17 research outputs found
Development of nodal diffusion code RAST-V for Vodo-Vodyanoi Energetichesky reactor analysis
Get PDFThis paper presents the development of a nodal diffusion code, RAST-V, and its verification and validation for VVER (vodo-vodyanoi energetichesky reactor) analysis. A VVER analytic solver has been imple-mented in an in-house nodal diffusion code, RAST-K. The new RAST-K version, RAST-V, uses the triangle -based polynomial expansion nodal method. The RAST-K code provides stand-alone and two-step computation modes for steady-state and transient calculations. An in-house lattice code (STREAM) with updated features for VVER analysis is also utilized in the two-step method for cross-section gen-eration. To assess the calculation capability of the formulated analysis module, various verification and validation studies have been performed with Rostov-II, and X2 multicycles, Novovoronezh-4, and the Atomic Energy Research benchmarks. In comparing the multicycle operation, rod worth, and integrated temperature coefficients, RAST-V is found to agree with measurements with high accuracy which RMS differences of each cycle are within +/- 47 ppm in multicycle operations, and +/- 81 pcm of the rod worth of the X2 reactor. Transient calculations were also performed considering two different rod ejection sce-narios. The accuracy of RAST-V was observed to be comparable to that of conventional nodal diffusion codes (DYN3D, BIPR8, and PARCS).(c) 2022 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/)
Enhancement of RAST-AI Deep Learning Cross-Section Generation Model by Adding Gadolinia Fuel Support
No full textOptimization of Traning Dataset Size for Predicting Homogenized Macroscopic Cross-Sections using Deep Neural Network
No full textImpact of Training Dataset Reduction on the Prediction Accuracy of Nuclear Design Parameters using Convolutional Neural Network
No full textRAST-AI: a Standalone Nodal Diffusion/Deep Neural Network Code for Reactor Analysis and Simulation
No full textMacroscopic Cross-Section Generation for Nodal Code RAST-K Using Artificial Neural Network
No full textAcceleration of Nodal Diffusion Calculation using Machine Learning-driven Generation of Homogenized Macroscopic Cross-sections
No full textOptimization of The Deep Neural Network Parameters for Generating Homogenized Fuel Assembly Data for Nodal Codes
No full textDevelopment of an artificial neural network model for generating macroscopic cross-sections for RAST-AI
No full textHomogenized macroscopic cross-sections (XS) are necessary for running core-wise nodal diffusion calculations. XS sets are usually generated using time-consuming lattice physics codes. In this study, a pre-trained artificial neural network was developed and used for XS generation. The model was trained to produce macroscopic XS, pin powers, and assembly discontinuity factors for 16 x 16 and 17 x 17 fuel assembly types with independent variable enrichments of each fuel pin loaded with fresh UO2 fuel without burnable poisons. The training dataset optimization method was described and used for defining the required number of variations in input parameters, such as pin arrangements and thermal hydraulics parameters. The optimized dataset's generation took only 248 core-hours, which is below 3 days on a modern 4-core CPU. For the worst-case out-of-range testing data, the maximum observed difference with the reference was found below 3% for pin powers, and below 4.5% for XS values
