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A two-equation local-correlation-based laminar-turbulent transition modeling scheme for external aerodynamics

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Abstract A local-correlation-based laminar-turbulent transition model has been developed for external aerodynamic problems and coupled with the Spalart-Allmaras ( S − A ) turbulence model. This laminar-turbulent transition modeling scheme… Click to show full abstract

Abstract A local-correlation-based laminar-turbulent transition model has been developed for external aerodynamic problems and coupled with the Spalart-Allmaras ( S − A ) turbulence model. This laminar-turbulent transition modeling scheme is then termed as the γ − S A model. It has been validated against a series of two- and three-dimensional test cases with a relatively wide range of Reynolds numbers and free-stream turbulence intensity levels. Results show that the γ − S A model can give accurate transition-onset-location predictions, as well as more accurate predictions on the force coefficients than the underlying S − A turbulence model at various angles of attack. Comparison with the classic S S T − γ − R e θ transition model shows that the γ − S A model can achieve comparable results, with the expect of less time or memory consumption, as the γ − S A model has two equations fewer than the former one. With a newly proposed local-farfield mixed turbulence intensity strategy, the turbulence intensity on the inlet or farfield boundary can be conveniently set directly with the value tested for the wind tunnel or the value as expected, while achieving local turbulence intensity variation. Moreover, the γ − S A model employs only local variables, which makes it totally modern-CFD-compatible and very appropriate for large-scale parallel running using unstructured grids. In the future, it can be modified into a robust and efficient DES-type model with the consideration of transition process.

Keywords: transition; turbulence; turbulent transition; model; laminar turbulent; aerodynamics

Journal Title: Aerospace Science and Technology
Year Published: 2020

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