Uncertainty quantification for a sailing yacht hull, using multi-fidelity kriging

被引:41
|
作者
de Baar, Jouke [1 ]
Roberts, Stephen [1 ]
Dwight, Richard [2 ]
Mallol, Benoit [3 ]
机构
[1] Australian Natl Univ, Canberra, ACT 0200, Australia
[2] Delft Univ Technol, NL-2600 AA Delft, Netherlands
[3] Numeca, Brussels, Belgium
关键词
Uncertainty quantification; Multi-fidelity; Kriging; RANS; Free-surface; OPTIMIZATION; DESIGN; MODELS; CFD;
D O I
10.1016/j.compfluid.2015.10.004
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
Uncertainty quantification (UQ) for CFD-based ship design can require a large number of simulations, resulting in significant overall computational cost. Presently, we use an existing method, multi-fidelity Kriging, to reduce the number of simulations required for the UQ analysis of the performance of a sailing yacht hull, considering uncertainties in the tank blockage, mass and centre of gravity. We compare the UQ results with experimental values. (C) 2015 Elsevier Ltd. All rights reserved.
引用
收藏
页码:185 / 201
页数:17
相关论文
共 50 条
  • [1] Multi-Fidelity Surrogate-Based Parameter Estimation for a Sailing Yacht Hull
    de Baar, Jouke H. S.
    Roberts, Stephen G.
    [J]. 21ST INTERNATIONAL CONGRESS ON MODELLING AND SIMULATION (MODSIM2015), 2015, : 105 - 111
  • [2] Multi-fidelity analysis and uncertainty quantification of beam vibration using co-kriging interpolation method
    Krishnan, K. V. Vishal
    Ganguli, Ranjan
    [J]. APPLIED MATHEMATICS AND COMPUTATION, 2021, 398
  • [3] ROBUST DESIGN OPTIMIZATION OF A COMPRESSOR ROTOR USING RECURSIVE COKRIGING BASED MULTI-FIDELITY UNCERTAINTY QUANTIFICATION AND MULTI-FIDELITY OPTIMIZATION
    Wiegand, Marcus
    Prots, Andriy
    Meyer, Marcus
    Schmidt, Robin
    Voigt, Matthias
    Mailach, Ronald
    [J]. PROCEEDINGS OF ASME TURBO EXPO 2024: TURBOMACHINERY TECHNICAL CONFERENCE AND EXPOSITION, GT2024, VOL 12D, 2024,
  • [4] Co-kriging based multi-fidelity uncertainty quantification of beam vibration using coarse and fine finite element meshes
    Rohit, R. Julian
    Ganguli, Ranjan
    [J]. INTERNATIONAL JOURNAL FOR COMPUTATIONAL METHODS IN ENGINEERING SCIENCE & MECHANICS, 2021, 23 (02): : 147 - 168
  • [5] Multi-fidelity Co-Kriging surrogate model for ship hull form optimization
    Liu, Xinwang
    Zhao, Weiwen
    Wan, Decheng
    [J]. OCEAN ENGINEERING, 2022, 243
  • [6] Enhanced multi-fidelity modeling for digital twin and uncertainty quantification
    Desai, Aarya Sheetal
    Navaneeth, N.
    Adhikari, Sondipon
    Chakraborty, Souvik
    [J]. PROBABILISTIC ENGINEERING MECHANICS, 2023, 74
  • [7] Multi-fidelity uncertainty quantification of particle deposition in turbulent flow
    Yao, Yuan
    Huan, Xun
    Capecelatro, Jesse
    [J]. JOURNAL OF AEROSOL SCIENCE, 2022, 166
  • [8] Kriging-based multi-fidelity optimization via information fusion with uncertainty
    Li, Chengshan
    Wang, Peng
    Dong, Huachao
    [J]. JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY, 2018, 32 (01) : 245 - 259
  • [9] Kriging-based multi-fidelity optimization via information fusion with uncertainty
    Chengshan Li
    Peng Wang
    Huachao Dong
    [J]. Journal of Mechanical Science and Technology, 2018, 32 : 245 - 259
  • [10] Multi-fidelity analysis and uncertainty quantification of beam vibration using correction response surfaces
    Iyappan, Praveen
    Ganguli, Ranjan
    [J]. INTERNATIONAL JOURNAL FOR COMPUTATIONAL METHODS IN ENGINEERING SCIENCE & MECHANICS, 2020, 21 (01): : 26 - 42