Multi-fidelity Co-Kriging surrogate model for ship hull form optimization

被引:62
|
作者
Liu, Xinwang [1 ,2 ]
Zhao, Weiwen [1 ]
Wan, Decheng [1 ]
机构
[1] Shanghai Jiao Tong Univ, Sch Naval Architecture Ocean & Civil Engn, Computat Marine Hydrodynam Lab CMHL, Shanghai 200240, Peoples R China
[2] Harbin Engn Univ, Coll Math Sci, Harbin 150001, Peoples R China
基金
中国国家自然科学基金;
关键词
Multi-fidelity; Hull form optimization; Co-Kriging surrogate model; Potential flow; Viscous flow; NEUMANN-MICHELL THEORY; UNCERTAINTY QUANTIFICATION; DESIGN; SIMULATION;
D O I
10.1016/j.oceaneng.2021.110239
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
For the simulation-based hull form optimization design, there are many methods to evaluate the hydrodynamic performance of the hull form. Although the high fidelity of the surrogate model can be guaranteed by evaluating a large number of new sample hulls based on viscous flow theory, the computational cost can be too high. Therefore, in order to release the burden of calculation, based on the traditional single-fidelity Kriging surrogate model, the multi-fidelity Co-Kriging surrogate model gives attention to both high accuracy and efficiency by combining the accuracy advantage of high-fidelity sample evaluation with the efficiency advantage of lowfidelity sample evaluation. This paper first introduces the construction process of the multi-fidelity Co-Kriging surrogate model, and then uses a series of numerical examples to illustrate the advantages of the multi-fidelity Co-Kriging surrogate model compared with the single-fidelity Kriging surrogate model in terms of fidelity and efficiency. Finally, a hull form optimization design for total drag of DTMB-5415 hull at the design speed is given in detail, where the viscous flow theory and potential flow theory are used for the hydrodynamic evaluations of the hull forms to obtain the high- and low-fidelity results respectively. Results show that the multi-fidelity CoKriging surrogate model can be established for hull form hydrodynamic performance optimization, which is superior to the single-fidelity Kriging surrogate model in accuracy, and the optimal hull obtained by Co-Kriging surrogate model has a better resistance optimization effect.
引用
收藏
页数:20
相关论文
共 50 条
  • [1] Rapid co-kriging based multi-fidelity surrogate assisted performance optimization of a transverse flux PMLSM
    Ahmed, Salman
    Koseki, Takafumi
    Norizuki, Kunihiko
    Aoyama, Yasuaki
    2019 12TH INTERNATIONAL SYMPOSIUM ON LINEAR DRIVES FOR INDUSTRY APPLICATIONS (LDIA), 2019,
  • [2] A generalized hierarchical co-Kriging model for multi-fidelity data fusion
    Qi Zhou
    Yuda Wu
    Zhendong Guo
    Jiexiang Hu
    Peng Jin
    Structural and Multidisciplinary Optimization, 2020, 62 : 1885 - 1904
  • [3] A generalized hierarchical co-Kriging model for multi-fidelity data fusion
    Zhou, Qi
    Wu, Yuda
    Guo, Zhendong
    Hu, Jiexiang
    Jin, Peng
    STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION, 2020, 62 (04) : 1885 - 1904
  • [4] Identification of groundwater pollution sources based on self-adaption Co-Kriging multi-fidelity surrogate model
    An, Yong-Kai
    Zhang, Yan-Xiang
    Yan, Xue-Man
    Zhongguo Huanjing Kexue/China Environmental Science, 2024, 44 (03): : 1376 - 1385
  • [5] Multi-fidelity wake modelling based on Co-Kriging method
    Wang, Y. M.
    Rethore, P-E
    van der Laan, M. P.
    Leon, J. P. Murcia
    Liu, Y. Q.
    Li, L.
    SCIENCE OF MAKING TORQUE FROM WIND (TORQUE 2016), 2016, 753
  • [6] An adaptive multi-fidelity optimization framework based on co-Kriging surrogate models and stochastic sampling with application to coastal aquifer management
    Christelis, Vasileios
    Kopsiaftis, George
    Regis, Rommel G.
    Mantoglou, Aristotelis
    ADVANCES IN WATER RESOURCES, 2023, 180
  • [7] Extended Co-Kriging interpolation method based on multi-fidelity data
    Xiao, Manyu
    Zhang, Guohua
    Breitkopf, Piotr
    Villon, Pierre
    Zhang, Weihong
    APPLIED MATHEMATICS AND COMPUTATION, 2018, 323 : 120 - 131
  • [8] MULTI-FIDELITY MODELING AND ADAPTIVE CO-KRIGING BASED OPTIMIZATION FOR ALL-ELECTRIC GEO SATELLITE SYSTEMS
    Shi, Renhe
    Liu, Li
    Long, Teng
    Wu, Yufei
    Wang, G. Gary
    PROCEEDINGS OF THE ASME INTERNATIONAL DESIGN ENGINEERING TECHNICAL CONFERENCES AND COMPUTERS AND INFORMATION IN ENGINEERING CONFERENCE, 2018, VOL 2B, 2018,
  • [9] Multi-fidelity modelling via recursive co-kriging and Gaussian-Markov random fields
    Perdikaris, P.
    Venturi, D.
    Royset, J. O.
    Karniadakis, G. E.
    PROCEEDINGS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES, 2015, 471 (2179):
  • [10] Kriging-Based Surrogate Model Combined with Weighted Expected Improvement for Ship Hull Form Optimization
    Liu, Xinwang
    Wan, Decheng
    Chen, Gang
    PROCEEDINGS OF THE ASME 37TH INTERNATIONAL CONFERENCE ON OCEAN, OFFSHORE AND ARCTIC ENGINEERING, 2018, VOL 7A, 2018,