1. A. Spentzos 1, G. Barakos 1, K. Badcock 1 P. Wernert 2, S. Schreck 3 & M. Raffel 4 1 CFD Laboratory, University of Glasgow, UK 2 Institute de Recherche de Saint Louis, France 3 National renewable energy laboratory USA 4 DLR - Institute for Aerodynamics and Flow Technology, Germany Numerical Simulation of 3D Dynamic Stall

2. Outline Background and Objectives Past efforts in 3D dynamic stall CFD requirements for validation Summary of selected tools 2D dynamic stall Validation cases and results Conclusions

3. Motivation and Objectives DS is encountered in rotorcraft and highly maneuverable aircraft Complex problem – prediction of loads and flow structure 3D studies are rare Study 3D DS, use a variety of turbulence models and simulation (LES) Improve existing turbulence models Understand flow physics Validate CFD so that industry can exploit Take things a bit further…

4. Background What is Dynamic Stall? Experimental and CFD work on DS The majority of the work performed on DS (experimental and CFD) has been done on 2-D Most CFD has been done for code validation rather than investigation of the flow physics. 2D CFD suggested that turbulence modelling is a key issue if fidelity is required Missing: 3D, centrifugal effects, dM/dt, interaction with wake

5. CFD requirements for validation Surface pressure Integral loads Boundary layers Information for turbulence levels in the tunnel and transition Higher Mach numbers Near-tip and flow-field measurements Measurements on rotating blades Measurements on more complex geometries

6. Summary of experiments Most experiments on DS are 2D 3D work has been done by the following:

7. Selected Validation Cases

8. CFD solver PMB solver of the Univ. of Glasgow Control volume method Parallel (distributed memory) Multi-block (complex geometry) structured grids Moving grids Unsteady RANS - Variety of turbulence models – LES Implicit time marching Osher's and Roe's schemes for convective fluxes MUSCL scheme for effectively 3rd order accuracy Central differences for viscous fluxes Conjugate gradient linear solver with pre-conditioning Validation database www.aero.gla.ac.uk/Research/CFD/validation

9. 2D Results for Ramping and Oscillating Aerofoils CFD results for dynamic stall of helicopter sections

10. Flow Field Comparison Sinusoidal pitch, k=0.15, Re=373,000, M=0.1 a) 22 Deg (upstroke) b) 23 Deg (upstroke) c) 24 Deg (upstroke)

11. Geometry – Grid Generation

12. One-block extruded tip

13. Geometry – Grid Generation C-O topology 4-block extruded tip

14. Grid and Time Convergence Three levels of refinement: 120k, 800k, 1,800k

15. Grid and Time Convergence Two levels of time refinement resolving frequencies up to 20 Hz and 40Hz

16. Experimental evidence of the  -shaped vortex Coton et al.

17. Surface Pressure Ramping motion, Re=69,000, M=0.1, K=0.1 Incidence 40.9 degrees Experiment CFD

18. Close the loop – Analysis ONERA model  CzCz CzCz  CzCz

20. Conclusions Experimentalists like CFD pictures! Are keen to collaborate and look in their databases for measurements They developed the ability to understand much about the flow from a small number of measurements They are getting used to the idea of CFD… or at least looking at CFD results

21. Conclusions CFD developers are always looking for good data and have many requirements Have sometimes to make a first step Have to be open about any limitations of their methods Perform simulations, validation, comparisons and maybe … some analysis!