Nuclear Data Uncertainty Propagation Research Articles

Assesment of covariance processing with GAIA for nuclear data uncertainty propagation

Assesment of covariance processing with GAIA for nuclear data uncertainty propagation

Nuclear data sensitivity and uncertainty analysis using a set of benchmarks containing [formula omitted]U comparing ENDF/B-VII.1 and ENDF/B- VIII.0 libraries

In nuclear reactor physics, the integral quantities (reaction rates, keff, etc.) are estimated by calculation codes (Monte Carlo and deterministic codes). The determination of the bias and uncertainty that may exist between the calculated results and the reality is necessary. These uncertainties are mainly due to the approximations used during modeling, the approximation associated with the calculation method, as well as the uncertainties of the nuclear data. It is necessary to perform the Sensitivity/Uncertainty Analysis using a set of benchmarks in order to provide an overview of nuclear data uncertainty propagation and to identify isotopes and reactions which can contribute to the uncertainty of integral parameters. In this study, a set of benchmarks contained 233U were selected in order to estimate the impact of the uncertainties related to the reaction of this isotope on keff and to validate the ENDF/B-VIII.0 library in comparison to ENDF/B-VII.1 nuclear data.

The impact of nuclear data uncertainty on identifying plutonium diversion in liquid-fueled Molten Salt Reactors

Molten salt-fueled reactors (MSRs) have gained popularity recently, but there are challenges in accounting for nuclear material due to the inability to count discrete items (e.g., fuel assemblies). The focus of this work is to calculate what changes would occur due to diversion of fissile material, and to evaluate the effect of nuclear data uncertainties on these quantities. The impact of other sources of uncertainty (e.g., measurement error) is out of the scope. A thermal-spectrum MSR was modeled using Serpent 2 and SCALE 6.3.b12 (beta), including plutonium diversion scenarios. The feed and removal capabilities were recently added into the depletion schemes of these codes. Protracted and abrupt diversion scenarios were developed for the removal of 1 and 10 significant quantities (SQs) of plutonium. The SCALE module Sampler was used to perform nuclear data uncertainty propagation along the depletion interval from neutron cross-sections, fission product yield and decay data. Results showed that along with plutonium species, other actinides and fission products presented significant changes between the reference scenario (i.e., no diversion) and the 10 SQ plutonium protracted diversion scenario. Considering their propagated nuclear data uncertainty from Sampler simulations, some actinides such Am and Cm showed changes higher than their nuclear data uncertainty, which goes from 2.23% for 241Am up to 4.88% for 242mAm. Some fission products also presented notable changes, Sr and Y isotopes are examples with positive changes, while Cd, Eu and Sm isotopes showed more prominent changes on the negative side, with uncertainties in the range of 1.89% for 149Sm to 3.31% for 113Cd. The analysis extended to nuclides regularly moved to other modeled inventories, showing that 105,106Ru can be good candidates given their low uncertainty that stands within the 1% range. Many other isotopes with considerable changes presented high uncertainty values and methods to improve their nuclear data uncertainty estimation will be sought in future work.

Propagation of Uncertainties due to Nuclear Data for the LCT086 Reactor System

An Organisation for Economic Co-operation and Development/Nuclear Energy Agency (OECD/NEA) benchmark has been established over recent years to satisfy an increasing demand from the nuclear community for best-estimate predictions accompanied by uncertainty and sensitivity analyses. The main objectives of the OECD/NEA benchmark activity are to determine uncertainties in modeling for reactor systems using best-estimate methods under steady-state and transient conditions and to quantify the impact of these uncertainties for each type of calculation in multiphysics analyses. In terms of light water reactor analyses, an international uncertainty analysis, “Benchmarks for Uncertainty Analysis in Modelling (UAM) for the Design, Operation and Safety Analysis of LWRs” is currently in progress, being coordinated by the OECD/NEA. In the neutronic phases of the benchmark, the uncertainty due to nuclear data is being studied for various LWR types. The LCT086 benchmark, which is a VVER-type reactor criticality benchmark experiment, has been identified to form part of the validation matrix for the uncertainty methodology development. Resulting from this, the main focus of the present work is to propagate the uncertainty due to the nuclear data for two cases (LCT086/Case1 and LCT086/Case3) presented in the LCT086 benchmark. Both fuel assembly and core models were used for the analysis. The code package used to perform the calculations was SCALE 6.2.1. In particular, the function modules KENO-VI, SAMPLER, and TSUNAMI-3D of SCALE 6.2.1 were used. MCNP 6.1 was also used for continuous-energy criticality calculations. In addition to propagating the uncertainty due to the nuclear data, the uncertainty due to selected input parameters as bounded by the manufacturing tolerances were also propagated so that the modeling methods employed could be verified against those reported in the LCT086 benchmark. The results obtained for the nuclear data uncertainty were further compared with nuclear data uncertainty propagation results for the OECD/NEA UAM Kozloduy-6 reactor system. The uncertainty in the multiplication factor k eff is reported in pcm together with the main contributors to the uncertainty from the nuclear data reported in %Δk / k .

An Analytic Benchmark for Neutron Boltzmann Transport with Downscattering: Part III. Uncertainty Propagation and Multigroup Covariance Matrices

An analytic benchmark for nuclear data uncertainty propagation in k-eigenvalue calculations is demonstrated. Flat-flux-weighted cross-section covariance matrices are available in the ENDF/B library for many isotopes. For application-specific purposes, flux-weighted multigroup cross sections with carefully constructed energy group boundaries are desired. In this paper, we use the covariance information from ENDF/B-VII.1 for the defined continuous-energy cross section and an artificially inflated variance version of the same covariance matrix for first-order and Monte Carlo propagation of uncertainty calculations. A flat-flux weighting function is used for the continuous-energy cross-section uncertainty collapse resulting in a higher propagated uncertainty on the k-eigenvalue as the group structure becomes coarser. The results of this analytic benchmark suggest that the reporting of flat-flux-weighted multigroup cross-section covariance matrices at the ENDF level may lead to inaccurate predictions of the uncertainty on the k-eigenvalue for certain applications. This work implies that not only should the resonance parameter uncertainties that go into the calculation of the continuous-energy cross sections be published, but the parameter uncertainties should also be processed into continuous-energy cross-section uncertainties that can be collapsed to application-specific multigroup cross-section covariance matrices.

Nuclear data uncertainty propagation and implications for radioactive waste management of fusion steels

Predictions of material activity in commercial fusion conditions predominantly rely on computational methods, due to a lack of data on long-term effects of high-energy neutron irradiation on structural steels. Consequently, this could result in a bias due to uncertainties in nuclear data used. This work focused on modelling neutron activation of four structural steels in a fusion reactor environment after 20 years of operation. Eurofer, F82H and G91, were assessed as candidate in-vessel materials, whereas SS316L(N)-IG was solely modelled in the vacuum vessel. Activation calculations were performed using the inventory code FISPACT-II using inputs from Monte-Carlo transport simulations performed with OpenMC. The study employed a one-dimensional reactor model with a Helium-Cooled Pebble Bed (HCPB) tritium-breeding blanket design. With the XSUN-2022 code package, a nuclear data sensitivity and uncertainty analysis on production cross-sections of relevant radio-nuclides was carried out. Eurofer and F82H steels exhibited significantly higher resistance to neutron activation than G91 and SS316L(N)-IG. At 100 years after shutdown, none of the steels reached UK low-level waste (LLW) activity levels in the first wall. In the rear of the back-support structure (BSS) of the reactor blanket, all assessed steels reached LLW levels within approximately 30 to 45 years of reactor shutdown. It was found that the vacuum vessel (SS316L(N)-IG) would not be classifiable as LLW for several centuries. Dominant radio-nuclides for each material were identified with FISPACT-II to carry out the uncertainty analyses. The calculated uncertainties were too small to affect the waste disposal options for the first wall within 100 years, but the time-to-reach LLW for BSS and vacuum vessel steel could be uncertain by up to approximately 3 and 6 years, respectively.

Analysis of the preliminary campaign for the PETALE program

The PETALE program aims to provide new experimental data to constrain the stainless steel nuclear data. In this frame, a preliminary measurement campaign has been performed to characterize the neutron flux in key positions of the CROCUS reactor and to develop analysis tools. During this preliminary campaign detailed in the present paper, an efficiency ratio technique has been developed and tested to speed up HPGe measurements by a factor of 30. A second objective of the campaign concerns the propagation of nuclear data uncertainty from the core neutron cross-sections to the reaction rates in the dosimeters. Uncertainties in the core cross sections, such as the uranium cross section, are nuisance parameters that add uncertainty to the dosimeter reaction rate calculation. This component must be fully characterized with covariances to constrain the metal reflector component for Bayesian assimilation. The experimental results are compared to the calculations with different nuclear databases for the nuclear data uncertainty propagation. A good agreement is obtained with the ENDF/B-VII.1 database and a systematic underestimation of around 5–10% in the fast range is observed with the ENDF/B-VIII.0 and JEFF-3.3 databases.

Nuclear data uncertainty propagation applied to the versatile test reactor conceptual design

The Versatile Test Reactor (VTR) currently under development is a 300 MWth sodium-cooled fast reactor (SFR) fueled with ternary metal alloy fuel, which aims to accelerate the testing of advanced nuclear fuels, materials, instrumentation, and sensors in high flux environments that are necessary to license the next generation of advanced reactor concepts. To support the VTR design process, uncertainties associated with the nuclear data has been propagated through the reactor core neutronics calculation to global parameters of interest, such as the core multiplication factor, kinetic parameters, and various reactivity feedback coefficients, following the sensitivity based uncertainty propagation approach. By folding the sensitivity coefficients, separately computed by the generalized perturbation theory code PERSENT and Monte Carlo code Serpent 2, with the variance–covariance matrices from COMMARA-2.0, we obtain the reaction-wise, isotope-wise, and overall uncertainties for each response of interest due to nuclear data uncertainty. With Serpent 2, the statistical error of the uncertainty is obtained by propagating the statistical error of the sensitivity coefficients through the same process using a newly developed uncertainty propagation method. From both codes, the overall top uncertainty contributors are found to be the cross section of Fe-56 elastic scattering, Na-23 elastic scattering, and U-238 inelastic scattering. The large contributions of the Fe-56 elastic scattering cross sections to global parameters are due to its relatively large relative uncertainty of 5–10% in nuclear data and the large volume of Fe-containing reflector assemblies in the fairly compact VTR core design. Both codes agreed well for the overall uncertainty estimates of all responses of interest, except the delayed neutron fraction, prompt neutron generation time, and the coolant density feedback coefficient, where Serpent 2 yielded a much larger value than PERSENT due to the large statistical error of sensitivity coefficients. The calculated uncertainties are also compared to those associated with other SFR cores. Another outcome of this study is a variance–covariance matrix of reactivity coefficients, which can be used in the subsequent uncertainty propagation to the system level to investigate the impact of identified uncertainties on system responses in the safety analysis.

FAST REACTOR MULTIPHYSICS AND UNCERTAINTY PROPAGATION WITHIN WIMS

For liquid metal-cooled fast reactors (LMFRs), improved predictive modelling is desirable to facilitate reactor licensing and operation and move towards a best estimate plus uncertainty (BEPU) approach. A key source of uncertainty in fast reactor calculations arises from the underlying nuclear data. Addressing the propagation of such uncertainties through multiphysics calculations schemes is therefore of importance, and is being addressed through international projects such as the Sodium-cooled Fast Reactor Uncertainty Analysis in Modelling (SFR-UAM) benchmark. In this paper, a methodology for propagation of nuclear data uncertainties within WIMS is presented. Uncertainties on key reactor physics parameters are calculated for selected SFR-UAM benchmark exercises, with good agreement with previous results. A methodology for coupled neutronic-thermal-hydraulic calculations within WIMS is developed, where thermal feedback is introduced to the neutronic solution through coupling with the ARTHUR subchannel code within WIMS and applied to steady-state analysis of the Horizon 2020 ESFR-SMART project reference core. Finally, integration of reactor physics and fuel performance calculations is demonstrated through linking of the WIMS reactor physics code to the TRAFIC fast reactor fuel performance code, through a Fortran-C-Python (FCP) interface. Given the 3D multiphysics calculation methodology, thermal-hydraulic and fuel performance uncertainties can ultimately be sampled alongside the nuclear data uncertainties. Together, these developments are therefore an important step towards enabling propagation of uncertainties through high fidelity, multiphysics SFR calculations and hence facilitate BEPU methodologies.

Nuclear Data Uncertainty Propagation in Complex Fusion Geometries

The ASUSD program package was designed to automate and simplify the process of deterministic nuclear data sensitivity and uncertainty quantification. The program package couples Denovo, a discrete ordinate 3D transport solver, as part of ADVANTG and SUSD3D, a deterministic first order perturbation theory based Sensitivity/Uncertainty code, using several auxiliary programs used for input data preparation and post processing. Because of the automation employed in ASUSD, it is useful for Sensitivity/Uncertainty analysis of complex fusion geometries. In this paper, ASUSD was used to quantify uncertainties in the JET KN2 irradiation position. The results were compared to previously obtained probabilistic-based uncertainties determined using TALYS-based random nuclear data samples and MCNP in a Total Monte Carlo computation scheme. Results of the two approaches, deterministic and probabilistic, to nuclear data uncertainty propagation are compared and discussed. ASUSD was also used to perform preliminary Sensitivity/Uncertainty (S/U) analyses of three JET3-NEXP streaming benchmark experimental positions (A1, A4 and A7).

Nuclear data uncertainty propagation and modeling uncertainty impact evaluation in neutronics core simulation

Uncertainty analysis is a critical requirement in reactor simulation as it is used to quantify the reliability of best-estimate calculation. A comprehensive uncertainty analysis should characterize all sources of uncertainties in a computationally-feasible and scientifically-defendable manner. This manuscript employs a well-established reduced order modeling (ROM) based uncertainty quantification methodology to propagate uncertainties throughout neutronic calculations. ROM relies on recent advances in randomized data mining techniques applied to large data streams. In our proposed implementation, the nuclear data uncertainties are first propagated from multi-group level through lattice physics calculation to generate few-group parameter uncertainties, described using a vector of mean values and a covariance matrix. Employing an ROM-based compression of the covariance matrix, the few-group uncertainties are then propagated through downstream core simulation in a computationally efficient manner. This straightforward approach, albeit efficient as compared to brute force forward and/or adjoint-based methods, often employs a number of assumptions that have been unquestioned in the literature of neutronic uncertainty analysis. This manuscript argues that these assumptions could introduce another source of uncertainty referred to as modeling uncertainties, whose magnitude needs to be quantified in tandem with nuclear data uncertainties. Thus, our primary goal is to explore the interactions between these two uncertainty sources in order to assess whether modeling uncertainties have an impact on parameter uncertainties. To explore this endeavor, the impact of a number of modeling assumptions on core attributes uncertainties is quantified. The study employs a CANDU reactor model, with Serpent and NEWT as lattice physics solvers and NESTLE-C as core simulator. The modeling assumptions investigated include those related with the uncertainty propagation method employed, e.g., deterministic vs. stochastic, the few-group energy structure employed to represent the cross-sections, the resonance treatment in lattice physics calculation, the reference values for the cross-section, and the number of samples employed to render ROM compression. Results indicate that some of the modeling assumptions could have a non-negligible impact on the core responses propagated uncertainties, highlighting the need for a more comprehensive approach to combine parameter and modeling uncertainties.

Nuclear Data Uncertainty Quantification and Propagation for Safety Analysis of Lead-Cooled Fast Reactors

In this study, the Best Estimate Plus Uncertainty (BEPU) approach is developed for the systematic quantification and propagation of uncertainties in the modelling and simulation of lead-cooled fast reactors (LFRs) and applied to the demonstration LFR (DLFR) initially investigated by Westinghouse. The impact of nuclear data uncertainties based on ENDF/B-VII.0 covariances is quantified on lattice level using the generalized perturbation theory implemented with the Monte Carlo code Serpent and the deterministic code PERSENT of the Argonne Reactor Computational (ARC) suite. The quantities of interest are the main eigenvalue and selected reactivity coefficients such as Doppler, radial expansion, and fuel/clad/coolant density coefficients. These uncertainties are then propagated through safety analysis, carried out using the MiniSAS code, following the stochastic sampling approach in DAKOTA. An unprotected transient overpower (UTOP) scenario is considered to assess the effect of input uncertainties on safety parameters such as peak fuel and clad temperatures. It is found that in steady state, the multiplication factor shows the most sensitivity to perturbations in 235U fission, 235U ν, and 238U capture cross sections. The uncertainties of 239Pu and 238U capture cross sections become more significant as the fuel is irradiated. The covariance of various reactivity feedback coefficients is constructed by tracing back to common uncertainty contributors (i.e., nuclide-reaction pairs), including 238U inelastic, 238U capture, and 239Pu capture cross sections. It is also observed that nuclear data uncertainty propagates to uncertainty on peak clad and fuel temperatures of 28.5 K and 70.0 K, respectively. Such uncertainties do not impose per se threat to the integrity of the fuel rod; however, they sum to other sources of uncertainties in verifying the compliance of the assumed safety margins, suggesting the developed BEPU method necessary to provide one of the required insights on the impact of uncertainties on core safety characteristics.

The Impact of Fueling Operations on Full Core Uncertainty Analysis in CANDU Reactors

Abstract Within emerging best-estimate-plus-uncertainty (BEPU) approaches, code output uncertainties can be inferred from the propagation of fundamental or microscopic uncertainties. This paper examines the propagation of fundamental nuclear data uncertainties though the entire analysis framework to predict macroscopic reactor physics phenomena, which can be measured in Canada Deuterium Uranium (CANDU) reactors. In this work, 151 perturbed multigroup cross sections libraries, each based on a set of perturbed microscopic nuclear data, were generated. Subsequently, these data were processed into few-group cross sections and used to generate full-core diffusion models in PARCS. The impact of these nuclear data perturbations leads to changes in core reactivity for a fixed set of fuel compositions of 4.5 mk. The impact of online fueling operations was simulated using a series of fueling rules, which attempted to mimic operator actions during CANDU operations such as studying the assembly powers and selecting fueling sites, which would minimize the deviation in power from some desirable reference condition or increasing or decreasing fueling frequency to manage reactivity. An important feature of this analysis was to perform long-transients (1–3 years) starting with each one of the 151 perturbed full core models. It was found that the operational feedback reduced the standard deviation in core reactivity by 99% from 0.0045 to 2.8 × 10−5. Overall, the conclusions demonstrate that while microscopic nuclear data uncertainties may give rise to large macroscopic variability during simple propagation, when important macrolevel feedback are considered the variability is significantly reduced.

Nuclear data sensitivity analysis and uncertainty propagation in the KYADJ whole-core transport code

Nuclear data sensitivity analysis and uncertainty propagation have been extensively applied to nuclear data adjustment and uncertainty quantification in the field of nuclear engineering. Sensitivity and Uncertainty (S&U) analysis is developed in the KYADJ whole-core transport code in order to meet the requirement of advanced reactor design. KYADJ aims to use two-dimension Method of Characteristic (MOC) and one-dimension discrete ordinate (SN) coupled method to solve the neutron transport equation and achieve one-step direct transport calculation of the reactor core. Developing sensitivity and uncertainty analysis module in KYADJ can minimize deviations caused by modeling approximation and enhance calculation efficiency. This work describes the application of the classic perturbation theory to the KYADJ transport solver. In order to obtain uncertainty, a technique is proposed for processing a covariance data file in 45-group energy grid instead of 44-group SCALE 6.1 covariance data which is extensively used in various codes. Numerical results for Uncertainty Analysis in Modelling (UAM) benchmarks and the SF96 benchmark are presented. The results agree well with the reference and the capability of S&U analysis in KYADJ is verified.

Application of Cross Sections Uncertainty Propagation Framework to Light and Heavy Water Reactor Systems

Abstract Uncertainty quantification has been recognized by the community as an essential component of best-estimate reactor analysis simulation because it provides a measure by which the credibility of the simulation can be assessed. In a companion paper, a framework for the propagation of nuclear data uncertainties from the multigroup level through lattice physics and core calculations and ultimately to core responses of interest has been developed. The overarching goal of this framework is to automate the propagation, prioritization, mapping, and reduction of uncertainties for reactor analysis core simulation. This paper employs both heavy and light water reactor systems to exemplify the application of this framework. Specifically, the paper is limited to the propagation of the nuclear data starting with the multigroup cross section covariance matrix and down to core responses, e.g., eigenvalue and power distribution, in steady-state core wide calculations. The goal is to demonstrate how the framework employs reduction techniques to compress the uncertainty space into a very small number of active degrees-of-freedom (DOFs), which renders the overall process computationally feasible for day-to-day engineering evaluations.

Nuclear-data uncertainty propagation in transient simulation for the C5G7-TD benchmark problem

In this study, uncertainty analysis has been performed to the time-dependent transient simulation of the C5G7-TD benchmark problem, propagating the nuclear-data uncertainties to the key parameters of interest. The detailed material compositions and geometries of C5G7-TD have been applied for the time-dependent transient simulation. For uncertainty analysis of the transient simulation, multigroup cross-section covariance library has been generated based on ENDF/B-VII.1 using the NJOY code. Our home-developed uncertainty-analysis code named UNICORN has been utilized for the uncertainty analysis, using the statistical sampling method. For the steady-state and transient simulations, our home-developed high-fidelity neutronics code NECP-X has been utilized. The relative uncertainties of the fuel-assembly normalized power and pin-wise power have been quantified as function of time. The numerical results show that the maximum relative uncertainties for the assembly normalized power can up to be about 2.14% and the value for the pin-wise power distributions can be about 3.54%.

An estimation of neutron-induced production of gases with propagation of uncertainties in nuclear data for structural materials of fission and fusion reactors

An accurate estimation of production of gases due to interactions of neutron in structural materials of nuclear systems is an important aspect within the purview of studies of primary radiation damage. Towards this objective, the indigenous code CRaD, which computes neutron primary radiation damage due to displacements of atoms, has been added with the capability to compute gas production for applications in Indian fast reactors. It includes the computation of gas production cross sections by using standard ENDF-6 procedure, estimation of gases in structural materials due to different types of neutron spectra and propagation of nuclear data uncertainties by Total Monte Carlo (TMC) methodology. A comparison of gas production cross sections from CRaD and NJOY (NJOY-2016.31 and NJOY21) is made and a few discrepancies noted with the processing of charged particle production cross sections using the above versions of NJOY are briefly outlined. A simple linear transmutation methodology is adapted in CRaD to estimate the concentrations of gases and validated with the published theoretical data which were obtained using a similar methodology. The study shows that the representation of charged particle data in different ENDF-6 files is not unique and spectrum-averaged gas production cross sections show large variations. The effect of spread in the activation cross sections from different libraries on the two-step processes of gas production in Nickel under different spectra is also discussed. Detailed TMC-based statistical analyses of gas production in Fe are performed and the mean, standard deviation, skewness and channel-wise and isotope-wise correlation coefficients are quantified. The uncertainties in the production of gases in Fe are estimated to be in the range of 16–56%, with the larger uncertainties being observed in the productions of tritium and helium-3 gases. The gas production parameters have shown non-Gaussian distributions.

Examples of Monte Carlo techniques applied for nuclear data uncertainty propagation

The aim of this work is to review different Monte Carlo techniques used to propagate nuclear data uncertainties. Firstly, we introduced Monte Carlo technique applied for Uncertainty Quantification studies in safety calculations of large scale systems. As an example, the impact of nuclear data uncertainty of JEFF-3.3 235U, 238U and 239Pu is demonstrated for the main design parameters of a typical 3-loop PWR Westinghouse unit. Secondly, the Bayesian Monte Carlo technique for data adjustment is presented. An example for 235U adjustment using criticality and shielding integral benchmarks shows the importance of performing joint adjustment based on different set of integral benchmarks.

Nuclear data uncertainty propagation for spectral reaction ratios

This paper describes several approaches to propagating nuclear data uncertainty in integral capture and fission reaction rates, focusing on the spectral hardness indices of 193Ir(n,n′)193mIr/191Ir(n,g)192Ir and 238U(n,f)/235U(n,f) in fast critical assemblies. Uncertainties in the nuclear data were propagated through the following methods: a sensitivity method (utilizing first order perturbation and covariance matrices), a BFMC method (random cross sections generated through covariance matrices), and Total Monte Carlo method (random cross sections generated from varying nuclear parameters). The uncertainty estimates from the three approaches are evaluated against the associated experimental uncertainties of a ZEUS/COMET critical assembly irradiation. The uncertainty estimates of the three approaches agree fairly well, and contribute a significant portion to the overall uncertainty of the spectral hardness indices.

Validation of PSI best estimate plus uncertainty methodology against SPERT-III reactivity initiated accident experiments

This research aims at validating the SIMULATE-3K code and complementing the results with the quantification of nuclear data uncertainties against the Special Power Excursion Reactor Test III (SPERT-III) experiments. To that aim, the SHARK-X methodology, under development at PSI, for the propagation of nuclear data uncertainties in CASMO5 2-D lattice calculations to 3-D core transient simulations is applied for the analysis of a SPERT-III super-prompt critical test conducted at cold startup conditions. Concerning transient results, both total power and reactivity show a good agreement with the measurements at the initial phase of power excursion, while a slight discrepancy is obtained at the final phase of the transient. The estimated uncertainties regarding both steady-state parameters such as k-eff and static reactivity worth, as well as dynamical quantities such as power pulse width and enthalpies are presented. Results show non-negligible sensitivity upon the employed nuclear data library. The uncertainty quantification results show relatively small biases for k-eff and reactivity. The uncertainty in peak power is around 3%, while it is negligible for the time to peak power and the pulse width. The time evolution of the standard deviation and skewness of the total power showed special shapes with relatively high maximum values. In addition, the uncertainty due to nuclear data in the two important safety parameters, i.e. maximum nodal fuel temperature and enthalpy reaches maximum value around 2% and 10%, respectively.