Browsing Journal of Verification, Validation and Uncertainty Quantification by Issue Date
Now showing items 1-20 of 199
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Integrating Bayesian Calibration, Bias Correction, and Machine Learning for the 2014 Sandia Verification and Validation Challenge Problem
(The American Society of Mechanical Engineers (ASME), 2016) -
A Validation of Flare Combustion Efficiency Predictions From Large Eddy Simulations
(The American Society of Mechanical Engineers (ASME), 2016) -
Verification Assessment of Piston Boundary Conditions for Lagrangian Simulation of Compressible Flow Similarity Solutions
(The American Society of Mechanical Engineers (ASME), 2016) -
Special Issue: Sandia V&V Challenge Problem
(The American Society of Mechanical Engineers (ASME), 2016) -
A New Extrapolation-Based Uncertainty Estimator for Computational Fluid Dynamics
(The American Society of Mechanical Engineers (ASME), 2016)A new Richardson extrapolation-based uncertainty estimator is developed which utilizes a global order of accuracy. The most significant difference between the proposed uncertainty estimator (referred to as the global ... -
Effective Convergence Checks for Verifying Finite Element Stresses at Two-Dimensional Stress Concentrations
(The American Society of Mechanical Engineers (ASME), 2016)The accurate determination of stresses at two-dimensional (2D) stress risers is both an important and a challenging problem in engineering. Finite element analysis (FEA) has become the method of choice in making such ... -
Reliability Analysis With Model Uncertainty Coupling With Parameter and Experiment Uncertainties: A Case Study of 2014 Verification and Validation Challenge Problem
(The American Society of Mechanical Engineers (ASME), 2016) -
Sandia Verification and Validation Challenge Problem: A PCMM-Based Approach to Assessing Prediction Credibility
(The American Society of Mechanical Engineers (ASME), 2016) -
Bayesian Uncertainty Integration for Model Calibration, Validation, and Prediction
(The American Society of Mechanical Engineers (ASME), 2016) -
Introduction: The 2014 Sandia Verification and Validation Challenge Workshop
(The American Society of Mechanical Engineers (ASME), 2016) -
Summary of the 2014 Sandia Verification and Validation Challenge Workshop
(The American Society of Mechanical Engineers (ASME), 2016) -
Uncertainty Quantification of Large-Eddy Spray Simulations
(The American Society of Mechanical Engineers (ASME), 2016) -
New Metrics for Validation of Data-Driven Random Process Models in Uncertainty Quantification
(The American Society of Mechanical Engineers (ASME), 2016) -
Probability Bounds Analysis Applied to the Sandia Verification and Validation Challenge Problem
(The American Society of Mechanical Engineers (ASME), 2016) -
Economic Analysis of Model Validation for a Challenge Problem
(The American Society of Mechanical Engineers (ASME), 2016) -
Optimal Test Selection for Prediction Uncertainty Reduction
(The American Society of Mechanical Engineers (ASME), 2016)Economic factors and experimental limitations often lead to sparse and/or imprecise data used for the calibration and validation of computational models. This paper addresses resource allocation for calibration and validation ... -
Risk Analysis of Reactor Pressure Vessels Considering Modeling-Induced Uncertainties
(The American Society of Mechanical Engineers (ASME), 2016)Uncertainties in simulation models arise not only from the parameters that are used within the model, but also due to the modeling process itself—specifically the identification of a model that most accurately predicts the ... -
Editorial
(The American Society of Mechanical Engineers (ASME), 2016) -
Conduction Invariance in Similarity Solutions for Compressible Flow Code Verification
(The American Society of Mechanical Engineers (ASME), 2016) -
The Effect of Grid Resolution and Reaction Models in Simulation of a Fluidized Bed Gasifier Through Nonintrusive Uncertainty Quantification Techniques
(The American Society of Mechanical Engineers (ASME), 2016)To improve quality of numerical models used in simulations of a fluidized bed gasifier at any scale, the sources of uncertainty in the simulation have to be identified and quantified. There are several sources of uncertainty ...