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Comparative Study on the Prediction of Aerodynamic Characteristics of Aircraft with Turbulence Models

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Abstract

The RANS equations are widely used to analyze complex flows over aircraft. The equations require a turbulence model for turbulent flow analyses. A suitable turbulence must be selected for accurate predictions of aircraft aerodynamic characteristics. In this study, numerical analyses of three-dimensional aircraft are performed to compare the results of various turbulence models for the prediction of aircraft aerodynamic characteristics. A 3-D RANS solver, MSAPv, is used for the aerodynamic analysis. The four turbulence models compared are the Sparlart–Allmaras (SA) model, Coakley’s \(q-\omega \) model, Huang and Coakley’s \(k-\varepsilon \) model, and Menter’s \(k-\omega \) SST model. Four aircrafts are considered: an ARA-M100, DLR-F6 wing–body, DLR-F6 wing–body–nacelle–pylon from the second drag prediction workshop, and a high wing aircraft with nacelles. The CFD results are compared with experimental data and other published computational results. The details of separation patterns, shock positions, and \(C_{p}\) distributions are discussed to find the characteristics of the turbulence models.

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Abbreviations

u :

Velocity component in the x direction

v :

Velocity component in the y direction

w :

Velocity component in the z direction

\(\rho \) :

Density

p :

Pressure

\(C_{L}\) :

Lift coefficient

\(C_{D}\) :

Drag coefficient

\(\hbox {Re}\) :

Reynolds number

\(\alpha \) :

Angle of attack

\(\eta \) :

Normalized span location

k :

Turbulent kinetic energy

q :

Turbulent velocity scale

\(\omega \) :

Specific dissipation rate

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Acknowledgements

This work was supported by Korea Aerospace Research Institute (KARI) and Inha University.

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Correspondence to Seungsoo Lee.

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An earlier version of this paper was presented at APISAT 2017, Seoul, Korea, October 2017.

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Jang, Y., Huh, J., Lee, N. et al. Comparative Study on the Prediction of Aerodynamic Characteristics of Aircraft with Turbulence Models. JASS 19, 13–23 (2018). https://doi.org/10.1007/s42405-018-0022-6

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  • DOI: https://doi.org/10.1007/s42405-018-0022-6

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