.1
^{240}Pu (n,f)  ^{240}Pu (n,f)
0.59

0.01
0.01
^{238}U (n,γ)  ^{238}U (n,γ)
0.3

0.24
0.27
^{239}Pu χ  ^{239}Pu χ
0.46
0.22

0.24
^{239}Pu (n,f)  ^{239}Pu (n,f)
0.31
0.55
0.71
0.25
^{238}U (n,n‘)  ^{238}U (n,n‘)
0.48
0.24
0.15
0.7
^{239}Pu (n, γ)  ^{239}Pu (n, γ)

0.25
0.28
0.29
Total uncertainty
1.05
0.8
0.88
0.9
The total BN600 and ESFR uncertainties are close to each other. In BN600, the uncertainty of ^{238}U (n,n′) is decreased from 0.7 to 0.15%. The uncertainty of ^{239}Pu (n,f) is increased from 0.25 to 0.7% when changing library from ENDF/BVII.1 to ENDF/BVIII.0. In ESFR, using ENDF/BVIII.0 instead of JEFF3.3, the uncertainty of ^{238}U (n,n′) changed from 0.48 to 0.24%, and the uncertainty of ^{239}Pu (n,f) changed from 0.31 to 0.55%. The total uncertainty, using ENDF/BVIII.0, has the smallest value and the closest one for sodium and leadcooled reactors.
Analysis of the SCALE calculated reactor results
The BR1200 and BN1200 reactor models were calculated with three fuel types: uranium dioxide, MOX and MNUP fuel. The reactor calculations were performed using the TSUNAMI3D module, the 252group ENDF/BVII.1 nuclear data library and 252group covariance matrices were used. The k_{eff} calculation statistical error did not exceed 0.0001.
Fig. 2 presents the most important k_{eff} sensitivity coefficients for reactors with MOX and MNUP fuels. Coolant or fuel type difference does not lead to a great effect on the k_{eff} sensitivity coefficients. The sensitivity coefficients to ^{239}Pu reactions are a little smaller for BN compared to BR. We should note that the sensitivity coefficients to ^{238}U are smaller than to ^{239}Pu, but the uncertainties for ^{238}U are larger than for ^{239}Pu.
Top 5 integrated k_{eff} sensitivity coefficients for BR and BN reactors with MNUP and MOX fuel.
https://binary.pensoft.net/fig/923166
Tables 6 and 7 present the k_{eff} uncertainties introduced by their major contributors in BR1200 and BN1200 reactors. The calculated uncertainties mainly agree with the Andrianova et al. 2014 results.
k_{eff} nuclear data uncertainties in reactors with MNUP fuel, %
Covariance
BR1200
BN1200
^{238}U (n,n‘)  ^{238}U (n,n‘)
1.28
1.03
^{238}U (n,γ)  ^{238}U (n,γ)
0.32
0.31
^{239}Pu (n, γ)  ^{239}Pu (n, γ)
0.23
0.2
^{239}Pu (n,f)  ^{239}Pu (n,f)
0.22
0.2
^{239}Pu χ  ^{239}Pu χ
0.22
0.18
^{238}U χ  ^{238}U χ
0.17
0.18
^{238}U ν_{f}  ^{238}U ν_{f}
0.17
0.18
^{239}Pu (n,n‘)  ^{239}Pu (n,n‘)
0.13
0.09
^{56}Fe (n,n‘)  ^{56}Fe (n,n‘)
0.12
0.12
^{207}Pb (n,n‘)  ^{207}Pb (n,n‘)
0.11

^{23}Na (n,n)  ^{23}Na (n,n)

0.1
Total uncertainty
1.45
1.21
k_{eff} nuclear data uncertainties in reactors with MOX fuel, %
Covariance
BR1200
BN1200
^{238}U (n,n‘)  ^{238}U (n,n‘)
0.53
0.61
^{239}Pu (n, γ)  ^{239}Pu (n, γ)
0.31
0.25
^{238}U (n,γ)  ^{238}U (n,γ)
0.28
0.27
^{56}Fe (n,n)  ^{56}Fe (n,n)
0.23
0.08
^{239}Pu (n,f)  ^{239}Pu (n,f)
0.23
0.2
^{56}Fe (n, γ)  ^{56}Fe (n, γ)
0.17
0.2
^{239}Pu χ  ^{239}Pu χ
0.15
0.15
^{207}Pb (n,n‘)  ^{207}Pb (n,n‘)
0.13

^{56}Fe (n,n‘)  ^{56}Fe (n,n‘)
0.12
0.17
^{238}U ν_{f}  ^{238}U ν_{f}
0.11
0.12
Total uncertainty
0.85
0.86
It is to be noted that the major contributor to the k_{eff} uncertainty in reactors with MOX fuel is ^{238}U (n,n′) and its value is about 0.6%. The major contributor for reactors with uranium fuel is ^{235}U (n,γ) with an uncertainty of 2%. The contribution of the structural material reactions to the k_{eff} uncertainty is about 0.2%, which agrees with the papers analyzed above.
For reactors with MNUP fuel, the BR and BN total uncertainties differ by 20% and are defined, basically, by differences in the uncertainties of ^{238}U (n,n′), which requires an additional analysis. In general, the comparison of the uncertainties shows that the lead and sodiumcooled reactors have close nuclear data sensitivity using one and the same calculation tools, nuclear data libraries and fuel compositions.
Conclusions
The calculated results of the sensitivities and uncertainties for Generation IV sodium and leadcooled fast reactors have been analyzed. The SCALE code was used for BR1200 and BN1200 reactors with three fuel types to calculate the sensitivities and uncertainties for the multiplication factor due to nuclear data.
The major uncertainty contributors for multiplication factor have been identified. For MOX and MNUP fuel, these are uncertainties of inelastic scatter and capture crosssections for ^{238}U, and, to a smaller extent, uncertainties of the capture and fission crosssections and the fission neutron spectrum uncertainty for ^{239}Pu; for reactors with uranium fuel, these are the capture and fission crosssections and the fission neutron spectrum uncertainty for ^{235}U.
The operation experience of leadcooled reactors is not as comparably considerable as light water and sodiumcooled reactors one has to be. The experimental data insufficiency requires an indepth analysis of the initial data uncertainty during modeling.
The obtained results confirm the statement that nuclear data sensitivity is close to both lead and sodiumcooled reactors with analogous fuel compositions using one and the same computational tools and nuclear data libraries. This allows us to use the accumulated benchmarks of sodiumcooled reactors to prove the leadcooled reactor safety.
ReferencesAdamovEOIvanovVKMochalovYuSRachkovVIShadrinAYuKhomyakovYuSLachkanovEVOrlovAI (2022) On the question of different approaches to the national strategy for the development of nuclear energy.AndrianovaONGolovkoYuEJerdevGMZadornovDVKoscheevVNManturovGNPeregudovAATsibulyaAM (2014) Testing covariance matrices of uncertainties in the BNAB data system. Izvestia vysshikh uchebnykh zawedeniy.BostelmannFIlasGWieselquistWA (2021) Nuclear data sensitivity study for the EBRII fast reactor benchmark using SCALE with ENDF/BVII.1 and ENDF/BVIII.0.BostelmannFWiardaDArbanasGWieselquistWA (2022) Extension of SCALE/Sampler’s sensitivity analysis. Annals of Nuclear Energy 165: 108641. https://doi.org/10.1016/j.anucene.2021.108641CastelluccioDMGrassoGLodiFPelusoVGMengoniA (2021) Nuclear data target accuracy requirements for advanced reactors: The ALFRED case. Annals of Nuclear Energy 162: 108533. https://doi.org/10.1016/j.anucene.2021.108533GriseriMFioritoLStankovskiyAVan den EyndeG (2017) Nuclear data uncertainty propagation on a sodium fast reactor.MaXHuangYQuWZhouFPengXKuiHBinZ (2021) Uncertainty comparison between ENDF/BVIII.0 and ENDF/BVII.1 for fast reactor BN600 using highprecision sampling method. Annals of Nuclear Energy 161: 108457. https://doi.org/10.1016/j.anucene.2021.108457ManturovGNZabrodskayaSVZuikovAALevchenkoYuVMelegaNAMishinVAPanovaDVPeregudovAAPeregudovaOOSemenovMYuSlyunyaevMNTykleevaKV (2022) Status of development of nuclear constant databases for calculations of fast reactors based on ROSFOND and BNABRF libraries. VANT. Ser.RomojaroPAlvarezVelardeF (2020) Evolution of the importance of neutroninduced reactions along the cycle of an LFR. EPJ Web of Conferences 239: 22010. https://doi.org/10.1051/epjconf/202023922010RomojaroPAlvarezVelardeFHerranzN (2017a) SUMMON: A Sensitivity and Uncertainty Methodology for MONte Carlo Codes. Int. Conf. on Mathematics & Computational Methods Applied to Nuclear Science & Engineering. Jeju, Korea, April 16–20, 2017. https://www.kns.org/files/int_paper/paper/MC2017_2017_2/P139S0206RomojaroP.pdf [accessed Mar. 21, 2023]RomojaroPAlvarezVelardeFCabellosOGarciaHerranzNJimenezCarrascosaA (2021) On the importance of target accuracy assessments and data assimilation for the codevelopment of nuclear data and fast reactors: MYRRHA and ESFR. Annals of Nuclear Energy 161: 108416. https://doi.org/10.1016/j.anucene.2021.108416RomojaroPAlvarezVelardeFKodeliIStankovskiyADiezCJCabellosOGarciaHerranzNHeyseJSchillebeeckxPVan den EyndeGZherovnikG (2017b) Nuclear data sensitivity and uncertainty analysis of effective neutron multiplication factor in various MYRRHA core configurations.TernovykhMYBogdanovaEV (2020) Testing the multigroup, group and subgroup options of the CONSYST/ABBNRF system on criticality calculations of fast reactor models with MNUP fuel. Journal of Physics: Conference Series 1689: 012059. https://doi.org/10.1088/17426596/1689/1/012059TernovykhMTikhomirovGKhomyakovYuSuslovI (2017) Determination of equilibrium fuel composition for fast reactor in closed fuel cycle. EPJ Web of Conferences 153: 07034. https://doi.org/10.1051/epjconf/201715307034TikhomirovGTernovykhMKhomyakovYSuslovI (2021) Independent testing of new generation codes of the “Proryv” project. Nuclear Engineering and Design 384: 111497. https://doi.org/10.1016/j.nucengdes.2021.111497TrivediIHouJGrassoGIvanovKFranceschiniF (2020) Nuclear data uncertainty quantification and propagation for safety analysis of leadcooled fast reactors. Science and Technology of Nuclear Installations 2020: 3961095. https://doi.org/10.1155/2020/3961095TrottierAAdamsFPLevinskyARoubtsovD (2018) Nuclear data sensitivity for reactor physics parameters in a leadcooled reactor.VuTMHartantoD (2021) Study on the sensitivity and uncertainty of nuclear data to the sodiumcooled linear breedandburn fast reactor using SCALE 6.2 code. Science and Technology of Nuclear Installations 2021: 9997867. https://doi.org/10.1155/2021/9997867WanCHuangYZhengYCaoLWuH (2020) Nucleardata adjustment based on the continueenergy crosssection library for the fast reactor. Annals of Nuclear Energy 143: 107453. https://doi.org/10.1016/j.anucene.2020.107453ZhengYQiaoLZhaiZDuXXuZ (2018) SARAX: A new code for fast reactor analysis part II: Verification, validation and uncertainty quantification.
Russian text published: Izvestiya vuzov. Yadernaya Energetika (ISSN 02043327), 2023, n. 1, pp. 162–174.