Sensitivity and Uncertainty Study of CTF Using the Uncertainty Analysis in Modeling Benchmark

Open Access
Porter, Nathan Wayne
Graduate Program:
Nuclear Engineering
Master of Science
Document Type:
Master Thesis
Date of Defense:
Committee Members:
  • Maria Nikolova Avramova, Thesis Advisor
  • nuclear code
  • thermal hydraulics
  • subchannel analysis
  • CTF
  • Dakota
  • uncertainty quantification
  • sensitivity analysis
This work describes the results of a quantitative sensitivity and uncertainty analysis of the thermal hydraulic subchannel code, Coolant-Boiling in Rod Arrays-Three Field (COBRA-TF). Four steady state cases from Phase II, Exercise 3 of the Organisation for Economic Co-operation and Development/Nuclear Energy Agency Uncertainty Analysis in Modeling Benchmark (OECD/NEA UAM) are analyzed using the statistical analysis tool, Design Analysis Kit for Optimization and Terascale Applications (Dakota). The input uncertainties include boundary condition and geometry uncertainties specified in the benchmark, as well as modeling uncertainties which are selected based on preliminary sensitivity studies and expert judgment. A large variety of output parameters are analyzed for each case: maximum void fractions and temperatures, bundle pressure drop, and a variety of axial distributions for a central subchannel. The predicted uncertainty in all parameters remains below 10% for all cases. The dominant sources of uncertainty are inferred from sensitivity studies and rank correlation coefficients from the uncertainty analysis. The results agree well with comparable past studies, but with a number of important improvements. A thorough analysis of geometry uncertainties is used to conclude that these uncertainties are negligible for all UAM cases. Wilks' Formula is shown to be inadequate for sample size selection for uncertainty quantification studies of nuclear codes. In addition, the pitfalls of using traditional black box uncertainty analysis methods are explored. An in-depth description of the bulk mass transfer model is used as an example to demonstrate that current uncertainty analysis methods are vastly insufficient to fully understand the intricacies of complex computational tools. Significant improvements can be made, for example, Bayesian Calibration can be used to directly relate experimental results to input parameter uncertainty. This method is demonstrated for the Lee and Ryley correlation and can give much more accurate input uncertainties than expert opinion.