海底油气管道腐蚀速率及剩余强度预测研究

海底油气管道腐蚀速率及剩余强度预测研究摘要海底油气管道作为连接海洋油气资源与陆地市场的重要纽带，其安全性对经济发展和能源战略具有深远影响。海底管道的腐蚀问题一直是管道工程中的一个技术难题，它不仅会降低管道的使用寿命，还可能引发严重的环境污染和安全事故。因此，开展海底油气管道腐蚀速率和剩余强度的预测研究，对于管道的安全具有重要的意义。本研究在现有预测方法研究上结合了管道腐蚀的原理，通过文献调研和数据分析，收集了影响海底油气管道腐蚀的多种环境因素，包括海水的PH值、温度、压力、流速共9个影响腐蚀速率的因素，利用Z-scoe标准化将已有数据集进行标准化处理，利用经验模态分解(EMD)算法对标准化数据集中9个影响因素进行分解，提取出固有模态函数(MFs)与剩余变量共43个特征，并通过主成分分析(P℃A)降维技术提取分解后的6个主要特征，将6个主要特征数据集作为长短期记忆网络(LSTM)模型的数据输入，利用适应性动量估计算法(Adam)进行训练，并经过参数调优，最终得到模型的RMSE为0.099681，模型误差小从而实现了对腐蚀速率的高精度预测：利用COMSOL软件进行了海底油气管道腐蚀后的电化学腐蚀模拟，选取腐蚀电流密度较大的第4年进行分析，通过构建流-固耦合物理场分析了不同腐蚀深度、腐蚀长度、腐蚀宽度对管道应力分布的影响。由于LSTM模型对腐蚀速率具有高精度的预测，于是将LSTM网络应用于Von Mises应力预测，在优化不同腐蚀参数下的学习率和隐藏单元数量等模型参数后，得到模型R分别为0.97468、0.85552、0.89427，模型拟合效果好实现了对管道腐蚀后剩余强度的准确预测。关键词海底油气管道；腐蚀速率预测；剩余强度：长短期记忆网络(LSTM):on Mises应力-I.Research on the Corrosion Rate and RemainingStrength Prediction of Submarine Oil and GasPipelinesAbstractSubmarine oil and gas pipelines serve as vital links connecting offshore oiland gas resources with the onshore market,and their safety has profoundimplications for economic development and energy strategy.The issue ofpipeline corrosion has always been a technical challenge in pipeline engineering.It not only reduces the service life of the pipeline but may also lead to severeenvironmental pollution and safety accidents.Therefore,conducting research onthe prediction of corrosion rates and residual strength of submarine oil and gaspipelines is of significant importance for pipeline safety.This study,building upon existing prediction methods and combining theprinciples of pipeline corrosion,collected various environmental factorsaffecting the corrosion of submarine oil and gas pipelines through literaturereview and data analysis.These factors include the pH value,temperature,pressure,and flow rate of seawater,totaling nine factors that affect the corrosionrate.The existing dataset was standardized using the Z-score normalizationmethod.The Empirical Mode Decomposition (EMD)algorithm was used todecompose the standardized dataset of nine influencing factors,extracting 43characteristics including Intrinsic Mode Functions(IMFs)and residual variables.Principal Component Analysis (PCA)was then applied as a dimensionalityreduction technique to extract six main features from the decomposed data.These six main features were used as the input data for the Long Short-Term-Ⅱ-Memory (LSTM)network model.The model was trained using the AdaptiveMoment Estimation (Adam)algorithm,and after parameter tuning,the finalmodel achieved an RMSE of 0.099681,indicating a small model error and thushigh-precision prediction of the corrosion rate.The COMSOL software wasutilized to simulate the electrochemical corrosion of the submarine oil and gaspipelines after corrosion.The fourth year,with a larger corrosion current density,was selected for analysis.A fluid-solid coupling physical field was constructedto analyze the impact of different corrosion depths,lengths,and widths on thestress distribution of the pipeline.Due to the high precision