文献类型：期刊文献

英文题名：Development of sound pressure identification method to detect internal leak of safety relief valves in nuclear power plants

作者：Hu, Ming-Hui[1]

机构：[1]East China Univ Sci & Technol, Minist Educ, Key Lab Pressure Syst & Safety, Shanghai 200237, Peoples R China

年份：2020

卷号：366

外文期刊名：NUCLEAR ENGINEERING AND DESIGN

收录：;EI(收录号：20203008962959);WOS:【SCI-EXPANDED(收录号:WOS:000558755300004)】；

基金：This work was supported by the National Natural Science Foundation of China (Grant No. 51775188) and National Key R&D Program of China (Grant No. 2018YFC1902404). Author thanks Prof. Yu Xinhai, and Wujiang Dongwu Machinery Co. Ltd, China for experiment support.

语种：英文

外文关键词：Safety relief valve (SRV); Internal leak detection; Sound pressure level (SPL); Large eddy simulation (LES); Lighthill's acoustic analogy

摘要：This article presents a sound pressure identification method to detect internal leak of safety relief valve (SRV) during the sealing surface damaged between valve disk and valve seat. An adjustable acoustical generator was designed to generate ultrasonic waves and detect internal leak. This approach was established by using computational fluid dynamics (CFD) with large eddy simulation (LES) to compute flow field and applying ACTRAN software with Lighthill's acoustic analogy model to simulate acoustic field. The performance of the proposed method is evaluated by testing under the different leakage rates, as 0.5 L/min, 1 L/min to 5.5 L/min with every 0.5 L/min, respectively. Two microphone sensors were installed at external designed added device around the SRV to capture sound pressure level (SPL) of internal leakage signal. In order to ensure safety of the experiment, the high temperature dry saturated steam was replaced by compressed air as flow (10 MPa). The experimental results show that the proposed method can efficiently identify internal leakage rate higher than 1 L/min, and the sensitive frequency ranges from 7 kHz to 40 kHz. Hence, the acoustic signal of leakage can easily distinguish from noise because ambient noise focuses on low frequency domain. The study makes it possible to detect internal leak of SRV and prevent catastrophic failures in nuclear power plants.