Identification of Pipeline Inner Wall Geometry Based on the POD-RBF Method

YU Bo; TAO Yingying

doi:10.21656/1000-0887.430168

Volume 44 Issue 4

Apr. 2023

Turn off MathJax

Article Contents

Article Navigation > Applied Mathematics and Mechanics > 2023 > 44(4): 406-418

YU Bo, TAO Yingying. Identification of Pipeline Inner Wall Geometry Based on the POD-RBF Method[J]. Applied Mathematics and Mechanics, 2023, 44(4): 406-418. doi: 10.21656/1000-0887.430168

Citation:

YU Bo, TAO Yingying. Identification of Pipeline Inner Wall Geometry Based on the POD-RBF Method[J]. Applied Mathematics and Mechanics, 2023, 44(4): 406-418. doi: 10.21656/1000-0887.430168

Citation:

YU Bo, TAO Yingying. Identification of Pipeline Inner Wall Geometry Based on the POD-RBF Method[J]. Applied Mathematics and Mechanics, 2023, 44(4): 406-418. doi: 10.21656/1000-0887.430168

PDF( 5427 KB)

Identification of Pipeline Inner Wall Geometry Based on the POD-RBF Method

doi: 10.21656/1000-0887.430168

YU Bo^{1, 2
,
,},
TAO Yingying¹

1.
Department of Engineering Mechanics, School of Civil Engineering, Hefei University of Technology, Hefei 230009, P.R.China
2.
State Key Laboratory of Structural Analysis for Industrial Equipment, Dalian University of Technology, Dalian, Liaoning 116024, P.R.China

Received Date: 2022-05-18
Rev Recd Date: 2022-10-04
Publish Date: 2023-04-01

Abstract

Abstract

Based on the proper orthogonal decomposition-radial basis function (POD-RBF), a geometric identification method for pipeline inner wall was proposed to solve the internal corrosion detection problem of natural gas and oil pipelines. In view of the static magnetic field, the simplified finite element model for the pipelines was established, and the variable-geometry sample library was constructed, to realize the response prediction of arbitrary geometry by the POD-RBF. The proposed method achieves reduced-order analysis and avoids repeated solution of the stiffness matrix due to the geometrical change during the identification process. Hence, it can significantly improve the computation efficiency. Finally, the grey wolf optimization (GWO) algorithm was used to optimize the objective function and avoid the calculation of the sensitivity in the process of geometry change. The numerical examples show that, the proposed method has high efficiency and accuracy in the geometric identification of the pipeline inner wall, with good stability even under introduced noises.

FullText(HTML)

References(44)

References

[1]	丁舒婷, 唐建群, 巩建鸣. 含腐蚀缺陷油气管道适用性评价研究进展[J]. 机械制造与自动化, 2017, 46 (1): 226-229. https://www.cnki.com.cn/Article/CJFDTOTAL-ZZHD201701061.htm DING Shuting, TANG Jianqun, GONG Jianming. Overview of service ability assessment of oil and gas pipelines containing corrosives[J]. Machine Building & Automation, 2017, 46 (1): 226-229. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-ZZHD201701061.htm
[2]	苑玮琦, 李绍丽, 李德健. 基于子区域变尺度高斯拟合的木材表面缺陷识别[J]. 仪器仪表学报, 2016, 37 (4): 879-886. doi: 10.3969/j.issn.0254-3087.2016.04.021 YUAN Weiqi, LI Shaoli, LI Dejian. Wood surface defect recognition based on sub-region zoom Gaussian fitting[J]. Chinese Journal of Scientific Instrument, 2016, 37 (4): 879-886. (in Chinese) doi: 10.3969/j.issn.0254-3087.2016.04.021
[3]	孙彬, 王建华, 赫东锋, 等. 基于激光测量的航发叶片表面几何缺陷识别技术[J]. 自动化学报, 2020, 46 (3): 594-599. doi: 10.16383/j.aas.c180080 SUN Bin, WANG Jianhua, HE Dongfeng, et al. Identification of aero-engine blade surface geometric defects with laser measurement[J]. Acta Automatica Sinica, 2020, 46 (3): 594-599. (in Chinese) doi: 10.16383/j.aas.c180080
[4]	YU B, XU C, ZHOU H L, et al. A novel non-iterative method for estimating boundary conditions and geometry of furnace inner wall made of FGMs[J]. Applied Thermal Engineering, 2019, 147: 251-271. doi: 10.1016/j.applthermaleng.2018.10.075
[5]	KIM H M, PARK G S. A study on the estimation of the shapes of axially oriented cracks in CMFL type NDT system[J]. IEEE Transactions on Magnetics, 2014, 50 (2): 109-112. doi: 10.1109/TMAG.2013.2283343
[6]	VALLS M J, ULAPANE N, SHI L, et al. Robotic pipeline wall thickness evaluation for dense nondestructive testing inspection[J]. Journal of Field Robotics, 2018, 35 (8): 1293-1310. doi: 10.1002/rob.21828
[7]	PENG X, ANYAOHA U, LIU Z, et al. Analysis of magnetic-flux leakage (MFL) data for pipeline corrosion assessment[J]. IEEE Transactions on Magnetics, 2020, 56 (6): 1-15. doi: 10.1109/TMAG.2020.2994477
[8]	FISER R, FERKOLJ S. Application of a finite element method to predict damaged induction motor performance[J]. IEEE Transactions on Magnetics, 2001, 37 (5): 3635-3639. doi: 10.1109/20.952679
[9]	QUÉVAL L, LIU K, YANG W J, et al. Superconducting magnetic bearings simulation using an H-formulation finite element model[J]. Superconductor Science and Technology, 2018, 31 (8): 084001. doi: 10.1088/1361-6668/aac55d
[10]	MOURAD A, AISSA A, MEBAREK-OUDINA F, et al. Galerkin finite element analysis of thermal aspects of Fe₃O₄-MWCNT/water hybrid nanofluid filled in wavy enclosure with uniform magnetic field effect[J]. International Communications in Heat and Mass Transfer, 2021, 126: 105461. doi: 10.1016/j.icheatmasstransfer.2021.105461
[11]	BONABEAU E, DORIGO M, THERAULAZ G. Swarm Intelligence: From Natural to Artificial Systems[M]. Oxford: Oxford University Press, 1999.
[12]	GANDOMI A H, YANG X S, ALAVI A H. Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems[J]. Engineering With Computers, 2013, 29 (1): 17-35. doi: 10.1007/s00366-011-0241-y
[13]	DORIGO M, BIRATTARI M, STUTZLE T. Ant colony optimization[J]. IEEE Computational Intelligence Magazine, 2006, 1 (4): 28-39. doi: 10.1109/MCI.2006.329691
[14]	金树, 任宗栋, 李宏男, 等. 输电塔结构离散变量优化设计方法[J]. 工程力学, 2016, 33 (11): 84-94. doi: 10.6052/j.issn.1000-4750.2015.02.0102 JIN Shu, REN Zongdong, LI Hongnan, et al. Discrete variable optimal design method of transmission tower structure[J]. Engineering Mechanics, 2016, 33 (11): 84-94. (in Chinese) doi: 10.6052/j.issn.1000-4750.2015.02.0102
[15]	KENNEDY J, EBERHART R. Particle swarm optimization[C]//Proceedings of ICNN' 95 -International Conference on Neural Networks. Perth, Australia, 1995, 4: 1942-1948.
[16]	夏志远, 李爱群, 李建慧, 等. 基于GMPSO的有限元模型修正方法验证[J]. 工程力学, 2019, 36 (10): 66-74. https://www.cnki.com.cn/Article/CJFDTOTAL-GCLX201910009.htm XIA Zhiyuan, LI Aiqun, LI Jianhui, et al. Validation of finite element model updating methodology based on GMPSO[J]. Engineering Mechanics, 2019, 36 (10): 66-74. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-GCLX201910009.htm
[17]	MIRJALILI S, MIRJALILI S M, LEWIS A. Grey wolf optimizer[J]. Advances in Engineering Software, 2014, 69: 46-61. doi: 10.1016/j.advengsoft.2013.12.007
[18]	余波, 孙文涧. 基于比例边界有限元法和灰狼优化算法的裂纹尖端位置识别[J]. 应用数学和力学, 2021, 42 (11): 1177-1189. doi: 10.21656/1000-0887.410381 YU Bo, SUN Wenjian. Identification of crack tip positions based on the scaled boundary finite element method and the grey wolf optimization algorithm[J]. Applied Mathematics and Mechanics, 2021, 42 (11): 1177-1189. (in Chinese) doi: 10.21656/1000-0887.410381
[19]	KOHLI M, ARORA S. Chaotic grey wolf optimization algorithm for constrained optimization problems[J]. Journal of Computational Design and Engineering, 2018, 5 (4): 458-472. doi: 10.1016/j.jcde.2017.02.005
[20]	张晓凤, 王秀英. 灰狼优化算法研究综述[J]. 计算机科学, 2019, 46 (3): 30-38. https://www.cnki.com.cn/Article/CJFDTOTAL-JSJA201903004.htm ZHANG Xiaofeng, WANG Xiuying. Comprehensive review of grey wolf optimization algorithm[J]. Computer Science, 2019, 46 (3): 30-38. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-JSJA201903004.htm
[21]	蒋耀林. 模型降阶方法[M]. 北京: 科学出版社, 2010. JIANG Yaolin. Model Order Reduction Method[M]. Beijing: Science Press, 2010. (in Chinese)
[22]	HELLSTRÖM L H O, SMITS A J. Structure identification in pipe flow using proper orthogonal decomposition[J]. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 2017, 375 (2089): 20160086. doi: 10.1098/rsta.2016.0086
[23]	EFTEKHAR AZAM S, RAGEH A, LINZELL D. Damage detection in structural systems utilizing artificial neural networks and proper orthogonal decomposition[J]. Structural Control and Health Monitoring, 2019, 26 (2): e2288. doi: 10.1002/stc.2288
[24]	BOX G E P, WILSON K B. On the Experimental Attainment of Optimum Conditions[M]. New York: Springer, 1992.
[25]	KLEIJNEN J P C. Regression and Kriging metamodels with their experimental designs in simulation: a review[J]. European Journal of Operational Research, 2017, 256 (1): 1-16. doi: 10.1016/j.ejor.2016.06.041
[26]	STEIN M L. Interpolation of Spatial Data: Some Theory for Kriging[M]. New York: Springer, 1999.
[27]	XIAO M, ZHANG J H, GAO L. A system active learning Kriging method for system reliability-based design optimization with a multiple response model[J]. Reliability Engineering & System Safety, 2020, 199: 106935.
[28]	BUHMANN M D. Radial Basis Functions: Theory and Implementations[M]. Cambridge: Cambridge University Press, 2003.
[29]	SONG X G, LV L Y, SUN W, et al. A radial basis function-based multi-fidelity surrogate model: exploring correlation between high-fidelity and low-fidelity models[J]. Structural and Multidisciplinary Optimization, 2019, 60 (3): 965-981. doi: 10.1007/s00158-019-02248-0
[30]	潘兆东, 刘良坤, 谭平, 等. 大型结构自适应学习率RBF神经网络滑模分散控制研究[J]. 工程力学, 2019, 36 (9): 120-127. https://www.cnki.com.cn/Article/CJFDTOTAL-GCLX201909015.htm PAN Zhaodong, LIU Liangkun, TAN Ping, et al. Research on sliding mode decentralized control based on adaptive learning rate RBF neural network for large-scale engineering structures[J]. Engineering Mechanics, 2019, 36 (9): 120-127. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-GCLX201909015.htm
[31]	VANLUCHENE R D, SUN R. Neural networks in structural engineering[J]. Computer-Aided Civil and Infrastructure Engineering, 1990, 5 (3): 207-215. doi: 10.1111/j.1467-8667.1990.tb00377.x
[32]	HAN X, XIANG H Y, LI Y L, et al. Predictions of vertical train-bridge response using artificial neural network-based surrogate model[J]. Advances in Structural Engineering, 2019, 22 (12): 2712-2723. doi: 10.1177/1369433219849809
[33]	YU B, CAO G Y, REN S H, et al. An isogeometric boundary element method for transient heat transfer problems in inhomogeneous materials and the non-iterative inversion of loads[J]. Applied Thermal Engineering, 2022, 212: 118600. doi: 10.1016/j.applthermaleng.2022.118600
[34]	余波, 吴月, 聂川宝, 等. 动载荷识别的非迭代法研究[J]. 应用数学和力学, 2019, 40 (5): 473-489. doi: 10.21656/1000-0887.390211 YU Bo, WU Yue, NIE Chuanbao, et al. A non-iterative method for dynamic load identification[J]. Applied Mathematics and Mechanics, 2019, 40 (5): 473-489. (in Chinese) doi: 10.21656/1000-0887.390211
[35]	JIN R, CHEN W, SIMPSON T W. Comparative studies of metamodelling techniques under multiple modelling criteria[J]. Structural and Multidisciplinary Optimization, 2001, 23 (1): 1-13. doi: 10.1007/s00158-001-0160-4
[36]	JING Z, CHEN J, LI X. RBF-GA: an adaptive radial basis function metamodeling with genetic algorithm for structural reliability analysis[J]. Reliability Engineering & System Safety, 2019, 189: 42-57.
[37]	LIU X, LIU X, ZHOU Z H, et al. An efficient multi-objective optimization method based on the adaptive approximation model of the radial basis function[J]. Structural and Multidisciplinary Optimization, 2021, 63 (3): 1385-1403. doi: 10.1007/s00158-020-02766-2
[38]	KHATIR S, WAHAB M A. Fast simulations for solving fracture mechanics inverse problems using POD-RBF XIGA and Jaya algorithm[J]. Engineering Fracture Mechanics, 2019, 205: 285-300. doi: 10.1016/j.engfracmech.2018.09.032
[39]	HENNERON T, PIERQUIN A, CLENET S. Surrogate model based on the POD combined with the RBF interpolation of nonlinear magnetostatic FE model[J]. IEEE Transactions on Magnetics, 2019, 56 (1): 1-4.
[40]	WANG S, KHATIR S, WAHAB M A. Proper orthogonal decomposition for the prediction of fretting wear characteristics[J]. Tribology International, 2020, 152: 106545. doi: 10.1016/j.triboint.2020.106545
[41]	金建铭. 电磁场有限元方法[M]. 西安: 西安电子科技大学出版社, 1998. JIN Jianming. Finite Element Method of Electromagnetic Field[M]. Xi'an: Xidian Electronic University Press, 1998. (in Chinese)
[42]	钱丽佳. 深海油气输送管道的缺陷定位与识别研究[D]. 硕士学位论文. 天津: 天津大学, 2017. QIAN Lijia. Research on the localization and identification of defects of deep sea oil and gas pipeline[D]. Master Thesis. Tianjin: Tianjin University, 2017. (in Chinese)
[43]	WANG G G. Adaptive response surface method using inherited Latin hypercube design points[J]. Journal of Mechanical Design, 2003, 125 (2): 210-220. doi: 10.1115/1.1561044
[44]	NIE C B, YU B. Inversing heat flux boundary conditions based on precise integration FEM without iteration and estimation of thermal stress in FGMs[J]. International Journal of Thermal Sciences, 2019, 140: 201-224. doi: 10.1016/j.ijthermalsci.2019.03.003