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
  • [1] ARTIFICIAL NEURAL NETWORKS APPLIED TO FLEXIBLE PIPES FATIGUE CALCULATIONS
    Chaves, Victor
    Sagrilo, Luis V. S.
    Machado da Silva, Vinicius Ribeiro
    Vignoles, Mario Alfredo
    PROCEEDINGS OF THE ASME 34TH INTERNATIONAL CONFERENCE ON OCEAN, OFFSHORE AND ARCTIC ENGINEERING, 2015, VOL 5B, 2015,
  • [2] Structural optimization using artificial neural networks
    Kaveh, A.
    Iranmanesh, A.
    Amirkabir (Journal of Science and Technology), 10 (40):
  • [3] Using artificial neural networks in financial optimization
    Dorneanu, Liliana
    Untaru, Mircea
    Darvasi, Doina
    Rotarescu, Vasile
    Cernescu, Lavinia
    RECENT ADVANCES IN BUSINESS ADMINISTRATION, 2011, : 93 - 96
  • [4] Locating defects using dynamic strain analysis and artificial neural networks
    Hernandez-Gomez, L. H.
    Durodola, J. F.
    Fellows, N. A.
    Urriolagoitia-Calderon, G.
    Advances in Experimental Mechanics IV, 2005, 3-4 : 325 - 330
  • [5] PREDICTION OF CALCIUM CARBONATE SCALING IN PIPES USING ARTIFICIAL NEURAL NETWORKS
    Paz, Paulo A.
    Caprace, Jean-David
    Cajaiba, Joao F.
    Netto, Theodoro A.
    PROCEEDINGS OF THE ASME 36TH INTERNATIONAL CONFERENCE ON OCEAN, OFFSHORE AND ARCTIC ENGINEERING, 2017, VOL 5A, 2017,
  • [6] Optimization of job scheduling for flexible manufacturing systems using ANOVA technique and artificial neural networks
    Raju, G. S.
    Vizhian, S. Paul
    Kumar, M. Vijay
    Biradar, Iranna M.
    Patil, Nagraj
    INTERNATIONAL JOURNAL OF INTERACTIVE DESIGN AND MANUFACTURING - IJIDEM, 2024, : 1883 - 1893
  • [7] Multiobjective optimization of the dynamic aperture using surrogate models based on artificial neural networks
    Kranjcevic, M.
    Riemann, B.
    Adelmann, A.
    Streun, A.
    PHYSICAL REVIEW ACCELERATORS AND BEAMS, 2021, 24 (01)
  • [8] Fast dynamic stability analysis of a power system using artificial neural networks
    Kukolj, D
    Popovic, D
    Kulic, F
    Gorecan, Z
    EUROPEAN TRANSACTIONS ON ELECTRICAL POWER, 1998, 8 (03): : 207 - 212
  • [9] Balancing of a highly flexible rotor by using artificial neural networks
    Saldarriaga, Manuel Villafafie
    Mahfoud, Jarir
    Steffen, Valder, Jr.
    Hagopian, Johan Der
    PROCEEDINGS OF THE ASME INTERNATIONAL DESIGN ENGINEERING TECHNICAL CONFERENCE AND INFORMATION IN ENGINEERING CONFERENCE, VOL 1, PTS A-C, 2008, : 1093 - 1099
  • [10] Optimization of Artificial Neural Networks using Wavelet Transforms
    N. Vershkov
    M. Babenko
    A. Tchernykh
    V. Kuchukov
    N. Kucherov
    N. Kuchukova
    A. Yu. Drozdov
    Programming and Computer Software, 2022, 48 : 376 - 384