scholarly journals Comparison of Bayesian Methods on Parameter Identification for a Viscoplastic Model with Damage

Metals ◽  
2020 ◽  
Vol 10 (7) ◽  
pp. 876 ◽  
Author(s):  
Ehsan Adeli ◽  
Bojana Rosić ◽  
Hermann G. Matthies ◽  
Sven Reinstädler ◽  
Dieter Dinkler
Keyword(s):  
Experimental Data ◽  
Parameter Identification ◽  
Measurement Errors ◽  
Surface Displacement ◽  
Life Time ◽  
Material Model ◽  
Model Parameters ◽  
Bayesian Approaches ◽  
Model Response ◽  
The Difference

The state of materials and accordingly the properties of structures are changing over the period of use, which may influence the reliability and quality of the structure during its life-time. Therefore, identification of the model parameters of the system is a topic which has attracted attention in the content of structural health monitoring. The parameters of a constitutive model are usually identified by minimization of the difference between model response and experimental data. However, the measurement errors and differences in the specimens lead to deviations in the determined parameters. In this article, the focus is on the identification of material parameters of a viscoplastic damaging material using a stochastic simulation technique to generate artificial data which exhibit the same stochastic behavior as experimental data. It is proposed to use Bayesian inverse methods for parameter identification and therefore the model and damage parameters are identified by applying the Transitional Markov Chain Monte Carlo Method (TMCMC) and Gauss-Markov-Kalman filter (GMKF) approach. Identified parameters by using these two Bayesian approaches are compared with the true parameters in the simulation and with each other, and the efficiency of the identification methods is discussed. The aim of this study is to observe which one of the mentioned methods is more suitable and efficient to identify the model and damage parameters of a material model, as a highly non-linear model, using a limited surface displacement measurement vector and see how much information is indeed needed to estimate the parameters accurately.

scholarly journals Bayesian Parameter Determination of a CT-Test Described by a Viscoplastic-Damage Model Considering the Model Error

Metals ◽  
2020 ◽  
Vol 10 (9) ◽  
pp. 1141 ◽  
Author(s):  
Ehsan Adeli ◽  
Bojana Rosić ◽  
Hermann G. Matthies ◽  
Sven Reinstädler ◽  
Dieter Dinkler
Keyword(s):  
Experimental Data ◽  
Health Monitoring ◽  
Measurement Errors ◽  
Model Updating ◽  
Surface Displacement ◽  
Life Time ◽  
Model Error ◽  
Image Correlation ◽  
Parameter Determination ◽  
Model Parameters

The state of materials and accordingly the properties of structures are changing over the period of use, which may influence the reliability and quality of the structure during its life-time. Therefore identification of the model parameters of the system is a topic which has attracted attention in the content of structural health monitoring. The parameters of a constitutive model are usually identified by minimization of the difference between model response and experimental data. However, the measurement errors and differences in the specimens lead to deviations in the determined parameters. In this article, the Choboche model with a damage is used and a stochastic simulation technique is applied to generate artificial data which exhibit the same stochastic behavior as experimental data. Then the model and damage parameters are identified by applying the sequential Gauss-Markov-Kalman filter (SGMKF) approach as this method is determined as the most efficient method for time consuming finite element model updating problems among filtering and random walk approaches. The parameters identified using this Bayesian approach are compared with the true parameters in the simulation, and further, the efficiency of the identification method is discussed. The aim of this study is to observe whether the mentioned method is suitable and efficient to identify the model and damage parameters of a material model, as a highly non-linear model, for a real structural specimen using a limited surface displacement measurement vector gained by Digital Image Correlation (DIC) and to see how much information is indeed needed to estimate the parameters accurately even by considering the model error and whether this approach can also practically be used for health monitoring purposes before the occurrence of severe damage and collapse.

Constitutive flow models for characterizing the rheology of fresh mortar and concrete

2013 ◽  
Vol 40 (5) ◽  
pp. 475-482 ◽  
Author(s):  
S.E. Chidiac ◽  
F. Mahmoodzadeh
Keyword(s):  
Experimental Data ◽  
Constitutive Equations ◽  
Measurement Errors ◽  
Fresh Concrete ◽  
Material Model ◽  
High Concentration ◽  
Flow Models ◽  
Static Interaction ◽  
Constitutive Material Model ◽  
The Difference

Constitutive equations for fresh mortar and fresh concrete provide the characterization of the mixture’s flow and the quantification of the rheological properties. This paper presents a constitutive material model for mortar and concrete that builds on previous research. It postulates that (a) the shear stress is the sum of three components: static interaction between particles, dynamic interaction between particles, and collision of particles; and (b) the cell is a representative volume of the mixture. For fresh concrete, the effects of particle collisions are negligible due to high concentration, and the equation reduces to the Bingham model. Experimental data reported in the literature was employed to evaluate the predictive capabilities of the constitutive equations. The model results are found to compare very well with the measured experimental data and the difference is within the measurement errors.

scholarly journals BAYESIAN INFERENCE OF HETEROGENEOUS VISCOPLASTIC MATERIAL PARAMETERS

2018 ◽  
Vol 15 ◽  
pp. 41-45
Author(s):  
Eliška Janouchová ◽  
Anna Kučerová
Keyword(s):  
Experimental Data ◽  
Bayesian Inference ◽  
Material Model ◽  
Isotropic Hardening ◽  
Model Parameters ◽  
Viscoplastic Material ◽  
Loading Test ◽  
Model Response ◽  
Probabilistic Description ◽  
Probabilistic Setting

<p>Modelling of heterogeneous materials based on randomness of model input parameters involves parameter identification which is focused on solving a stochastic inversion problem. It can be formulated as a search for probabilistic description of model parameters providing the distribution of the model response corresponding to the distribution of the observed data</p><p>In this contribution, a numerical model of kinematic and isotropic hardening for viscoplastic material is calibrated on a basis of experimental data from a cyclic loading test at a high temperature. Five material model parameters are identified in probabilistic setting. The core of the identification method is the Bayesian inference of uncertain statistical moments of a prescribed joint lognormal distribution of the parameters. At first, synthetic experimental data are used to verify the identification procedure, then the real experimental data are processed to calibrate the material model of copper alloy.</p>

Nonlinear Optimization Methods for the Determination of Heat Source Model Parameters

2016 ◽  
Vol 879 ◽  
pp. 2008-2013
Author(s):  
Udo Hartel ◽  
Alexander Ilin ◽  
Steffen Sonntag ◽  
Vesselin Michailov
Keyword(s):  
Experimental Data ◽  
Heat Source ◽  
Parameter Identification ◽  
Nonlinear Optimization ◽  
Laser Beam Welding ◽  
Source Model ◽  
Transient Temperature ◽  
Surface Model ◽  
Model Parameters ◽  
Heat Source Model

In this paper the technique of parameter identification is investigated to reconstruct the 3D transient temperature field for the simulation of laser beam welding. The reconstruction bases on volume heat source models and makes use of experimental data. The parameter identification leads to an inverse heat conduction problem which cannot be solved exactly but in terms of an optimal alignment of the simulation and experimental data. To solve the inverse problem, methods of nonlinear optimization are applied to minimize a problem dependent objective function.In particular the objective function is generated based on the Response Surface Model (RSM) technique. Sampling points on the RSM are determined by means of Finite-Element-Analysis (FEA). The scope of this research paper is the evaluation and comparison of gradient based and stochastic optimization algorithms. The proposed parameter identification makes it possible to determine the heat source model parameters in an automated way. The methodology is applied on welds of dissimilar material joints.

Parameter Identification for HAPC System in Hot Tandem Mill

2010 ◽  
Vol 154-155 ◽  
pp. 781-786
Author(s):  
Xu Li ◽  
Wen Xue Zhang ◽  
Dian Hua Zhang ◽  
Dan Yan
Keyword(s):  
Experimental Data ◽  
Mathematical Model ◽  
Parameter Identification ◽  
Actual Data ◽  
Automation System ◽  
Model Parameters ◽  
Simulation Data ◽  
Matlab Software ◽  
Identification Method ◽  
Curve Fit

Under condition that exact values of model parameters can not be calculated accurately in hot tandem mill system and change with the time passing, model parameters are identified by adopting identification method based on the parameter model and sampling the datum on site; Basic automation system is used for the sampling of actual data, MATLAB software is adopted for curve fit. By comparing the experimental data and simulation data, the consequence of simulation testifies the accuracy of identified mathematical model.

STRAIN-CONTROLLED BIAXIAL TENSION OF NATURAL RUBBER: NEW EXPERIMENTAL DATA

2014 ◽  
Vol 87 (1) ◽  
pp. 120-138 ◽  
Author(s):  
Francesco Q. Pancheri ◽  
Luis Dorfmann
Keyword(s):  
Experimental Data ◽  
Natural Rubber ◽  
Excellent Agreement ◽  
Biaxial Tension ◽  
Pure Shear ◽  
Material Model ◽  
Loading Path ◽  
Model Parameters ◽  
Biaxial Strain ◽  
Strain Control

ABSTRACT We present a new experimental method and provide data showing the response of 40A natural rubber in uniaxial, pure shear, and biaxial tension. Real-time biaxial strain control allows for independent and automatic variation of the velocity of extension and retraction of each actuator to maintain the preselected deformation rate within the gage area of the specimen. We also focus on the Valanis–Landel hypothesis that is used to verify and validate the consistency of the data. We use a three-term Ogden model to derive stress–stretch relations to validate the experimental data. The material model parameters are determined using the primary loading path in uniaxial and equibiaxial tension. Excellent agreement is found when the model is used to predict the response in biaxial tension for different maximum in-plane stretches. The application of the Valanis–Landel hypothesis also results in excellent agreement with the theoretical prediction.

Finite Element Modeling of Aortic Tissue Using High Speed Experimental Data

2005 ◽  
Author(s):  
Muralikrishna Maddali ◽  
Chirag S. Shah ◽  
King H. Yang
Keyword(s):  
Experimental Data ◽  
Finite Element ◽  
High Speed ◽  
Material Model ◽  
Traumatic Rupture ◽  
Aortic Tissue ◽  
Model Parameters ◽  
Fe Model ◽  
Rubber Material ◽  
Material Constants

Traumatic rupture of the aorta (TRA) is responsible for 10% to 20% of motor vehicle fatalities [1]. Both finite element (FE) modeling and experimental investigations have enhanced our understanding of the injury mechanisms associated with TRA. Because accurate material properties are essential for the development of correct and authoritative FE model predictions, the objective of the current study was to identify a suitable material model and model parameters for aorta tissue that can be incorporated into FE aorta models for studying TRA. An Ogden rubber material (Type 77B in LS-DYNA 970) was used to simulate a series of high speed uniaxial experiments reported by Mohan [2] using a dumbbell shaped FE model representing human aortic tissue. Material constants were obtained by fitting model simulation results against experimentally obtained corridors. The sensitivity of the Ogden rubber material model was examined by altering constants G and alpha (α) and monitoring model behavior. One single set of material constants (α = 25.3, G = 0.02 GPa, and μ = 0.6000E-06 GPa) was found to fit uniaxial data at strain rates of approximately 100 s−1 for both younger and older aortic tissue specimens. Until a better material model is derived and other experimental data are obtained, it is recommended that the Ogden material model and associated constants derived from the current study be used to represent aorta tissue properties when using FE methods to investigate mechanisms of TRA.

Characterization of Thermoplastic Elastomers for Design Efforts

2014 ◽  
Vol 905 ◽  
pp. 161-166
Author(s):  
Zoltan Major ◽  
Matei C. Miron ◽  
Umut D. Cakmak
Keyword(s):  
Experimental Data ◽  
Thermoplastic Elastomer ◽  
Material Model ◽  
Thermoplastic Elastomers ◽  
Failure Behavior ◽  
Model Parameters ◽  
Material Models ◽  
Fitting Procedures ◽  
Relevant Material

Different grades of several thermoplastic elastomer types were selected and are investigated over a wide frequency/time, temperature and loading range in a research project of the authors. Relevant material models are selected for different loading situations and based on these experimental data the material model parameters were determined either directly or by applying fitting procedures. These models along with the proper data were used for modeling the deformation and the failure behavior of typical engineering thermoplastic elastomer components. Furthermore, based on the modeling of various elastomers under different service relevant loading situation several design proposals were formulated.

scholarly journals Population-Based Parameter Identification for Dynamical Models of Biological Networks with an Application to Saccharomyces cerevisiae

Processes ◽  
2021 ◽  
Vol 9 (1) ◽  
pp. 98
Author(s):  
Ewelina Weglarz-Tomczak ◽  
Jakub M. Tomczak ◽  
Agoston E. Eiben ◽  
Stanley Brul
Keyword(s):  
Experimental Data ◽  
Saccharomyces Cerevisiae ◽  
Systems Biology ◽  
Parameter Identification ◽  
Dynamic Models ◽  
Optimization Methods ◽  
Population Based ◽  
Model Parameters ◽  
K Nearest Neighbor ◽  
Dynamic Simulations

One of the central elements in systems biology is the interaction between mathematical modeling and measured quantities. Typically, biological phenomena are represented as dynamical systems, and they are further analyzed and comprehended by identifying model parameters using experimental data. However, all model parameters cannot be found by gradient-based optimization methods by fitting the model to the experimental data due to the non-differentiable character of the problem. Here, we present POPI4SB, a Python-based framework for population-based parameter identification of dynamic models in systems biology. The code is built on top of PySCeS that provides an engine to run dynamic simulations. The idea behind the methodology is to provide a set of derivative-free optimization methods that utilize a population of candidate solutions to find a better solution iteratively. Additionally, we propose two surrogate-assisted population-based methods, namely, a combination of a k-nearest-neighbor regressor with the Reversible Differential Evolution and the Evolution of Distribution Algorithm, that speeds up convergence. We present the optimization framework on the example of the well-studied glycolytic pathway in Saccharomyces cerevisiae.

Simulations of Bending Experiments of Concrete Beams by Stochastic Discrete Model

2014 ◽  
Vol 627 ◽  
pp. 457-460
Author(s):  
Jana Kaděrová ◽  
Jan Eliáš
Keyword(s):  
Experimental Data ◽  
Material Properties ◽  
Discrete Model ◽  
Concrete Beams ◽  
Particle Model ◽  
Model Parameters ◽  
Three Point Bending ◽  
Model Response ◽  
Different Types ◽  
Lattice Particle

The paper describes results of numerical simulations of experiments on concrete beams loaded in three-point bending. Stochastic lattice-particle model has been applied in which the material was represented by discrete particles of random size and location. Additional spatial variability of material properties was introduced by stationary autocorrelated random field. Three different types of geometrically similar beams were modeled: half-notched, fifth-notched and unnotched, each in four different sizes. The deterministic and stochastic model parameters were identified via automatic procedure based on comparison to a subset of experimental data, so that the adequacy of the model response could be validated by comparison with the remaining experimental data.

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