OPTIMIZATION OF FLEXIBLE PIPES DYNAMIC ANALYSIS USING ARTIFICIAL NEURAL NETWORKS

被引:0
|
作者
Chaves, Victor [1 ]
Sagrilo, Luis V. S. [2 ]
Machado da Silva, Vinicius Ribeiro [2 ]
机构
[1] ETP Artificial Intelligence, Rio De Janeiro, Brazil
[2] Univ Fed Rio de Janeiro, COPPE, Rio De Janeiro, Brazil
关键词
D O I
暂无
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Irregular wave dynamic analysis is an extremely computational expensive process on flexible pipes design. One emerging method that aims to reduce these computational costs is the hybrid methodology that combines Finite Element Analyses (FEA) and Artificial Neural Network (ANN). The proposed hybrid methodology aims to predict flexible pipe tension and curvatures in the bend stiffener region. Firstly using short FEA simulations to train the ANN, and then using only the ANN and the prescribed floater motions to get the rest of the response histories. Two approaches are developed with respect to the training data. One uses an ANN for each sea state in the wave scatter diagram and the other develops an ANN for each wave incidence direction. In order to evaluate the accuracy of the proposed approaches, a local analysis is applied, based on the predicted tension and curvatures, to calculate stresses in tension armour wires and the corresponding flexible pipe fatigue lifes. The results are compared to those from full nonlinear FEM simulation.
引用
收藏
页数:11
相关论文
共 50 条
  • [11] Gradientless shape optimization using artificial neural networks
    Pathak, Krishna K.
    Sehgal, D. K.
    STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION, 2010, 41 (05) : 699 - 709
  • [12] Backcalculation of flexible pavement moduli using artificial neural networks
    Meier, Roger W.
    Rix, Glenn J.
    1600, National Research Council, Washington, DC, United States
  • [13] Optimization of cellulose membranes using artificial neural networks
    Sulbaran, Belkis
    Romero Arellano, Victor Hugo
    Guzman Gonzalez, Carlos Alberto
    Zuniga Grajeda, Virgilio
    Gurubel Tun, Kelly Joel
    ABSTRACTS OF PAPERS OF THE AMERICAN CHEMICAL SOCIETY, 2019, 257
  • [14] Optimization of Artificial Neural Networks using Wavelet Transforms
    Vershkov, N.
    Babenko, M.
    Tchernykh, A.
    Kuchukov, V.
    Kucherov, N.
    Kuchukova, N.
    Drozdov, A. Yu.
    PROGRAMMING AND COMPUTER SOFTWARE, 2022, 48 (06) : 376 - 384
  • [15] CRYSTALLIZATION PROCESS OPTIMIZATION USING ARTIFICIAL NEURAL NETWORKS
    WOINAROSCHY, A
    ISOPESCU, R
    FILIPESCU, L
    CHEMICAL ENGINEERING & TECHNOLOGY, 1994, 17 (04) : 269 - 272
  • [16] Gradientless shape optimization using artificial neural networks
    Krishna K. Pathak
    D. K. Sehgal
    Structural and Multidisciplinary Optimization, 2010, 41 : 699 - 709
  • [17] Modeling Prosopagnosia Using Dynamic Artificial Neural Networks
    Vandermeulen, Robyn
    Morissette, Laurence
    Chartier, Sylvain
    2011 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), 2011, : 2074 - 2079
  • [18] Flexible pavement condition evaluation using deflection basin parameters and dynamic finite element analysis implemented by artificial neural networks
    Kim, YR
    Lee, YC
    Ranjithan, SR
    NONDESTRUCTIVE TESTING OF PAVEMENTS AND BACKCALCULATION OF MODULI, 3RD VOLUME, 2000, 1375 : 514 - 530
  • [19] Artificial Neural Networks for Flexible Pavement
    Bayat, Ramin
    Talatahari, Siamak
    Gandomi, Amir H.
    Habibi, Mohammadreza
    Aminnejad, Babak
    INFORMATION, 2023, 14 (02)
  • [20] Training Artificial Neural Networks Using a Global Optimization Method That Utilizes Neural Networks
    Tsoulos, Ioannis G.
    Tzallas, Alexandros
    AI, 2023, 4 (03) : 491 - 508