{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,27]],"date-time":"2026-08-27T02:18:27Z","timestamp":1787797107310,"version":"build-2784847793"},"reference-count":30,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2025,11,29]],"date-time":"2025-11-29T00:00:00Z","timestamp":1764374400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Research and Development and Application Project on Identification and Reading Technology of Fluid Flow Characteristic Parameters in Fuqing Nuclear Power Pipelines","award":["FQ0FY-23004769-000"],"award-info":[{"award-number":["FQ0FY-23004769-000"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Acoustic emission detection technology is widely employed for leakage detection in water supply systems. However, this approach heavily relies on extensive field data to develop feature extraction and analysis models. Since field data cannot comprehensively cover all operational conditions\u2014such as variations in pressure, pipe diameter, and leakage size\u2014the limited generalization capability of these models often results in high rates of false negatives and false positives. To address these issues, this study utilizes Large Eddy Simulation (LES) to analyze leakage flow fields, establishing correlations between diverse operating conditions and flow field characteristics, including the areas of negative pressure zones and pressure pulsations. Based on these flow field findings, Computational Aeroacoustics (CAA) is applied to analyze the acoustic radiation field at leakage locations, thus clarifying the sound generation mechanisms of leakage-related acoustic signals, demonstrating strong agreement between simulation results and experimental data. Furthermore, wavelet packet energy ratio, centroid frequency, and frequency entropy are extracted as key feature parameters. A leakage detection model based on Support Vector Machine (SVM) is subsequently developed, achieving an accuracy of 98.6% across a wide range of operating conditions. This research enhances the capability for high-accuracy leakage detection with limited field data, offering valuable technical insights for the development of low-computation and low-hardware-cost leakage detection systems.<\/jats:p>","DOI":"10.3390\/s25237281","type":"journal-article","created":{"date-parts":[[2025,12,5]],"date-time":"2025-12-05T17:35:17Z","timestamp":1764956117000},"page":"7281","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Mechanism of Pipeline Leakage Sound Generation and Leak Detection Technology Under Multiple Operational Conditions"],"prefix":"10.3390","volume":"25","author":[{"given":"Fei","family":"Chen","sequence":"first","affiliation":[{"name":"Fuqing Nuclear Power Co., Ltd., Sanshan Town, Fuqing 350318, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Taikeng","family":"Jiang","sequence":"additional","affiliation":[{"name":"Fuqing Nuclear Power Co., Ltd., Sanshan Town, Fuqing 350318, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Latao","family":"Jiang","sequence":"additional","affiliation":[{"name":"Fuqing Nuclear Power Co., Ltd., Sanshan Town, Fuqing 350318, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chen","family":"Rong","sequence":"additional","affiliation":[{"name":"College of Optoelectronic Engineering, Chongqing University, Chongqing 400044, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-0179-1716","authenticated-orcid":false,"given":"Xiaohang","family":"Li","sequence":"additional","affiliation":[{"name":"College of Physics and Electronic Engineering, Chongqing Normal University, Chongqing 401331, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liang","family":"Chen","sequence":"additional","affiliation":[{"name":"Xiamen Silicon Microelectronics Technology Co., Ltd., Unit 701, No. 370 Chengyi Avenue, Jimei District, Xiamen 361021, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xuefei","family":"Xu","sequence":"additional","affiliation":[{"name":"Xiamen Silicon Microelectronics Technology Co., Ltd., Unit 701, No. 370 Chengyi Avenue, Jimei District, Xiamen 361021, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jin","family":"Yang","sequence":"additional","affiliation":[{"name":"College of Optoelectronic Engineering, Chongqing University, Chongqing 400044, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,11,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"055012","DOI":"10.1088\/1361-6501\/ad2740","article-title":"Liquid-filled pipeline leak detection and localization based on multi-scale residual networks","volume":"35","author":"Zhao","year":"2024","journal-title":"Meas. Sci. Technol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"04022055","DOI":"10.1061\/(ASCE)PS.1949-1204.0000695","article-title":"Acoustic Model for Leak Detection of Water Supply Pipeline","volume":"14","author":"Zhang","year":"2023","journal-title":"J. Pipeline Syst. Eng. Pract."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"396","DOI":"10.1177\/00202940221088713","article-title":"Pipeline leak detection based on empirical mode decomposition and deep belief network","volume":"56","author":"Yan","year":"2023","journal-title":"Meas. Control"},{"key":"ref_4","unstructured":"Lin, W., Kong, L., and Wang, F. (2019, January 1\u20133). Non-intrusive acoustic detection method for pipeline leak of hazardous substances. Proceedings of the 2019 14th IEEE International Conference on Electronic Measurement & Instruments (ICEMI), Changsha, China."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"108021","DOI":"10.1016\/j.measurement.2020.108021","article-title":"Single-point location algorithm based on an acceleration sensor for pipeline leak detection","volume":"163","author":"Yin","year":"2020","journal-title":"Measurement"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"103249","DOI":"10.1016\/j.tust.2019.103249","article-title":"Leakage detection techniques for oil and gas pipelines: State-of-the-art","volume":"98","author":"Lu","year":"2020","journal-title":"Tunn. Undergr. Space Technol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"109906","DOI":"10.1016\/j.measurement.2021.109906","article-title":"Noise-Pressure interaction model for gas pipeline leakage detection and location","volume":"184","author":"Li","year":"2021","journal-title":"Measurement"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Kumar, D., Tu, D., Zhu, N., Shah, R.A., Hou, D., and Zhang, H. (2017). The Free-Swimming Device Leakage Detection in Plastic Water-filled Pipes through Tuning the Wavelet Transform to the Underwater Acoustic Signals. Water, 9.","DOI":"10.3390\/w9100731"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"100074","DOI":"10.1016\/j.jpse.2022.100074","article-title":"Review and analysis of pipeline leak detection methods","volume":"2","author":"Korlapati","year":"2022","journal-title":"J. Pipeline Sci. Eng."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"9600211","DOI":"10.1109\/TIM.2022.3150864","article-title":"Acoustic Leak Localization Method for Pipelines in High-Noise Environment Using Time-Frequency Signal Segmentation","volume":"71","author":"Kousiopoulos","year":"2022","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"107994","DOI":"10.1016\/j.ymssp.2021.107994","article-title":"A comprehensive review of acoustic based leak localization method in pressurized pipelines","volume":"161","author":"Hu","year":"2021","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"449","DOI":"10.1016\/j.jlp.2011.04.003","article-title":"Detection of small leakage from long transportation pipeline with complex noise","volume":"24","author":"Hu","year":"2011","journal-title":"J. Loss Prev. Process Ind."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Adegboye, M.A., Fung, W.-K., and Karnik, A. (2019). Recent Advances in Pipeline Monitoring and Oil Leakage Detection Technologies: Principles and Approaches. Sensors, 19.","DOI":"10.20944\/preprints201905.0041.v1"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"188","DOI":"10.1016\/j.measurement.2015.09.048","article-title":"Adaptive noise cancellation based on EMD in water-supply pipeline leak detection","volume":"79","author":"Guo","year":"2016","journal-title":"Measurement"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"426","DOI":"10.1016\/j.proeng.2015.08.904","article-title":"Contaminant Intrusion through Leaks in Water Distribution System: Experimental Analysis","volume":"119","author":"Fontanazza","year":"2015","journal-title":"Procedia Eng."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"104243","DOI":"10.1016\/j.ijpvp.2020.104243","article-title":"Machine learning supported acoustic emission technique for leakage detection in pipelines","volume":"188","author":"Banjara","year":"2020","journal-title":"Int. J. Press. Vessel. Pip."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1016\/j.jclepro.2018.07.127","article-title":"CFD analysis of natural gas emission from damaged pipelines: Correlation development for leakage estimation","volume":"199","author":"Arabkoohsar","year":"2018","journal-title":"J. Clean. Prod."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"71","DOI":"10.15764\/EH.2014.02009","article-title":"Theoretical analysis of natural-gas leakage in urban medium-pressure pipelines","volume":"1","author":"Lu","year":"2014","journal-title":"J. Environ. Hum."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1016\/j.compfluid.2015.12.002","article-title":"CFD modeling of transient flow in pressurized pipes","volume":"126","author":"Martins","year":"2016","journal-title":"Comput. Fluids"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"104159","DOI":"10.1016\/j.ijpvp.2020.104159","article-title":"Numerical study on the impact distance of a jet fire following the rupture of a natural gas pipeline","volume":"187","author":"Shan","year":"2020","journal-title":"Int. J. Press. Vessel. Pip."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"35527","DOI":"10.1016\/j.ijhydene.2022.08.122","article-title":"Numerical study on the flow characteristics of pressurized hydrogen leaking into the confined space through different shaped orifices","volume":"47","author":"Zhang","year":"2022","journal-title":"Int. J. Hydrogen Energy"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"106581","DOI":"10.1016\/j.petrol.2019.106581","article-title":"Gas pipeline leakage detection based on sensor fusion under model-based fault detection framework","volume":"184","author":"Doshmanziari","year":"2020","journal-title":"J. Pet. Sci. Eng."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1016\/j.compfluid.2015.06.023","article-title":"Computational fluid dynamic simulation of pressure perturbations generation for gas pipelines leakage","volume":"119","author":"Liu","year":"2015","journal-title":"Comput. Fluids"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"847","DOI":"10.3813\/AAA.918568","article-title":"Experimental Investigation into the Characteristics of In-Pipe Leak Noise in Plastic Water Filled Pipes","volume":"98","author":"Papastefanou","year":"2012","journal-title":"Acta Acust. United Acust."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1016\/j.apacoust.2017.01.022","article-title":"Study on acoustic, vibration and flow induced noise characteristics of T-shaped pipe with a square cross-section","volume":"120","author":"Mori","year":"2017","journal-title":"Appl. Acoust."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Shi, S., Jiang, B., Ludwig, S., Xu, L., Wang, H., Huang, Y., and Yan, F. (2023). Optimization for Pipeline Corrosion Sensor Placement in Oil-Water Two-Phase Flow Using CFD Simulations and Genetic Algorithm. Sensors, 23.","DOI":"10.3390\/s23177379"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"105529","DOI":"10.1016\/j.jlp.2024.105529","article-title":"Consequence analysis of accidental gas leak from storage tank group using LES method","volume":"94","author":"Zhang","year":"2025","journal-title":"J. Loss Prev. Process Ind."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Li, J., Wan, J., Liu, H., Yi, X., He, Y., Chen, K., and Zhao, X. (2023). Numerical Study of Casing Microleakage Flow Field Sensitivity and Acoustic Field Characteristics. Materials, 16.","DOI":"10.3390\/ma16010386"},{"key":"ref_29","first-page":"841","article-title":"Support-Vector-Machine Phase Classification of Downhole Leak Flows Based on Acoustic Signals","volume":"59","author":"Venna","year":"2018","journal-title":"Petrophysics"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"3850","DOI":"10.1080\/09593330.2022.2074320","article-title":"Leak detection in real water distribution networks based on acoustic emission and machine learning","volume":"44","author":"Fares","year":"2023","journal-title":"Environ. Technol."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/25\/23\/7281\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,5]],"date-time":"2025-12-05T17:39:07Z","timestamp":1764956347000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/25\/23\/7281"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,29]]},"references-count":30,"journal-issue":{"issue":"23","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["s25237281"],"URL":"https:\/\/doi.org\/10.3390\/s25237281","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,29]]}}}