Hostname: page-component-586b7cd67f-dlnhk Total loading time: 0 Render date: 2024-11-22T06:29:13.043Z Has data issue: false hasContentIssue false
  • English
  • Français

Maximising the impact of CFD in the design office: ARA's role

Published online by Cambridge University Press:  04 July 2016

A. J. Peace
A. J. Peace*
Affiliation:
Aircraft Research Association (ARA)Research DepartmentBedford, UK
Get access Rights & Permissions [Opens in a new window]

Abstract

Computational fluid dynamics (CFD) tools are an integral part of the processes within design and performance assessment offices in the aerospace industry. As timescales from initial concept to design-freeze become shorter, there is an increasing need for the CFD process to be timely and cost effective and for confidence in the predictive capability of the CFD tool to be high. Only in this way will the impact of CFD be maximised. The CFD software development and support team plays a key role in achieving this goal. This paper describes how the team at ARA has met the challenge of delivering to industry what it requires in terms of both the CFD tools and the people who supply them. The qualities needed to do this extend beyond the ability to achieve technical advancement, although prime emphasis is still placed on this aspect. In particular, the importance of developing a close relationship between CFD team and end user — an integrated team approach — is stressed. This leads to a precise and unambiguous understanding of customer needs and the delivery of the required product and its maintenance. The demonstration of recent technical achievements and the discussion of supporting team attributes exemplify these points.

Type
Research Article
Information
The Aeronautical Journal , Volume 106 , Issue 1066 , December 2002 , pp. 675 - 685
Copyright
Copyright © Royal Aeronautical Society 2002 

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. Bocci, A.J. CFD at ARA, 1992, ARA 40th Anniversary Symposium.Google Scholar
2. Baker, T.J. and Forsey, C.R. A fast algorithm for the calculation of transonic flow over wing/body combinations, AIAA J, 1983, 21, pp 60–67.Google Scholar
3. Weatherill, N.P. and Forsey, C.R. Grid generation and flow calculations for aircraft geometries, J Aircr, 1985, 22, pp 855–860.Google Scholar
4. Shaw, J.A. and Weatherill, NP, Automatic topology generation for multi-block grids, J Appl Maths and Computations, 1992, 52, pp 355–388.Google Scholar
5. Childs, P.N., Shaw, J.A., Peace, A.J. and Georgala, J.M. SAUNA: A system for grid generation and flow simulation using hybrid structured/unstructured grids, 1992, First ECCOMAS conference.Google Scholar
6. Cross, M.R. Collaborative aerodynamic simulation toolset — an overview of the UK CAST programme, 2002, Paper 49, CEAS Aerodynamics Research Conference, Cambridge.Google Scholar
7. Chappell, J.A., Shaw, J.A. and Leatham, M. The generation of hybrid grids incorporating prismatic regions for viscous flow calculations, 1996 Numerical Grid Generation in Computational Field Simulation, SONI, B.K. et al (Eds), Mississippi State University.Google Scholar
8. Shaw, J.A. and Peace, A.J. Simulating three-dimensional aeronautical flows on mixed block-structured/semi-structured/unstructured grids, 1998, ICAS Paper 98-2.7.1.Google Scholar
9. Smith, R.J. and Leschziner, M.A. A novel Cartesian grid method for complex aerodynamic CFD applications, loc. cit. 6.Google Scholar
10. Leatham, M., Stokes, S., Shaw, J.A., Cooper, J., Appa, J. and Blaylock, T.A. Automatic mesh generation for rapid-response Navier-Stokes calculations, 2000, AIAA-2000-2247.Google Scholar
11. Appa, J., Hughes, R.A., Porter, L., Woods, P.D., Hunt, D.L. and Rham, S. Generating rapid response NS 2-equation turbulence models, 2000, AIAA-2000-2677.Google Scholar
12. May, N.E. Intake/S-bend diffuser flow prediction using linear and non linear eddy-viscosity and second-moment closure turbulence models, 1999, Engineering turbulence modelling and experiment 4, Rodi, W. et al (Eds), Elsevier.Google Scholar
13. Aerodynamics of 3-D aircraft afterbodies, 1995, AGARD AR 318.Google Scholar
14. Air intakes for high-speed vehicles, 1991, AGARD AR 270.Google Scholar
15. May, N.E. A new vortex generator model for use in complex configuration CFD solvers, 2001, AIAA-2001-2434.Google Scholar
16. Hunt, D.L., Childs, M.A. and Maina, M. QUACC, a novel method for predicting unsteady flow — including propellers and store release, Aeronaut J, August 2001, 105, (1050), pp 427–434.Google Scholar
17. Forsey, C.R., Maina, M. and Scrase, N. Development and validation of an Euler code for propeller flow field prediction and thrust/drag accounting, 1993, RAeS European Forum: Recent developments and applications of CFD.Google Scholar
18. Stokes, S., Chappell, J.A. and Leatham, M. Efficient numerical store trajectory prediction for complex aircraft-store configurations, 1999, AIAA-99-3712.Google Scholar
19. Leatham, M. and Chappell, J.A. On the rapid generation of hybrid meshes due to design driven geometry perturbation, 1998, Numerical Grid Generation in Computational Field Simulation, Cross, M.A. et al (Eds), Mississippi State University.Google Scholar
20. Hunt, D.L. Development and application of farfield drag extraction techniques for complex viscous flows, Aeronaut J, March 2001, 105, (1045), pp 161–169.Google Scholar
21. Maina, M. and Wong, P.W.C. Transition prediction and design philosophy for hybrid laminar flow control for military aircraft, 2000, RTA-AVT Symposium on active control technology for enhanced performance operation capabilities of military aircraft, land vehicles and sea vehicles.Google Scholar
22. Wong, P.W.C. and Maina, M. Studies of methods and philosophies for designing hybrid laminar flow wings, 2000, ICAS Paper 2000-2.8.2.Google Scholar