scholarly journals Arguments for the Generality and Effectiveness of “Discrete Direct” Model Calibration and Uncertainty Propagation vs. Other Calibration-UQ Approaches

2022 ◽  
Author(s):  
Vicente J. Romero
Keyword(s):  
Model Calibration ◽  
Uncertainty Propagation ◽  
Direct Model

Discrete-Direct Model Calibration and Uncertainty Propagation Method Confirmed on Multi-Parameter Plasticity Model Calibrated to Sparse Random Field Data

2021 ◽  
Vol 7 (2) ◽  
Author(s):  
Vicente J. Romero ◽  
Justin G. Winokur ◽  
George E. Orient ◽  
James F. Dempsey
Keyword(s):  
Strain Rate ◽  
Model Calibration ◽  
Structural Model ◽  
Uncertainty Propagation ◽  
Material Strength ◽  
Rate Dependent ◽  
Direct Model ◽  
Von Mises ◽  
Von Mises Stresses ◽  
Force Displacement

Abstract A discrete direct (DD) model calibration and uncertainty propagation approach is explained and demonstrated on a 4-parameter Johnson-Cook (J-C) strain-rate dependent material strength model for an aluminum alloy. The methodology's performance is characterized in many trials involving four random realizations of strain-rate dependent material-test data curves per trial, drawn from a large synthetic population. The J-C model is calibrated to particular combinations of the data curves to obtain calibration parameter sets which are then propagated to “Can Crush” structural model predictions to produce samples of predicted response variability. These are processed with appropriate sparse-sample uncertainty quantification (UQ) methods to estimate various statistics of response with an appropriate level of conservatism. This is tested on 16 output quantities (von Mises stresses and equivalent plastic strains) and it is shown that important statistics of the true variabilities of the 16 quantities are bounded with a high success rate that is reasonably predictable and controllable. The DD approach has several advantages over other calibration-UQ approaches like Bayesian inference for capturing and utilizing the information obtained from typically small numbers of replicate experiments in model calibration situations—especially when sparse replicate functional data are involved like force–displacement curves from material tests. The DD methodology is straightforward and efficient for calibration and propagation problems involving aleatory and epistemic uncertainties in calibration experiments, models, and procedures.

scholarly journals Basic Framework and Main Methods of Uncertainty Quantification

2020 ◽  
Vol 2020 ◽  
pp. 1-18
Author(s):  
Juan Zhang ◽  
Junping Yin ◽  
Ruili Wang
Keyword(s):  
Sensitivity Analysis ◽  
Bayesian Inference ◽  
Uncertainty Quantification ◽  
Model Uncertainty ◽  
Model Calibration ◽  
Uncertainty Propagation ◽  
Propagation Model ◽  
Practical Significance ◽  
The Core ◽  
Basic Framework

Since 2000, the research of uncertainty quantification (UQ) has been successfully applied in many fields and has been highly valued and strongly supported by academia and industry. This review firstly discusses the sources and the types of uncertainties and gives an overall discussion on the goal, practical significance, and basic framework of the research of UQ. Then, the core ideas and typical methods of several important UQ processes are introduced, including sensitivity analysis, uncertainty propagation, model calibration, Bayesian inference, experimental design, surrogate model, and model uncertainty analysis.

Two-stage nested optimization-based uncertainty propagation method for model calibration

2016 ◽  
Vol 07 (01) ◽  
pp. 1541003 ◽  
Author(s):  
Xiaochao Qian ◽  
Wei Li ◽  
Ming Yang
Keyword(s):  
Experimental Data ◽  
Objective Function ◽  
Model Calibration ◽  
Epistemic Uncertainty ◽  
Uncertainty Propagation ◽  
Simulation Method ◽  
Optimization Method ◽  
Aleatory Uncertainty ◽  
Unknown Parameters ◽  
Two Stage

Model calibration is the procedure that adjusts the unknown parameters in order to fit the model to experimental data and improve predictive capability. However, it is difficult to implement the procedure because of the aleatory uncertainty. In this paper, a new method of model calibration based on uncertainty propagation is investigated. The calibration process is described as an optimization problem. A two-stage nested uncertainty propagation method is proposed to resolve this problem. Monte Carlo Simulation method is applied for the inner loop to propagate the aleatory uncertainty. Optimization method is applied for the outer loop to propagate the epistemic uncertainty. The optimization objective function is the consistency between the result of the inner loop and the experimental data. Thus, different consistency measurement methods for unary output and multivariate outputs are proposed as the optimization objective function. Finally, the thermal challenge problem is given to validate the reasonableness and effectiveness of the proposed method.

Relationships Between Bidirectional Work-Family Interactions and Psychological Well-Being

2014 ◽  
Vol 13 (2) ◽  
pp. 87-96 ◽  
Author(s):  
Xi-Chao Zhang ◽  
Oi Ling Siu ◽  
Jing Hu ◽  
Weiwei Zhang
Keyword(s):  
Family Conflict ◽  
Structural Equation ◽  
Time Lag ◽  
Well Being ◽  
Equation Modeling ◽  
Family Interactions ◽  
Work Family Conflict ◽  
Psychological Well Being ◽  
Direct Model ◽  
Work Family

This study investigated the direct, reversed, and reciprocal relationships between bidirectional work-family conflict/work-family facilitation and psychological well-being (PWB). We administered a three-wave questionnaire survey to 260 married Chinese employees using a time lag of one month. Cross-lagged structural equation modeling analysis was conducted and demonstrated that the direct model was better than the reversed causal or the reciprocal model. Specifically, work-to-family conflict at Time 1 negatively predicted PWB at Time 2, and work-to-family conflict at Time 2 negatively predicted PWB at Time 3; further, work-to-family facilitation at Time 1 positively predicted PWB at Time 2. In addition, family-to-work facilitation at Time 1 positively predicted PWB at Time 2, and family-to-work conflict at Time 2 negatively predicted PWB at Time 3.

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