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Water hammer analysis of essential service water system in nuclear power plant

  • Sheng Yan ORCID logo , Jiayue Chen , Yanyu Yang , Lv Li , Xiaolong He , Xinming Huang , Rongyan Huang und Hong Xu EMAIL logo
Veröffentlicht/Copyright: 17. September 2025
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Abstract

In the essential service water system (SEC) of the nuclear power plants (NPPs), pressure surges (water hammer) are possible due to the operation of components such as valves, pumps, and heat exchangers, which can cause damage to the pipeline. The main focus of this study is to predict the occurrence of water hammer and to optimize the operation of these components in order to minimize pressure surges to the greatest extent possible. This paper focuses on the SEC of a typical pressurized water reactor (PWR), by employing a thermal-hydraulic system code to model the operation of the SEC. The accuracy of the model is verified by comparing the experimental results with those from the calculations. Through the sensitivity analysis of the time step and the control volume length, the minimum time step is selected as 0.001 to predict the pressure peak under actual working conditions. Two water hammer types are analyzed: 1) void-induced water hammer; 2) rapid operation of the valve-induced water hammer. Void-induced water hammer, the pressure peaks are moderate, and the pressure waves propagate continuously, gradually diminish. In contrast, rapid valve closure generates higher pressure peaks. Upon reaching air-containing section, the pressure waves caused by the valve closure can superimpose, resulting in irregular pressure fluctuations. By tracking pressure and flow variations, we can study the variation process of the water hammer phenomenon better, and provide a reference for the operation and control of SECs in the PWR, and the future research.


Corresponding author: Hong Xu, Sun Yat-Sen University, Zhuhai, 519000, China, E-mail:

  1. Research ethics: Not applicable.

  2. Informed consent: Not applicable.

  3. Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.

  4. Use of Large Language Models, AI and Machine Learning Tools: None declared.

  5. Conflict of interest: The authors state no conflict of interest.

  6. Research funding: None declared.

  7. Data availability: Not applicable.

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Received: 2025-04-18
Accepted: 2025-08-25
Published Online: 2025-09-17
Published in Print: 2025-10-27

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Heruntergeladen am 11.12.2025 von https://www.degruyterbrill.com/document/doi/10.1515/kern-2025-0038/html
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