Predicting reverse flow characteristics in equal-height-difference passive heat removal system for ocean nuclear power

Wang, Zhiwei; He, Yanping; Duan, Zhongdi; Huang, Chao; Yuan, Yuchao; Liu, Shiwen; Li, Mingzhi

doi:10.1016/j.pnucene.2022.104333

Cited by 5 publications

(2 citation statements)

References 34 publications

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“…Zhang et al predicted hydrogen defagration to detonation transition by analyzing oxygen concentration in inert diluent and verifed the feasibility and applicability of the method [5]. Wang et al use dimensionless Reynolds number, Grashf number, and steam volume fraction to predict reverse fow characteristics, and the prediction error is within ±15% [6]. Data-driven methods require some data support.…”

Section: Introductionmentioning

confidence: 99%

“…Chen et al combined genetic algorithm with neural network and optimized the neural network model using genetic algorithm, which made the model function better for nonlinear system parameter prediction [13]. At present, more scholars prefer to study the application of data-driven methods in the prediction of nuclear power system parameters [14,15] and a few scholars study the mechanism model-driven method to predict the parameters of the nuclear power system [6]. For the outage loop of the foating nuclear power plant, the temperature within the outage loop and the efects brought about by it should be obtained if the more accurate guidance is needed.…”

Section: Introductionmentioning

confidence: 99%

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Methods for Predicting the Minimum Temperature of the Outage Loop and the Maximum Power Caused by the Low-Temperature Coolant

Liu

Lei

Hao

et al. 2023

Science and Technology of Nuclear Installations

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When the feedwater valve at the outage loop of the floating nuclear power plant leaks, thermal stratification occurs in the steam generator. It causes lower water temperature in the outage loop. The extent of hazard of this phenomenon cannot be directly determined by the existing measurement parameters, which poses a threat to the operational safety of the reactor. Therefore, this study adopts two routes: data-driven combined with safety analysis system (DSAS) and mechanism model-driven combined with safety analysis system (MSAS), to propose the prediction methods for the minimum temperature of the outage loop and the maximum power caused by the low-temperature coolant. Then, the actual data are used to verify these methods and the prediction results under different initial conditions are analyzed. The results show that both the DSAS method and the MSAS method can predict the minimum temperature of the steam generator in the outage loop and the maximum power when the outage loop is put into operation, but the DSAS method has better performance under certain conditions. These methods can provide guidance to the operators to avoid reactivity insertion accident.

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Section: Introductionmentioning

confidence: 99%