Bayesian analysis for estimating statistical parameter distributions of elasto-viscoplastic material models

Chakraborty, Aritra; Messner, Mark C.

doi:10.1016/j.probengmech.2021.103153

Cited by 8 publications

(6 citation statements)

References 43 publications

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“…The viscoelastic strain rate given by Equation 3 is suitable for long-term monotonic loading and was developed for design analyses, such as the primary load design in the American Society of Mechanical Engineers (ASME) Boiler and Pressure Vessel Code. Chakraborty and Messner [14 ] used a Bayesian Markov Chain Monte Carlo analysis to fit distributions of viscoplastic parameters in Equation 3 to a range of Grade 91 experimental data. In this work, we use the mean values from their distributions fit to data for 550 • C, given in Table 2 .…”

Section: Grade 91mentioning

confidence: 99%

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High-Temperature Creep Test Suite for Grizzly

Singh

Messner

Munday

et al. 2022

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A recent development in Grizzly has focused on improving its ability to model the high-temperature response of alloys, enabling its use for the predictive modeling of material response for advanced reactor applications. This report documents an effort to expand the example and assessment test suites in Grizzly to include a set of problems that exercise the models for the high-temperature response of components relevant to advanced reactors. These problems can be used to assess the accuracy of models, provide examples for users interested in modeling this class of problems, monitor performance on large-scale problems, and use as test cases for future feature development.

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Section: Grade 91mentioning

confidence: 99%

“…where values for the adjustable damage parameters , , and , given in Table 2 , are obtained from a linear Larson-Miller fit [14 ].…”

Section: Grade 91mentioning

confidence: 99%

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High-Temperature Creep Test Suite for Grizzly

Singh

Messner

Munday

et al. 2022

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“…Subsequently, Adeli et al 20,21 also applied the Bayesian method to identify the parameters of the viscoplastic damage model. Chakraborty and Messner 22 investigated the effect of different aspects of the Bayesian method on the parameter identification of the viscoplastic damage model.…”

Section: Introductionmentioning

confidence: 99%

“…Vigliotti et al 33 employed synthetic experimental data to validate the effectiveness of the Bayesian method. Chakraborty and Messner 22 discussed different aspects of Bayesian parameter identification by synthetic reference data. Although synthetically generated experimental data can exhibit the approximate stochastic behavior as real experimental data to a certain extent, it cannot entirely replace real experimental data.…”

Section: Introductionmentioning

confidence: 99%

Parameter identification of elastoplastic model for CuCrZr alloy by the neural network‐aided Bayesian inference

Chen

Jin

et al. 2023

Fatigue Fract Eng Mat Struct

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The constitutive model serves as the foundation for executing structure analysis to obtain the deformation and stress/strain. In this paper, a neural network‐assisted Bayesian parameter identification framework is presented to calibrate parameters of the constitutive model while considering the unavoidable uncertainties. The low‐cycle fatigue test of the CuCrZr alloy at 700 K is first performed to provide realistic data. The posterior distributions are obtained by applying the transitional Markov Chain Monte Carlo method. To accelerate the identification, the neural network is adopted to directly predict the likelihood function value given material parameters. The effect of prior distributions on the identification parameters is also studied. The characteristic parameters of the normal distribution have almost no effect on the identification results. In the absence of prior information, uniform prior distributions can be used to perform Bayesian identification of material parameters, and satisfactory identification parameters can also be acquired.

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Data-Driven Accelerated Parameter Identification for Chaboche-Type Visco-Plastic Material Models to Describe the Relaxation Behavior of Copper Alloys

Morand,

Norouzi,

Weber

et al. 2024

Exp Mech

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Background Calibrating material models to experimental measurements is crucial for realistic computational analysis of components. For complex material models, however, optimization-based identification procedures can become time-consuming, particularly if the optimization problem is ill-posed. Objective The objective of this paper is to assess the feasibility of using machine learning to identify the parameters of a Chaboche-type material model that describes copper alloys. Specifically, we apply and analyze this identification approach using short-term uniaxial relaxation tests on a C19010 copper alloy. Methods A genetic algorithm forms the basis for identifying the parameters of the Chaboche-type material model. The approach is accelerated by replacing the numerical simulation of the experimental setup by a neural network surrogate. The neural networks-based approach is compared against a classic approach using both, synthetic and experimental data. Results The results show that on the one hand, a sufficiently accurate identification of the material model parameters can be achieved by a classic but time-consuming genetic algorithm. On the other hand, it is shown that machine learning enables a much more time-efficient identification procedure, however, suffering from the ill-posedness of the identification problem. Conclusion Compared to classic parameter identification approaches, machine learning techniques can significantly accelerate the identification procedure for parameters of Chaboche-type material models with acceptable loss of accuracy.

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Bayesian analysis for estimating statistical parameter distributions of elasto-viscoplastic material models

Cited by 8 publications

References 43 publications

High-Temperature Creep Test Suite for Grizzly

High-Temperature Creep Test Suite for Grizzly

Parameter identification of elastoplastic model for CuCrZr alloy by the neural network‐aided Bayesian inference

Data-Driven Accelerated Parameter Identification for Chaboche-Type Visco-Plastic Material Models to Describe the Relaxation Behavior of Copper Alloys

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