Research on a Simulation Platform for Typical Internal Corrosion Defects in Natural Gas Pipelines Based on Big Data Analysis

The accuracy and reliability of non-destructive testing (NDT) approaches in detecting interior corrosion problems are critical, yet research in this field is limited. This work describes a novel way to monitor the structural integrity of steel gas pipelines that uses advanced numerical modeling techniques to anticipate fracture development and corrosion effects. The objective is to increase pipeline dependability and safety through more precise, real-time health evaluations. Compared to previous approaches, our solution provides higher accuracy in fault detection and quantification, making it ideal for pipeline integrity monitoring in real-world applications. To solve this issue, statistical analysis was conducted on the size and directional distribution features of about 380,000 sets of internal corrosion faults, as well as simulations of erosion and wear patterns on bent pipes. Using real defect morphologies, we developed a modeling framework for typical interior corrosion flaws. We evaluated and validated the applicability and effectiveness of in-service inspection processes, as well as conducted on-site comparison tests. The results show that (1) the length and width of corrosion defects follow a log-normal distribution, the clock orientation follows a normal distribution, and the peak depth follows a Freundlich EX function distribution pattern; (2) pipeline corrosion defect data can be classified into three classes using the K-means clustering algorithm, allowing rapid and convenient acquisition of typical size and orientation characteristics of internal corrosion defects; (3) the applicability range and boundary conditions of various NDT techniques were verified, establishing comprehensive selection principles for internal corrosion defect detection technology; (4) on-site inspection results showed a 31% The simulation and validation platform for typical interior corrosion issues greatly enhances the accuracy and reliability of detection data.