Structured treatment of model uncertainty in complex thermal-hydraulics codes: Technical challenges, prospective and characterization

Code structure uncertainties (model uncertainty) are a crucial source of uncertainty quantification for thermal-hydraulics (TH) system codes, an assembly of models and correlations that simulate physical phenomena and the behavior of system. Technical challenges dealing with the subject are discussed in this paper with the prospective of TH codes model uncertainty characterization. A literature review was conducted on the subject matter to evaluate the state of the art on the topic. A key characteristic of thermal-hydraulics systems codes is complexity. This complexity has its roots in the composite structure of these systems that comprise of many different elements (or sub-systems) whose state inevitably affects the state of the whole system. Its main implication is the dynamic (i.e., many interdependent variables in time) and/or non-linear behavior. Several different situations are met dealing with the TH model uncertainty. In some cases there are alternative sub-models, or several different correlations for calculating a specific phenomenon of interest. There are also “user options” for choosing one of several models, or correlations in performing a specific code computation. Dynamic characteristics of TH add more complexity to the code calculation, meaning, for example, that specific code models and correlations invoked are sequence-dependent, and require certain (dynamic) conditions. This paper discusses the techniques developed in the Integrated Methodology for Thermal-Hydraulics Uncertainty Analysis (IMTHUA), specifically for the treatment of uncertainties due to code structure and models. The methodology comprehensively covers various aspects of complex code uncertainty assessments for important accident transients. It explicitly examines the TH code structural uncertainties by treating internal sub-model uncertainties and by propagating such model uncertainties in the code calculations, including uncertainties about input parameters. Structural uncertainty assessment (model uncertainty) for a single model will be discussed in terms of “correction factor,” “bias,” and Bayesian sub-model output updating with available experimental evidence. In case of multiple alternative models, several techniques, including dynamic model switching, user-controlled model selection, and model mixing, are discussed. Examples from different applications, including Marviken Blowdown, LOFT LBLOCA, and typical PWR LOCA scenario calculations, are provided for greater clarification of the proposed techniques.