A comparison of two stochastic model updating methods using the DLR AIRMOD test structure

Govers, Yves; Khodaparast, Hamed Haddad; Link, Michael; Mottershead, John E.

doi:10.1016/j.ymssp.2014.06.003

Cited by 40 publications

(37 citation statements)

References 19 publications

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“…Covariance formulas produced by Haddad Khodaparast et al [18] and Govers and Link [19] were shown to be identical within an assumption of small perturbation by Silva et al [26]. Sensitivity methods for covariance and interval updating [34] have recently been compared [35] using data obtained by repeated disassembly and reassembly of the DRL AIRMOD structure [9,10]-a replica of the GARTEUR SM-AG19 testbed [36].…”

Section: The Sensitivity Methodsmentioning

confidence: 99%

Sensitivity or Bayesian model updating: a comparison of techniques using the DLR AIRMOD test data

et al. 2017

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Deterministic model updating is now a mature technology widely applied to large-scale industrial structures. It is concerned with the calibration of the parameters of a single model based on one set of test data. It is, of course, well known that different analysts produce different finite element models, make different physicsbased assumptions, and parameterize their models differently. Also, tests carried out on the same structure, by different operatives, at different times, under different ambient conditions produce different results. There is no unique model and no unique data. Therefore, model updating needs to take account of modeling and test-data variability. Much emphasis is now placed on what has become known as stochastic model updating where data are available from multiple nominally identical test structures. In this paper two currently prominent stochastic model updating techniques (sensitivity-based updating and Bayesian model updating) are described and applied to the DLR AIRMOD structure.

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Section: The Sensitivity Methodsmentioning

confidence: 99%

Sensitivity or Bayesian model updating: a comparison of techniques using the DLR AIRMOD test data

et al. 2017

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“…According to the sufficient sample statistical information, the aleatory uncertainty is usually quantified as random variable or stochastic process by probability theory . A lot of investigations have been conducted on the aleatory uncertainty‐based inverse analysis –. Capasso et al described a framework for solving nonlinear inverse problems in a random environment and adopted the stochastic regularization approach to deal with scale‐dependent modeling errors .…”

Section: Introductionmentioning

confidence: 99%

Novel interval theory‐based parameter identification method for engineering heat transfer systems with epistemic uncertainty

Wang

Matthies

2018

Numerical Meth Engineering

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Summary The parameter identification problem with epistemic uncertainty, where only a small amount of experimental information is available, is a challenging issue in engineering. To overcome the drawback of traditional probabilistic methods in dealing with limited data, this paper proposes a novel interval theory‐based inverse analysis method. First, the interval variables are introduced to represent the input uncertainties, whose lower and upper bounds are to be identified. Subsequently, an unbiased estimation method is presented to quantify the experimental response interval from limited measurements. Meanwhile, a quantitative metric is defined to characterize the relative errors between computational and experimental response intervals by which the interval parameter identification can be constructed as a nested‐loop optimization procedure. To improve the computational efficiency of response prediction with respect to various interval variables, a universal surrogate model is established in the support box via Legendre polynomial chaos expansion, where the expansion coefficients can be evaluated by a collocation method under Clenshaw‐Curtis points and Smolyak algorithm. Eventually, a heat conduction example is provided to verify the feasibility of proposed method, especially in the case with noise‐contaminated temperature measurements.

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“…11 The advantages and limitations of FRF-based model updating in engineering application were introduced by Hemez and Brown. Some researchers [16][17][18] have obtained a better results based on the Kriging model. The relationship between the input and output of the FEA is considered as a black-box system by the metamodel technique.…”

Section: Introductionmentioning

confidence: 99%

Cantilever structure model updating of end effector based on frequency response data under thermal effects

Wang

Liu

Zhao

2018

Adaptive Control & Signal

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The effect of thermal on the structural dynamic response is gaining attention, and should be taken into account during model updating. Meanwhile, metamodel technique is applied to be a surrogate model for its low computational cost. In this work, the structural model updating under thermal field is investigated, an attempt has been made to combine Kriging metamodel and frequency response function based model updating. The Kriging model, taken as a surrogate, is constructed based on the input variables selected by F-test method, which is applied on the results of design of experiment. The errors of response signal between analytical model and experimental model are used to create response of design of experiment. The performed example demonstrates the effectiveness of the proposed approach. KEYWORDS frequency response signal, metamodel, model updating, thermal preload Int J Adapt Control Signal Process. 2019;33:1257-1264.wileyonlinelibrary.com/journal/acs

show abstract

A comparison of two stochastic model updating methods using the DLR AIRMOD test structure

Cited by 40 publications

References 19 publications

Sensitivity or Bayesian model updating: a comparison of techniques using the DLR AIRMOD test data

Sensitivity or Bayesian model updating: a comparison of techniques using the DLR AIRMOD test data

Novel interval theory‐based parameter identification method for engineering heat transfer systems with epistemic uncertainty

Cantilever structure model updating of end effector based on frequency response data under thermal effects

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