Evaluation of Turbulence-Model Performance in Jet Flows

The importance of reducing jet noise in both commercial and military aircraft applications has made jet acoustics a significant area of research. A technique for jet noise prediction commonly employed in practice is the MGB approach, based on the Lighthill acoustic analogy. This technique requires as aerodynamic input mean flow quantities and turbulence quantities like the kinetic energy and the dissipation. The purpose of the present paper is to assess existing capabilities for predicting these aerodynamic inputs. Two modern Navier-Stokes flow solvers, coupled with several modern turbulence models, are evaluated by comparison with experiment for their ability to predict mean flow properties in a supersonic jet plume. Potential weaknesses are identified for further investigation. Another comparison with similar intent is discussed by Barber et al. The ultimate goal of this research is to develop a reliable flow solver applicable to the low-noise, propulsion-efficient, nozzle exhaust systems being developed in NASA focused programs. These programs address a broad range of complex nozzle geometries operating in high temperature, compressible, flows. Seiner et al. previously discussed the jet configuration examined here. This convergent-divergent nozzle with an exit diameter of 3.6 inches was designed for an exhaust Mach number of 2.0 and a total temperature of 1680 F. The acoustic and aerodynamic data reported by Seiner et al. covered a range of jet total temperatures from 104 F to 2200 F at the fully-expanded nozzle pressure ratio. The aerodynamic data included centerline mean velocity and total temperature profiles. Computations were performed independently with two computational fluid dynamics (CFD) codes, ISAAC and PAB3D. Turbulence models employed include the k-epsilon model, the Gatski-Speziale algebraic-stress model and the Girimaji model, with and without the Sarkar compressibility correction. Centerline values of mean velocity and mean temperature are compared with experimental data.

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