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Global Model Calibration of High-Temperature Gas-Cooled Reactor Pebble-Bed Module Using an Adaptive Experimental Design

Author

Listed:
  • 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)

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.

Suggested Citation

  • Yao Tong & Duo Zhang & Zhijiang Shao & Xiaojin Huang, 2023. "Global Model Calibration of High-Temperature Gas-Cooled Reactor Pebble-Bed Module Using an Adaptive Experimental Design," Energies, MDPI, vol. 16(12), pages 1-25, June.
  • Handle: RePEc:gam:jeners:v:16:y:2023:i:12:p:4653-:d:1168942
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    References listed on IDEAS

    as
    1. Zhe Dong, 2014. "Saturated Adaptive Output-Feedback Power-Level Control for Modular High Temperature Gas-Cooled Reactors," Energies, MDPI, vol. 7(11), pages 1-20, November.
    2. Locatelli, Giorgio & Mancini, Mauro & Todeschini, Nicola, 2013. "Generation IV nuclear reactors: Current status and future prospects," Energy Policy, Elsevier, vol. 61(C), pages 1503-1520.
    3. Song Xu & Yiu Hin Martin Lu & Meiheriayi Mutailipu & Kanti Yan & Yaoli Zhang & Staffan Qvist, 2022. "Repowering Coal Power in China by Nuclear Energy—Implementation Strategy and Potential," Energies, MDPI, vol. 15(3), pages 1-27, January.
    4. Zhe Dong & Zhonghua Cheng & Yunlong Zhu & Xiaojin Huang & Yujie Dong & Zuoyi Zhang, 2023. "Review on the Recent Progress in Nuclear Plant Dynamical Modeling and Control," Energies, MDPI, vol. 16(3), pages 1-19, February.
    5. Carlos Ruiz & Carlos M. Alaíz & José R. Dorronsoro, 2020. "Multitask Support Vector Regression for Solar and Wind Energy Prediction," Energies, MDPI, vol. 13(23), pages 1-21, November.
    6. Jung, Yongsu & Lee, Ikjin, 2021. "Optimal design of experiments for optimization-based model calibration using Fisher information matrix," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    7. Jiang, Di & Dong, Zhe, 2020. "Dynamic matrix control for thermal power of multi-modular high temperature gas-cooled reactor plants," Energy, Elsevier, vol. 198(C).
    8. Sungho Shin & Ophelia S Venturelli & Victor M Zavala, 2019. "Scalable nonlinear programming framework for parameter estimation in dynamic biological system models," PLOS Computational Biology, Public Library of Science, vol. 15(3), pages 1-29, March.
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