[1] CAI Yun, WANG Lianjie, WANG Liangzi et al. Preliminary conceptual design of ultra-high flux fast neutron test reactor core[J]. Nuclear Power Engineering, 44, 222-226(2023).
[2] Antipina M M, Bibilashvili Y K, Golovnin I S et al. Testing of experimental BN-600-type fuel elements in the BOR-60 reactor up to different burnups[J]. Soviet Atomic Energy, 40, 14-25(1976).
[3] Izhutov A L, Krasheninnikov Y M, Zhemkov I Y et al. Prolongation of the BOR-60 reactor operation[J]. Nuclear Engineering and Technology, 47, 253-259(2015).
[4] Gulevich A V, Klinov D A. Multilateral research program international research center MBIR proposal on R&D activities (fuels and materials) for the Ten Year Period 2025-2035[R].
[5] Eliseev V A, Korobeynikova L V, Maslov P A et al. ON feasibility of using nitride and metallic fuel in the MBIR reactor core[J]. Nuclear Energy and Technology, 2, 179-182(2016).
[6] Heidet F, Grandy C, Sumner T et al. FASt TEst Reactor (FASTER) design overview[J]. Progress in Nuclear Energy, 108, 465-473(2018).
[7] Roglans-Ribas J, Pasamehmetoglu K, O'Connor T J. The versatile test reactor project: mission, requirements, and description[J]. Nuclear Science and Engineering, 196, 1-10(2022).
[8] Balderrama S, Sabharwall P, Wachs D. Versatile test reactor for advanced reactor testing[J]. Transactions of the American Nuclear Society, 119, 942(2018).
[9] JI Nan, YI Jinhao, ZHAO Pengcheng et al. Research on adaptive RBF neural network prediction method for core thermal-hydraulic parameters of fast reactor[J]. Nuclear Techniques, 45, 090601(2022).
[10] Ridluan A, Manic M, Tokuhiro A. EBaLM-THP–a neural network thermohydraulic prediction model of advanced nuclear system components[J]. Nuclear Engineering and Design, 239, 308-319(2009).
[11] JIN Shuang, LIU Xiaojing, CHENG Xu. Optimization method of CFD coarse grid numerical simulation based on neural network[J]. Nuclear Techniques, 44, 060601(2021).
[12] Mazrou H. Performance improvement of artificial neural networks designed for safety key parameters prediction in nuclear research reactors[J]. Nuclear Engineering and Design, 239, 1901-1910(2009).
[13] ZHANG Xiangwen, FAN Chenguang, HE An et al. Performance prediction and structural parameter optimization of control rod hydraulic buffer based on GA-BP neural network[J]. Nuclear Power Engineering, 44, 162-169(2023).
[14] ZHANG Haiming, ZHANG Haochun. Prediction method of effective multiplication factor of reactor core based on convolution neural network model[J]. Modern Applied Physics, 13, 61-66(2022).
[15] McKay M D, Beckman R J, Conover W J. A comparison of three methods for selecting values of input variables in the analysis of output from a computer code[J]. Technometrics, 42, 55-61(2000).
[16] SONG Mengyan, ZHANG Chaoqi, SUN Xiaoying. Probability SSI analysis of a nuclear island building based on Latin hypercube sampling[J]. Nuclear Science and Engineering, 41, 306-312(2021).
[17] WANG Qun. Multiobjective evolutionary algorithm based on symmetric Latin hypercube designs[D](2011).
[18] Wang K, Li Z G, She D et al. RMC–a Monte Carlo code for reactor core analysis[J]. Annals of Nuclear Energy, 82, 121-129(2015).
[19] Sánchez V, Imke U, Ivanov A et al. SUBCHANFLOW: a thermal-hydraulic sub-channel program to analyse fuel rod bundles and reactor cores[C], 1-18(2010).
[20] OECD, Agency N E. Handbook on lead-bismuth eutectic alloy and lead properties, materials compatibility, thermalhydraulics and technologies[M]. OECD(2015).
[21] Sobol I. Sensitivity estimates for nonlinear mathematical models[J]. Matematicheskoe Modelirovanie, 2, 112-118(1993).
[22] LIU Tianyi, LI Xiaoming. Sensitivity analysis of solenoid valve parameters based on Sobol index method[C], 4, 2-9(2023).
[23] YIN Wenjin, ZHANG Jingyuan, RAO Zhe et al. Global sensitivity analysis method based on sobol index method[J]. Marine Electric & Electronic Engineering, 35, 19-21, 25(2015).
