Inverse uncertainty quantification by hierarchical Bayesian inference for TRACE physical model parameters based on BFBT benchmark

Keyphrases

• Parameter Dependence 100%
• Physical Model 100%
• Hierarchical Bayesian Inference 100%
• Inverse Uncertainty Quantification 100%
• Posterior Distribution 75%
• Hierarchical Model 50%
• Input Uncertainty 50%
• Steady State 25%
• Sampling Methods 25%
• Calculation Results 25%
• Sampling Problems 25%
• Simulation Model 25%
• Void Fraction 25%
• Small Dataset 25%
• Markov Chain Monte Carlo Algorithm 25%
• Large Uncertainty 25%
• Gradient-based 25%
• Different Datasets 25%
• Self-assessment 25%
• Uncertainty Analysis 25%
• Expert Opinion 25%
• Thermal-hydraulics 25%
• Nuclear Engineering 25%
• Data Assimilation 25%
• Prediction Uncertainty 25%
• Experimental Case Study 25%
• Sampling Process 25%
• Best Known Codes 25%
• Monte Carlo (MC) Simulation 25%
• Bayesian Calibration 25%
• Best Estimate Plus Uncertainty 25%
• Quantification Framework 25%
• Bayesian Inference Approach 25%
• Stable Frameworks 25%
• No-U-Turn Sampler 25%

Engineering

• Model Parameter 100%
• Physical Model 100%
• Uncertainty Quantification 100%
• Posterior Distribution 75%
• Input Uncertainty 50%
• Simulation Model 25%
• Input Parameter 25%
• Void Fraction 25%
• Thermal Hydraulics 25%
• Nuclear Engineering 25%
• So Framework 25%
• Expert Judgment 25%
• Sampling Process 25%

Computer Science

• Physical Model 100%
• Posterior Distribution 75%
• markov chain monte-carlo 50%
• Hierarchical Model 50%
• Sampling Problem 25%
• Simulation Model 25%
• Input Parameter 25%
• Sampling Process 25%
• Expert Judgment 25%