Handling Bias and Uncertainty in Model Verification and Validation associated with Heated Pipes Pressurized to Failure

[1]  Christopher J. Roy,et al.  Verification and Validation in Scientific Computing: Design and execution of validation experiments , 2010 .

[2]  Christopher J. Roy,et al.  A Complete Framework for Verification, Validation, and Uncertainty Quantification in Scientific Computing (Invited) , 2010 .

[3]  Vicente Jose Romero,et al.  Elements of a pragmatic approach for dealing with bias and uncertainty in experiments through predictions : experiment design and data conditioning; %22real space%22 model validation and conditioning; hierarchical modeling and extrapolative prediction. , 2011 .

[4]  W. Oberkampf,et al.  Model validation and predictive capability for the thermal challenge problem , 2008 .

[5]  Vicente J. Romero.  Type X and Y Errors and Data & Model Conditioning for Systematic Uncertainty in Model Calibration, Validation, and Extrapolation , 2008 .

[6]  Gerald William Wellman,et al.  A simple approach to modeling ductile failure. , 2012 .

[7]  Timothy G. Trucano,et al.  General Concepts for Experimental Validation of ASCI Code Applications , 2002 .

[8]  Matthew F. Barone,et al.  Measures of Agreement Between Computation and Experiment: Validation Metrics , 2004 .

[9]  V. Romero.  Validated Model? Not So Fast. The Need for Model "Conditioning" as an Essential Addendum to Model Validation 1 , 2007 .

[10]  James Thomas Nakos,et al.  Uncertainty analysis of thermocouple measurements used in normal and abnormal thermal environment experiments at Sandia's Radiant Heat Facility and Lurance Canyon Burn Site. , 2004 .

[11]  Vicente J. Romero,et al.  Application of a Versatile "Real-Space" Validation Methodology to a Fire Model , 2010 .

[12]  Vicente J. Romero,et al.  Data & Model Conditioning for Multivariate Systematic Uncertainty in Model Calibration, Validation, and Extrapolation 1 , 2010 .

[13]  Vicente J. Romero,et al.  Some Statistical Procedures to Refine Estimates of Uncertainty when Sparse Data are Available for Model Validation and Calibration. , 2011 .

[14]  Vicente J. Romero,et al.  A Comparison of Methods for Representing and Aggregating Uncertainties involving Sparsely Sampled Random Variables - Final Results , 2013 .

[15]  Richard Guy Hills,et al.  Roll-up of validation results to a target application. , 2013 .

[16]  G. Schuëller,et al.  The use of kernel densities and confidence intervals to cope with insufficient data in validation experiments , 2008 .

[17]  Christopher J. Roy,et al.  A comprehensive framework for verification, validation, and uncertainty quantification in scientific computing , 2011 .

[18]  W. Gill,et al.  Shroud boundary condition characterization experiments at the Radiant Heat Facility. , 2004 .

[19]  Vicente J. Romero,et al.  A comparison of methods for representing sparsely sampled random quantities. , 2013 .

[20]  Vicente J. Romero,et al.  A Method for Projecting Uncertainty from Sparse Samples of Discrete Random Functions - Example of Multiple Stress-Strain Curves. , 2012 .

[21]  Vicente J. Romero,et al.  Development and Validation of a Component Failure Model , 2005 .