Spalart-Allmaras

Spalart-Allmaras Turbulence Model: ”Standard” Spalart-Allmaras One-Equation Model (SA). The following equations represents the most commonly-used implementation of the Spalart-Allmaras model (written in non-conservation form). The primary reference is: Spalart, P. R. and Allmaras, S. R., ”A One-Equation Turbulence Model for Aerodynamic Flows,” Recherche Aerospatiale, No. 1, 1994, pp. 5-21. (paper provided with permission of authors)


References in zbMATH (referenced in 528 articles )

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  1. Basile, Francesca; Chapelier, Jean-Baptiste; de la Llave Plata, Marta; Laraufie, Romain; Frey, Pascal: Unstructured (h)- and \textithp-adaptive strategies for discontinuous Galerkin methods based on \textitaposteriori error estimation for compressible flows (2022)
  2. Duru, Cihat; Alemdar, Hande; Baran, Ozgur Ugras: A deep learning approach for the transonic flow field predictions around airfoils (2022)
  3. Karbasian, Hamid R.; Vermeire, Brian C.: Application of physics-constrained data-driven reduced-order models to shape optimization (2022)
  4. Langer, Stefan; Suarez, Guillermo: Loosely coupled and coupled solution methods for the RANS equations and a one-equation turbulence model (2022)
  5. Maeyama, Hirotaka; Imamura, Taro; Osaka, Jun; Kurimoto, Naoki: Unsteady aerodynamic simulations by the lattice Boltzmann method with near-wall modeling on hierarchical Cartesian grids (2022)
  6. Okubo, Carlos M. jun.; Sá, Luís F. N.; Kiyono, César Y.; Silva, Emílio C. N.: A discrete adjoint approach based on finite differences applied to topology optimization of flow problems (2022)
  7. Sun, Xuxiang; Cao, Wenbo; Liu, Yilang; Zhu, Linyang; Zhang, Weiwei: High Reynolds number airfoil turbulence modeling method based on machine learning technique (2022)
  8. Zauner, M.; Mons, V.; Marquet, O.; Leclaire, B.: Nudging-based data assimilation of the turbulent flow around a square cylinder (2022)
  9. Zhu, Hong; Sun, Qinglin; Tao, Jin; Chen, Zengqiang; Dehmer, Matthias; Xie, Guangming: Flexible modeling of parafoil delivery system in wind environments (2022)
  10. Aleksandrov, A. V.; Dorodnitsyn, L. W.; Duben’, A. P.; Kolyukhin, D. R.: Generation of anisotropic turbulent velocity fields based on the randomized spectral method (2021)
  11. Alessi, G.; Verstraete, T.; Koloszar, L.; Blocken, B.; van Beeck, J. P. A. J.: Adjoint shape optimization coupled with LES-adapted RANS of a U-bend duct for pressure loss reduction (2021)
  12. Assonitis, Alessia; Paciorri, Renato; Bonfiglioli, Aldo: Numerical simulation of shock/boundary-layer interaction using an unstructured shock-fitting technique (2021)
  13. Balin, Riccardo; Jansen, K. E.: Direct numerical simulation of a turbulent boundary layer over a bump with strong pressure gradients (2021)
  14. Bosnyakov, S. M.; Matyash, I. S.; Mikhaĭlov, S. V.: Numerical simulation of low frequency fluctuations in jet mixing layer of real scale wind tunnel and experience of actuators modelling for ones suppressing (2021)
  15. Busquet, Denis; Marquet, Olivier; Richez, François; Juniper, Matthew; Sipp, Denis: Bifurcation scenario for a two-dimensional static airfoil exhibiting trailing edge stall (2021)
  16. Chen, Zhi; Pan, Honglu; Zhang, Liang: Numerical study on the supersonic near-wake flow structures of a cylinder at an angle of attack (2021)
  17. Degrigny, Johan; Cai, Shang-Gui; Boussuge, Jean-François; Sagaut, Pierre: Improved wall model treatment for aerodynamic flows in LBM (2021)
  18. Di Mascio, Andrea; Zaghi, Stefano: An immersed boundary approach for high order weighted essentially non-oscillatory schemes (2021)
  19. Di Stefano, Martin A.; Hosder, Serhat; Baurle, Robert A.: Effect of turbulence model uncertainty on scramjet strut injector flow field analysis (2021)
  20. Donoghue, Geoff; Yano, Masayuki: Spatio-stochastic adaptive discontinuous Galerkin methods (2021)

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