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Comparison of drag prediction using RANS models and DDES for the DLR-F6 configuration using high order schemes
Gan JY; Shen YQ(申义庆); Zha GC
Source Publication54th AIAA Aerospace Sciences Meeting
Conference Name54th AIAA Aerospace Sciences Meeting, 2016
Conference DateJanuary 4, 2016 - January 8, 2016
Conference PlaceSan Diego, CA, United states
AbstractThis paper compares the accuracy and robustness of steady state RANS, unsteady RANS, and DDES turbulence models with high order schemes for predicting the drag of the DLR-F6 configuration. The implicit time marching method with unfactored Gauss-Seidel line relaxation is used with a 5th order WENO finite difference scheme for Navier-Stokes equations. The viscous terms are discretized using a 4th order conservative central differencing. The effect of grid size on the accuracy of drag prediction by using the different turbulent models are conducted on the coarse, medium and fine mesh models at the same angle of attack. The coarse mesh has about 10 drag counts deviation from the experiment, the medium mesh has 28 counts, and the fine mesh has about 15 counts difference. The RANS method achieves almost the same results as URANS and DDES at angle of attack of 0.49. The DDES have the least deviation from the experimental drag result and the closest pressure distribution to the experiment in the trailing edge separation zone. However, since the DDES uses the same mesh as the RANS model in this paper, the DDES results should not be considered as conclusive. A more rigorous mesh refinement study for the DDES is in progress. © 2016, American Institute of Aeronautics and Astronautics Inc, AIAA, All rights reserved.
KeywordCentral Differencing Drag Prediction Finite Difference Scheme High Order Scheme Line Relaxation Mesh Refinement Trailing Edges Turbulent Models
Indexed ByEI
Document Type会议论文
Recommended Citation
GB/T 7714
Gan JY,Shen YQ,Zha GC. Comparison of drag prediction using RANS models and DDES for the DLR-F6 configuration using high order schemes[C]54th AIAA Aerospace Sciences Meeting,2016.
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