Determination of Nuclear Reaction Cross-sections for Neutron-Induced Reactions in Some Odd – A Nuclides

The effect of the odd-even nature and mass of target nucleus on neutron reaction cross-sections have been investigated for the energy range of 1 – 30 MeV for neutron-induced reactions in the odd – A nuclides: ,  and  using the EXIFON code. This code which uses one global parameter set and is based on the analytical model for statistical multistep direct and multistep compound reactions, was used to calculate cross-sections for the (n, ?), (n, p), and (n, 2n) reactions. Calculations were compared with experimental data (EXFOR) and evaluated data (ENDF) from the IAEA nuclear data bank. Results show that charged particle emissions ((n, ?) and (n, p)) are the most dominant reaction channels in the light and intermediate mass nuclei considered. Results also show that the Na (n, 2n) reaction cross-section over predicted experimental and evaluated data, while the Na (n, ?) reaction agrees with experimental data and was in fair agreement with evaluated data while the Na (n, p) cross-section under predicted experimental and evaluated data. For magnesium, the (n, 2n) cross-section partially agree with evaluated cross-section, while the (n, ?) and (n, p) cross-sections under predicted evaluated data. And for aluminium, the calculated (n, 2n) cross-section over predicted experimental and evaluated data, the (n, ?) data fairly reproduced experimental and evaluated cross-sections while the (n, p) reaction cross-section was in fair agreement with experimental and evaluated data. The explanations for the discrepancies and trends observed have been given. Keywords: Nuclear reaction cross-section, Neutron-induced reaction, nuclear data, EXIFON cod

Nigeria’s Nuclear Power Generation Project: Current State and Future Prospects

The industrialization programme of any nation is driven by its power sector so that the industrialization process becomes epileptic when the power sector becomes epileptic. This has been the challenge facing Nigeria. The national grid has an installed capacity of 6,000 MW, but only about 4,000 MW is obtainable. Also, pipeline vandalisation disrupts the supply lines to the few functional plants, while water shortage and irregular supply incapacitate the effective functioning of the nation’s hydroelectric power plants. These factors along with the increasing national energy demand for both domestic and industrial purposes made the nuclear power option attractive to Nigeria and informed the nation’s pursuit of the nuclear power option. Nuclear power has not only been adjudged economically competitive and environmentally friendly, but is also a viable alternative for long-term energy security. Nuclear power plants have low operational costs and the added advantage of long life spans. This paper examines Nigeria’s nuclear power generation programme with emphasis on how far Nigeria has gone, the successes recorded, the problems encountered and the plans to be implemented for the first nuclear power plant to become functional. The various issues of concern in deploying nuclear power plants for electricity generation are also discussed. Keywords: Nigeria atomic energy commission, nuclear power plants, nuclear reactors, energ

Validation of nuclide depletion capabilities in Monte Carlo code MCS

In this work, the depletion capability implemented in Monte Carlo code MCS is investigated to predict the isotopic compositions of spent nuclear fuel (SNF). By comparison of MCS calculation results to post irradiation examination (PIE) data obtained from one pressurized water reactor (PWR), the validation of this capability is conducted. The depletion analysis is performed with the ENDF/B-VII.1 library and a fuel assembly model. The transmutation equation is solved by the Chebyshev Rational Approximation Method (CRAM) with a depletion chain of 3820 isotopes. 18 actinides and 19 fission products are analyzed in 14 SNF samples. The effect of statistical uncertainties on the calculated number densities is discussed. On average, most of the actinides and fission products analyzed are predicted within +/- 6% of the experiment. MCS depletion results are also compared to other depletion codes based on publicly reported information in literature. The code-to-code analysis shows comparable accuracy. Overall, it is demonstrated that the depletion capability in MCS can be reliably applied in the prediction of SNF isotopic inventory. (c) 2020 Korean Nuclear Society, Published by Elsevier Korea LLC. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Uncertainty quanti fi cation of PWR spent fuel due to nuclear data and modeling parameters

Uncertainties are calculated for pressurized water reactor (PWR) spent nuclear fuel (SNF) characteristics. The deterministic code STREAM is currently being used as an SNF analysis tool to obtain isotopic in-ventory, radioactivity, decay heat, neutron and gamma source strengths. The SNF analysis capability of STREAM was recently validated. However, the uncertainty analysis is yet to be conducted. To estimate the uncertainty due to nuclear data, STREAM is used to perturb nuclear cross section (XS) and resonance integral (RI) libraries produced by NJOY99. The perturbation of XS and RI involves the stochastic sam-pling of ENDF/B-VII.1 covariance data. To estimate the uncertainty due to modeling parameters (fuel design and irradiation history), surrogate models are built based on polynomial chaos expansion (PCE) and variance-based sensitivity indices (i.e., Sobol & rsquo; indices) are employed to perform global sensitivity analysis (GSA). The calculation results indicate that uncertainty of SNF due to modeling parameters are also very important and as a result can contribute significantly to the difference of uncertainties due to nuclear data and modeling parameters. In addition, the surrogate model offers a computationally effi-cient approach with significantly reduced computation time, to accurately evaluate uncertainties of SNF integral characteristics. (c) 2020 Korean Nuclear Society, Published by Elsevier Korea LLC. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Propagation of radiation source uncertainties in spent fuel cask shielding calculations

The propagation of radiation source uncertainties in spent nuclear fuel (SNF) cask shielding calculations is presented in this paper. The uncertainty propagation employs the depletion and source term outputs of the deterministic code STREAM as input to the transport simulation of the Monte Carlo (MC) codes MCS and MCNP6. The uncertainties of dose rate coming from two sources: nuclear data and modeling parameters, are quantified. The nuclear data uncertainties are obtained from the stochastic sampling of the cross-section covariance and perturbed fission product yields. Uncertainties induced by perturbed modeling parameters consider the design parameters and operating conditions. Uncertainties coming from the two sources result in perturbed depleted nuclide inventories and radiation source terms which are then propagated to the dose rate on the cask surface. The uncertainty analysis results show that the neutron and secondary photon dose have uncertainties which are dominated by the cross section and modeling parameters, while the fission yields have relatively insignificant effect. Besides, the primary photon dose is mostly influenced by the fission yield and modeling parameters, while the cross-section data have a relatively negligible effect. Moreover, the neutron, secondary photon, and primary photon dose can have uncertainties up to about 13%, 14%, and 6%, respectively

Verification and validation of isotope inventory prediction for back-end cycle management using two-step method

This paper presents the verification and validation (V&V) of a calculation module for isotope inventory prediction to control the back-end cycle of spent nuclear fuel (SNF). The calculation method presented herein was implemented in a two-step code system of a lattice code STREAM and a nodal diffusion code RAST-K. STREAM generates a cross section and provides the number density information using branch/ history depletion branch calculations, whereas RAST-K supplies the power history and three history indices (boron concentration, moderator temperature, and fuel temperature). As its primary feature, this method can directly consider three-dimensional core simulation conditions using history indices of the operating conditions. Therefore, this method reduces the computation time by avoiding a recalculation of the fuel depletion. The module for isotope inventory calculates the number densities using the Lagrange interpolation method and power history correction factors, which are applied to correct the effects of the decay and fission products generated at different power levels. To assess the reliability of the developed code system for back-end cycle analysis, validation study was performed with 58 measured samples of pressurized water reactor (PWR) SNF, and code-to-code comparison was conducted with STREAM-SNF, HELIOS-1.6 and SCALE 5.1. The V&V results presented that the developed code system can provide reasonable results with comparable confidence intervals. As a result, this paper successfully demonstrates that the isotope inventory prediction code system can be used for spent nuclear fuel analysis. (c) 2021 Korean Nuclear Society, Published by Elsevier Korea LLC. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Uncertainty quantification in decay heat calculation of spent nuclear fuel by STREAM/RAST-K

This paper addresses the uncertainty quantification and sensitivity analysis of a depleted light-water fuel assembly of the Turkey Point-3 benchmark. The uncertainty of the fuel assembly decay heat and isotopic densities is quantified with respect to three different groups of diverse parameters: nuclear data, assembly design, and reactor core operation. The uncertainty propagation is conducted using a two-step analysis code system comprising the lattice code STREAM, nodal code RAST-K, and spent nuclear fuel module SNF through the random sampling of microscopic cross-sections, fuel rod sizes, number densities, reactor core total power, and temperature distributions. Overall, the statistical analysis of the calculated samples demonstrates that the decay heat uncertainty decreases with the cooling time. The nuclear data and assembly design parameters are proven to be the largest contributors to the decay heat uncertainty, whereas the reactor core power and inlet coolant temperature have a minor effect. The majority of the decay heat uncertainties are delivered by a small number of isotopes such as 241Am, 137Ba, 244Cm, 238Pu, and 90Y. (c) 2021 Korean Nuclear Society, Published by Elsevier Korea LLC. All rights reserved. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Development of a Spent Nuclear Fuel Radiation Source Term Framework

Department of Nuclear EngineeringA Spent Nuclear Fuel (SNF) radiation source term framework is developed to improve the accuracy, computational efficiency, and uncertainty quantification (UQ) of spent nuclear fuel (SNF). In SNF forward UQ, the quantification of design parameters and operating condition uncertainties in pressurized water reactor (PWR) SNF assemblies source terms is limited. Besides, the design parameters uncertainties i.e., manufacturing tolerances are often ???proprietary???, and these uncertainties, including those of the operating conditions, are unknown and available values from literature are either based on personal opinions or ad-hoc expert judgement which lack theoretical basis. Furthermore, the propagation of radiation source spectra uncertainties in SNF cask shielding has not been conducted to quantify the dose rate uncertainties. Moreover, the propagation of uncertainties can be computationally expensive in cases where large number of repeated code calculations need to be performed. These problems decrease prediction accuracy, and causes computationally inefficient uncertainty analysis, as they prevent the performance of detailed UQ. The SNF framework develops data-driven, non-intrusive Verification, Validation, and Uncertainty Quantification (VVUQ) techniques which are based on surrogate and machine learning (ML) models. At first, a source term capability is implemented in the Steady state and Transient Reactor Analysis code with Method of Characteristics (STREAM). Secondly, the decay heat calculations are validated by 91 calorimetric decay heat measurements of 52 Pressurized Water Reactor (PWR) fuel assemblies. Thirdly, the forward UQ of SNF assembly source terms and cask shielding is conducted due to perturbed cross section, fission yield, design parameters and operating conditions. High accuracy and computationally efficient surrogate models are developed based on Polynomial Chaos Expansion (PCE), to propagate design parameters and operating condition uncertainties through assembly source term calculations. Fourthly, a new application of decay heat measurement data is proposed based on inverse UQ (IUQ) and model calibration. This solves a model inversion problem to find the design parameters and operating conditions probability distribution functions (PDFs) that are consistent with decay heat measurements. Additionally, this improves the decay heat calculation-experiment agreement and decreases the decay heat uncertainty. The IUQ couples the PCE based surrogate models with the Bayesian method and Markov Chain Monte Carlo (MCMC) to obtain an inverse solution and calibrate the modeling parameters of PWR SNF assemblies. In the fifth step, ML models are developed based on calorimetric decay heat measurement data, to predict decay heat of discharged PWR and boiling water reactor (BWR) assemblies, with speed and accuracy. The calorimetric measurement dataset is a small one. Consequently, the method of multiple runs and generation of synthetic data having the same statistical characteristics as the original measured data, are proposed. Then, the uncertainties in the ML predictions are quantified to ensure reliability of the results. While the use of surrogate models, Bayesian method, MCMC and ML are well known state-of-the-art techniques of statistical learning, their implementation in spent fuel VVUQ, forward and inverse uncertainty analysis, model calibration and inversion, represent novelty, to the best of the author???s knowledge. The outcomes are promising and demonstrate the potentials of the techniques excellently for SNF applications.ope

Bayesian method and polynomial chaos expansion based inverse uncertainty quantification of spent fuel using decay heat measurements

This work presents a new application of decay heat measurement based on the calibration and inverse uncertainty quantification (IUQ) of modeling parameters of pressurized water reactor (PWR) fuel assemblies. This work (i) solves the problem encountered in forward UQ i.e., the lack of fuel vendor proprietary information (manufacturing tolerances of fuel assembly design) and operating condition uncertainties which are based on adhoc expert judgement or personal opinion (ii) calibrates the parameters of a fuel assembly model for improved code calculation-to-experiment agreement. The IUQ is conducted under the Bayesian framework and finds the fuel assembly model parameters that are consistent with decay heat measurements. The forward model implements a polynomial chaos expansion (PCE) based surrogate model for a computationally efficient inverse analysis. The approach introduced is then tested with the decay heat calculations of the deterministic code STREAM. Eight input parameters: fuel density, enrichment, pellet radius, clad outer radius, fuel temperature, power density, moderator temperature and boron concentration, are considered. The outcomes of this work include quantification of uncertainties and determination of probability distribution function (PDF) of these parameters, in addition to reducing code calculation-to-experiment discrepancies. These outcomes are important for future studies on the forward propagation of model parameters uncertainties