Study on Quantification of Parameter Uncertainty in Reflooding Model Based on Random Forest Algorithm

Lei Meng; Li Dong; Zhang Ziyue; Hao Rao

doi:10.13832/j.jnpe.2024.070031

Volume 46 Issue 2

Apr. 2025

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Lei Meng, Li Dong, Zhang Ziyue, Hao Rao. Study on Quantification of Parameter Uncertainty in Reflooding Model Based on Random Forest Algorithm[J]. Nuclear Power Engineering, 2025, 46(2): 98-106. doi: 10.13832/j.jnpe.2024.070031

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Lei Meng, Li Dong, Zhang Ziyue, Hao Rao. Study on Quantification of Parameter Uncertainty in Reflooding Model Based on Random Forest Algorithm[J]. Nuclear Power Engineering, 2025, 46(2): 98-106. doi: 10.13832/j.jnpe.2024.070031

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Study on Quantification of Parameter Uncertainty in Reflooding Model Based on Random Forest Algorithm

doi: 10.13832/j.jnpe.2024.070031

Lei Meng^1
,,
Li Dong^{1
,
,},
Zhang Ziyue¹,
Hao Rao²

1.
College of Energy and Mechanical Engineering, Shanghai University of Electric Power, Shanghai, 201306, China
2.
Xi’an Electric Power College, Xi’an, 710032, China

Received Date: 2024-07-09
Rev Recd Date: 2024-08-30

Available Online: 2025-01-15

Publish Date: 2025-04-02

Abstract

Abstract

In order to assess the uncertainty of physical models (inputs) of complex accidents, an inverse uncertainty quantification method based on Random Forest algorithm combined with PSO-Kriging surrogate model and KDE-SJ nonparametric statistics is proposed, and it is applied to the model assessment of reflooding in large breach accidents. The probability density distributions of the model parameters were obtained through the degree of consistency between the calculation results (output) of the system program and the FEBA experimental data as a classification criterion for the Random Forest algorithm. The validation results show that the 95% uncertainty bands obtained by randomly sampling 93 groups of calculations on the probability density distributions can completely envelope the experimental data, but the calibration effect of the model using mode or mean may not be as good as the maximum posterior mean obtained by Bayesian method.
- Inverse uncertainty quantification,
- Random Forest,
- Surrogate model,
- Nonparametric statistics,
- Reflooding phenomenon

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References(23)

References

[1]	李冬. 最佳估算模型的不确定性量化方法研究及再淹没模型评估的应用[D]. 上海: 上海交通大学,2017.
[2]	WU X, XIE Z Y, ALSAFADI F, et al. A comprehensive survey of inverse uncertainty quantification of physical model parameters in nuclear system thermal-hydraulics codes[J]. Nuclear Engineering and Design, 2021, 384: 111460. doi: 10.1016/j.nucengdes.2021.111460
[3]	KOVTONYUK A. Development of methodology for evaluation of uncertainties of system thermal-hydraulic codes’ input parameters[D]. Pisa: Università di Pisa, 2014.
[4]	SKOREK T. Input uncertainties in uncertainty analyses of system codes: quantification of physical model uncertainties on the basis of CET (combined effect tests)[J]. Nuclear Engineering and Design, 2017, 321: 301-317. doi: 10.1016/j.nucengdes.2016.10.028
[5]	DEMPSTER A P, LAIRD N M, RUBIN D B. Maximum likelihood from incomplete data via the EM algorithm[J]. Journal of the Royal Statistical Society: Series B (Methodological), 1977, 39(1): 1-22. doi: 10.1111/j.2517-6161.1977.tb01600.x
[6]	GELMAN A, CARLIN J B, STERN H S, et al. Bayesian data analysis[M]. New York: Chapman and Hall/CRC, 2013: 1-675.
[7]	DAMBLIN G, GAILLARD P. A Bayesian framework for quantifying the uncertainty of physical models integrated into thermal-hydraulic computer codes[C]//Proceedings of the Best Estimate Plus Uncertainty International Conference. Lucca: BEUP, 2018.
[8]	DAMBLIN G, GAILLARD P. Bayesian inference and non-linear extensions of the CIRCE method for quantifying the uncertainty of closure relationships integrated into thermal-hydraulic system codes[J]. Nuclear Engineering and Design, 2020, 359: 110391. doi: 10.1016/j.nucengdes.2019.110391
[9]	WU X, KOZLOWSKI T, MEIDANI H, et al. Inverse uncertainty quantification using the modular Bayesian approach based on Gaussian Process, part 2: application to TRACE[J]. Nuclear Engineering and Design, 2018, 335: 417-431. doi: 10.1016/j.nucengdes.2018.06.003
[10]	WANG C, WU X, KOZLOWSKI T. Surrogate-based Bayesian calibration of thermal-hydraulics models based on PSBT time-dependent benchmark data[C]//Proceedings of the ANS Best Estimate Plus Uncertainty International Conference. Lucca, Italy: BEPU, 2018.
[11]	WANG C, WU X, KOZLOWSKI T. Gaussian process–based inverse uncertainty quantification for trace physical model parameters using steady-state PSBT benchmark[J]. Nuclear Science and Engineering, 2019, 193(1-2): 100-114. doi: 10.1080/00295639.2018.1499279
[12]	WANG C, WU X, KOZLOWSKI T. Inverse uncertainty quantification by hierarchical Bayesian inference for trace physical model parameters based on BFBT benchmark[C]//American Nuclear Society. Proceedings of NURETH-2019. Portland, Oregon, USA: American Nuclear Society, 2019.
[13]	DOMITR P, WŁOSTOWSKI M. The use of machine learning for inverse uncertainty quantification in TRACE code based on Marviken experiment[J]. Nuclear Engineering and Design, 2021, 384: 111498. doi: 10.1016/j.nucengdes.2021.111498
[14]	DOMITR P, WŁOSTOWSKI M, LASKOWSKI R, et al. Comparison of inverse uncertainty quantification methods for critical flow test[J]. Energy, 2023, 263: 125640. doi: 10.1016/j.energy.2022.125640
[15]	邵旻晖. 决策树典型算法研究综述[J]. 电脑知识与技术,2018, 14(8): 175-177.
[16]	曹正凤. 随机森林算法优化研究[D]. 北京: 首都经济贸易大学,2014.
[17]	WANG N, LI D, PENG C, et al. INVESTIGATION OF SURROGATE MODEL FOR UNCERTAINTY QUANTIFICATION OF NUCLEAR SYSTEM[C]//The Proceedings of the International Conference on Nuclear Engineering (ICONE) 2023.30. The Japan Society of Mechanical Engineers, 2023: 1085.
[18]	李冬,王念峰.基于再淹没现象的RBF神经网络和Kriging的代理模型应用及误差分析[J].上海电力大学学报,2022, 38(03): 269-273. 李冬, 王念峰.基于再淹没现象的RBF神经网络和Kriging的代理模型应用及误差分析[J].上海电力大学学报, 2022, 38(03): 269-273.
[19]	IAEA. Status of small reactor designs without on-site refuelling: IAEA-TECDOC-CD-1536[R]. Vienna: International Atomic Energy Agency, 2007.
[20]	MEHOLIC M J. The development of a non-equilibrium dispersed flow film boiling heat transfer modeling package[D]. State College: The Pennsylvania State University, 2011.
[21]	BERAR O A, PROŠEK A, MAVKO B. RELAP5 and TRACE assessment of the Achilles natural reflood experiment[J]. Nuclear Engineering and Design, 2013, 261(8): 306-316.
[22]	IHLE P, RUST K. FEBA-flooding experiments with blocked arrays: evaluation report[M]. Karlsruhe: Kernforschungszentrum Karlsruhe, 1984: 1-543.
[23]	WILKS S S. Determination of sample sizes for setting tolerance limits[J]. The Annals of Mathematical Statistics, 1941, 12(1): 91-96. doi: 10.1214/aoms/1177731788