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>

Bayesian Updating of Aleatory Uncertainties in Heterogeneous Materials

2017 ◽  
Vol 1144 ◽  
pp. 136-141
Author(s):  
Eliška Janouchová ◽  
Anna Kučerová ◽  
Jan Sýkora
Keyword(s):  
Bayesian Inference ◽  
Probability Distribution ◽  
Parameter Identification ◽  
Material Properties ◽  
Model Parameters ◽  
Experimental Measurements ◽  
Identification Procedure ◽  
Model Response ◽  
Probabilistic Description ◽  
Probabilistic Setting

Advances in meta-modelling and increasing computational capacity of modern computerspermitted many researches to focus on parameter identification in probabilistic setting. Increasinglypopular Bayesian inference allows to estimate model parameters together with corresponding epistemicuncertainties from indirect experimental measurements. However in case of a heterogeneousmaterial model, the identification procedure has to be able to quantify the aleatory uncertainties capturingthe variability of the material properties. Parameter identification of a heterogeneous materialmodel can be formulated as a search for probabilistic description of its parameters providing the distributionof the model response corresponding to the distribution of the observed data, i.e. a stochasticinversion problem. By prescribing a specific type of probability distribution to the model parameterswith corresponding uncertain moments, the task changes to the identification of these so-calledhyperparameters of the distribution which can be inferred in the Bayesian way.

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.

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.

Stochastic Model Calibration Based on Measurements from Different Experiments

2016 ◽  
Vol 825 ◽  
pp. 135-140
Author(s):  
Eliška Janouchová ◽  
Anna Kučerová ◽  
Jan Sýkora
Keyword(s):  
Stochastic Model ◽  
Model Calibration ◽  
Heterogeneous Material ◽  
Material Model ◽  
Identification Of Parameters ◽  
Model Response ◽  
Probabilistic Description ◽  
Different Types

The calibration of a heterogeneous material model can be formulated as a search for probabilistic description of its parameters providing the distribution of the model response corresponding to the distribution of the observed data. This contribution is focused on developing a method for identification of parameters along with their variations based on combining measurements from different types of destructive experiments.

Modeling of Crack Propagation in Defective X100 Line Pipes

2020 ◽  
Author(s):  
Lufeng Xue ◽  
Marcelo Paredes ◽  
Aida Nonn ◽  
Tomasz Wierzbicki
Keyword(s):  
Crack Propagation ◽  
Fracture Initiation ◽  
Material Model ◽  
Ring Expansion ◽  
Yield Function ◽  
Experimental Program ◽  
Isotropic Hardening ◽  
Model Parameters ◽  
Expansion Test ◽  
Initiation And Propagation

Abstract A comprehensive experimental program is carried out to determine material parameters for fracture initiation and propagation in X100 pipeline steels. The quadratic Hill’48 yield function along with an isotropic hardening are used to describe plastic flow at large deformation and a phenomenological fracture criterion to predict fracture initiation. Fracture mechanics SENT specimens are used to calibrate post-initiation softening parameters necessary for ductile crack propagation in thick components. Once the material model parameters set is complete a final comparison is conducted with ring expansion test on same material.

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.

Yield Surface Investigation of Alloys During Model Disk Spin Tests

2014 ◽  
Author(s):  
Anton N. Servetnik ◽  
Evgeny P. Kuzmin
Keyword(s):  
Experimental Approach ◽  
Eddy Currents ◽  
Material Model ◽  
Isotropic Hardening ◽  
Model Parameters ◽  
Element Analysis ◽  
Strain Fields ◽  
Von Mises ◽  
Yield Conditions ◽  
Incremental Theory Of Plasticity

Results of quasi-static numerical simulation of spin tests of model disk made from high-temperature forged alloy are presented. To determine stress-strain state of disk during loading finite element analysis is used. Simulation of elastic-plastic strain fields was carried out using incremental theory of plasticity with isotropic hardening. Model sensitivity from Von mises and Tresca yield conditions and hardening conditions was investigated. To identify the material model parameters an experimental approach of rim radial displacement measurement by eddy currents sensor during the load-unload of spin test was used. Calculation made using different material models were compared with the experimental results.

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.

scholarly journals Online Estimation of Terminal Airspace Sector Capacity from ATC Workload

2021 ◽  
Author(s):  
Majeed Mohamed
Keyword(s):  
Neural Network ◽  
Dynamical System ◽  
Experimental Data ◽  
Neural Model ◽  
Model Parameters ◽  
Air Traffic Controller ◽  
Single Output ◽  
Estimated Parameters ◽  
Model Response ◽  
Relative Standard

Neural Partial Differentiation (NPD) approach is applied to estimate terminal airspace sector capacity in real-time from the ATC (Air Traffic Controller) dynamical neural model with permissible safe separation and affordable workload. A neural model of a multi-input-single-output (MISO) ATC dynamical system is primarily established and used to estimate parameters from the experimental data using NPD. Since the relative standard deviations of these estimated parameters are lesser, the predicted neural model response is well matched with the intervention of ATC workload. Moreover, the proposed neural network-based approach works well with the experimental data online as it does not require the initial values of model parameters that are unknown in practice.

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