BAYESIAN PARAMETER IDENTIFICATION IN PLASTICITY

被引：0

作者：

Adeli, Ehsan ^{[1
]}

Rosic, Bojana ^{[1
]}

Matthies, Hermann G. ^{[1
]}

Reinstaeler, Sven ^{[2
]}

机构：

[1] Tech Univ Carolo Wilhelmina Braunschweig, Inst Sci Comp, Braunschweig, Germany

[2] Tech Univ Carolo Wilhelmina Braunschweig, Inst Struct Anal, Braunschweig, Germany

来源：

COMPUTATIONAL PLASTICITY XIV: FUNDAMENTALS AND APPLICATIONS | 2017年

关键词：

Viscoplastic Model; Uncertainty Quantification; Probabilistic Inverse Approach; Polynomial Chaos; CONSTITUTIVE MODEL; INELASTIC FLOW;

D O I：

暂无

中图分类号：

TH [机械、仪表工业];

学科分类号：

0802 ;

摘要：

To evaluate the cyclic behaviour under different loading conditions using the kinematic and isotropic hardening theory of steel a Chaboche visco-plastic material model is employed. The parameters of a constitutive model are usually identified by minimization of the distance between model response and experimental data. However, measurement errors and differences in the specimens lead to deviations in the determined parameters. In this article the Choboche model is used and a stochastic simulation technique is applied to generate artificial data which exhibit the same stochastic behaviour as experimental data. Then the model parameters are identified by applying a variaty of Bayes's theorem. Identified parameters are compared with the true parameters in the simulation and the efficiency of the identification method is discussed.

引用

页码：247 / 255

页数：9

共 50 条

[21] Bayesian Target-Vector Optimization for Efficient Parameter Reconstruction [J].

Plock, Matthias ;

Andrle, Kas ;

Burger, Sven ;

Schneider, Philipp-Immanuel .

ADVANCED THEORY AND SIMULATIONS, 2022, 5 (10)

[22] Bayesian estimates of parameter variability in the k-ε turbulence model [J].

Edeling, W. N. ;

Cinnella, P. ;

Dwight, R. P. ;

Bijl, H. .

JOURNAL OF COMPUTATIONAL PHYSICS, 2014, 258 :73-94

[23] Bayesian Reservoir History Matching Considering Model and Parameter Uncertainties [J].

Elsheikh, A. H. ;

Jackson, M. D. ;

Laforce, T. C. .

MATHEMATICAL GEOSCIENCES, 2012, 44 (05) :515-543

[24] Hierarchical Bayesian model updating for structural identification [J].

Behmanesh, Iman ;

Moaveni, Babak ;

Lombaert, Geert ;

Papadimitriou, Costas .

MECHANICAL SYSTEMS AND SIGNAL PROCESSING, 2015, 64-65 :360-376

[25] Sparse Bayesian learning for structural damage identification [J].

Chen, Zhao ;

Zhang, Ruiyang ;

Zheng, Jingwei ;

Sun, Hao .

MECHANICAL SYSTEMS AND SIGNAL PROCESSING, 2020, 140

[26] A new efficient Bayesian parameter inference strategy: Application to flow and pesticide transport through unsaturated porous media [J].

Younes, Anis ;

Mara, Thierry A. ;

Voltz, Marc ;

Guellouz, Lamia ;

Baalousha, Husam Musa ;

Fahs, Marwan .

JOURNAL OF HYDROLOGY, 2018, 563 :887-899

[27] Bayesian Inference Based Parameter Calibration of the LuGre-Friction Model [J].

C.M. Gehb ;

S. Atamturktur ;

R. Platz ;

T. Melz .

Experimental Techniques, 2020, 44 :369-382

[28] Bayesian Inference Based Parameter Calibration of the LuGre-Friction Model [J].

Gehb, C. M. ;

Atamturktur, S. ;

Platz, R. ;

Melz, T. .

EXPERIMENTAL TECHNIQUES, 2020, 44 (03) :369-382

[29] Ensemble Bayesian method for parameter distribution inference: application to reactor physics [J].

Zeng, Jia-Qin ;

Zhang, Hai-Xiang ;

Gong, He-Lin ;

Luo, Ying-Ting .

NUCLEAR SCIENCE AND TECHNIQUES, 2023, 34 (12)

[30] Parameter Identification in Uncertain Scalar Conservation Laws Discretized with the Discontinuous Stochastic Galerkin Scheme [J].

Schlachter, Louisa ;

Totzeck, Claudia .

COMMUNICATIONS IN COMPUTATIONAL PHYSICS, 2020, 28 (04) :1585-1608

← 1 2 3 4 5 →