Hostname: page-component-848d4c4894-8bljj Total loading time: 0 Render date: 2024-06-16T17:25:31.478Z Has data issue: false hasContentIssue false

Rapid, parallel CFD grid generation using octrees

Published online by Cambridge University Press:  27 January 2016

G. J. Page*
Affiliation:
Loughborough University, Loughborough, UK

Abstract

As Large Eddy Simulation is increasingly applied to flows containing complex geometry, grid generation becomes difficult and time consuming when using software originally developed for RANS flow solvers. The traditional ‘pipeline’ approach of grid generation → solve → visualise entails the time consuming transfer of large files and conversion of file formats. This work demonstrates a grid generation methodology developed specifically to be integrated with parallel LES. The current approach is to use a Cartesian grid with adaptive refinement based upon geometry intersection, surface detail and surface curvature. The grid is defined by an octree data structure with the geometry defined by triangular facets using the STL file format. The result is a set of ‘cubical’ subdomains, each with identical numbers of cells and uniform distributions within the cube. Some subdomains will be entirely fluid and can be solved using straightforward CFD techniques, whilst some cubes will be cut by the surfaces. Individual cells are then tagged as ‘solid’, ‘fluid’ or ‘cut’ with the solver expected to use an immersed boundary approach to model the surface. A key feature is the design of the algorithm to be parallelisable on both shared and distributed memory systems. The distributed memory parallel dynamically partitions the grid as it is being generated, so that the partitioning is suitable for a subsequent flow solver. Grid generation testing has been carried out on a variety of input CAD files ranging up to 350,000 facets. A landing gear case shows how the grid generator correctly finds the fluid inside of the tire and other cavities within the hub. In scalar mode, a grid with 4,916 cubes and 468 million cells is generated in less than 100 seconds, whilst in parallel on 32 processor cores this can be achieved in 4·6 seconds.

Type
Research Article
Copyright
Copyright © Royal Aeronautical Society 2013 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

1. De Zeeuw, D. and Powell, K.G. An adaptively refined Cartesian mesh solver for the Euler equations, J Computational Physics, 1993, 104, (5668).Google Scholar
2 Aftosmis, M.J., Berger, M.G. and Adomavicius, G. A parallel multilevel method for adaptively refined Cartesian grids with embedded boundaries, In proceedings of the 38th Aerospace Sciences Meeting and Exhibit, 10-13 January 2000, Reno, Nevada, USA. AIAA-2000-0808.Google Scholar
3. Tseng, Y-H. and Ferziger, J.H. A ghost-cell immersed boundary method for flow in complex geometry, J Computational Physics, 2003, 192, (593623).Google Scholar
4. Emblemsvag, J-E., Suzuki, R. and Candler, G.V. Cartesian grid method for moderate-Reynolds-number flows around complex moving objects, AIAA J, 2005, 43, (1), pp 7686.Google Scholar
5. Kang, S. Iaccarino, G. Moin, P. Accurate immersed-boundary reconstructions for viscous flow simulations, AIAA J, 2009, 47, (7), pp 17501760.Google Scholar
6 Kamatsuchi, T. Flow simulation around complex geometries with solution adaptive Cartesian grid method, In proceedings of the 18th AIAA Computational Fluid Dynamics conference, 25-28 June 2007, Miami, Florida, USA. AIAA-2007-4189.Google Scholar
7. Dawes, W.N., Harvey, S.A., Fellows, S., Favaretto, C.F. and Velivelli, A. Viscous Layer Meshes from Level Sets on Cartesian Meshes, In proceedings of the 45th Aerospace Sciences Meeting and Exhibit, 8-11 January 2007, Reno, Nevada, USA. AIAA-2007-0555.Google Scholar
8. Dawes, W.N., Harvey, S.A., Fellows, S., Eccles, N., Jaeggi, D. and Kellar, W.P. A practical demonstration of scalable, parallel mesh generation, In proceedings of the 47th Aerospace Sciences Meeting and Exhibit, 5-8 January 2009, Orlando, Florida, USA. AIAA-2009-0981 (2009)Google Scholar
9. Ishida, T., Takahashi, S. and Nakahashi, K. Efficient Cartesian mesh approach for flow computations around moving and deforming bodies, In proceedings of the 19th AIAA Computational Fluid Dynamics conference, 22-25 June 2009, San Antonio, Texas, USA. AIAA-2009-3879.Google Scholar
10. Akenine-Möller, T. Fast 3D triangle-box overlap testing, In proceedings of the SIGGRAPH ‘05: ACM SIGGRAPH 2005 Courses, (Los Angeles, California, USA, July 31 – August 04, 2005). Fujii, J.,. doi http://doi.acm.org/10.1145/1198555.1198747 (2005)Google Scholar
11. Surazhsky, T., Magid, E., Soldea, O., Elber, G. and Rivlin, E. A comparison of Gaussian and mean curvatures estimation methods on triangular meshes, In proceedings of the IEEE International Conference on Robotics and Automation, 1, (10211026), 2003.Google Scholar
12. Möller, T. and Trumbore, B. Fast, minimum storage ray/triangle intersection, In proceedings of the SIGGRAPH ‘05: ACM SIGGRAPH 2005 Courses, (Los Angeles, California, USA, July 31 – August 04, 2005). Fujii, J., Ed., doi http://doi.acm.org/10.1145/1198555.1198746 (2005).Google Scholar
13. McGinley, C., Jenkins, L. and Watson, R. 3D high-lift flow physics experiment – transition measurements, In proceedings of the AIAA Fluid Dynamics Conference, 6-9 June 2005, Toronto, Canada. AIAA-2005-5148.Google Scholar
14. Anon. AIAA CFD High Lift Prediction Workshop (HiLiftPW) Series (online). Available at: http://hiliftpw.larc.nasa.gov/ (Accessed 20 December 2011).Google Scholar
15. Anon. (online) Available at: http://www.3dvia.com/ (Accessed 20 December 2011).Google Scholar
16. Li, Y., Satti, R., Lew, P-T., Shock, R. and Noelting, S. Computational aeroacoustic analysis of flow around a complex nose landing gear conÞguration, In proceedings of the 14th AIAA/CEAS Aeroacoustics Conference, 5-7 May 2008 – June 2009, Vancouver, British Columbia, Canada. AIAA-2008-2916 (2008).Google Scholar