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http://dx.doi.org/10.1016/j.net.2022.06.019

Design optimization of a nuclear main steam safety valve based on an E-AHF ensemble surrogate model  

Chaoyong Zong (School of Mechanical Engineering, Dalian University of Technology)
Maolin Shi (School of Agricultural Engineering, Jiangsu University)
Qingye Li (School of Mechanical Engineering, Dalian University of Technology)
Fuwen Liu (School of Mechanical Engineering, Dalian University of Technology)
Weihao Zhou (School of Mechanical Engineering, Dalian University of Technology)
Xueguan Song (School of Mechanical Engineering, Dalian University of Technology)
Publication Information
Nuclear Engineering and Technology / v.54, no.11, 2022 , pp. 4181-4194 More about this Journal
Abstract
Main steam safety valves are commonly used in nuclear power plants to provide final protections from overpressure events. Blowdown and dynamic stability are two critical characteristics of safety valves. However, due to the parameter sensitivity and multi-parameter features of safety valves, using traditional method to design and/or optimize them is generally difficult and/or inefficient. To overcome these problems, a surrogate model-based valve design optimization is carried out in this study, of particular interest are methods of valve surrogate modeling, valve parameters global sensitivity analysis and valve performance optimization. To construct the surrogate model, Design of Experiments (DoE) and Computational Fluid Dynamics (CFD) simulations of the safety valve were performed successively, thereby an ensemble surrogate model (E-AHF) was built for valve blowdown and stability predictions. With the developed E-AHF model, global sensitivity analysis (GSA) on the valve parameters was performed, thereby five primary parameters that affect valve performance were identified. Finally, the k-sigma method is used to conduct the robust optimization on the valve. After optimization, the valve remains stable, the minimum blowdown of the safety valve is reduced greatly from 13.30% to 2.70%, and the corresponding variance is reduced from 1.04 to 0.65 as well, confirming the feasibility and effectiveness of the optimization method proposed in this paper.
Keywords
Safety valve; E-AHF ensemble model; Robust optimization; Valve blowdown;
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1 X.G. Song, G.Y. Sun, G.Y. Li, W.Z. Gao, Q. Li, Crashworthiness optimization of foam-filled tapered thin-walled structure using multiple surrogate models, Struct. Multidiscipl. Opt. 47 (2013) 221-231.   DOI
2 X.G. Song, L.Y. Lv, J.L. Li, W. Sun, J. Zhang, An advanced and robust ensemble surrogate model: extended adaptive hybrid functions, J. Mech. Des. (Transaction of ASME) 140 (4) (2018), 041402.
3 L.T. Wang, S.K. Zheng, X. Liu, H. Xie, J. Dou, Flow resistance optimization of link lever butterfly valve based on combined surrogate model, Struct. Multidiscipl. Opt. 64 (2021) 4255-4270.   DOI
4 R.H. Myers, D.C. Montgomery, Response Surface Methodology: Process and Product Optimization Using Designed Experiments, Wiley, New York, 1995.
5 R.F. Gunst, Response surface methodology: process and product optimization using designed experiments, J. Stat. Plan. Inferen. 59 (1997) 185-186.   DOI
6 J. Sacks, W.J. Welch, T.J. Mitchell, H.P. Wynn, Design and analysis of computer experiments, Stat. Sci. 4 (4) (1989) 409-435.
7 A.J. Smola, B. Scholkopf, A tutorial on support vector regression, Stat. Comput. 14 (3) (2004) 199-222.   DOI
8 B.G.R. Lloyd, Support vector machines for classification and regression, Analyst 135 (2) (2010) 230-267.   DOI
9 H.M. Gutmann, A radial basis function method for global optimization, J. Global Opt. 19 (3) (2001) 201-227.   DOI
10 G.Y. Sun, G.Y. Li, M. Stone, Q. Li, A two-stage multi-fidelity optimization procedure for honeycomb-type cellular materials, Comput. Mater. Sci. 49 (3) (2010) 500-511.   DOI
11 G.Y. Sun, G.Y. Li, Z.H. Gong, G.Q. He, Q. Li, Radial basis functional model for multiobjective sheet metal forming optimization, Eng. Opt. 12 (43) (2011) 1351-1366.
12 Han Y., Zhou L., Bai L., Xue P., Lv W.N., Shi W.D., Huang G.Y., Transient Simulation and Experiment Validation on the Opening and Closing Process of a Ball Valve. Nucl. Eng. Technol., article in press.
13 F.J. Zheng, C.Y. Zong, W. Dempster, F.Z. Qu, X.G. Song, A multidimensional and Multiscale model for pressure analysis in a reservoir-pipe-valve system, J. Pres. Vessel Technol. 141 (2019), 051603-1.
14 W.Q. Li, L. Zhao, Y. Yue, J.Y. Wu, Z.J. Jin, J.Y. Qian, Thermo-mechanical stress analysis of feed-water valves in nuclear power plants, Nucl. Eng. Technol. 54 (2022) 849-859.   DOI
15 N.L. Scuro, E. Angelo, G. Angelo, D.A. Andrade, A CFD analysis of the flow dynamics of a directly-operated safety relief valve, Nucl. Eng. Design 328 (2018) 321-332.   DOI
16 R. Darby, A.A. Aldeeb, The dynamic response of pressure relief valves in vapor or gas service. Part III: model validation, J. Loss Prevent. Proc. Indus. 31 (2014) 133-141.   DOI
17 A. Beune, J.G.M. Kuerten, M.P.C. van Heumen, CFD analysis with fluid-structure interaction of opening high-pressure safety valves, Comput. Fluids 64 (2012) 108e116. https://doi.org/10.1016/j.compfluid.2012.05.010.   DOI
18 R. Darby, The dynamic response of pressure relief valves in vapor or gas service, Part I: mathematical Model, J. Loss Prevent. Proc. Indus. 26 (2013) 1262-1268.   DOI
19 X.G. Song, L.T. Wang, Y.C. Park, W. Sun, A fluid-structure interaction analysis of the spring-loaded pressure safety valve during popping off, Procedia Eng. 130 (2015) 87-94.   DOI
20 C.J. Hos, A.R. Champneys, K. Paul, M. McNeely, Dynamic behavior of direct spring loaded pressure relief valves in gas service: model development, measurements and instability mechanisms, J. Loss Prevent. Proc. Indus. 31 (2014) 70-81.   DOI
21 T. Goel, R.T. Haftka, W. Shyy, V.Q. Nestor, Ensemble of surrogates, Struct. Multidiscipl. Opt. 33 (3) (2007) 199-216.   DOI
22 R.G. Regis, C.A. Shoemaker, Constrained global optimization of expensive black box Functions using radial basis functions, J. Global Opt. 31 (1) (2005) 153-171.   DOI
23 A.A. Mullur, A. Messac, Metamodeling using extended radial basis functions: a comparative approach, Eng. Comput. 21 (3) (2006) 203-217.   DOI
24 D. Zhao, D.Y. Xue, A comparative study of metamodeling methods considering sample quality merits, Struct. Multidiscipl. Opt. 42 (6) (2010) 923-938.   DOI
25 E. Denimal, L. Nechak, J.J. Sinou, S. Nacivet, A novel hybrid surrogate model and its application on a mechanical system subjected to friction-induced vibration, J. Sound Vibrat. 434 (2018) 456-474.   DOI
26 P. Ye, G. Pan, Z. Dong, Ensemble of surrogate based global optimization methods using hierarchical design space reduction, Struct. Multidiscipl. Opt. 58 (2) (2018) 537-554.   DOI
27 Y. Ye, Z. Wang, X. Zhang, An optimal pointwise weighted ensemble of surrogates based on minimization of local mean square error, Struct. Multidiscipl. Opt. 62 (2) (2020) 529-542.   DOI
28 C. Zhou, H. Zhang, Q. Chang, X. Song, C. Li, An adaptive ensemble of surrogate models based on hybrid measure for reliability analysis, Struct. Multidiscipl. Opt. 65 (1) (2022) 1-18.   DOI
29 L. Chen, H. Qiu, C. Jiang, X. Cai, L. Gao, Ensemble of surrogates with hybrid method using global and local measures for engineering design, Struct. Multidiscipl. Opt. 57 (4) (2018) 1711-1729.   DOI
30 Sizing, Selection, and Installation of Pressure-relieving Devices, Part I-Sizing and Selection, API STANDARD 520, NINTH EDITION, July (2014), 7-8.
31 G.Y. Sun, X.G. Song, S. Baek, Q. Li, Robust optimization of foam-filled thin-walled structure based on sequential Kriging metamodel, Struct. Multidiscipl. Opt. 49 (6) (2014) 897-913.   DOI
32 A. Olsson, G. Sandberg, O. Dahlblom, On Latin hypercube sampling for structural reliability analysis, Struct. Safety 25 (1) (2003) 47-68.   DOI
33 I.M. Sobol, Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates, Math. Comput. Simul. 55 (1-3) (2001) 271-280.   DOI