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 (...
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Online Access: | http://dx.doi.org/10.1155/2018/9034865 |
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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 |