Research on the on-the-fly homogenization method based on RMC code for criticality calculations-Reference-Cited by-同舟云学术

Research on the on-the-fly homogenization method based on RMC code for criticality calculations

Published:2020-01 Issue: Volume:135 Page:106985
ISSN:0306-4549
Container-title:Annals of Nuclear Energy
language:en
Short-container-title:Annals of Nuclear Energy

Author:

Yuan Yuan,Xing Ji,Huo Xiaodong,Wang Kan

Funder

NSFC

Publisher

Elsevier BV

Subject

Nuclear Energy and Engineering

Reference17 articles.

1. Extension and application of the reactor dynamics code dyn3d for block-type high temperature reactors;Baier;Nucl. Eng. Des.,2014

2. Reactor Agnostic Multi-Group Cross Section Generation for Fine-Mesh Deterministic Neutron Transport Simulations, Thesis Massachusetts;Boyd,2017

3. Conceptual design of a small modular natural circulation lead cooled fast reactor snclfr-100;Chen;Int. J. Hydrogen Energy,2016

4. Cho, N.Z, 2000, Benchmark Problem 1A: MOX Fuel-Loaded Small PWR Core (MOX Fuel with Zoning). KAIST/NurapT, June 2000, http://nurapt.kaist.ac.kr/benchmark/.

5. On the use of the serpent monte carlo code for few-group cross section generation;Fridman;Ann. Nucl. Energy,2011

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1. Analysis of neutron physics and thermal hydraulics for fuel assembly of small modular reactor loaded with ATFs;Annals of Nuclear Energy;2021-03

2. Verification of on-the-fly homogenization method based on the analysis of HTTR;Annals of Nuclear Energy;2021-01

3. Implementation of Grey Wolf Optimization (GWO) algorithm to multi-objective loading pattern optimization of a PWR reactor;Annals of Nuclear Energy;2020-12

4. PWR core pattern optimization using grey wolf algorithm based on artificial neural network;Progress in Nuclear Energy;2020-11