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This article provides a reduced order modelling framework for turbulentcompressible flows discretized by the use of finite volume approaches. Thebasic idea behind this work is the construction of reduced order models capableof providing fully consistent solutions with respect to the high fidelity flowfields. Full order solutions are often obtained through the use of segregatedsolvers, employing slightly modified conservation laws so that they can bedecoupled and then solved one at a time. Classical reduction architectures, onthe contrary, rely on the Galerkin projection of a complete Navier-Stokessystem to be projected all at once, causing a mild discrepancy with the highorder solutions. In this article we rely on segregated reduced order algorithmsfor the resolution of turbulent and compressible flows in the context ofphysical and geometrical parameters. At the full order level turbulence ismodeled using an eddy viscosity approach. Since there are a variety ofdifferent turbulence models for the approximation of this supplementaryviscosity, one of the aims of this work is to provide reduced order modelswhich are independent on this selection. This goal is reached by theapplication of hybrid methods where Navier-Stokes equations are projected in astandard way while the viscosity field is approximated by the use ofdata-driven interpolation methods or by the evaluation of a properly trainedneural network. By exploiting the aforementioned expedients it is possible toreduced the computational cost associated wit fluid flow problems characterizedby high Reynolds numbers and elevated Mach numbers.

A segregated reduced order model of a pressure-based solver for turbulent compressible flows