  In Computational Fluid Dynamics (CFD) studies composed of the coupling of
different simulations, the uncertainty in one stage may be propagated to the
following stage and affect the accuracy of the prediction. In this paper, a
framework for uncertainty quantification is applied to the two-step simulation
of the mechanical design of a swirling jet flow generated by a rotating pipe
(\textit{Simulation 1}) impinging on a flat plate to provide convective heat
transfer (\textit{Simulation 2}). The first approach is the Stochastic
Collocation Method (SCM) with Clenshaw-Curtis sparse grids. The conclusion
drawn from the analysis is that the simulated system does not exhibit a
significant sensitivity to stochastic variations of model input parameters,
over the tested uncertainty ranges.
  Additionally, a set of non-linear regression models for the stochastic
velocity and turbulent profiles for the pipe nozzle are created and tested,
since impinging jets at Reynolds number of $Re=23000$ are very frequent in the
literature, but stochastic inlet conditions have never been provided. Numerical
results demonstrate a negligible difference in the predicted convective heat
transfer with respect to the use of the profiles simulated via CFD. These
suggested surrogate models can be directly embedded onto other CFD applications
(e.g arrays of jets or jet flows impinging on plates with different shapes) in
which a realistic swirling flow under uncertainty can be of interest.
