REFERENCES
- C. J. Roy and W. L. Oberkampf, “A Comprehensive Framework for Verification, Validation, and Uncertainty Quantification in Scientific Computing,” Comput. Methods Appl. Mech. Eng., 200, 2131 (2011); http://dx.doi.org/10.1016/j.cma.2011.03.016.
- S. M. Hoseyni et al., “A Systematic Framework for Effective Uncertainty Assessment of Severe Accident Calculations; Hybrid Qualitative and Quantitative Methodology,” Reliab. Eng. Syst. Saf., 125, 22 (2014); http://dx.doi.org/10.1016/j.ress.2013.06.037.
- M. Pourgol-Mohammad, “Thermal-Hydraulics System Codes Uncertainty Assessment: A Review of the Methodologies,” Ann. Nucl. Energy, 36, 1774 (2009); http://dx.doi.org/10.1016/j.anucene.2009.08.018.
- M. Pourgol-Mohammad, A. Mosleh, and M. Modarres, “Structured Treatment of Model Uncertainty in Complex Thermal-Hydraulics Codes: Technical Challenges, Prospective and Characterization,” Nucl. Eng. Des., 241, 285 (2010); http://dx.doi.org/10.1016/j.nucengdes.2010.10.035.
- L. N. Kmetyk, “MELCOR 1.8.1 Assessment: LOFT Integral Experiment LP-FP-2,” SAND92-1373, Sandia National Laboratories (1992).
- “Standard for Level 1/Large Early Release Frequency Probabilistic Risk Assessment for Nuclear Power Plant Applications,” ASME (2009).
- T. Bjerga, T. Aven, and E. Zio, “An Illustration of the Use of an Approach for Treating Model Uncertainties in Risk Assessment,” Reliab. Eng. Syst. Saf., 125, 46 (2014); http://dx.doi.org/10.1016/j.ress.2014.01.014.
- Code ofFederal Regulations, Title 10, “Energy,” Parts 1 through 50, U.S. Nuclear Regulatory Commission (2003).
- R. O. Gauntt, “Uncertainty Analyses Using the MELCOR Severe Accident Analysis Code,” Proc. Workshop Evaluation of Uncertainties in Relation to Severe Accidents and Level-2 Probabilistic Safety Analysis, Aix-En-Provence, France, 2005.
- M. Pourgol-Mohamad, “Integrated Methodology for Thermal-Hydraulic Code Uncertainty Analysis with Application,” Nucl. Technol., 165, 333 (2009); http://dx.doi.org/10.13182/NT165-333.
- M. Pourgol-Mohamad, “Integrated Methodology on Thermal Hydraulics Uncertainty Analysis (IMTHUA),” PhD Thesis, University of Maryland (2007).
- M. Pourgol-Mohamad, A. Mosleh, and M. Modarres, “Methodology for the Use of Experimental Data to Enhance Model Output Uncertainty Assessment in Thermal Hydraulics Codes,” Reliab. Eng. Syst. Saf., 95, 77 (2010); http://dx.doi.org/10.1016/j.ress.2009.08.003.
- S. M. Hoseyni and M. Pourgol-Mohammad, “Model Uncertainty Assessment; Review of Available Approaches,” Proc. Int. Reliability Engineering Conf. (IREC2014), Tehran, Iran, 2014.
- W. Yao et al., “Review of Uncertainty-Based Multidisciplinary Design Optimization Methods for Aerospace Vehicles,” Prog. Aerosp. Sci., 47, 450 (2011); http://dx.doi.org/10.1016/j.paerosci.2011.05.001.
- D. Draper, “Assessment and Propagation of Model Uncertainty,” J. R. Stat. Soc. Ser. B Stat. Methodol., 57, 1, 45 (1995).
- K. B. Laskey, “Model Uncertainty: Theory and Practical Implications,” IEEE Trans. Syst. Man. Cybern. Part C Appl. Rev., 26, 3, 340 (1996); http://dx.doi.org/10.1109/3468.487959.
- R. Rebba, S. Mahadevan, and R. Zhang, “Validation of Uncertainty Propagation Models,” Proc. 44th AIAA/ASME/ASCE/AHS Structures, Structural Dynamics and Materials Conf., Norfolk, Virginia, 2003, AIAA-2003-1913, American Institute of Aeronautics and Astronautics (2003).
- S. Mahadevan and R. Rebba, “Validation of Reliability Computational Models Using Bayes Networks,” Reliab. Eng. Syst. Saf, 87, 2, 223 (2005); http://dx.doi.org/10.1016/j.ress.2004.05.001.
- J. Faragher, “Probabilistic Methods for the Quantification of Uncertainty and Error in Computational Fluid Dynamics Simulations,” DSTO-TR-1633, Defence Science and Technology Organisation (2004).
- W. L. Oberkampf and T. G. Trucano, “Verification and Validation in Computational Fluid Dynamics,” Prog. Aerosp. Sci., 38, 3, 209 (2002); http://dx.doi.org/10.1016/S0376-0421(02)00005-2.
- “Approaches and Tools for Severe Accident Analysis for Nuclear Power Plants,” IAEA SRS-56, International Atomic Energy Agency (2008).
- I. Park and R. V. Grandhi, “Quantification of Model-Form and Parametric Uncertainty Using Evidence Theory,” Struct. Saf, 39, 44 (2012); http://dx.doi.org/10.1016/strusafe.2012.08.003.
- J. C. Helton et al., “A Sampling-Based Computational Strategy for the Representation of Epistemic Uncertainty in Model Predictions with Evidence Theory,” Comput. Methods AppJ. Mech. Eng., 196, 3980 (2007); http://dx.doi.org/10.1016/j.cma.2006.10.049.
- E. Zio and G. E. Apostolakis, “Two Methods for the Structural Assessment of Model Uncertainty by Experts in Performance Assessments of Radioactive Waste Repositories,” Reliab. Eng. Syst. Saf, 54, 2-3, 225 (1996); http://dx.doi.org/10.1016/S0951-8320(96)00078-6.
- Y. Ling and S. Mahadevan, “Quantitative Model Validation Techniques: New Insights,” ReJiab. Eng. Syst. Saf., 111, 217 (2013); http://dx.doi.org/10.1016/j.ress.2012.11.011.
- E. L. Droguett, “Methodology for the Treatment of Model Uncertainty,” PhD Thesis, University of Maryland (1999).
- I. Park and R. V. Grandhi, “A Bayesian Statistical Method for Quantifying Model Form Uncertainty and Two Model Combination Methods,” Reliab. Eng. Syst. Saf., 129, 46 (2014); http://dx.doi.org/10.1016/j.ress.2014.04.023.
- R. Zhang and S. Mahadevan, “Model Uncertainty and Bayesian Updating in Reliability-Based Inspection,” Struct. Saf., 22, 2, 145 (2000); http://dx.doi.org/10.1016/S0167-4730(00)00005-9.
- A. Raftery, D. Madigan, and J. Hoeting, “Bayesian Model Averaging for Linear Regression Models,” J. Am. Stat. Assoc., 92, 179 (1993); http://dx.doi.org/10.1080/01621459.1997.10473615.
- I. Park, “Quantification of Multiple Types of Uncertainty in Physics-Based Simulation,” PhD Thesis, Wright State University (2012).
- I. Park and R. V. Grandhi, “Quantifying Multiple Types of Uncertainty in Physics-Based Simulation Using Bayesian Model Averaging,” AIAA J., 49, 5, 1038 (2011); http://dx.doi.org/10.2514/1.J050741.
- E. L. Droguett and A. Mosleh, “Bayesian Treatment of Model Uncertainty for Partially Applicable Models,” Risk Anal., 34, 2, 252 (2014); http://dx.doi.org/10.1111/risa.12121.
- E. L. Droguett and A. Mosleh, “Bayesian Methodology for Model Uncertainty Using Model Performance Data,” Risk Anal., 28, 5, 1457 (2008); http://dx.doi.org/10.1111/j.1539-6924.2008.01117.x.
- Nuclear Safety in Light Water Reactors: Severe Accident Phenomenology, B.R. Sehgal, Ed., Academic Press (2012).
- S. E. Yakush, P. Kudinov, and N. T. Lubchenko, “Coolability of Heat-Releasing Debris Bed. Part 1: Sensitivity Analysis and Model Calibration,” Ann. Nucl. Energy, 52, 59 (2013); http://dx.doi.org/10.1016/j.anucene.2012.06.024.
- I. Park, H. K. Amarchinta, and R. V. Grandhi, “A Bayesian Approach for Quantification of Model Uncertainty,” Reliab. Eng. Syst. Saf., 95, 7, 777 (2010); http://dx.doi.org/10.1016/j.ress.2010.02.015.
- B. L. Lewis et al., “Overview of Experimental Programs on Core Melt Progression and Fission Product Release Behaviour,” J. Nucl. Mater., 380, 126 (2008); http://dx.doi.org/10.1016/j.jnucmat.2008.07.005.
- R. O. Gauntt et al., “MELCOR 1.8.6 Computer Code Manuals, Volume 1 & 2,” NUREG/CR-6119, U.S. Nuclear Regulatory Commission (2005).
- “In-Vessel Core Degradation Code Validation Matrix Update 1996–1999,” Organisation for Economic Co-operation and Development/Nuclear Energy Agency (2001).
- R. O. Gauntt, “MELCOR 1.8.5 Modeling Aspects of Fission Product Release, Transport and Deposition,” SAND 2010-1635, Sandia National Laboratories (2010).
- T. Haste et al., “MELCOR/MACCS Simulation of the TMI-2 Severe Accident and Initial Recovery Phases, Off-Site Fission Product Release and Consequences,” Nucl. Eng. Des., 236, 10, 1099 (2006); http://dx.doi.org/10.1016/j.nucengdes.2005.11.012.
- “MELCOR Modeling of Phebus FTP1,” Korea Atomic Energy Research Institute (1998).
- T. A. Oliver et al., “Validating Predictions of Unobserved Quantities,” Comput. Methods Appl. Mech. Eng., 283, 1310 (2015); http://dx.doi.org/10.1016/j.cma.2014.08.023.