Uncertainty quantification of a refrigeration pool utilizing k-epsilon turbulence reduced order model

The uncertainty of a three-dimensional turbulent natural convection transient is quantified in a geometry representing an idealized refrigeration pool.

The assessment is carried out through the creation of a surrogate model, built utilizing Proper Orthogonal Decomposition and Galerkin projection applied to a K-epsilon turbulent Navier-Stokes formulation.

We discuss the uncertainties in the flow created by the heat released by an element during its cooling. Concretely, we consider the hypothesis that the heat released suffers deviations that follow the normal distribution around a certain nominal value, with different standard deviations and sample size. We study the effect of this on the mean results and their accuracy.

The expected results and its uncertainty are computed by Monte-Carlo method, calculating numerous solutions of the Initial Value Problem with the surrogate model.

The performance of the method has been also assessed, demonstrating the possibility of performing ten thousand calculations inside the working time of a day with a common desktop computer. Considering computational power, this methodology is usable for common practitioners.