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Development of nodal diffusion code RAST-V for Vodo–Vodyanoi Energetichesky reactor analysis
장재림,Dzianisau Siarhei,이덕중 한국원자력학회 2022 Nuclear Engineering and Technology Vol.54 No.9
This paper presents the development of a nodal diffusion code, RAST-V, and its verification and validation for VVER (vodoevodyanoi energetichesky reactor) analysis. A VVER analytic solver has been implemented in an in-house nodal diffusion code, RAST-K. The new RAST-K version, RAST-V, uses the trianglebased 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 generation. 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 scenarios. The accuracy of RAST-V was observed to be comparable to that of conventional nodal diffusion codes (DYN3D, BIPR8, and PARCS)
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.