Multi-fidelity analysis and uncertainty quantification of beam vibration using correction response surfaces

被引:6
|
作者
Iyappan, Praveen [1 ]
Ganguli, Ranjan [1 ]
机构
[1] Indian Inst Sci, Dept Aerosp Engn, Bangalore, Karnataka, India
来源
INTERNATIONAL JOURNAL FOR COMPUTATIONAL METHODS IN ENGINEERING SCIENCE & MECHANICS | 2020年 / 21卷 / 01期
关键词
Multi-fidelity; Finite element model; Correction response surface; High-fidelity; Low-fidelity; Uncertainty quantification; DESIGN; OPTIMIZATION;
D O I
10.1080/15502287.2020.1729898
中图分类号
O3 [力学];
学科分类号
08 ; 0801 ;
摘要
A multi-fidelity model for beam vibration is developed by coupling a low-fidelity Euler-Bernoulli beam finite element model with a high-fidelity Timoshenko beam finite element model. Natural frequencies are used as the response measure of the physical system. A second order response surface is created for the low-fidelity Euler-Bernoulli model using the face centered design. Correction response surfaces for multi-fidelity analysis are created by utilizing the high-fidelity finite element predictions and the low-fidelity finite element predictions. It is shown that the multi-fidelity model gives accurate results with high computational efficiency when compared to the high-fidelity finite element model.
引用
收藏
页码:26 / 42
页数:17
相关论文
共 50 条
  • [41] A Multi-Fidelity Polynomial Chaos Approach for Uncertainty Quantification of MWCNT Interconnect Networks in the Presence of Imperfect Contacts
    Guglani, Surila
    Dimple, Km
    Kaushik, Brajesh K.
    Roy, Sourajeet
    Sharma, Rohit
    SPI 2021: 25TH IEEE WORKSHOP ON SIGNAL AND POWER INTEGRITY, 2021,
  • [42] PyApprox: A software package for sensitivity analysis, Bayesian inference, optimal experimental design, and multi-fidelity uncertainty quantification and surrogate modeling
    Jakeman, J. D.
    ENVIRONMENTAL MODELLING & SOFTWARE, 2023, 170
  • [43] Hybrid uncertainty propagation based on multi-fidelity surrogate model
    Liu, Jinxing
    Shi, Yan
    Ding, Chen
    Beer, Michael
    COMPUTERS & STRUCTURES, 2024, 293
  • [44] Multi-fidelity uncertainty quantification of high Reynolds number turbulent flow around a rectangular 5:1 Cylinder
    Sakuma, Mayu
    Pepper, Nick
    Warnakulasuriya, Suneth
    Montomoli, Francesco
    Wuech-ner, Roland
    Bletzinger, Kai-Uwe
    WIND AND STRUCTURES, 2022, 34 (01) : 127 - 136
  • [45] Multi-fidelity aerodynamic data analysis by using composite neural network
    Zhu, Xingyu
    Mei, Liquan
    Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University, 42 (02): : 328 - 334
  • [46] Multi-fidelity robust aerodynamic design optimization under mixed uncertainty
    Shah, Harsheel
    Hosder, Serhat
    Koziel, Slawomir
    Tesfahunegn, Yonatan A.
    Leifsson, Leifur
    AEROSPACE SCIENCE AND TECHNOLOGY, 2015, 45 : 17 - 29
  • [47] Efficient aerodynamic analysis and optimization under uncertainty using multi-fidelity polynomial chaos-Kriging surrogate model
    Zhao, Huan
    Gao, Zheng-Hong
    Xia, Lu
    COMPUTERS & FLUIDS, 2022, 246
  • [48] A Bilevel Multi-Fidelity Polynomial Chaos Approach for the Uncertainty Quantification of MWCNT Interconnect Networks With Variable Imperfect Contact Resistances
    Guglani, Surila
    Dimple, Km
    Roy, Sourajeet
    Sharma, Rohit
    Kaushik, Brajesh K.
    IEEE ACCESS, 2022, 10 : 109925 - 109936
  • [49] Multi-fidelity machine-learning with uncertainty quantification and Bayesian optimization for materials design: Application to ternary random alloys
    Tran, Anh
    Tranchida, Julien
    Wildey, Tim
    Thompson, Aidan P.
    JOURNAL OF CHEMICAL PHYSICS, 2020, 153 (07):
  • [50] Towards efficient uncertainty quantification in complex and large-scale biomechanical problems based on a Bayesian multi-fidelity scheme
    Biehler, Jonas
    Gee, Michael W.
    Wall, Wolfgang A.
    BIOMECHANICS AND MODELING IN MECHANOBIOLOGY, 2015, 14 (03) : 489 - 513