Hostname: page-component-78c5997874-4rdpn Total loading time: 0 Render date: 2024-11-19T13:37:39.376Z Has data issue: false hasContentIssue false
  • English
  • Français

Validation of CFD simulations for X-31 wind-tunnel models

Published online by Cambridge University Press:  27 January 2016

M. Ghoreyshi ,
A. D. H. Kim ,
A. Jirasek ,
A. J. Lofthouse  and
R. M. Cummings
A. D. H. Kim
Affiliation:
High Performance Computing Research Center, US Air Force Academy, Colorado, USA
A. Jirasek
Affiliation:
High Performance Computing Research Center, US Air Force Academy, Colorado, USA
A. J. Lofthouse
Affiliation:
High Performance Computing Research Center, US Air Force Academy, Colorado, USA
R. M. Cummings
Affiliation:
High Performance Computing Research Center, US Air Force Academy, Colorado, USA
Get access Rights & Permissions [Opens in a new window]

Abstract

Computational Fluid Dynamics (CFD) has become an attractive method of choice in the design of many aerospace vehicles because of advances in numerical algorithms and convergence acceleration methods. However, the flow around an advanced fighter aircraft is complicated and usually unsteady due to the presence of vortex-dominated flows. The accuracy and predictability of conventional turbulence models for these applications may be questionable and therefore results obtained from these models must be validated and evaluated on the basis of experimental data from wind tunnels and/or flight tests. This work aims to validate CFD simulations of X-31 wind-tunnel models with and without a belly-mounted sting. The sting setup facilitates forced sinusoidal oscillations in one of three modes of: pitch, yaw, and roll. However, the results show that measured aerodynamic data are altered by the turbulent wake behind the sting, even at small angles of attack. The high angle-of-attack flow around the X-31 is also very complicated and unsteady due to canard and wing vortices. Therefore, validation of CFD models for predicting these complex flows can be a very challenging task. The X-31 wind-tunnel experiments were carried out in the German Dutch low-speed wind tunnel at Braunschweig and include aerodynamic force and moment measurement as well as span-wise pressure distributions at locations of 60% and 70% chord length. This data set is used to validate the Cobalt and Kestrel flow solvers and the results are similar and match quiet well with experiments for small to moderate angles of attack. The main discrepancies between CFD and measurements occur close to the wing tip, where leading-edge flaps are located.

Type
Research Article
Information
The Aeronautical Journal , Volume 119 , Issue 1214 , April 2015 , pp. 479 - 500
Copyright
Copyright © Royal Aeronautical Society 2015

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.Cummings, R.M.Update on computational aerodynamics education at the US Air Force Academy, Int J Aerodynamics, 2012, 2, (1), pp 93–109.CrossRefGoogle Scholar
2.Pamadi, B.N.Performance, Stability, Dynamics, and Control of Airplanes, AIAA Education Series, AIAA, Reston, Virginia, USA, 2004.CrossRefGoogle Scholar
3.Tu, E.L.Effects of canard deflection on close-coupled canard-wing-body aerodynamics, J Aircr, 1994, 31, (1), pp 138–145CrossRefGoogle Scholar
4.Nelson, R.C. and Pelletier, A.The unsteady aerodynamics of slender wings and aircraft undergoing large amplitude maneuvers, Progress in Aerospace Sciences, 2003, 39, (2), pp 185–284.CrossRefGoogle Scholar
5.Rein, M., Höhler, G., Schütte, A., Bergmann, A. and Löser, T. Ground-Based Simulation of Complex Maneuvers of a Delta-Wing Aircraft, AIAA Paper 2006–3149, June 2006.CrossRefGoogle Scholar
6.Strang, W.Z., Tomaro, R.F. and Grismer, M.J. The defining methods of cobalt: a parallel, implicit, unstructured Euler/Navier-Stokes flow solver, AIAA Paper 1999–0786, January 1999.CrossRefGoogle Scholar
7.Morton, S.A., McDaniel, D.R., Sears, D.R., Tillman, B. and Tuckey, T.R. Kestrel: a fixed wing virtual aircraft product of the CREATE program. AIAA Paper 2009-0338, January 2009.CrossRefGoogle Scholar
8.Gottlieb, J.J. and Groth, C.P.T.Assessment of Riemann solvers for unsteady one-dimensional inviscid flows of perfect gasses, J of Computational Physics, 1988, 78, (2), pp 437–458.CrossRefGoogle Scholar
9.Godunov, S.K.A difference scheme for numerical computation of discontinuous solution of hydrodynamic equations, Sbornik Mathematics, 1959, 47, pp 271–306.Google Scholar
10.Schütte, A., Einarsson, G., Raichle, A., Schoning, B., Mönnich, W. and Forkert, T., Numerical simulation of manoeuvreing aircraft by aerodynamic, flight mechanics, and structural mechanics coupling, J Airc, 2009, 46, (1), pp 53–64.CrossRefGoogle Scholar
11.Schütte, A., Boelens, O.J., Löser, T. and Oehlke, M. Prediction of the Flow around the X-31 aircraft using two different CFD methods, AIAA Paper 2010-4692, June-July 2010.CrossRefGoogle Scholar
12.Boelens, O.J. CFD Analysis of the flow around the X-31 aircraft at high angle-of-attack, AIAA Paper 2009-3628, June 2009.Google Scholar
13.Anton, N. and Botez, R.M.Aircraft X-31 stability analysis and validation with experimental data. Proceedings of the IASTED International Conference on Applied Simulation and Modeling, 25-27 June 2012, Naples, Italy, pp 114–118, Calgary, Acta Press.Google Scholar
14.Anton, N., Botez, R.M. and Popescu, D.Stability derivatives for a delta-wing X-31 aircraft validated using wind tunnel test data, J Aerospace Engineering, 2011, 225, pp 403–416.Google Scholar
15.Alcon, C.W., Croom, M.A. and Francis, M.S. The X-31 Experience – Aerodynamic impediments to post-stall agility, AIAA Paper 1995–362, January 1995.CrossRefGoogle Scholar
16.Williams, D.L., Nelson, R.C. and Fisher, D. An Investigation of X-31 roll characteristics at high angle-of-attack through subscale model testing, AIAA Paper 1994–806, January 1994.CrossRefGoogle Scholar
17.Tomaro, R.F., Strang, W.Z. and Sankar, L.N. An implicit algorithm for solving time dependent flows on unstructured grids, AIAA Paper 1997–0333, January 1997.CrossRefGoogle Scholar
18.Spalart, P.R. and Allmaras, S.R.A. One equation turbulence model for aerodynamic flows. AIAA Paper 1992-0439, January 1992.CrossRefGoogle Scholar
19.Wilcox, D.C.Reassesment of the scale determining equation for advanced turbulence models, AIAA J, 1988, 26, pp 1299–1310.CrossRefGoogle Scholar
20.Menter, F.Eddy viscosity transport equations and their relation to the model, ASME Journal of Fluids Engineering, 1997, 119, pp 876–884.CrossRefGoogle Scholar
21.Roth, G.L., Morton, S.A. and Brooks, G.P. Integrating CREATE-AV products DaVinci and Kestrel: experiences and lessons learned. AIAA Paper 2012-1063, January 2012.CrossRefGoogle Scholar
22.Grismer, M.J., Strang, W.Z., Tomaro, R.F. and Witzmman, F.C.Cobalt: a parallel, implicit, unstructured Euler/Navier-stokes solver, Advanced Engineering Software, 1998, 29, (3-6), pp 365–373.CrossRefGoogle Scholar
23.Churchfield, M.J. and Blaisdell, G.A.Reynolds stress relaxation turbulence modeling applied to a wingtip vortex flow, AIAA J, 2013, 51, (11), pp 2643–2655.CrossRefGoogle Scholar
24.Swanson, R.C. and Rumsey, C.L. Numerical issues for circulation control calculations, AIAA Paper 2008-3008, June 2008.Google Scholar
25.Bigarella, E. and Azevedo, J.Advanced eddy-viscosity and Reynolds-stress turbulence model simulations of aerospace applications, AIAA J, 2007, 45, (10), pp 2369–2390.CrossRefGoogle Scholar
26.Spalart, P.R. and Shur, M.L.On the sensitization of turbulence models to rotation and curvature, Aerospace Science Technology, 1997, 1, pp 297–302.CrossRefGoogle Scholar
27.Spalart, P.R., Jou, W.-H., Strelets, M. and Allmaras, S.R.Comments on the Feasibility of LES for Wings, and on a Hybrid RANS/LES Approach. In Proceedings, 1st AFSOR International Conference on DNS/LES, Greyden Press, Columbus, Ohio, USA, 1997, pp 137–147.Google Scholar
28.Trapier, S., Deck, S. and Duveau, P.Delayed detached-eddy simulation and analysis of supersonic inlet buzz, AIAA J, 2008, 46, (1), pp 118–131.CrossRefGoogle Scholar
29.Thomas, J.L., Krist, S.T. and Anderson, W.K.Navier-Stokes computations of vortical flows over low-aspect-ratio wings, 1990, AIAA J, 28, (2), pp 205–212.Google Scholar
30.Gurul, I., Gordnier, R. and Visbal, M.Unsteady aerodynamics of nonslender delta wings, Progress in Aerospace Sciences, 2005, 41, pp 515–557.CrossRefGoogle Scholar
31.Vallespin, D., Daronch, A., Badcok, K.J. and Boelens, O.J.Vortical flow prediction validation for an unmanned combat air vehicle model, J Aircr, 2011, 48, (6), pp 1948–1959.CrossRefGoogle Scholar
32.Taylor, G.S., Schnorbus, T. and Gursul, I. An investigation of vortex flows over low speed delta wings. AIAA Paper 2003-4021, June 2003.CrossRefGoogle Scholar
33.Cummings, R.M., Forsythe, J.R., Morton, S.A. and Squires, K.D.Challenges in high angle-of-attack flow prediction, Progress in Aerospace Sciences, 2003, 39, (5), pp 369–384.CrossRefGoogle Scholar
34.Cummings, R.M. and Schütte, A. An integrated computational/experimental approach to UCAV stability & control estimation: overview of NATO RTO AVT-161, AIAA Paper 2010–4392, June-July 2010.CrossRefGoogle Scholar
35.Schefter, J.X-31: How They´re inventing a radical new way to fly, Popular Science, 1989, 234, (2), pp 58–64.Google Scholar
36.Tyssel, L. Hybrid grid generation for complex 3D geometries, Proceedings of the 7th International Conference on Numerical Grid Generation in Computational Field Simulation, 2000, pp 337–346.Google Scholar
37.Tyssel, L. The TRITET grid generation system, International Society of Grid Generation (ISGG), Proceedings of the 10the International Conference on Numerical Grid Generation in Computational Field Simulations, 2000.Google Scholar
38.Löser, T. and Bergmann, A. Develop of the dynamic wind tunnel testing capabilities at DNW-NWB. AIAA Paper 2003-453, January 2003.CrossRefGoogle Scholar