A wall-boundary-natural transitional Reynolds-stress model for high-order wing-body simulations
The precise simulation of full-size wing-body configuration in real flight conditions is still a challenge in computational fluid dynamics in which transition and flow separation are the most crucial issues. To predict these problems robustly by high-order numerical methods, this paper proposes a new transitional Reynolds-stress model, which combines λ-scale (λ=τ8)-based SSG (Speziale, Sarkar, and Gatski)/LRR (Launder, Reece, and Rodi) model with γ−Reθt transition model. Compared with the ω-scale, the λ-scale variable has a natural boundary condition on the wall (helpful for numerical stability) and avoids an additional modification during the transition from laminar to turbulent flow. The T3 series plates with/without pressure gradient, 30P-30N multi-element airfoil, and DLR (German Aerospace Center) 6:1 prolate spheroid are carried out to validate the reliability of the new nine-equation transition model. Furthermore, the new model is applied to the analysis of National Aeronautics and Space Administration juncture flow . Numerical results show that the new transitional model has an obvious advantage in the prediction of Reynolds stresses over the traditional γ−Reθt SST(shear stress transport) k−ω (k is the turbulence kinetic energy, ω is the specific dissipation rate) model, and then, more physical junction separation as well as transition onset can be obtained.
Authors
- Shengye Wang
- Xiang Fu (ORCID: https://orcid.org/0000-0001-6577-2499)
- Shunshun Wang (ORCID: https://orcid.org/0009-0003-3552-8649)
- Xiaogang Deng (ORCID: https://orcid.org/0000-0002-9316-9539)
Institutions
- National University of Defense Technology (CN)
- China Aerodynamics Research and Development Center (CN)
Publication Details
- Journal
- Physics of Fluids
- Published
- 2024-08-01
- DOI
- https://doi.org/10.1063/5.0219939
- Citations
- 5
- Primary Topic
- Computational Fluid Dynamics and Aerodynamics
- Type
- article
- Field-Weighted Citation Impact
- 1.87
Funders
- National Natural Science Foundation of China