{"title":"Leakage Prediction of a Bolted-Flange-Joint Subjected to Axial Cyclic Loading Based on Magnetic Measurement Method","authors":"Y. Wang, J. Fan, H. Jiang","doi":"10.1007/s40799-025-00779-4","DOIUrl":null,"url":null,"abstract":"<div><p>In oil and gas production operations, complex and variable cyclic external loads can trigger bolt loosening, leading to flange leakage, blowout, and other severe accidents. In-service flange leak monitoring is challenging owing to the limited working conditions and equipment integration costs. In addition, most current seal evaluation methods are not applicable to verification in vibrational working conditions. This study proposes a method for predicting the leakage rate of bolted flange joints based on the degree of bolt loosening under dynamic load conditions, which can directly monitor and evaluate the sealing status of flanges in service in real time. This method employs a low-cost, highly stress-sensitive, and stable magnetic measurement method to determine the change in the loosening index of the bolt, analyse its influence on the contact stress of the gasket by finite-element simulation, and propose a leakage prediction model based on critical contact mechanical parameters by simulating contact with a rough surface. The method was verified by a bolt loosening and flange leakage monitoring test under axial cyclic loadings, and provides a new method for evaluating the sealing performance of in-service flanges.</p></div>","PeriodicalId":553,"journal":{"name":"Experimental Techniques","volume":"49 4","pages":"727 - 741"},"PeriodicalIF":1.9000,"publicationDate":"2025-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Experimental Techniques","FirstCategoryId":"5","ListUrlMain":"https://link.springer.com/article/10.1007/s40799-025-00779-4","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENGINEERING, MECHANICAL","Score":null,"Total":0}
引用次数: 0
Abstract
In oil and gas production operations, complex and variable cyclic external loads can trigger bolt loosening, leading to flange leakage, blowout, and other severe accidents. In-service flange leak monitoring is challenging owing to the limited working conditions and equipment integration costs. In addition, most current seal evaluation methods are not applicable to verification in vibrational working conditions. This study proposes a method for predicting the leakage rate of bolted flange joints based on the degree of bolt loosening under dynamic load conditions, which can directly monitor and evaluate the sealing status of flanges in service in real time. This method employs a low-cost, highly stress-sensitive, and stable magnetic measurement method to determine the change in the loosening index of the bolt, analyse its influence on the contact stress of the gasket by finite-element simulation, and propose a leakage prediction model based on critical contact mechanical parameters by simulating contact with a rough surface. The method was verified by a bolt loosening and flange leakage monitoring test under axial cyclic loadings, and provides a new method for evaluating the sealing performance of in-service flanges.
期刊介绍:
Experimental Techniques is a bimonthly interdisciplinary publication of the Society for Experimental Mechanics focusing on the development, application and tutorial of experimental mechanics techniques.
The purpose for Experimental Techniques is to promote pedagogical, technical and practical advancements in experimental mechanics while supporting the Society''s mission and commitment to interdisciplinary application, research and development, education, and active promotion of experimental methods to:
- Increase the knowledge of physical phenomena
- Further the understanding of the behavior of materials, structures, and systems
- Provide the necessary physical observations necessary to improve and assess new analytical and computational approaches.