  Model-form uncertainties in complex mechanics systems are a major obstacle
for predictive simulations. Reducing these uncertainties is critical for
stake-holders to make risk-informed decisions based on numerical simulations.
For example, Reynolds-Averaged Navier-Stokes (RANS) simulations are
increasingly used in mission-critical systems involving turbulent flows.
However, for many practical flows the RANS predictions have large model-form
uncertainties originating from the uncertainty in the modeled Reynolds
stresses. Recently, a physics-informed Bayesian framework has been proposed to
quantify and reduce model-form uncertainties in RANS simulations by utilizing
sparse observation data. However, in the design stage of engineering systems,
measurement data are usually not available. In the present work we extend the
original framework to scenarios where there are no available data on the flow
to be predicted. In the proposed method, we first calibrate the model
discrepancy on a related flow with available data, leading to a statistical
model for the uncertainty distribution of the Reynolds stress discrepancy. The
obtained distribution is then sampled to correct the RANS-modeled Reynolds
stresses for the flow to be predicted. The extended framework is a Bayesian
calibration-prediction method. The merits of the proposed method are
demonstrated on two flows that are challenging to standard RANS models. By not
requiring observation data on the flow to be predicted, the present
calibration-prediction method will gain wider acceptance in practical
engineering application. While RANS modeling is chosen to demonstrate the
merits of the proposed framework, the methodology is generally applicable to
other complex mechanics models involving solids, fluids flows, or the coupling
between the two, where model-form uncertainties are present in the constitutive
relations.
