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What are the advantages and disadvantages of the Spalart-Allmaras model?

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Advantages:


(a) Computational efficiency:


The standard k-ε model is a classical model developed by turbulence researchers in the early 1970's, whereas the SA model is a recent model developed in the early 1990's with the objective of numerical efficiency and robustness. The SA model can perform much faster than the k-ε model for the same or better level of accuracy.


(B) Accuracy as Low-Re Model:


Inherently, the SA model is effective as a low-Reynolds number model and provides a superior accuracy than the standard k-ε model for wall-bounded and adverse pressure gradients flows in boundary layers. The k-ε model does not perform well in boundary layers and requires additional terms to be added to the governing equations to produce boundary layer profiles.


© Mathematics & Numerics:


The standard k-ε model involves a two equation coupled differential system, which can lead to stiff algebraic system for non-diffusive & accurate flow solver like AcuSolve. Some numerically dissipative solvers can easily handle such stiff differential equations. On the contrary, the SA model possess a well-behaved one equation differential system.


 


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Disadvantages:


The primary disadvantage of the Spalart-Allmaras model is seen when applied to free jet flows. For these applications, the rapid change in length scales associated with the transition from wall bounded to free shear proves to be problematic and alternative models may provide better predictions.

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How reliable is SA for calculating drag forces on a blunt rear face body? Example: an aircraft with a blunt rear on the fuselage. SA is a great model for every other part of the aircraft, but will it throw off drag results due to the severe separation at the back?


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The Spalart-Allmaras model is as good as any RANS based approach for simulating bluff bodies.  The challenge that all RANS models face for massive separation is that the Reynolds Stresses can become very anisotropic, which violates the assumptions of the RANS approach.  In general, you should expect fairly good results for the drag under theses types of scenerios (hard to place a percentage on it, but lets say within 10-20% of reality).  If you need higher accuracy, then you can move to unsteady RANS or DES.  Both approaches should improve the results over steady RANS for  massively separated flows.


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