Development of a Surrogate Model for Structural Health Monitoring of a UAV Wing Spar

Adrielly H. Razzini, Iddo Kressel, Yoav Ofir, Moshe Tur, Tal Yehoshua, Michael D. Todd*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

A critical part to implementing a structural health monitoring system is being able to understand the structural response under different operational and environmental conditions. In this work, a detailed finite element model of an unmanned aerial vehicle’s wings’ spar was developed to serve as a synthetic data generator. A probabilistic understanding of the aerodynamic loads and debonding damages at different locations and with different sizes were implemented to simulate observations of the spar’s performance in service. The target measurements are uniaxial strain, measured in several paths throughout the spar. Given measured strain, the damage assessment problem is probabilistically formulated by defining local buckling from debonding as the observable damage, which is fundamentally characterized by load-dependent buckling eigenvalues. This FE physical model is highly computationally intensive, so a Gaussian process regressor and a multilayer artificial neural network (MANN) were designed to serve as a “run time” surrogate model to learn the relationships between inputs (loads and damage conditions) and outputs (strain measurements and buckling eigenvalues). The results illustrate that the surrogate models presented are a reliable replacement to the computationally expensive inverse finite element model in damage identification.

Original languageEnglish
Title of host publicationData Science in Engineering, Volume 9 - Proceedings of the 40th IMAC, A Conference and Exposition on Structural Dynamics, 2022
EditorsRamin Madarshahian, Francois Hemez
PublisherSpringer
Pages99-102
Number of pages4
ISBN (Print)9783031041211
DOIs
StatePublished - 2022
Event40th IMAC, A Conference and Exposition on Structural Dynamics, 2022 - Orlando, United States
Duration: 7 Feb 202210 Feb 2022

Publication series

NameConference Proceedings of the Society for Experimental Mechanics Series
ISSN (Print)2191-5644
ISSN (Electronic)2191-5652

Conference

Conference40th IMAC, A Conference and Exposition on Structural Dynamics, 2022
Country/TerritoryUnited States
CityOrlando
Period7/02/2210/02/22

Keywords

  • Finite element
  • Gaussian process regressors
  • Neural networks
  • Structural health monitoring
  • Surrogate model

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