Turbulence model verification and validation in an open source environment
by Daniel Wei; Seymour M.J. Spence; Ahsan Kareem
Progress in Computational Fluid Dynamics, An International Journal (PCFD), Vol. 18, No. 2, 2018

Abstract: In this paper, the verification of two low-Reynolds-number turbulence models in an open source environment is reported. The two models are the Spalart-Allmaras model without the ft2 term and the k - ω SST model using vorticity in production estimation. Grid convergence is achieved in all verification cases, while reasonable agreements with other codes are found. The two turbulence models are also validated through comparison with some well-known validation cases, the results of which show good agreement with experimental data. The differences between the results obtained from incompressible and compressible codes are also discussed. The impact of wall distance estimation on the performance of turbulence modelling is also evaluated through grid convergence studies. Within the realm of search algorithms for wall distance estimation, a three-level search approach is found to be necessary in order to achieve correct and converged results, especially for skewed meshes. It is shown, through a 2D bump flow grid convergence study, that the use of a one-level search approach can not only lead to a maximum friction coefficient deviation of 3.8%, but also cause an inconsistent convergence behaviour.

Online publication date: Tue, 13-Mar-2018

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the Progress in Computational Fluid Dynamics, An International Journal (PCFD):
Login with your Inderscience username and password:

    Username:        Password:         

Forgotten your password?


Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com