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Assessment of Turbulent Shock-Boundary Layer Interaction Computations Using the OVERFLOW Code
Author(s) -
A. Brandon Oliver,
Randolph P. Lillard,
Alan Schwing,
Gregory A. Blaisdell,
Anastasios S. Lyrintzis
Publication year - 2007
Publication title -
45th aiaa aerospace sciences meeting and exhibit
Language(s) - English
Resource type - Conference proceedings
DOI - 10.2514/6.2007-104
Subject(s) - boundary layer , computation , computer science , code (set theory) , shock (circulatory) , turbulence , boundary (topology) , mechanics , parallel computing , computational science , physics , algorithm , programming language , mathematics , mathematical analysis , medicine , set (abstract data type)
The performance of two popular turbulence models, the Spalart-Allmaras model and Menter s SST model, and one relatively new model, Olsen & Coakley s Lag model, are evaluated using the OVERFLOWcode. Turbulent shock-boundary layer interaction predictions are evaluated with three different experimental datasets: a series of 2D compression ramps at Mach 2.87, a series of 2D compression ramps at Mach 2.94, and an axisymmetric coneflare at Mach 11. The experimental datasets include flows with no separation, moderate separation, and significant separation, and use several different experimental measurement techniques (including laser doppler velocimetry (LDV), pitot-probe measurement, inclined hot-wire probe measurement, preston tube skin friction measurement, and surface pressure measurement). Additionally, the OVERFLOW solutions are compared to the solutions of a second CFD code, DPLR. The predictions for weak shock-boundary layer interactions are in reasonable agreement with the experimental data. For strong shock-boundary layer interactions, all of the turbulence models overpredict the separation size and fail to predict the correct skin friction recovery distribution. In most cases, surface pressure predictions show too much upstream influence, however including the tunnel side-wall boundary layers in the computation improves the separation predictions.

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