Bayesian calibration of strength model parameters from Taylor impact data

2021 ◽  
pp. 110999
Author(s):  
David Rivera ◽  
Jason Bernstein ◽  
Kathleen Schmidt ◽  
Amanda Muyskens ◽  
Matthew Nelms ◽  
...  
Keyword(s):  
Model Parameters ◽  
Strength Model ◽  
Bayesian Calibration ◽  
Taylor Impact ◽  
Impact Data

A New Approach to Determine Dynamic Strength Model Parameters under Taylor Impact Test

2012 ◽  
Vol 525-526 ◽  
pp. 377-380
Author(s):  
F. Xu ◽  
Wei Guo Guo ◽  
Q.J. Wang ◽  
Zhi Yin Zeng
Keyword(s):  
Impact Test ◽  
Dynamic Strength ◽  
Hopkinson Bar ◽  
Optimization Techniques ◽  
Model Parameters ◽  
Strength Model ◽  
New Approach ◽  
Taylor Impact ◽  
Bar Test ◽  
Taylor Impact Test

In this paper, to determine the dynamic strength model for steels, a new approach which does not rely on the Hopkinson bar test has been proposed. As the DH36 steel for example, using the results of Taylor impact test and the quasi-static compression test, the initial parameters of Johnson-Cook plastic strength model have been fitted out, then the initial strength parameters have been optimized using the optimization techniques of the sparse Taylor impact cylinder. It has been shown that the optimized results in numerical simulation are consistent with results of Taylor impact test, and the optimized Johnson-Cook model can also well describe flow stress curve fitted from the Hopkinson bar test.

scholarly journals Evaluation of strength model parameters from Taylor impact tests

2011 ◽  
Vol 10 ◽  
pp. 3453-3458 ◽  
Author(s):  
Julien Nussbaum ◽  
Norbert Faderl
Keyword(s):  
Model Parameters ◽  
Strength Model ◽  
Impact Tests ◽  
Taylor Impact

Optimizing material strength constants numerically extracted from taylor impact data

1997 ◽  
Vol 37 (3) ◽  
pp. 333-338 ◽  
Author(s):  
D. J. Allen ◽  
W. K. Rule ◽  
S. E. Jones
Keyword(s):  
Material Strength ◽  
Taylor Impact ◽  
Impact Data

scholarly journals Bayesian calibration of terrestrial ecosystem models: a study of advanced Markov chain Monte Carlo methods

2017 ◽  
Vol 14 (18) ◽  
pp. 4295-4314 ◽  
Author(s):  
Dan Lu ◽  
Daniel Ricciuto ◽  
Anthony Walker ◽  
Cosmin Safta ◽  
William Munger
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Likelihood Function ◽  
Terrestrial Ecosystem ◽  
Error Model ◽  
Model Parameters ◽  
Posterior Distributions ◽  
Ecosystem Models ◽  
Bayesian Calibration

Abstract. Calibration of terrestrial ecosystem models is important but challenging. Bayesian inference implemented by Markov chain Monte Carlo (MCMC) sampling provides a comprehensive framework to estimate model parameters and associated uncertainties using their posterior distributions. The effectiveness and efficiency of the method strongly depend on the MCMC algorithm used. In this work, a differential evolution adaptive Metropolis (DREAM) algorithm is used to estimate posterior distributions of 21 parameters for the data assimilation linked ecosystem carbon (DALEC) model using 14 years of daily net ecosystem exchange data collected at the Harvard Forest Environmental Measurement Site eddy-flux tower. The calibration of DREAM results in a better model fit and predictive performance compared to the popular adaptive Metropolis (AM) scheme. Moreover, DREAM indicates that two parameters controlling autumn phenology have multiple modes in their posterior distributions while AM only identifies one mode. The application suggests that DREAM is very suitable to calibrate complex terrestrial ecosystem models, where the uncertain parameter size is usually large and existence of local optima is always a concern. In addition, this effort justifies the assumptions of the error model used in Bayesian calibration according to the residual analysis. The result indicates that a heteroscedastic, correlated, Gaussian error model is appropriate for the problem, and the consequent constructed likelihood function can alleviate the underestimation of parameter uncertainty that is usually caused by using uncorrelated error models.

Modeling of Sleeved Taylor Impact Specimens

2003 ◽  
Author(s):  
William Keith Rule
Keyword(s):  
Stress State ◽  
Impact Test ◽  
Sliding Friction ◽  
Experimental Studies ◽  
Impact Testing ◽  
Strength Model ◽  
The Core ◽  
Post Test ◽  
Taylor Impact ◽  
Sliding Friction Coefficient

Recently experimental studies have been conducted using a novel form of the Taylor impact test consisting of sleeved cylinders. A soft material of known properties (OFHC Cu) was used for the core and the tight fitting sleeve was fabricated from the material of interest (AF1410 steel). On impact the mushrooming and sliding core places the sleeve in a stress state not normally found in Taylor impact testing. This paper describes a study conducted to evaluate the feasibility of backing out Johnson-Cook strength model coefficients from measured (post-test) deformed geometries of sleeved specimens using an explicit impact code (EPIC). In addition, modifications to the sleeved concept geometry (tapered and capped core) are also explored numerically as well as the sleeve/core sliding friction coefficient.

Energy Criterion for Fatigue Strength of Wood Structural Members

1996 ◽  
Vol 118 (3) ◽  
pp. 375-378 ◽  
Author(s):  
Jen Y. Liu ◽  
Robert J. Ross
Keyword(s):  
Fatigue Strength ◽  
Good Description ◽  
Energy Criterion ◽  
Element Model ◽  
Cyclic Stress ◽  
Model System ◽  
Model Parameters ◽  
Strength Model ◽  
Structural Members ◽  
Linear Spring

This report describes a mathematical model for fatigue strength of cellulosic materials under sinusoidal loading. The model is based on the Reiner-Weissenberg thermodynamic theory of strength in conjunction with a nonlinear Eyring’s three-element model. This theory states that failure depends on a maximum value of the intrinsic free energy that can be stored elastically in a volume element of the material. The three-element mechanical model, which consists of a linear spring in series with a parallel array of another linear spring and an Eyring dashpot, provides a good description of rheological material properties. The strength model system was able to predict rupture occurrence of polymers and wood structural members under constant and ramp loading with satisfactory results. For sinusoidal loading, the present study shows that the strength model system can predict time at fracture as a function of applied mean stress, amplitude of cyclic stress, and stress frequency. Numerical examples with model parameters evaluated for small Douglas-fir beams are presented.

scholarly journals Mathematical Modeling and Robustness Analysis to Unravel COVID-19 Transmission Dynamics: The Italy Case

Biology ◽  
2020 ◽  
Vol 9 (11) ◽  
pp. 394
Author(s):  
Chiara Antonini ◽  
Sara Calandrini ◽  
Fabrizio Stracci ◽  
Claudio Dario ◽  
Fortunato Bianconi
Keyword(s):  
National Level ◽  
Robustness Analysis ◽  
Calibration Method ◽  
Model Parameters ◽  
Civil Protection ◽  
Bayesian Calibration ◽  
Computational Framework ◽  
Hospitalization Rates ◽  
Intervention Measures ◽  
Umbria Region

This study started from the request of providing predictions on hospitalization and Intensive Care Unit (ICU) rates that are caused by COVID-19 for the Umbria region in Italy. To this purpose, we propose the application of a computational framework to a SEIR-type (Susceptible, Exposed, Infected, Removed) epidemiological model describing the different stages of COVID-19 infection. The model discriminates between asymptomatic and symptomatic cases and it takes into account possible intervention measures in order to reduce the probability of transmission. As case studies, we analyze not only the epidemic situation in Umbria but also in Italy, in order to capture the evolution of the pandemic at a national level. First of all, we estimate model parameters through a Bayesian calibration method, called Conditional Robust Calibration (CRC), while using the official COVID-19 data of the Italian Civil Protection. Subsequently, Conditional Robustness Analysis (CRA) on the calibrated model is carried out in order to quantify the influence of epidemiological and intervention parameters on the hospitalization rates. The proposed pipeline properly describes the COVID-19 spread during the lock-down phase. It also reveals the underestimation of new positive cases and the need of promptly isolating asymptomatic and presymptomatic cases. The results emphasize the importance of the lock-down timeliness and provide accurate predictions on the current evolution of the pandemic.

scholarly journals Sensitivity-Based Parameter Calibration and Model Validation Under Model Error

2017 ◽  
Vol 140 (1) ◽  
Author(s):  
Na Qiu ◽  
Chanyoung Park ◽  
Yunkai Gao ◽  
Jianguang Fang ◽  
Guangyong Sun ◽  
...  
Keyword(s):  
Parameter Estimation ◽  
Calibration Method ◽  
Model Error ◽  
Model Parameters ◽  
Observation Data ◽  
Parameter Calibration ◽  
Bayesian Calibration ◽  
Modeling Errors ◽  
Model Discrepancy ◽  
Better Than

In calibrating model parameters, it is important to include the model discrepancy term in order to capture missing physics in simulation, which can result from numerical, measurement, and modeling errors. Ignoring the discrepancy may lead to biased calibration parameters and predictions, even with an increasing number of observations. In this paper, a simple yet efficient calibration method is proposed based on sensitivity information when the simulation model has a model error and/or numerical error but only a small number of observations are available. The sensitivity-based calibration method captures the trend of observation data by matching the slope of simulation predictions and observations at different designs and then utilizing a constant value to compensate for the model discrepancy. The sensitivity-based calibration is compared with the conventional least squares calibration method and Bayesian calibration method in terms of parameter estimation and model prediction accuracies. A cantilever beam example, as well as a honeycomb tube crush example, is used to illustrate the calibration process of these three methods. It turned out that the sensitivity-based method has a similar performance with the Bayesian calibration method and performs much better than the conventional method in parameter estimation and prediction accuracy.

scholarly journals The Transmuted Odd Lindley-G Family of Distributions

2018 ◽  
pp. 1-25 ◽  
Author(s):  
Hesham Reyad ◽  
Farrukh Jamal ◽  
Soha Othman ◽  
G. G. Hamedani
Keyword(s):  
Information Matrix ◽  
Real Data ◽  
Maximum Likelihood Estimates ◽  
Model Parameters ◽  
Data Sets ◽  
Strength Model ◽  
New Family ◽  
Mathematical Properties ◽  
Probability Weighted Moments ◽  
Family Of Distributions

We propose a new generator of univariate continuous distributions with two extra parameters called the transmuted odd-Lindley generator which extends the odd Lindely-G family introduced by Gomes-Silva et al. [1]. Some mathematical properties of the new generator such as, the ordinary and incomplete moments, generating function, stress strength model, Rényi entropy, probability weighted moments and order statistics are investigated. Certain characterisations of the proposed family are estimated. We discuss the maximum likelihood estimates and the observed information matrix for the model parameters. The potentiality of the new family is illustrated by means of five applications to real data sets.  

A Sequential Calibration and Validation Framework for Model Parameter Updating and Bias Correction

2020 ◽  
Author(s):  
Chen Jiang ◽  
Yixuan Liu ◽  
Zhen Hu ◽  
Zissimos P. Mourelatos ◽  
David Gorsich ◽  
...  
Keyword(s):  
Model Validation ◽  
Bias Correction ◽  
Model Parameters ◽  
Model Bias ◽  
Bayesian Calibration ◽  
Prior Distributions ◽  
Model Parameter ◽  
Calibration And Validation ◽  
Validation Framework ◽  
Uncertain Model

Abstract Model parameter updating and bias correction plays an essential role in improving the validity of Modeling and Simulation (M&S) in engineering design and analysis. However, it is observed that the existing methods may either be misled by potentially wrong information if the computer model cannot adequately capture the underlying true physics, or be affected by the prior distributions of the unknown model parameters. In this paper, a sequential model calibration and validation (SeCAV) framework is proposed to improve the efficacy of both model parameter updating and model bias correction, where the model validation and Bayesian calibration are implemented in a sequential manner. In each iteration, the model validation assessment is employed as a filter to select the best experimental data for Bayesian calibration, and to update the prior distributions of uncertain model parameters for the next iteration. The calibrated parameters are then integrated with model bias correction to improve the prediction accuracy of the M&S. A mathematical example is employed to demonstrate the advantages of the SeCAV method.

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