Nuclear Engineering and Technology

  • 제53권12호
  • /
  • Pages.4166-4178
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  • 2021
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  • 1738-5733(pISSN)
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  • 2234-358X(eISSN)
한국원자력학회 (Korean Nuclear Society)
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Analysis and optimization research on latch life of control rod drive mechanism based on approximate model

  • Ling, Sitong (School of Mechanical Engineering, Sichuan University) ;
  • Li, Wenqiang (School of Mechanical Engineering, Sichuan University) ;
  • Yu, Tianda (Science and Technology on Reactor System Design Technology Laboratory, Nuclear Power Institute of China) ;
  • Deng, Qiang (Science and Technology on Reactor System Design Technology Laboratory, Nuclear Power Institute of China) ;
  • Fu, Guozhong (Science and Technology on Reactor System Design Technology Laboratory, Nuclear Power Institute of China)
  • 투고 : 2020.12.04
  • 심사 : 2021.06.06
  • 발행 : 2021.12.25
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초록

The Control Rod Drive Mechanism (CRDM) is an essential part of the reactor, which realizes the start-stop and power adjustment of the reactor by lifting and lowering the control rod assembly. As a moving part in CRDM, the latch directly contacts with the control rod assembly, and the life of latch is closely related to the service life of the reactor. In this paper, the relationship between the life of the latch and the step stress, friction stress, and impact stress in the process of movement is analyzed, and the optimization methodology and process of latch life based on the approximate model are proposed. The design variables that affect the life of the latch are studied through the experimental design, and the optimization objective of design variables based on the latch life is established. Based on this, an approximate model of the life of the latch is built, and the multi-objective optimization of the life of the latch is optimized through the NSGA-II algorithm.

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과제정보

This work is supported by the National Natural Science Foundation of China (No. 52075350) and the Sichuan Major Science and Technology Project (No.2019ZDZX0001).

참고문헌

  1. T.-T. Tran, A.-T. Cao, T.-H.-X. Nguyen, et al., Fragility assessment for electric cabinet in nuclear power plant using response surface methodology, Nuclear Engineering and Technology 51 (3) (2019) 894-903. https://doi.org/10.1016/j.net.2018.12.025
  2. Z.R. Lu Yonggang, Qiang Fu, Xiuli Wang, Ce An, Jing Chen, Research on the structure design of the LBE reactor coolant pump in the lead base heap, Nuclear Engineering and Technology 51 (2019) 546-555. https://doi.org/10.1016/j.net.2018.09.023
  3. J.B.-G. Kim Sung-Wan, Hahm Dae-Gi, Kim Min-Kyu Seismic fragility evaluation of the base-isolated nuclear power plant piping system using the failure criterion based on stress-strain, Nuclear Engineering and Technology 51 (2019) 561-572. https://doi.org/10.1016/j.net.2018.10.006
  4. M.R. Kacal, F. Akman, M.I. Sayyed, et al., Evaluation of gamma-ray and neutron attenuation properties of some polymers, Nuclear Engineering and Technology 51 (3) (2019) 818-824. https://doi.org/10.1016/j.net.2018.11.011
  5. S. Abdullahi, A.F. Ismail, S. Samat, Determination of indoor doses and excess lifetime cancer risks caused by building materials containing natural radionuclides in Malaysia, Nuclear Engineering and Technology 51 (1) (2019) 325-336. https://doi.org/10.1016/j.net.2018.09.017
  6. A. Tanaka, K. Futahashi, K. Takanabe, et al., Development of a 3-D simulation analysis system for PWR control rod drive mechanism, Int. J. Pres. Ves. Pip. 85 (9) (2008 Sep) 655-661. https://doi.org/10.1016/j.ijpvp.2008.04.007
  7. Z. Lin, L. Zhai, L. Zhu, et al., Control rod drop dynamic analysis in the TMSR - SF1 based on numerical simulation and experiment, Nucl. Eng. Des. 322 (2017) 131-137. https://doi.org/10.1016/j.nucengdes.2017.06.031
  8. J. Park, W. Lee, M. Lee, et al., Development of an in-vessel CEDM for small modular reactors, Nucl. Technol. 206 (3) (2019) 435-443. https://doi.org/10.1080/00295450.2019.1635363
  9. J.H. Lee, S. Kim, Y.-S. Yoo, et al., Bottom-mounted control rod drive mechanism for KJRR, Nucl. Eng. Des. 300 (2016) 222-230. https://doi.org/10.1016/j.nucengdes.2016.01.036
  10. K. Ahn, J.-S. Lee, Dynamic equivalent model of a SMART control rod drive mechanism for a seismic analysis, Nuclear Engineering and Technology 52 (8) (2020) 1834-1846. https://doi.org/10.1016/j.net.2020.01.009
  11. F. Wang, Q. Shen, Aging mechanism and effect analysis of control rod drive mechanism, Mech. Eng. (6) (2014) 94-97.
  12. X. Tang, B. Yang, X. Chen, et al., Research on lift Pole thread fatigue of magnetic lifting control rod drive mechanism, Nucl. Power Eng. 34 (S1) (2013) 112-115.
  13. H. Zhen, Y. Zhong, Y. Wang, et al., Longeval control rod drive M echanism hot life test for advance pressurized water reactor, Nucl. Power Eng. (S1) (2002) 70-73.
  14. D. Hertz, Approach to analysis of wear mechanisms in the case of RCCAs and CRDM latch arms: from observation to understanding, Wear 261 (9) (2006) 1024-1031. https://doi.org/10.1016/j.wear.2006.03.037
  15. Z. Sun, S. Liu, X. Ran, et al., Analysis and optimization of kinematic pair force in control rod drive mechanism, Nucl. Power Eng. 36 (3) (2015) 90-93.
  16. X. Chen, F. Yang, X. Yang, et al., Research on the optimal design and manufacture of ACP1000 control rod drive mechanism latch, Machine Design and Manufacturing Engineering 45 (10) (2016) 74-78.
  17. F.L. Gao, Y.C. Bai, C. Lin, et al., A time-space Kriging-based sequential metamodeling approach for multi-objective crashworthiness optimization, Appl. Math. Model. 69 (2019) 378-404. https://doi.org/10.1016/j.apm.2018.12.011
  18. M. Ou, L. Yan, W. Huang, et al., Design exploration of combinational spike and opposing jet concept in hypersonic flows based on CFD calculation and surrogate model, Acta Astronaut. 155 (2019) 287-301. https://doi.org/10.1016/j.actaastro.2018.12.012
  19. J-h Wu, X-w Zhen, G. Liu, et al., Optimization design on the riser system of next generation subsea production system with the assistance of DOE and surrogate model techniques, Appl. Ocean Res. 85 (2019) 34-44. https://doi.org/10.1016/j.apor.2019.01.035
  20. J. Grobbel, S. Brendelberger, M. Henninger, et al., Calibration of parameters for DEM simulations of solar particle receivers by bulk experiments and surrogate functions, Powder Technol. 364 (2020) 831-844. https://doi.org/10.1016/j.powtec.2019.11.028
  21. M. Xi, D. Lu, D. Gui, et al., Calibration of an agricultural-hydrological model (RZWQM2) using surrogate global optimization, J. Hydrol. 544 (2017) 456-466. https://doi.org/10.1016/j.jhydrol.2016.11.051
  22. M. Mishra, R. Derakhshani, G.C. Paiva, et al., Nonlinear surrogate modeling of tibio-femoral joint interactions, Biomed. Signal Process Contr. 6 (2) (2011) 164-174. https://doi.org/10.1016/j.bspc.2010.08.005
  23. S. Vakili, M.S. Gadala, Low cost surrogate model based evolutionary optimization solvers for inverse heat conduction problem, Int. J. Heat Mass Tran. 56 (1-2) (2013) 263-273. https://doi.org/10.1016/j.ijheatmasstransfer.2012.09.009
  24. P. Westermann, R. Evins, Surrogate modelling for sustainable building design - a review, Energy Build. 198 (2019) 170-186. https://doi.org/10.1016/j.enbuild.2019.05.057
  25. Y. Ni, X. Du, P. Ye, et al., Frequent pattern mining assisted energy consumption evolutionary optimization approach based on surrogate model at GCC compile time, Swarm and Evolutionary Computation 50 (2019). https://doi.org/10.1016/j.swevo.2019.01.009
  26. O.B. Ericok, A.K. Ozbek, A.T. Cemgil, et al., Gaussian process and design of experiments for surrogate modeling of optical properties of fractal aggregates, J. Quant. Spectrosc. Radiat. Transf. 239 (2019).
  27. M. Belyaev, E. Burnaev, E. Kapushev, et al., GTApprox: surrogate modeling for industrial design, Adv. Eng. Software 102 (2016) 29-39. https://doi.org/10.1016/j.advengsoft.2016.09.001
  28. M.D. Pandey, F.J. Tallavo, N.C. Christodoulou, et al., Understanding the mechanics of creep deformation to develop a surrogate model for contact assessment in CANDU® fuel channels, Nucl. Eng. Des. 330 (2018) 141-156. https://doi.org/10.1016/j.nucengdes.2018.01.032
  29. S.R. Prabhu, M.D. Pandey, N. Christodoulou, et al., A surrogate model for the 3D prediction of in-service deformation in CANDU® fuel channels, Nucl. Eng. Des. (2020) 369.
  30. G.A. Banyay, M.D. Shields, J.C. Brigham, Efficient global sensitivity analysis for flow-induced vibration of a nuclear reactor assembly using Kriging surrogates, Nucl. Eng. Des. 341 (2019) 1-15. https://doi.org/10.1016/j.nucengdes.2018.10.013
  31. Q. Wei, Y. Li, X. Ran, Dynamic analysis of CRDM loads during stepping motion, Nucl. Power Eng. 35 (4) (2014) 13-16.
  32. A.N. Eraslan, Stress distributions in elastic-plastic rotating disks with elliptical thickness profiles using Tresca and von Mises criteria, ZAMM - Journal of Applied Mathematics and Mechanics/Z. Angew. Math. Mech. 85 (4) (2005) 252-266. https://doi.org/10.1002/zamm.200210177
  33. C.J. Zhu, X.L. Xie, L. Lai, et al., Study of Two Important Plastic Indexes of Tresca Yield Criterion and Mises Yield Criterion, 2005.
  34. L. Li, F. Wang, Y. Wang, Friction pair test of latch of control rod drive mechanism, Physical Testing and Chemical Analysis(Part A:Physical Testing) 52 (6) (2016) 384-388.
  35. L. Li, F. Wang, Y. Wang, Study on manufacturing process of Stellite6 alloy investment castings of control rod drive mechanism latch, Hot Work. Technol. 45 (15) (2016) 75-77.
  36. E. Acar, Effects of the correlation model, the trend model, and the number of training points on the accuracy of Kriging metamodels, Expet Syst. 30 (5) (2013) 418-428. https://doi.org/10.1111/j.1468-0394.2012.00646.x
  37. A. Hasani, S.M.H. Hosseini, A bi-objective flexible flow shop scheduling problem with machine-dependent processing stages: trade-off between production costs and energy consumption, Appl. Math. Comput. (2020) 386.