Physics based multi-fidelity data fusion for efficient characterization of mode shape variation under uncertainties

K. Zhou, J. Tang

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

Abstract

Efficient prediction of mode shape variation under uncertainties is important for design and control. While Monte Carlo simulation (MCS) is straightforward, it is computationally expensive and not feasible for complex structures with high dimensionalities. To address this issue, in this study we develop a multi-fidelity data fusion approach with an enhanced Gaussian process (GP) architecture to evaluate mode shape variation. Since the process to acquire high-fidelity data from full-scale physical model usually is costly, we involve an order-reduced model to rapidly generate a relatively large amount of low-fidelity data. Combining these with a small amount of high-fidelity data altogether, we can establish a Gaussian process meta-model and use it for efficient model shape prediction. This enhanced meta-model allows one to capture the intrinsic correlation of model shape amplitudes at different locations by incorporating a multi-response strategy. Comprehensive case studies are performed for methodology validation.

Original languageEnglish
Title of host publicationIntelligent Transportation/Vehicles; Manufacturing; Mechatronics; Engine/After-Treatment Systems; Soft Actuators/Manipulators; Modeling/Validation; Motion/Vibration Control Applications; Multi-Agent/Networked Systems; Path Planning/Motion Control; Renewable/Smart Energy Systems; Security/Privacy of Cyber-Physical Systems; Sensors/Actuators; Tracking Control Systems; Unmanned Ground/Aerial Vehicles; Vehicle Dynamics, Estimation, Control; Vibration/Control Systems; Vibrations
PublisherAmerican Society of Mechanical Engineers
ISBN (Electronic)9780791884287
DOIs
Publication statusPublished - 2020
Externally publishedYes
EventASME 2020 Dynamic Systems and Control Conference, DSCC 2020 - Virtual, Online
Duration: 5 Oct 20207 Oct 2020

Publication series

NameASME 2020 Dynamic Systems and Control Conference, DSCC 2020
Volume2

Conference

ConferenceASME 2020 Dynamic Systems and Control Conference, DSCC 2020
CityVirtual, Online
Period5/10/207/10/20

Keywords

  • Mode shape
  • Multi-level Gaussian process
  • Multi-response Gaussian process
  • Order-reduction
  • Uncertainty quantification

ASJC Scopus subject areas

  • Artificial Intelligence
  • Energy Engineering and Power Technology
  • Aerospace Engineering
  • Automotive Engineering
  • Biomedical Engineering
  • Control and Systems Engineering
  • Mechanical Engineering
  • Control and Optimization

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