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

被引：44

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

来源：

COMPUTERS & FLUIDS | 2015年 / 123卷

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

页码：185 / 201

页数：17

共 50 条

[11] Multi-fidelity analysis and uncertainty quantification of beam vibration using correction response surfaces
Iyappan, Praveen
Ganguli, Ranjan
INTERNATIONAL JOURNAL FOR COMPUTATIONAL METHODS IN ENGINEERING SCIENCE & MECHANICS, 2020, 21 (01): : 26 - 42
[12] Error correction in multi-fidelity molecular dynamics simulations using functional uncertainty quantification
Reeve, Samuel Temple
Strachan, Alejandro
JOURNAL OF COMPUTATIONAL PHYSICS, 2017, 334 : 207 - 220
[13] MULTI-FIDELITY MODEL FUSION AND UNCERTAINTY QUANTIFICATION USING HIGH DIMENSIONAL MODEL REPRESENTATION
Kubicek, Martin
Mehta, Piyush M.
Minisci, Edmondo
Vasile, Massimiliano
SPACEFLIGHT MECHANICS 2016, PTS I-IV, 2016, 158 : 1987 - 2002
[14] A MULTI-FIDELITY NEURAL NETWORK SURROGATE SAMPLING METHOD FOR UNCERTAINTY QUANTIFICATION
Motamed, Mohammad
INTERNATIONAL JOURNAL FOR UNCERTAINTY QUANTIFICATION, 2020, 10 (04) : 315 - 332
[15] Aleatory uncertainty quantification based on multi-fidelity deep neural networks
Li, Zhihui
Montomoli, Francesco
RELIABILITY ENGINEERING & SYSTEM SAFETY, 2024, 245
[16] Multi-fidelity uncertainty quantification of particle deposition in turbulent pipe flow
Yao, Yuan
Huan, Xun
Capecelatro, Jesse
Journal of Aerosol Science, 2022, 166
[17] Multi-fidelity neural network for uncertainty quantification of chemical reaction models
Liu, Chengcheng
Lin, Keli
Wang, Yiru
Yang, Bin
COMBUSTION AND FLAME, 2023, 258
[18] Multi-fidelity nonlinear unsteady aerodynamic modeling and uncertainty estimation based on Hierarchical Kriging
Peng, Xuhao
Kou, Jiaqing
Zhang, Weiwei
APPLIED MATHEMATICAL MODELLING, 2023, 122 : 1 - 21
[19] Multi-fidelity orbit uncertainty propagation
Jones, Brandon A.
Weisman, Ryan
ACTA ASTRONAUTICA, 2019, 155 : 406 - 417
[20] Multi-fidelity information fusion based on prediction of kriging
Huachao Dong
Baowei Song
Peng Wang
Shuai Huang
Structural and Multidisciplinary Optimization, 2015, 51 : 1267 - 1280

← 1 2 3 4 5 →