Hostname: page-component-cd9895bd7-gxg78 Total loading time: 0 Render date: 2024-12-22T21:29:16.377Z Has data issue: false hasContentIssue false
  • English
  • Français

The aerodynamic optimisation of a low-Reynolds paper plane with adjoint method

Published online by Cambridge University Press:  24 August 2020

Y. Zhang Open the ORCID record for Y. Zhang [Opens in a new window] ,
X. Zhang  and
G. Chen
Y. Zhang*
Affiliation:
State Key Laboratory of Mechanical Structure Strength and Vibration, Xi’an Jiaotong University, Xi’an, China
X. Zhang
Affiliation:
State Key Laboratory of Mechanical Structure Strength and Vibration, Xi’an Jiaotong University, Xi’an, China
G. Chen
Affiliation:
State Key Laboratory of Mechanical Structure Strength and Vibration, Xi’an Jiaotong University, Xi’an, China
*
[email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

The aerodynamic performance of a deployable and low-cost unmanned aerial vehicle (UAV) is investigated and improved in present work. The parameters of configuration, such as airfoil and winglet, are determined via an optimising process based on a discrete adjoint method. The optimised target is locked on an increasing lift-to-drag ratio with a limited variation of pitching moments. The separation that will lead to a stall is delayed after optimisation. Up to 128 design variables are used by the optimised solver to give enough flexibility of the geometrical transformation. As much as 20% enhancement of lift-to-drag ratio is gained at the cruise angle-of-attack, that is, a significant improvement in the lift-to-drag ratio adhering to the preferred configuration is obtained with increasing lift and decreasing drag coefficients, essentially entailing an improved aerodynamic performance.

Keywords

aerodynamic designoptimisationadjoint methodnumerical simulation
Type
Research Article
Information
The Aeronautical Journal , Volume 125 , Issue 1285 , March 2021 , pp. 510 - 524
Copyright
© The Author(s), 2020. Published by Cambridge University Press on behalf of Royal Aeronautical Society

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

REFERENCES

Gabriel, E. and Mueller, T. Low-aspect-ratio wing aerodynamics at low reynolds number, AIAA Journal, 2004, 42, pp 865–873.Google Scholar
Schlüter, J.U., Aerodynamic study of the dart paper airplane for micro air vehicle application, Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, 2014, 228, pp 567–576.Google Scholar
Pounds, P.E. and Singh, S.P. Integrated electro-aeromechanical structures for low-cost, self-deploying environment sensors and disposable uavs, In: 2013 IEEE International Conference on Robotics and Automation, IEEE, pp 4459–4466.CrossRefGoogle Scholar
Kaplan, S., Altman, A. and Ol, M. Wake vorticity measurements for low aspect ratio wings at low reynolds number, Journal of Aircraft, 2007, 44, pp 241–251.CrossRefGoogle Scholar
Luckring, J. Reynolds number and leading-edge bluntness effects on a 65-deg delta wing, In: 40th AIAA Aerospace Sciences Meeting & Exhibit, p 419.Google Scholar
Boschetti, P., Cárdenas, E. and Amerio, A. Aerodynamic optimization of an UAV design, In: AIAA 5th ATIO and16th Lighter-Than-Air Sys Tech. and Balloon Systems Conferences, p. 7399.Google Scholar
Secanell, M., Suleman, A. and Gamboa, P. Design of a morphing airfoil for a Light unmanned aerial vehicle using high-fidelity aerodynamics shape optimization[C]//AIAA/ASME/ASCE/ AHS/ASC/ Structures, Structural Dynamics & Materials Conference. 2005.CrossRefGoogle Scholar
Secanell, M., Suleman, A. and Gamboa, P. Design of a morphing airfoil using aerodynamic shape optimization, AIAA Journal, 2006, 44, pp 1550–1562.CrossRefGoogle Scholar
Rajagopal, S. and Ganguli, R. Conceptual design of UAV using kriging-based multi-objective genetic algorithm, The Aeronautical Journal, 2008, 112, pp 653–662.CrossRefGoogle Scholar
Lee, D.-S., Gonzalez, L.F., Srinivas, K., Auld, D. and Wong, K.C. Aerodynamic shape optimization of unmanned aerial vehicles using hierarchical asynchronous parallel evolutionary algorithms, International Journal of Computational Intelligence Research, 2007, 3, pp 231–253.CrossRefGoogle Scholar
Ahuja, V. and Hartfield, R. Optimization of UAV designs for aerodynamic performance using genetic algorithms, In: 51st AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference 18th AIAA/ASME/AHS Adaptive Structures Conference 12th, p. 2759.Google Scholar
Boutemedjet, A., Samardžić, M., Rebhi, L., Rajić, Z. and Mouada, T. UAV aerodynamic design involving genetic algorithm and artificial neural network for wing preliminary computation, Aerospace Science and Technology, 2019, 84, pp 464–483.CrossRefGoogle Scholar
Kontogiannis, S.G. and Ekaterinaris, J.A. Design, performance evaluation and optimization of a UAV, Aerospace Science and Technology, 2013, 29, pp 339–350.CrossRefGoogle Scholar
Jameson, A., Martinelli, L. and Pierce, N. Optimum aerodynamic design using the Navier–Stokes equations, Theoretical and Computational Fluid Dynamics, 1998, 10, pp 213–237.CrossRefGoogle Scholar
Nadarajah, S. and Jameson, A. A comparison of the continuous and discrete adjoint approach to automatic aerodynamic optimization, In: 38th Aerospace Sciences Meeting and Exhibit, p 667.Google Scholar
Kim, H.-J., Sasaki, D., Obayashi, S. and Nakahashi, K. Aerodynamic optimization of supersonic transport wing using unstructured adjoint method, AIAA Journal, 2001, 39, pp 1011–1020.CrossRefGoogle Scholar
Nielsen, E.J. and Anderson, W.K. Recent improvements in aerodynamic design optimization on unstructured meshes, AIAA Journal, 2002, 40, pp 1155–1163.CrossRefGoogle Scholar
Nemec, M., Zingg, D.W. and Pulliam, T.H. Multipoint and multi-objective aerodynamic shape optimization, AIAA Journal, 2004, 42, pp 1057–1065.CrossRefGoogle Scholar
Dwight, R.P. and Brezillon, J. Effect of approximations of the discrete adjoint on gradient- based optimization, AIAA Journal, 2006, 44, pp 3022–3031.CrossRefGoogle Scholar
Jameson, A., Shankaran, S. and Martinelli, L. Continuous adjoint method for unstructured grids, AIAA Journal, 2008, 46, pp 1226–1239.CrossRefGoogle Scholar
Hicken, J.E. and Zingg, D.W. Aerodynamic optimization algorithm with integrated geometry parameterization and mesh movement, AIAA Journal, 2010, 48, pp 400–413.CrossRefGoogle Scholar
Bueno-Orovio, A., Castro, C., Palacios, F. and Zuazua, E. Continuous adjoint approach for the Spalart-Allmaras model in aerodynamic optimization, AIAA Journal, 2012, 50, pp 631–646.CrossRefGoogle Scholar
Roe, P.L. Approximate Riemann solvers, parameter vectors, and difference schemes, Journal of Computational Physics, 1981, 43, pp 357–372.CrossRefGoogle Scholar
Venkatakrishnan, V. Convergence to steady state solutions of the Euler equations on unstructured grids with limiters, Journal of Computational Physics, 1995, 118, pp 120–130.CrossRefGoogle Scholar
Spalart, P. and Allmaras, S. A one-equation turbulence model for aerodynamic flows, In: 30th aerospace sciences meeting and exhibit, p. 439. E. J. Nielsen, J. Lu, M. A. Park, D.Google Scholar
Darmofal, L. An implicit, exact dual adjoint solution method for turbulent flows on unstructured grids, Computers & Fluids, 2004, 33, pp 1131–1155.Google Scholar
Saad, Y. and Schultz, M.H. Gmres: A generalized minimal residual algorithm for solving nonsymmetric linear systems, SIAM Journal on Scientific and Statistical Computing, 1986, 7, pp 856–869.CrossRefGoogle Scholar
Jameson, A. and Kim, S. Reduction of the adjoint gradient formula for aerodynamic shape optimization problems, AIAA Journal, 2003, 41, pp 2114–2129.CrossRefGoogle Scholar
Samareh, J.A. Novel multidisciplinary shape parameterization approach, Journal of Aircraft, 2001, 38, pp 1015–1024.CrossRefGoogle Scholar
Gursul, I. Recent developments in delta wing aerodynamics, The Aeronautical Journal, 2004, 108, pp 437–452.CrossRefGoogle Scholar
Colella, P. A direct Eulerian MUSCL scheme for gas dynamics, SIAM Journal on Scientific and Statistical Computing, 1985, 6, pp 104–117.CrossRefGoogle Scholar