基于机器学习算法的油嘴冲蚀磨损预测  

Prediction of Nozzle Erosion Wear Based on Machine Learning Algorithm

作　　者：李若雯 刘少胡[1] 徐泽庆 王锁男 LI Ruo-wen;LIU Shao-hu;XU Ze-qing;WANG Suo-nan(School of Mechanical Engineering,Yangtze University,Jingzhou 434023,China)

机构地区：[1]长江大学机械工程学院,荆州434023

出　　处：《科学技术与工程》2025年第11期4526-4533,共8页Science Technology and Engineering

基　　金：国家自然科学基金(52374002,51974036);湖北省高等学校优秀中青年科技创新团队计划(T2021035)。

摘　　要：压裂后返排高速液体携带固体颗粒对油嘴造成严重冲蚀,难以保证油嘴稳定运行。针对油嘴冲蚀严重的问题,采用数值模拟的方法对油嘴冲蚀磨损进行研究,分析了含砂比、砂粒直径、砂粒密度、泵排量和液体黏度对油嘴冲蚀磨损的影响规律。研究表明:含砂比和液体黏度增大时,最大冲蚀率呈线性增长;砂粒密度和泵排量的增大时,最大冲蚀率呈指数增长;砂粒直径增大时,最大冲蚀率呈指数降低。采用正交试验法判断各个因素的显著性,影响油嘴冲蚀磨损的因素依次为:含沙比>泵排量>砂粒密度>砂粒直径>液体黏度。基于数值模拟的结果,采用机器学习的方法,对比分析支持向量机回归(support vector regression, SVR)、卷积神经网络(convolutional neural network, CNN)、BP(back propagation)神经网络神经网络和随机森林回归(random forest regression, RFR)算法分别进行油嘴冲蚀磨损结果预测。优选SVR算法,采用粒子群算法对预测模型进行优化,得到较优油嘴冲蚀预测模型。After fracturing,the solid particles carried by the high speed liquid will cause serious erosion to the oil nozzle,and it is difficult to ensure the stable operation of the oil nozzle.To address the serious erosion problem of the nozzle,numerical simulation was employed to study the erosion wear of the nozzle,and the influence patterns of sand content,sand grain diameter,sand grain density,pump displacement,and liquid viscosity on the erosion wear of the nozzle were analyzed.The research indicates that:when the sand content and liquid viscosity increase,the maximum erosion rate exhibits linear growth;when the sand grain density and pump displacement increase,the maximum erosion rate exhibits exponential growth;and when the sand grain diameter increases,the maximum erosion rate shows exponential decrease.The orthogonal test method is used to judge the significance of each factor.The factors affecting the erosion wear of the nozzle are as follows:sand content ratio>pump displacement>sand density>sand diameter>liquid viscosity.Based on the results of numerical simulation,the machine learning method is used to compare and analyze SVR(support vector regression),CNN(convolutional neural network),BP(back propagation)neural network and RFR(random forest regression)algorithm to predict the erosion wear results of oil nozzle respectively.By preferring the SVR algorithm and adopting the particle swarm optimization algorithm to optimize the prediction model,a better nozzle erosion prediction model is obtained.

关 键 词：油嘴 冲蚀磨损 正交试验 模型优化 机器学习 

分 类 号：TE931[石油与天然气工程—石油机械设备]