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
  • [31] Bayesian Uncertainty Quantification with Multi-Fidelity Data and Gaussian Processes for Impedance Cardiography of Aortic Dissection
    Ranftl, Sascha
    Melito, Gian Marco
    Badeli, Vahid
    Reinbacher-Koestinger, Alice
    Ellermann, Katrin
    von der Linden, Wolfgang
    ENTROPY, 2020, 22 (01) : 58
  • [32] Multi-fidelity uncertainty quantification of film cooling flow under random operational and geometrical conditions
    Mohammadi-Ahmar, Akbar
    Raisee, Mehrdad
    INTERNATIONAL JOURNAL OF HEAT AND MASS TRANSFER, 2020, 152
  • [33] Multi-Fidelity Design Optimization under Epistemic Uncertainty
    Hou, Liqiang
    Tan, Wei
    Ma, Hong
    2016 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC), 2016, : 4452 - 4459
  • [34] A Machine Learning Based Hybrid Multi-Fidelity Multi-Level Monte Carlo Method for Uncertainty Quantification
    Khan, Nagoor Kani Jabarullah
    Elsheikh, Ahmed H.
    FRONTIERS IN ENVIRONMENTAL SCIENCE, 2019, 7
  • [35] Multi-fidelity uncertainty propagation using polynomial chaos and Gaussian process modeling
    Fenggang Wang
    Fenfen Xiong
    Shishi Chen
    Jianmei Song
    Structural and Multidisciplinary Optimization, 2019, 60 : 1583 - 1604
  • [36] Multi-fidelity uncertainty propagation using polynomial chaos and Gaussian process modeling
    Wang, Fenggang
    Xiong, Fenfen
    Chen, Shishi
    Song, Jianmei
    STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION, 2019, 60 (04) : 1583 - 1604
  • [37] The effects of scale factor and correction on the multi-fidelity model
    Son, Seok-Ho
    Choi, Dong-Hoon
    JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY, 2016, 30 (05) : 2075 - 2081
  • [38] The effects of scale factor and correction on the multi-fidelity model
    Seok-Ho Son
    Dong-Hoon Choi
    Journal of Mechanical Science and Technology, 2016, 30 : 2075 - 2081
  • [39] DEEP LEARNING ENHANCED COST-AWARE MULTI-FIDELITY UNCERTAINTY QUANTIFICATION OF A COMPUTATIONAL MODEL FOR RADIOTHERAPY
    Vitullo, Piermario
    Franco, Nicola rares
    Zunino, Paolo
    FOUNDATIONS OF DATA SCIENCE, 2024,
  • [40] Multi-Fidelity Surrogate-Based Process Mapping with Uncertainty Quantification in Laser Directed Energy Deposition
    Menon, Nandana
    Mondal, Sudeepta
    Basak, Amrita
    MATERIALS, 2022, 15 (08)