Global Model Calibration of High-Temperature Gas-Cooled Reactor Pebble-Bed Module Using an Adaptive Experimental Design
Yao Tong,
Duo Zhang,
Zhijiang Shao (szj@zju.edu.cn) and
Xiaojin Huang
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Yao Tong: College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China
Duo Zhang: College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China
Zhijiang Shao: College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China
Xiaojin Huang: Institute of Nuclear and New Energy Technology, Tsinghua University, Beijing 100084, China
Energies, 2023, vol. 16, issue 12, 1-25
Abstract:
The world’s first high-temperature gas-cooled reactor pebble-bed module (HTR-PM) nuclear power plant adopts an innovative reactor type and a modular structure design. Parameter estimation and model calibration are of great significance prior to the implementation of model-based control and optimization. This paper focuses on identifying the thermal hydraulic parameters of HTR-PM over the global operating domain. The process technology and model mechanism of HTR-PM are reviewed. A parameter submodel named global parameter mapping is presented to quantify the relationship between an unknown model parameter and different operating conditions in a data-driven manner. The ideal construction of such a mapping requires reliable estimates, a well-poised sample set and an appropriate global surrogate. An adaptive model calibration scheme is designed to tackle these three issues correspondingly. First, a systematic parameter estimation approach is developed to ensure reliable estimates via heuristic subset selection consisting of estimability analysis and reliability evaluation. To capture the parameter behavior among the multiple experimental conditions and meanwhile reduce the operating cost, an adaptive experimental design is employed to guide condition testing. Experimental conditions are sequentially determined by comprehensively considering the criteria of sampling density, local nonlinearity and parameter uncertainty. Support vector regression is introduced as the global surrogate due to its capability of small-sample learning. Finally, the effectiveness of the model calibration scheme and its application performance in HTR-PM are validated by the simulation results.
Keywords: HTR-PM; system modeling; parameter estimation; model calibration; experimental design (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:16:y:2023:i:12:p:4653-:d:1168942
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