  In the framework of BEPU (Best Estimate plus Uncertainty) methodology, the
uncertainties involved in the simulations must be quantified to prove that the
investigated design is acceptable. The output uncertainties are usually
calculated by propagating input uncertainties through the simulation model,
which requires knowledge of the model input uncertainties. However, in some
best-estimate Thermal-Hydraulics (TH) codes such as TRACE, the physical model
parameters used in empirical correlations may have large uncertainties, which
are unknown to the code users. Therefore, obtaining uncertainty distributions
of those parameters becomes crucial if we want to study the predictive
uncertainty or output sensitivity.
  In this study, we present a Modular Bayesian approach that considers the
presence model discrepancy during Bayesian calibration. Several TRACE physical
model parameters are selected as calibration parameters in this work. Model
discrepancy, also referred to as model inadequacy or model bias, accounts for
the inaccuracy in computer simulation caused by underlying missing/insufficient
physics, numerical approximation errors, and other errors of a computer code,
even if all its parameters are fixed at their "true" values. Model discrepancy
always exists in computer models because they are reduced representations of
the reality. The consideration of model discrepancy is important because it can
help avoid the "overfitting" problem in Bayesian calibration. This paper uses a
set of steady-state experimental data from PSBT benchmark and it mainly aims
at: (1) quantifying the uncertainties of TRACE physical model parameters based
on experiment data; (2) quantifying the uncertainties in TRACE outputs based on
inversely quantified physical model parameters uncertainties.
