- About this Journal
- Abstracting and Indexing
- Aims and Scope
- Article Processing Charges
- Articles in Press
- Author Guidelines
- Bibliographic Information
- Citations to this Journal
- Contact Information
- Editorial Board
- Editorial Workflow
- Free eTOC Alerts
- Publication Ethics
- Reviewers Acknowledgment
- Submit a Manuscript
- Subscription Information
- Table of Contents
International Journal of Aerospace Engineering
Volume 2012 (2012), Article ID 149461, 11 pages
doi:10.1155/2012/149461
A DES Procedure Applied to a Wall-Mounted Hump
1Thermal/Fluid Science & Engineering Department, Sandia National Laboratories, Livermore, CA 945501, USA
2Mechanical and Aero Engineering Department, University of California at Davis, Davis, CA 95616, USA
Received 1 March 2012; Revised 21 June 2012; Accepted 5 July 2012
Academic Editor: Linda L. Vahala
Copyright © 2012 Radoslav Bozinoski and Roger L. Davis. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract
This paper describes a detached-eddy simulation (DES) for the flow over a wall-mounted hump. The Reynolds number based on the hump chord is with an in-let Mach number of 0.1. Solutions of the three-dimensional Reynolds-averaged Navier-Stokes (RANS) procedure are obtained using the Wilcox equations. The DES results are obtained using the model presented by Bush and Mani and are compared with RANS solutions and experimental data from NASA's 2004 Computational Fluid Dynamics Validation on Synthetic Jets and Turbulent Separation Control Workshop. The DES procedure exhibited a three-dimensional flow structure in the wake, with a 13.65% shorter mean separation region compared to RANS and a mean reattachment length that is in good agreement with experimental measurements. DES predictions of the pressure coefficient in the separation region also exhibit good agreement with experiment and are more accurate than RANS predictions.