摘要
天然气管道地理环境复杂、运行工况多变,以蒙特卡罗模拟为代表的不确定性仿真是目前腐蚀管道可靠性评价的主要方法。然而天然气管道高设计可靠度特性所带来的高次模拟问题,使蒙特卡罗模拟十分耗时。为解决这一问题,本文采用神经网络算法取代蒙特卡罗模拟的可靠性评价方法,建立管道基本参数与可靠度的非线性模型。针对目前神经网络算法应用过程中存在的先验信息与神经网络模型的融合问题,本文创新性地提出智能优化算法与神经网络算法相结合的方法。该方法能够将腐蚀管道可靠度变化规律融入到建模过程中。建立了从特征变量的选择、样本数据的生成与处理、神经网络模型构建及模型预测效果评价一体化计算流程。在多种工况下采用神经网络模型对管道结构可靠度进行预测,结果表明该模型能够在极短的时间内获得与蒙特卡罗模拟高度近似的评价结果。相比于传统的神经网络模型,该方法建立的模型在可靠度预测准确性及可靠度变化规律的反映能力方面均有大幅度提高。
Natural gas pipelines have complex geographical environments and varied operating conditions.Uncertainty simulation represented by Monte Carlo methods has become the main method for pipeline corrosion reliability assessment.However,the high-order simulation problems caused by the high design reliability of natural gas pipelines make Monte Carlo simulations very time-consuming.In order to solve this problem,this paper uses a neural network algorithm rather than Monte Carlo simulation to establish a nonlinear model of basic pipeline parameters and reliability.Because of the difficulty of combining prior information in the modeling process,this paper proposes an innovative method that combines an intelligent optimization algorithm and a neural network algorithm.This method can incorporate the pipe corrosion reliability variation into the modeling process.An integrated computational flow from the selection of feature variables,the generation and processing of sample data,the construction of neural network models and the evaluation of model prediction effects are proposed.Under various working conditions,the neural network model constructed by the method proposed in this paper predicts the reliability of pipeline structures.The results show that the model can obtain the calculation results highly similar to Monte Carlo simulation in a very short time.Compared with the traditional neural network model,the model established by this method has greatly improved the reliability of prediction and the ability to reflect changes in reliability.
作者
何蕾
温凯
吴长春
宫敬
HE Lei;WEN Kai;WU Changchun;GONG Jing(College of Mechanical and Transportation Engineering,China University of Petroleum-Beijing,Beijing 102249,China)
出处
《石油科学通报》
2019年第3期310-322,共13页
Petroleum Science Bulletin
基金
国家自然科学基金青年基金资助项目“基于状态空间模型的天然气管网瞬态优化控制研究”(51504271)资助
关键词
腐蚀天然气管道
可靠性
人工神经网络建模方法改进
模拟退火算法
拉丁超立方抽样
遗传算法
corroded gas pipelines
reliability
artificial neural network modeling method improvement
simulated annealing algorithm
Latin hypercube sampling
genetic algorithm
