Design optimization of a nuclear main steam safety valve based on an E-AHF ensemble surrogate model
0
조회
0
다운로드
1
인용
0
참고문헌
키워드
초록
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 ksigma 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
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 ksigma 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
참고문헌 (33)
- Han Y., "Transient Simulation and Experiment Validation on the Opening and Closing Process of a Ball Valve", Nucl. Eng. Technol
- F. J. Zheng, "A multidimensional and Multiscale model for pressure analysis in a reservoir-pipe-valve system", J. Pres. Vessel Technol, 141, pp.051603~051601 (2019)
- Wen-qing Li, "Thermo-mechanical stress analysis of feed-water valves in nuclear power plants", Nuclear Engineering and Technology, 54(3), pp.849~859 (2022), 한국원자력학회[DOI]
- N. L. Scuro, "A CFD analysis of the flow dynamics of a directly-operated safety relief valve", Nucl. Eng. Design, 328, pp.321~332 (2018)
- R. Darby, "The dynamic response of pressure relief valves in vapor or gas service. Part III : model validation", J. Loss Prevent. Proc. Indus, 31, pp.133~141 (2014)
- A. Beune, "CFD analysis with fluidstructure interaction of opening high-pressure safety valves", Comput. Fluids, 64, pp.108~116 (2012)[DOI]
- R. Darby, "The dynamic response of pressure relief valves in vapor or gas service, Part I: mathematical Model", J. Loss Prevent. Proc. Indus, 26, pp.1262~1268 (2013)
- X. G. Song, "A fluid-structure interaction analysis of the spring-loaded pressure safety valve during popping off", Procedia Eng, 130, pp.87~94 (2015)
- C. J. H}os, "Dynamic behavior of direct spring loaded pressure relief valves in gas service: model development, measurements and instability mechanisms", J. Loss Prevent. Proc. Indus, 31, pp.70~81 (2014)
- X. G. Song, "Crashworthiness optimization of foam-filled tapered thin-walled structure using multiple surrogate models", Struct. Multidiscipl. Opt, 47, pp.221~231 (2013)
- X. G. Song, "An advanced and robust ensemble surrogate model : extended adaptive hybrid functions", J. Mech. Des. (Transaction of ASME), 140(4), pp.041402~ (2018)
- L. T. Wang, "Flow resistance optimization of link lever butterfly valve based on combined surrogate model", Struct. Multidiscipl. Opt, 64, pp.4255~4270 (2021)
- R. H. Myers, "Response Surface Methodology : Process and Product Optimization Using Designed Experiments" (1995), Wiley
- R. F. Gunst, "Response surface methodology : process and product optimization using designed experiments", J. Stat. Plan. Inferen, 59, pp.185~186 (1997)
- J. Sacks, "Design and analysis of computer experiments", Stat. Sci, 4(4), pp.409~435 (1989)
- A. J. Smola, "A tutorial on support vector regression", Stat. Comput, 14(3), pp.199~222 (2004)
- B. G. R. Lloyd, "Support vector machines for classification and regression", Analyst, 135(2), pp.230~267 (2010)
- H. M. Gutmann, "A radial basis function method for global optimization", J. Global Opt, 19(3), pp.201~227 (2001)
- G. Y. Sun, "A two-stage multi-fidelity optimization procedure for honeycomb-type cellular materials", Comput. Mater. Sci, 49(3), pp.500~511 (2010)
- G. Y. Sun, "Radial basis functional model for multiobjective sheet metal forming optimization", Eng. Opt, 12(43), pp.1351~1366 (2011)
- R. G. Regis, "Constrained global optimization of expensive black box Functions using radial basis functions", J. Global Opt, 31(1), pp.153~171 (2005)
- A. A. Mullur, "Metamodeling using extended radial basis functions : a comparative approach", Eng. Comput, 21(3), pp.203~217 (2006)
- D. Zhao, "A comparative study of metamodeling methods considering sample quality merits", Struct. Multidiscipl. Opt, 42(6), pp.923~938 (2010)
- T. Goel, "Ensemble of surrogates", Struct. Multidiscipl. Opt, 33(3), pp.199~216 (2007)
- E. Denimal, "A novel hybrid surrogate model and its application on a mechanical system subjected to friction-induced vibration", J. Sound Vibrat, 434, pp.456~474 (2018)
- P. Ye, "Ensemble of surrogate based global optimization methods using hierarchical design space reduction", Struct. Multidiscipl. Opt, 58(2), pp.537~554 (2018)
- Y. Ye, "An optimal pointwise weighted ensemble of surrogates based on minimization of local mean square error", Struct. Multidiscipl. Opt, 62(2), pp.529~542 (2020)
- C. Zhou, "An adaptive ensemble of surrogate models based on hybrid measure for reliability analysis", Struct. Multidiscipl. Opt, 65(1), pp.1~18 (2022)
- L. Chen, "Ensemble of surrogates with hybrid method using global and local measures for engineering design", Struct. Multidiscipl. Opt, 57(4), pp.1711~1729 (2018)
- Sizing, Selection, and Installation of Pressure-relieving Devices, Part I-Sizing and Selection, , API, 7~8, pp.API STANDARD 520 (2014)
- A. Olsson, "On Latin hypercube sampling for structural reliability analysis", Struct. Safety, 25(1), pp.47~68 (2003)
- I. M. Sobol, "Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates", Math. Comput. Simul, 55(1-3), pp.271~280 (2001)
- G. Y. Sun, "Robust optimization of foam-filled thinwalled structure based on sequential Kriging metamodel", Struct. Multidiscipl. Opt, 49(6), pp.897~913 (2014)