Acceleration of convective dissolution by an instantaneous chemical reaction: A comparison of experimental and numerical results

Cherezov, Ilia,Cardoso, Silvana S.S.,Kim, Min Chan Elsevier 2018 Chemical engineering science Vol.181 No.-

<P><B>Abstract</B></P> <P>The effect of an instantaneous chemical reaction on convective dissolution in a Hele-Shaw cell is analyzed experimentally and numerically. We experimentally show that the non-dimensional onset time and growth rate of instability is determined only by the reactant concentration-ratio. Also, nonlinear numerical simulations are conducted to obtain the temporal evolution of the dissolution flux. The experimental and numerical results reveal a sharp increase of up to 300% in the dissolution flux, accompanied by a decrease in the convection onset time, when the reactant concentration-ratio approaches the stoichiometric value. These results have important implications for the optimization of carbon-dioxide storage in porous saline aquifers in the Earth’s subsurface.</P> <P><B>Highlights</B></P> <P> <UL> <LI> We study of an instantaneous reaction for the geological storage of carbon dioxide. </LI> <LI> We identify conditions to maximise the solute (carbon dioxide) dissolution. </LI> <LI> The maximum dissolution rate occurs for stoichiometric reactant concentrations. </LI> <LI> Theory and experiment predict an increase of up to 300% in the dissolution flux. </LI> </UL> </P>

A reduced-basis element method for pin-by-pin reactor core calculations in diffusion and _{SP 3} approximations

Cherezov, Alexey,Sanchez, Richard,Joo, Han Gyu Elsevier 2018 Annals of nuclear energy Vol.116 No.-

<P><B>Abstract</B></P> <P>The reduced order model (ROM) methods allow to significantly improve the computation time and memory usage. Therefore these methods are very useful for real-time or many-query reactor core simulation analysis. The ROM approach is based on the proper orthogonal decomposition methods which generate an optimal truncated subspace from a given set of test solutions or snapshots. In the case of high-fidelity real-size problems the calculation of one snapshot might be too expensive or not possible due to memory limitations. To circumvent this problem we apply the reduced basis element method (RBEM). This method is based on a spatial decomposition of the core domain and on the application of the ROM approach on each subdomain. In this work the RBEM is developed for diffusion and <SUB> SP 3 </SUB> equations and the results are illustrated with several two-dimensional reactor core calculations.</P> <P><B>Highlights</B></P> <P> <UL> <LI> The Reduced Basis Element Method (RBEM) is suggested as an alternative to computationally intensive large high-fidelity calculations. </LI> <LI> The RBEM is based on the calculation of ad-hoc reduced basis functions and the application of the Discontinuous Galerkin method. </LI> <LI> The RBEM is applied to multigroup diffusion and <I>SP</I> <SUB>3</SUB> reactor core pin-by-pin calculations. </LI> </UL> </P>

Machine learning of LWR spent nuclear fuel assembly decay heat measurements

Ebiwonjumi, Bamidele,Cherezov, Alexey,Dzianisau, Siarhei,Lee, Deokjung Korean Nuclear Society 2021 Nuclear Engineering and Technology Vol.53 No.11

Measured decay heat data of light water reactor (LWR) spent nuclear fuel (SNF) assemblies are adopted to train machine learning (ML) models. The measured data is available for fuel assemblies irradiated in commercial reactors operated in the United States and Sweden. The data comes from calorimetric measurements of discharged pressurized water reactor (PWR) and boiling water reactor (BWR) fuel assemblies. 91 and 171 measurements of PWR and BWR assembly decay heat data are used, respectively. Due to the small size of the measurement dataset, we propose: (i) to use the method of multiple runs (ii) to generate and use synthetic data, as large dataset which has similar statistical characteristics as the original dataset. Three ML models are developed based on Gaussian process (GP), support vector machines (SVM) and neural networks (NN), with four inputs including the fuel assembly averaged enrichment, assembly averaged burnup, initial heavy metal mass, and cooling time after discharge. The outcomes of this work are (i) development of ML models which predict LWR fuel assembly decay heat from the four inputs (ii) generation and application of synthetic data which improves the performance of the ML models (iii) uncertainty analysis of the ML models and their predictions.

Verification of a two-step code system MCS/RAST-F to fast reactor core analysis

Tuan Quoc Tran,Alexey Cherezov,Xianan Du,이덕중 한국원자력학회 2022 Nuclear Engineering and Technology Vol.54 No.5

RAST-F is a new full-core analysis code based on the two-step approach that couples a multi-group crosssection generation Monte-Carlo code MCS and a multi-group nodal diffusion solver. To demonstrate thefeasibility of using MCS/RAST-F for fast reactor analysis, this paper presents the coupled nodal codeverification results for the MET-1000 and CAR-3600 benchmark cores. Three different multi-group crosssection calculation schemes are employed to improve the agreement between the nodal and referencesolutions. The reference solution is obtained by the MCS code using continuous-energy nuclear data. Additionally, the MCS/RAST-F nodal solution is verified with results based on cross-section generated bycollision probability code TULIP. A good agreement between MCS/RAST-F and reference solution isobserved with less than 120 pcm discrepancy in keff and less than 1.2% root-mean-square error in powerdistribution. This study confirms the two-step approach MCS/RAST-F as a reliable tool for the threedimensional simulation of reactor cores with fast spectrum

Uncertainty quantification of PWR spent fuel due to nuclear data and modeling parameters

Ebiwonjumi, Bamidele,Kong, Chidong,Zhang, Peng,Cherezov, Alexey,Lee, Deokjung Korean Nuclear Society 2021 Nuclear Engineering and Technology Vol.53 No.3

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 inventory, 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 sampling 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' 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 efficient approach with significantly reduced computation time, to accurately evaluate uncertainties of SNF integral characteristics.

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

Jang, Jaerim,Ebiwonjumi, Bamidele,Kim, Wonkyeong,Cherezov, Alexey,Park, Jinsu,Lee, Deokjung Korean Nuclear Society 2021 Nuclear Engineering and Technology Vol.53 No.7

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.

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

Jang, Jaerim,Kong, Chidong,Ebiwonjumi, Bamidele,Cherezov, Alexey,Jo, Yunki,Lee, Deokjung Korean Nuclear Society 2021 Nuclear Engineering and Technology Vol.53 No.9

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.

Application of TULIP/STREAM code in 2-D fast reactor core high-fidelity neutronic analysis

Xianan Du,최지원,최수영,이웅희,Alexey Cherezov,임재용,이민재,이덕중 한국원자력학회 2019 Nuclear Engineering and Technology Vol.51 No.8

The deterministic MOC code STREAM of the Computational Reactor Physics and Experiment (CORE) laboratory of Ulsan National Institute of Science and Technology (UNIST), was initially designed for the calculation of pressurized water reactor two- and three-dimensional assemblies and cores. Since fast reactors play an important role in the generation-IV concept, it was decided that the code should be upgraded for the analysis of fast neutron spectrum reactors. This paper presents a coupled code - TULIP/STREAM, developed for the fast reactor assembly and core calculations. The TULIP code produces self-shielded multi-group cross-sections using a one-dimensional cylindrical model. The generated cross-section library is used in the STREAM code which solves eigenvalue problems for a two-dimensional assembly and a multi-assembly whole reactor core. Multiplication factors and steady-state power dis-tributions were compared with the reference solutions obtained by the continuous energy Monte-Carlo code MCS. With the developed code, a sensitivity study of the number of energy groups, the order of anisotropic PN scattering, and the multi-group cross-section generation model was performed on the keff and power distribution. The 2D core simulation calculations show that the TULIP/STREAM code gives a keff error smaller than 200 pcm and the root mean square errors of the pin-wise power distributions within 2%.