Co-kriging based multi-fidelity uncertainty quantification of beam vibration using coarse and fine finite element meshes

被引:4
|
作者
Rohit, R. Julian [1 ]
Ganguli, Ranjan [1 ]
机构
[1] Indian Inst Sci, Dept Aerosp Engn, Bangalore, Karnataka, India
关键词
Beam vibration; co-kriging; Latin hypercube sampling; Monte Carlo simulation; multi-fidelity; uncertainty quantification; SHAPE OPTIMIZATION; APPROXIMATION; SIMULATION; MODELS;
D O I
10.1080/15502287.2021.1921883
中图分类号
O3 [力学];
学科分类号
08 ; 0801 ;
摘要
Multi-fidelity models have exploded in popularity as they promise to circumvent the computational complexity of a high-fidelity model without sacrificing accuracy. In this paper, we demonstrate the process of building a multi-fidelity model and illustrate its advantage through an uncertainty quantification study using the beam vibration problem. A multi-fidelity co-kriging model is built with data from low- and high-fidelity models, which are finite element models with coarse and fine discretization, respectively. The co-kriging model's predictive capabilities are excellent, achieving accuracy within 1% of the high-fidelity model while providing 98% computational savings over the high-fidelity model in the uncertainty quantification study.
引用
收藏
页码:147 / 168
页数:22
相关论文
共 50 条
  • [31] Multi-objective Geometry Optimization of a Gas Cyclone Using Triple-Fidelity Co-Kriging Surrogate Models
    Singh, Prashant
    Couckuyt, Ivo
    Elsayed, Khairy
    Deschrijver, Dirk
    Dhaene, Tom
    [J]. JOURNAL OF OPTIMIZATION THEORY AND APPLICATIONS, 2017, 175 (01) : 172 - 193
  • [32] Multi-objective Geometry Optimization of a Gas Cyclone Using Triple-Fidelity Co-Kriging Surrogate Models
    Prashant Singh
    Ivo Couckuyt
    Khairy Elsayed
    Dirk Deschrijver
    Tom Dhaene
    [J]. Journal of Optimization Theory and Applications, 2017, 175 : 172 - 193
  • [33] Multi-Fidelity Surrogate-Based Process Mapping with Uncertainty Quantification in Laser Directed Energy Deposition
    Menon, Nandana
    Mondal, Sudeepta
    Basak, Amrita
    [J]. MATERIALS, 2022, 15 (08)
  • [34] Efficient aerodynamic analysis and optimization under uncertainty using multi-fidelity polynomial chaos-Kriging surrogate model
    Zhao, Huan
    Gao, Zheng-Hong
    Xia, Lu
    [J]. COMPUTERS & FLUIDS, 2022, 246
  • [35] Multi-fidelity Uncertainty Quantification for Homogenization Problems in Structure-Property Relationships from Crystal Plasticity Finite Elements
    Anh Tran
    Pieterjan Robbe
    Theron Rodgers
    Hojun Lim
    [J]. JOM, 2024, 76 : 3007 - 3020
  • [36] Multi-fidelity Uncertainty Quantification for Homogenization Problems in Structure-Property Relationships from Crystal Plasticity Finite Elements
    Tran, Anh
    Robbe, Pieterjan
    Rodgers, Theron
    Lim, Hojun
    [J]. JOM, 2024, 76 (06) : 3007 - 3020
  • [37] Uncertainty quantification in analytical and finite element beam models using experimental data
    Langer, P.
    Sepahvand, K.
    Marburg, S.
    [J]. EURODYN 2014: IX INTERNATIONAL CONFERENCE ON STRUCTURAL DYNAMICS, 2014, : 2753 - 2758
  • [38] Uncertainty Quantification for Numerical Solutions of the Nonlinear Partial Differential Equations by Using the Multi-Fidelity Monte Carlo Method
    Du, Wenting
    Su, Jin
    [J]. APPLIED SCIENCES-BASEL, 2022, 12 (14):
  • [39] 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.
    [J]. BIOMECHANICS AND MODELING IN MECHANOBIOLOGY, 2015, 14 (03) : 489 - 513
  • [40] Towards efficient uncertainty quantification in complex and large-scale biomechanical problems based on a Bayesian multi-fidelity scheme
    Jonas Biehler
    Michael W. Gee
    Wolfgang A. Wall
    [J]. Biomechanics and Modeling in Mechanobiology, 2015, 14 : 489 - 513