Life-cycle integrity design method considering load robustness of offshore oil and gas equipment: SCSSV as a case study

被引:1
作者
Gao, Chuntan [1 ]
Cai, Baoping [1 ,2 ]
机构
[1] China Univ Petr, Coll Mech & Elect Engn, Qingdao 266580, Shandong, Peoples R China
[2] Natl Engn Res Ctr Marine Geophys Prospecting & Exp, Qingdao 266580, Shandong, Peoples R China
基金
中国国家自然科学基金;
关键词
Life -cycle design; Reliability design; Particle swarm algorithm; Load robustness; Offshore oil and gas equipment;
D O I
10.1016/j.oceaneng.2024.117434
中图分类号
U6 [水路运输]; P75 [海洋工程];
学科分类号
0814 ; 081505 ; 0824 ; 082401 ;
摘要
Components that are vulnerable to the external environment and harsh working conditions easily fail during their life cycle under variable loads of offshore oil and gas equipment. The reliability of the system in the life cycle is reduced, various risks are increased, and substantial maintenance costs are generated. However, the current reliability-based design optimization method has difficulty meeting the situation that the life cycle is taken as the time dimension of design considering reliability, risk, and maintenance. To solve the problem, a life cycle integrity design optimization method considering robustness is proposed. A simplified integrity model is established through random load sampling and global sensitivity analysis. A multiobjective particle swam hierarchical optimization algorithm is proposed, in which hierarchical optimization order is determined by the priority of objectives, and the optimization design with structural integrity as the optimization objective is completed. The method is applied in the design of pressure pipeline of all-electric surface-controlled subsurface safety valve system.
引用
收藏
页数:14
相关论文
共 33 条
  • [1] A parallel hybrid PSO-GA algorithm for the flexible flow-shop scheduling with transportation
    Amirteimoori, Arash
    Mahdavi, Iraj
    Solimanpur, Maghsud
    Ali, Sadia Samar
    Tirkolaee, Erfan Babaee
    [J]. COMPUTERS & INDUSTRIAL ENGINEERING, 2022, 173
  • [2] [Anonymous], 2015, DNVGLRP0501
  • [3] [Anonymous], 2017, Norsok Standard No. M - 506
  • [4] Artificial Intelligence Enhanced Reliability Assessment Methodology With Small Samples
    Cai, Baoping
    Sheng, Chaoyang
    Gao, Chuntan
    Liu, Yonghong
    Shi, Mingwei
    Liu, Zengkai
    Feng, Qiang
    Liu, Guijie
    [J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2023, 34 (09) : 6578 - 6590
  • [5] Long-Time gap crowd prediction with a Two-Stage optimized spatiotemporal Hybrid-GCGRU
    Cheng, Jack C. P.
    Poon, Kwok Ho
    Wong, Peter Kok-Yiu
    [J]. ADVANCED ENGINEERING INFORMATICS, 2022, 54
  • [6] Mahalanobis classification system (MCS) integrated with binary particle swarm optimization for robust quality classification of complex metallic turbine blades
    Cheng, Liangliang
    Yaghoubi, Vahid
    Van Paepegem, Wim
    Kersemans, Mathias
    [J]. MECHANICAL SYSTEMS AND SIGNAL PROCESSING, 2021, 146
  • [7] Towards optimal reliability-based design of wind turbines towers using artificial intelligence
    De Anda, Jonathan
    Ruiz, Sonia E.
    Bojorquez, Eden
    Inzunza-Aragon, Indira
    [J]. ENGINEERING STRUCTURES, 2023, 294
  • [8] Optimization of wind energy extraction for variable speed wind turbines using fuzzy backstepping sliding mode control based on multi objective PSO
    Eskandari, Ahmadreza
    Vatankhah, Ramin
    Azadi, Ehsan
    [J]. OCEAN ENGINEERING, 2023, 285
  • [9] An optimization neural network model for bridge cable force identification
    Gai, Tongtong
    Yu, Dehu
    Zeng, Sen
    Lin, Jerry Chun-Wei
    [J]. ENGINEERING STRUCTURES, 2023, 286
  • [10] Life Cycle Structural Integrity Design Approach for the Components of Subsea Production System: SCSSV as a Case Study
    Gao, Chuntan
    Cai, Baoping
    Sheng, Chaoyang
    Liu, Yonghong
    Liu, Keyang
    Khan, Javed Akbar
    Shi, Mingwei
    Liu, Zengkai
    Ji, Renjie
    [J]. IEEE-ASME TRANSACTIONS ON MECHATRONICS, 2024, 29 (04) : 2768 - 2778