scholarly journals The influence of flow model selection on finite element model parameter estimation using Bayesian inference

2021 ◽  
Vol 1 (4) ◽  
pp. 045204
Author(s):  
Paul J. Hadwin ◽  
Byron D. Erath ◽  
Sean D. Peterson
Keyword(s):  
Finite Element ◽  
Parameter Estimation ◽  
Bayesian Inference ◽  
Model Selection ◽  
Finite Element Model ◽  
Flow Model ◽  
Element Model ◽  
Model Parameter ◽  
Model Parameter Estimation

Identification of cracks in an Euler–Bernoulli beam using Bayesian inference and closed-form solution of vibration modes

2020 ◽  
pp. 146442072096971
Author(s):  
Tianyu Wang ◽  
Mohammad Noori ◽  
Wael A. Altabey
Keyword(s):  
Finite Element ◽  
Bayesian Inference ◽  
Finite Element Model ◽  
Crack Detection ◽  
Closed Form Solution ◽  
Element Model ◽  
Form Solution ◽  
Vibration Modes ◽  
Vibration Data ◽  
The Finite Element Model

Over the past two decades, extensive research has been carried out in the field of structural health monitoring for damage detection in structural systems. Some crack detection methods are based on the finite element model of a beam and use vibration data are developed. These methods identify the crack by updating of the finite element model according to the vibration data of structure. This paper proposes a novel method for crack detection in Euler–Bernoulli beams based on the closed-form solution of mode shapes using Bayesian inference. The expression of vibration modes is derived analytically with the crack parameters as unknown variables. Subsequently, the Bayesian inference is used to obtain the probability density function of crack parameters and to evaluate the uncertainty of the modes. Finally, the method is applied to a series of numerical examples, including a beam with a single-crack and multi-cracks, to verify the effectiveness of this method.

Numerical Analysis of the Influence of Material Model on Response of Compressed Imperfect Thin-Walled Channel

2014 ◽  
Vol 611 ◽  
pp. 188-193 ◽  
Author(s):  
Vladimír Ivančo ◽  
Gabriel Fedorko ◽  
Ladislav Novotný
Keyword(s):  
Finite Element ◽  
Numerical Analysis ◽  
Model Selection ◽  
Finite Element Model ◽  
Material Model ◽  
Element Model ◽  
Material Models ◽  
Geometric Imperfections ◽  
Walled Channel ◽  
Thin Walled

In the paper, the influence of material model selection on the behaviour of Finite Element model of a compressed thin-walled channel is studied. Results of three material models of channels of two different lengths and two types of geometric imperfections are compared and discussed.

A Dynamic Material Parameter Estimation Procedure for Soft Tissue Using a Poroelastic Finite Element Model

1994 ◽  
Vol 116 (1) ◽  
pp. 19-29 ◽  
Author(s):  
J. P. Laible ◽  
D. Pflaster ◽  
B. R. Simon ◽  
M. H. Krag ◽  
M. Pope ◽  
...  
Keyword(s):  
Finite Element ◽  
Parameter Estimation ◽  
Finite Element Model ◽  
Three Dimensional ◽  
Estimation Method ◽  
Estimation Procedure ◽  
Element Model ◽  
Sequential Optimization ◽  
Displacement Control ◽  
Starting Values

A three-dimensional finite element model for a poroelastic medium has been coupled with a least squares parameter estimation method for the purpose of assessing material properties based on intradiscal displacement and reactive forces. Parameter optimization may be based on either load or displacement control experiments. In this paper we present the basis of the finite element model and the parameter estimation process. The method is then applied to a test problem and the computational behavior is discussed. Sequential optimization on different parameter groups was found to have superior convergence properties. Some guidelines for choosing the starting parameter values for optimization were deduced by considering the form of the objective function. For load control experiments, in which displacement data is used for the optimization, the starting values for the elastic modulus should be lower in magnitude than an “anticipated” modulus. The permeability starting values should be higher than an anticipated permeability. For displacement control experiments, the reverse is true. The optimization scheme was also tested on data with random variations.

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