The poster highlights a CFD study that incorporates a combined Eulerian multi-fluid multiphase and a Population Balance Model (PBM) to study the flow inside a typical multiphase gravity separator vessel (GSV) found in oil and gas industry. The simulations were performed using Ansys Fluent CFD package running on KAUST supercomputer, Shaheen. Also, a highlight of a scalability study is presented. The effect of I/O bottlenecks and using Hierarchical Data Format (HDF5) for collective and independent parallel reading of case file is presented. This work is an outcome of a research collaboration on an Aramco project on Shaheen.

We consider uncertainty quantification problem in aerodynamic simulations. We identify input uncertainties, classify them, suggest an appropriate statistical model and, finally, estimate propagation of these uncertainties into the solution (pressure, velocity and density fields as well as the lift and drag coefficients). The deterministic problem under consideration is a compressible transonic Reynolds-averaged Navier-Strokes flow around an airfoil with random/uncertain data. Input uncertainties include: uncertain angle of attack, the Mach number, random perturbations in the airfoil geometry, mesh, shock location, turbulence model and parameters of this turbulence model. This problem requires efficient numerical/statistical methods since it is computationally expensive, especially for the uncertainties caused by random geometry variations which involve a large number of variables. In numerical section we compares five methods, including quasi-Monte Carlo quadrature, polynomial chaos with coefficients determined by sparse quadrature and gradient-enhanced version of Kriging, radial basis functions and point collocation polynomial chaos, in their efficiency in estimating statistics of aerodynamic performance upon random perturbation to the airfoil geometry [D.Liu et al '17]. For modeling we used the TAU code, developed in DLR, Germany.