Statistical Identification of Parameters for Damaged FGM Structures with Material Uncertainties in Thermal Environment

Considering that the statistic numerical characteristics are often required in the probability-based damage identification and safety assessment of functionally graded material (FGM) structures, an stochastic model updating-based inverse computational method to identify the second-order statistics (...

Full description

Bibliographic Details
Main Authors: Yalan Xu, Yu Qian, Kongming Guo
Format: Article
Language:English
Published: Hindawi-Wiley 2018-01-01
Series:Complexity
Online Access:http://dx.doi.org/10.1155/2018/9034865
id doaj-9513afb5d6db44b4ba9dd355f2ccc792
record_format Article
spelling doaj-9513afb5d6db44b4ba9dd355f2ccc7922020-11-24T20:49:14ZengHindawi-WileyComplexity1076-27871099-05262018-01-01201810.1155/2018/90348659034865Statistical Identification of Parameters for Damaged FGM Structures with Material Uncertainties in Thermal EnvironmentYalan Xu0Yu Qian1Kongming Guo2School of Mechano-Electronic Engineering, Xidian University, Xi’an 710071, ChinaSchool of Aerospace, Xi’an Jiaotong University, Xi’an 710049, ChinaSchool of Mechano-Electronic Engineering, Xidian University, Xi’an 710071, ChinaConsidering that the statistic numerical characteristics are often required in the probability-based damage identification and safety assessment of functionally graded material (FGM) structures, an stochastic model updating-based inverse computational method to identify the second-order statistics (means and variances) of material properties as well as distribution of constituents for damaged FGM structures with material uncertainties is presented by using measurable modal parameters of structures. The region truncation-based optimization method is employed to simplify the computational process in stochastic model updating. In order to implement the forward propagation of uncertainties required in the stochastic model updating and avoid large error resulting in the nonconvergence of the iteration process, an algorithm is proposed to compute the covariance between the modal parameters and the identified parameters for damaged FGM structures. The proposed method is illustrated by a numerically simulated damaged FGM beam with continuous spatial variation of material properties and verified by comparing with the Monte-Carlo simulation (MCS) method. The influences of the levels and sources of measured data uncertainties as well as the boundary conditions on the identification results are investigated. The numerical simulation results show the efficiency and effectiveness of the presented method for the identification of material parameter variability by using the measurable modal parameters of damaged FGM structures.http://dx.doi.org/10.1155/2018/9034865
collection DOAJ
language English
format Article
sources DOAJ
author Yalan Xu
Yu Qian
Kongming Guo
spellingShingle Yalan Xu
Yu Qian
Kongming Guo
Statistical Identification of Parameters for Damaged FGM Structures with Material Uncertainties in Thermal Environment
Complexity
author_facet Yalan Xu
Yu Qian
Kongming Guo
author_sort Yalan Xu
title Statistical Identification of Parameters for Damaged FGM Structures with Material Uncertainties in Thermal Environment
title_short Statistical Identification of Parameters for Damaged FGM Structures with Material Uncertainties in Thermal Environment
title_full Statistical Identification of Parameters for Damaged FGM Structures with Material Uncertainties in Thermal Environment
title_fullStr Statistical Identification of Parameters for Damaged FGM Structures with Material Uncertainties in Thermal Environment
title_full_unstemmed Statistical Identification of Parameters for Damaged FGM Structures with Material Uncertainties in Thermal Environment
title_sort statistical identification of parameters for damaged fgm structures with material uncertainties in thermal environment
publisher Hindawi-Wiley
series Complexity
issn 1076-2787
1099-0526
publishDate 2018-01-01
description Considering that the statistic numerical characteristics are often required in the probability-based damage identification and safety assessment of functionally graded material (FGM) structures, an stochastic model updating-based inverse computational method to identify the second-order statistics (means and variances) of material properties as well as distribution of constituents for damaged FGM structures with material uncertainties is presented by using measurable modal parameters of structures. The region truncation-based optimization method is employed to simplify the computational process in stochastic model updating. In order to implement the forward propagation of uncertainties required in the stochastic model updating and avoid large error resulting in the nonconvergence of the iteration process, an algorithm is proposed to compute the covariance between the modal parameters and the identified parameters for damaged FGM structures. The proposed method is illustrated by a numerically simulated damaged FGM beam with continuous spatial variation of material properties and verified by comparing with the Monte-Carlo simulation (MCS) method. The influences of the levels and sources of measured data uncertainties as well as the boundary conditions on the identification results are investigated. The numerical simulation results show the efficiency and effectiveness of the presented method for the identification of material parameter variability by using the measurable modal parameters of damaged FGM structures.
url http://dx.doi.org/10.1155/2018/9034865
work_keys_str_mv AT yalanxu statisticalidentificationofparametersfordamagedfgmstructureswithmaterialuncertaintiesinthermalenvironment
AT yuqian statisticalidentificationofparametersfordamagedfgmstructureswithmaterialuncertaintiesinthermalenvironment
AT kongmingguo statisticalidentificationofparametersfordamagedfgmstructureswithmaterialuncertaintiesinthermalenvironment
_version_ 1716806367357960192