Drag Decomposition Analysis of CFD Data of the DLR-F6 Model

Ueno, Michio; Akatsuka, Junichi; Hidaka, Akiko

doi:10.2514/6.2008-6903

Cited by 3 publications

(1 citation statement)

References 3 publications

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“…Figure 3 shows an example of these missed cells. This example uses the same flow 11,31,32 for viscosity based sensor, which indicates some arbitrariness in its selection and the necessity for parameter sensitivity analysis. The main reason is that there are large differences in eddy viscosity among different RANS flow solvers, though their mean flow solutions are very similar.…”

Section: Area Selection Strategymentioning

confidence: 99%

Far-field drag decomposition using hybrid formulas and vorticity based area sensors

Qiao

Sun

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part G:

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Numerical simulation of flow-field has become an indispensable tool for aerodynamic design. Usually, wall surface integration is a tool used to calculate values of pressure drag and skin friction drag, but the aerodynamic mechanism of drag production is still confusing. In present work, in order to decompose the total drag into viscous drag, wave drag, induced drag, and spurious drag, a far-field drag decomposition (FDD) method is developed. This method depends on axial velocity defect and area sensor functions. The present work proposes three hybrid formulas for velocity defect to tackle the negative square root issue by analyzing the existing axial velocity defect formulas. For dealing with the issue of detection failure for near-wall cells, a novel vorticity based viscous area sensor function is proposed. The new area sensor function is also independent of the turbulence model, which ensures easy application to general simulation methods for flow-field. Three tests cases are there to validate the proposed FDD method. The three dimensional transonic CRM test case shows that the present improvement is crucial for accurate drag decomposition. Excellent agreement between total decomposed drags and results from the near-field method or experimental data further confirms the correctness.

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Section: Area Selection Strategymentioning

confidence: 99%