内腐蚀海底管道剩余强度的FOA-GRNN模型

doi:10.16265/j.cnki.issn1003-3033.2020.06.012

中国安全科学学报 ›› 2020, Vol. 30 ›› Issue (6): 78-83.doi: 10.16265/j.cnki.issn1003-3033.2020.06.012

内腐蚀海底管道剩余强度的FOA-GRNN模型

毕傲睿^1,2 讲师, 骆正山² 教授, 宋莹莹², 张新生² 教授

1.淮阴工学院管理工程学院,江苏淮安 223003;
2.西安建筑科技大学管理学院,陕西西安 710055

收稿日期:2020-03-16 修回日期:2020-05-15 出版日期:2020-06-28 发布日期:2021-01-28
作者简介:毕傲睿(1987—),男,江苏淮安人,博士研究生,讲师,研究方向为油气工程风险管理。E-mail:bar126@163.com。
基金资助:
国家自然科学基金资助(41877527);陕西省社科基金资助(2018S34)。

Residual strength analysis of internally corroded submarine pipeline based on FOA-GRNN model

BI Aorui^1,2, LUO Zhengshan², SONG Yingying², ZHANG Xinsheng²

1. School of Management Engineering, Huaiyin Institute of Technology, Huai'an Jiangsu 223003, China;
2. School of Management, Xi'an University of Architecture & Technology, Xi'an Shaanxi 710055, China

Received:2020-03-16 Revised:2020-05-15 Online:2020-06-28 Published:2021-01-28

摘要/Abstract

摘要： 为探究内腐蚀海底管道剩余强度,保证管道安全运营,基于管道壁厚、直径,腐蚀深度、长度、宽度和极限抗拉强度等影响因素,提出果蝇优化算法(FOA)优化广义回归神经网络(GRNN)的剩余强度计算方法,应用GRNN构建剩余强度预测模型;采用FOA优化模型,人为设置光滑因子的负面影响;通过有限元模拟生成影响因素和剩余强度数据库,并采用FOA-GRNN模型训练和预测;以巴西国家石油研究中心的极限强度爆破试验数据为例,分析验证预测模型。结果表明:FOA-GRNN模型对有限元模拟数据的剩余强度预测平均相对误差(ARE)为16.53%,对试验数据预测ARE为7.81%,预测结果合理、准确。

关键词: 内腐蚀海底管道, 剩余强度, 果蝇优化算法(FOA), 广义回归神经网络(GRNN), 有限元

Abstract: In order to explore residual strength of submarine pipelines with internal corrosion, and provide reference for maintenance so as to ensure safe operation, a FOA-GRNN calculation method of residual strength was proposed and a prediction model was constructed by using GRNN based on influencing factors like wall thickness, diameter, corrosion depth, length, width and ultimate tensile strength. Then, FOA was used to optimize the model, and negative influence of smooth factors were set artificially. Influencing factors and residual strength database were simulated and generated by finite element method, and trained and predicted through FOA-GRNN model. Finally, with experimental data of pipeline ultimate strength blasting from PETROBRAS Research Institute as an example, the prediction model was verified. The results show that average relative error of FOA-GRNN model is 16.53% for residual strength prediction of finite element simulation data, and 7.81% for experimental data prediction, which are reasonable and accurate.

Key words: internally corroded submarine pipeline, residual strength, fruit fly optimization algorithm (FOA), generalized regression neural network (GRNN), finite element method

中图分类号:

X937

毕傲睿, 骆正山, 宋莹莹, 张新生. 内腐蚀海底管道剩余强度的FOA-GRNN模型[J]. 中国安全科学学报, 2020, 30(6): 78-83.

BI Aorui, LUO Zhengshan, SONG Yingying, ZHANG Xinsheng. Residual strength analysis of internally corroded submarine pipeline based on FOA-GRNN model[J]. China Safety Science Journal, 2020, 30(6): 78-83.

参考文献

[1] 狄彦, 帅健, 王晓霖, 等, 油气管道事故原因分析及分类方法研究[J].中国安全科学学报, 2013, 23(7): 109-115.
DI Yan, SHUAI Jian, WANG Xiaolin, et al. Study on methods for classifying oil ＆ gas pipeline incidents[J]. Chnia Safety Science Journal, 2013, 23(7): 109-115.
[2] 李新宏, 朱红卫, 陈国明, 等. 海底油气管道泄漏事故风险分析的贝叶斯动态模型[J].中国安全科学学报, 2015,25(4): 75-80.
LI Xinhong, ZHU Hongwei, CHEN Guoming, et al. Bayesian dynamic model for analyzing risk of meeting submarine oil & gas pipeline leakage accident[J].Chnia Safety Science Journal, 2015,35(4): 75-80.
[3] GOGOI B K, DUTTA N N, GOSWAMI P, et al. A case study of bioremediation of petroleum-hydrocarbon contaminated soil at a crude oil spill site[J]. Advances in Environmental Research, 2003,7(4): 767-782.
[4] ASME B31-1984, Manual for determining the remaining strength of corroded pipelines[S].
[5] ASME B31-1991, Manual for determining the remaining strength of corroded pipelines: a supplement to ASME B31 code for pressure piping[S].
[6] DNV RP F101-1999, Recommended practice corroded pipeline[S].
[7] STEPHENS D R, LEIS B N. Development of an alternative criterion for residual strength of corrosion defects in moderate to high toughness pipe[C]. Proceedings of International Pipeline Conference, 2000: 781-792.
[8] 帅健, 张春娥, 陈福来. 腐蚀管道剩余强度评价方法的对比研究[J].天然气工业,2006,26(11): 122-125.
SHUAI Jian, ZHANG Chun'e, CHEN Fulai. Comparison study on assessment methods for remaining strength of corroded pipeline[J]. Natural Gas Industry, 2006, 26(11): 122-125.
[9] 赵鹏程, 帅健, 唐雨, 等. 轴向应力对腐蚀缺陷管道极限内压的影响[J].中国安全科学学报, 2019, 29(3): 70-75.
ZHAO Pengcheng, SHUAI Jian, TANG Yu, et al. Impact of axial stress on ultimate internal pressure of corroded pipelines[J]. Chnia Safety Science Journal, 2019, 29(3): 70-75.
[10] 崔铭伟, 曹学文, 封子艳, 等. 局部腐蚀油气管道失效压力计算方法[J]. 中国石油大学学报:自然科学版, 2013, 37(6):123-128,134.
CUI Mingwei, CAO Xuewen, FENG Ziyan, et al. Calculation method of oil-gas pipeline failure pressure with localized corrosion[J]. Journal of China University of Petroleum:Edition of Social Sciences, 2013, 37(6):123-128,134.
[11] 焦李成, 杨淑媛, 刘芳, 等. 神经网络七十年:回顾与展望[J]. 计算机学报, 2016, 39(8): 1 697-1 716.
JIAO Licheng, YANG Shuyuan, LIU Fang, et al. Seventy years beyond neural networks: retrospect and prospect[J]. Chinese Journal of Computers, 2016, 39(8): 1 697-1 716.
[12] SPECHT D F. A general regression neural network[J]. IEEE Transactions on Neural Networks, 1991, 2(6): 568-576.
[13] 潘文超.果蝇最佳化演算法[M].台北:沧海书局, 2011: 4-10.
[14] 郑晓龙,王凌,王圣尧. 求解置换流水线调度问题的混合离散果蝇算法[J].控制理论与应用, 2014, 31(2): 159-164.
ZHENG Xiaolong, WANG Ling, WANG Shengyao. A hybrid discrete fruit fly optimization algorithm for solving permutation flow-shop scheduling problem[J].Control Theory and Applications, 2014, 31(2): 159-164.
[15] OSGOOD W R, RAMBERG W. Description of stress-strain curves by three parameters[R]. National Advisory Committee for Aeronautics, 1943.
[16] FU B, KIRKWOOD M G. Predicting failure pressure of internally corroded linepipe using the finite element method[C]. ASME OMAE 13^th International Conference of Mechanic Arctic Engineering, 1995: 175-184.
[17] FREIRE J L F, VIEIRA R D, CASTRO J T P, et al. Part 3: burst tests of pipeline with extensive longitudinal metal loss[J]. Experimental Techniques, 2006,30(6): 60-65.

内腐蚀海底管道剩余强度的FOA-GRNN模型

Residual strength analysis of internally corroded submarine pipeline based on FOA-GRNN model

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价

[1]	郑度奎, 程远鹏, 李昊燃, 何天隆. IAFSA-GRNN在油田集输管道CO₂腐蚀速率预测中的应用^*[J]. 中国安全科学学报, 2022, 32(1): 110-117.
[2]	张凯, 张科, 李昆. 主元分析-神经网络岩爆等级预测模型[J]. 中国安全科学学报, 2021, 31(3): 96-104.
[3]	方永, 夏成宇, 何宇航, 王旭东, 陈锟, 黄壮. 含体积缺陷隔水管安全区间研究[J]. 中国安全科学学报, 2020, 30(7): 101-106.
[4]	徐乾, 杨超, 郭鸿, 郭光玲. 损伤状态下某桩板式挡土墙稳定性预警[J]. 中国安全科学学报, 2020, 30(2): 47-53.
[5]	贡力, 康春涛, 王鸿, 杨轶群, 王忠慧. 盐冻耦合侵蚀作用下寒旱地区渡槽劣化机制[J]. 中国安全科学学报, 2020, 30(11): 43-52.
[6]	何光辉, 李晓伟, 李鑫奎, 沈志勇. 基于有限元与物联网的塔式起重机安全监测系统[J]. 中国安全科学学报, 2020, 30(11): 88-94.
[7]	杨志军, 张振龙, 吴忠义, 刘玉琢, 于永亮. 储罐壁板缺陷的漏磁场信号研究[J]. 中国安全科学学报, 2019, 29(5): 67-72.
[8]	支治, 郭世永. 护栏薄壁长杆贯穿胸部的损伤研究[J]. 中国安全科学学报, 2019, 29(4): 25-30.
[9]	沈小燕, 张凡, 吕卉焘, 闫艳, 刘浩学. 追尾碰撞下液罐车罐体变形失效仿真研究[J]. 中国安全科学学报, 2019, 29(4): 37-42.
[10]	彭聪, 张道文, 杨苓, 刘卓异. 偏置碰撞中轿车左后排乘员损伤的研究[J]. 中国安全科学学报, 2019, 29(3): 21-26.
[11]	赵鹏程, 帅健, 唐雨, 许葵. 轴向应力对腐蚀缺陷管道极限内压的影响[J]. 中国安全科学学报, 2019, 29(3): 70-75.
[12]	殷鹰, 蓝朝逊, 李俊, 毛丹, 史君林. 埋地PE燃气管道地质沉降破坏数值分析[J]. 中国安全科学学报, 2019, 29(10): 18-23.
[13]	李坤元, 刘佳亮. 磨料水射流多维参数对混凝土致裂性能影响特征[J]. 中国安全科学学报, 2019, 29(10): 110-116.
[14]	谢金玲, 张文庆, 谢春音. 某种轨检车构架强度及疲劳安全性能评估^*[J]. 中国安全科学学报, 2018, 28(S2): 143-148.
[15]	石磊, 帅健, 王晓霖, 许葵. 地基沉降对石油储罐象足屈曲的影响[J]. 中国安全科学学报, 2018, 28(9): 56-61.