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Optimisation of Ducted Propellers for Hybrid Air Vehicles Using High-Fidelity CFD

Published online by Cambridge University Press:  04 July 2016

M. Biava  and
G. N. Barakos
M. Biava
Affiliation:
CFD Laboratory, School of Engineering, James Watt South Building, University of Glasgow, Glasgow, United Kingdom
G. N. Barakos*
Affiliation:
CFD Laboratory, School of Engineering, James Watt South Building, University of Glasgow, Glasgow, United Kingdom
*
George.Barakos@glasgow.ac.uk
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Abstract

This paper presents performance analysis and design of ducted propellers for lighter-than-air vehicles. High-fidelity computational fluid dynamics simulations were first performed on a detailed model of the propulsor, and the results were in very good agreement with available experimental data. Additional simulations were performed using a simplified geometry, to quantify the effect of the duct and of the blade twist on the propeller performance. It was shown that the duct is particularly effective at low flight speed and that the blades with relatively high twist have better performance over the flight envelope. Design of the optimal twist distribution and of the duct shape was also attempted by coupling the flow solver with a quasi-Newton optimisation method. Flow gradients were computed by solving the discrete adjoint of the Reynolds-averaged Navier-Stokes equations using a fixed-point iteration scheme or a nested Krylov method with deflated restarting. The results show that the ducted propeller propulsive efficiency can be increased by 2%.

Keywords

Hybrid air vehiclesducted propellersoptimisationadjoint methodcomputational fluid dynamics
Type
Research Article
Information
The Aeronautical Journal , Volume 120 , Issue 1232 , October 2016 , pp. 1632 - 1657
Copyright
Copyright © Royal Aeronautical Society 2016 

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References

1. Private communication with Hybrid Air Vehicles Ltd., 2015, Bedford, UK.Google Scholar
2. Arnoldi, W.E. The principle of minimized iterations in the solution of the matrix eigenvalue problem, Quarterly of Applied Mathematics, 1951, 9, (1), pp 17–29.Google Scholar
3. Axelsson, O. Iterative Solution Methods, 1994, Cambridge University Press, Cambridge, Massachusetts, US.CrossRefGoogle Scholar
4. Barakos, G., Steijl, R., Badcock, K. and Brocklehurst, A. Development of CFD capability for full helicopter engineering analysis, 31st European Rotorcraft Forum [CD-ROM], number Paper 91, 2005, Council of European Aerospace Societies.Google Scholar
5. Benzi, M. Preconditioning techniques for large linear systems: a survey. J. Computational Physics, 2002, 182, (2), pp 418–477.CrossRefGoogle Scholar
6. Biava, M., Woodgate, M. and Barakos, G.N. Fully implicit discrete adjoint methods for rotorcraft applications. AIAA J., 2016, 54, (2), pp 735–749.Google Scholar
7. Bonet, J. and Peraire, J. An alternating digital tree (ADT) algorithm for 3D geometric searching and intersection problems, Int. J. Numerical Methods in Engineering, 1991, 31, (1), pp 1–17.Google Scholar
8. Campobasso, M.S. and Giles, M.B. Effects of flow instabilities on the linear analysis of turbomachinery aeroelasticity, J. Propulsion and Power, 2003, 19, (2), pp 250–259.CrossRefGoogle Scholar
9. Campobasso, M.S. and Giles, M.B. Stabilization of a linear flow solver for turbomachinery aeroelasticity using recursive projection method, AIAA J., 2004, 42, (9), pp 1765–1774.CrossRefGoogle Scholar
10. Christianson, B. Reverse accumulation and implicit functions, Optimization Methods and Software, 1998, 9, (4), 307–322.Google Scholar
11. Engquist, B. and Osher, S. Stable and entropy satisfying approximations for transonic flow calculations. Mathematics of Computation, 1980, 34, (149), pp 45–75.CrossRefGoogle Scholar
12. Giles, M.B. On the iterative solution of adjoint equations, In Corliss, G., Faure, C., Griewank, A., Hascoët, L., and Naumann, U., Eds, Automatic Differentiation of Algorithms, 2002, Springer, New York, New York, US, pp 145–151.Google Scholar
13. Giraud, L., Gratton, S., Pinel, X. and Vasseur, X. Flexible GMRES with deflated restarting, SIAM J. Scientific Computing, 2010, 32, (4), pp 1858–1878.Google Scholar
14. Hirsch, C. Numerical Computation of Internal and External Flows: The Fundamentals of Computational Fluid Dynamics, volume 1, 2007, Butterworth-Heinemann.Google Scholar
15. Jameson, A. Time dependent calculations using multigrid with application to unsteady flows past airfoils and wings, 10th Computational Fluid Dynamics Conference, AIAA Paper 1991-1596, 1991, Honolulu, HI, US.Google Scholar
16. Johnson, S.G. The NLopt Nonlinear-Optimization Package, http://ab-initio.mit.edu/nlopt. Technical report.Google Scholar
17. Kraft, D. Algorithm 733: TOMP-Fortran modules for optimal control calculations, ACM Transactions on Mathematical Software, 1994, 20, (3), pp 262–281.Google Scholar
18. Kulfan, B.M. Universal parametric geometry representation method, J. Aircraft, 2008, 45, (1), pp 142–158.CrossRefGoogle Scholar
19. Liou, M.S. A sequel to AUSM: AUSM+, J. Computational Physics, 1996, 129, (2), pp 364–382.Google Scholar
20. McCormick, B.W. Aerodynamics of V/STOL Flight, 1999, Dover Publications, Mineola, New York, USA.Google Scholar
21. Morgan, R.B. GMRES with deflated restarting. SIAM J. Scientific Computing, 2003, 24, (1), pp 20–37.Google Scholar
22. Pereira, J. Hover and Wind-Tunnel Testing of Shrouded Rotors for Improved Micro Air Vehicle Design, PhD thesis, 2008, University of Maryland, College Park, Maryland, US.Google Scholar
23. Renka, R.J. Multivariate Interpolation of Large Sets of Scattered Data. ACM Transactions on Mathematical Software, 1988, 14, (2), pp 139–148.CrossRefGoogle Scholar
24. Rieper, F. A low-Mach number fix for Roe’s approximate Riemann solver. J. Computational Physics, 2011, 230, (13), pp 5263–5287.CrossRefGoogle Scholar
25. Roe, P.L. Approximate Riemann solvers, parameter vectors, and difference schemes, J. Computational Physics, 1981, 43, (2), pp 357–372.Google Scholar
26. Saad, Y. A flexible inner-outer preconditioned GMRES algorithm, SIAM J. Scientific Computing, 1993, 14, (2), pp 461–469.Google Scholar
27. Saad, Y. Iterative Methods for Sparse Linear Systems, 1996, PWS Publishing, Boston, Massachusetts, US.Google Scholar
28. Saad, Y. and Schultz, M.H. GMRES: a generalized minimal residual algorithm for solving nonsymmetric linear systems, SIAM J. Scientific and Statistical Computing, 1986, 7, (3), pp 856–869.Google Scholar
29. Shepard, D. A two-dimensional interpolation function for irregularly-spaced data, Proceedings of the 1968 23rd ACM National Conference, 1968, Association for Computing Machinery, New York, New York, US, pp 517-524.CrossRefGoogle Scholar
30. Shroff, G.M. and Keller, H.B. Stabilization of unstable procedures: the recursive projection method, SIAM J. Numerical Analysis, 1993, 30, (4), 1099–1120.Google Scholar
31. Steijl, R., Barakos, G.N. and Badcock, K. A framework for CFD analysis of helicopter rotors in hover and forward flight, Int. J. Numerical Methods in Fluids, 2006, 51, (8), pp 819–847.CrossRefGoogle Scholar
32. Van Albada, G.D., Van Leer, B. and Roberts, W.W. Jr A comparative study of computational methods in cosmic gas dynamics, Astronomy and Astrophysics, 1982, 108, pp 76–84.Google Scholar
33. Wilcox, D.C. Re-assessment of the scale-determining equation for advanced turbulence nodels, AIAA J., 1988, 26, (11), pp 1299–1310.Google Scholar
34. Woodgate, M.A., Pastrikakis, V.A. and Barakos, G.N. Method for calculating rotors with active gurney flaps, J. Aircraft, 2016, 53, (3), pp 605–626.CrossRefGoogle Scholar