Hostname: page-component-78c5997874-4rdpn Total loading time: 0 Render date: 2024-11-19T13:27:54.680Z Has data issue: false hasContentIssue false

Constrained multi-objective aerofoil design using a multi-level optimisation strategy

Published online by Cambridge University Press:  27 January 2016

J. Early
Affiliation:
School of Mechanical and Aerospace Engineering, Queen’s University, Belfast, UK
R. McRoberts
Affiliation:
School of Mechanical and Aerospace Engineering, Queen’s University, Belfast, UK
M. Price
Affiliation:
School of Mechanical and Aerospace Engineering, Queen’s University, Belfast, UK

Abstract

A novel approach for the multi-objective design optimisation of aerofoil profiles is presented. The proposed method aims to exploit the relative strengths of global and local optimisation algorithms, whilst using surrogate models to limit the number of computationally expensive CFD simulations required. The local search stage utilises a re-parameterisation scheme that increases the flexibility of the geometry description by iteratively increasing the number of design variables, enabling superior designs to be generated with minimal user intervention. Capability of the algorithm is demonstrated via the conceptual design of aerofoil sections for use on a lightweight laminar flow business jet. The design case is formulated to account for take-off performance while reducing sensitivity to leading edge contamination. The algorithm successfully manipulates boundary layer transition location to provide a potential set of aerofoils that represent the trade-offs between drag at cruise and climb conditions in the presence of a challenging constraint set. Variations in the underlying flow physics between Pareto-optimal aerofoils are examined to aid understanding of the mechanisms that drive the trade-offs in objective functions.

Type
Research Article
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.Nemec, M., Zingg, D.W. and Pulliam, T.H.Multipoint and multi-objective aerodynamic shape optimization, AIAA J, June 2004, 42, (6), pp 10571065.CrossRefGoogle Scholar
2.Gonzalez, L.F., Lee, D.S., Srivinas, K. and Wong, K.C.Single and multi-objective UAV aerofoil optimisation via hierarchical asynchronous parallel evolutionary algorithm, Aeronaut J, 2006, 110, pp 659672.CrossRefGoogle Scholar
3.Cameron, L., Early, J.M. and McRoberts, R.Metamodel Assisted Multi-Objective Global Optimisation of Natural Laminar Flow Aerofoils, AIAA 2011-3001, 29th Applied Aerodynamics Conference, Honolulu, Hawaii, USA, June 2011.CrossRefGoogle Scholar
4.Arguelles, P.et alEuropean Aeronautics: A Vision for 2020, European Commission, Belgium, 2001.Google Scholar
5.Braun, M., Wicke, K. and Koch, A.Analysis of Natural Laminar Flow Aircraft Based on Airline Network Design and Fleet Assignment, AIAA 2011-6807, 11th AIAA Aviation Technology, Integration and Operations (ATIO) Conference, Virginia Beach, Virginia, USA, September 2011.CrossRefGoogle Scholar
6.Atkin, C.Laminar Flow Control: Leap or Creep, AIAA 2008-3986, 38th Fluid Dynamics Conference and Exhibit, Seattle, Washington, USA, June 2008.Google Scholar
7.Green, B.E., Whiteside, J.L., Campbell, R.L. and Mineck, R.E.Method for the constrained design of natural laminar flow airfoils, J Aircr, 1997, 34, (6), pp 706712.CrossRefGoogle Scholar
8.Amoignon, O., Pralits, J., Hanifi, A., Berggren, M. and Henningson, D.Shape optimization for delay of laminar-turbulent transition, AIAA J, 2006, 44, (5), pp 10091023.CrossRefGoogle Scholar
9.Driver, J. and Zingg, D.W.Optimized Natural-Laminar Flow Airfoils, AIAA 2006-247, 44th AIAA Aerospace Sciences Meeting and Exhibit, oReno, Nevada, USA, January 2006.CrossRefGoogle Scholar
10.Khurana, M. and Winarto, H.Development and validation of an efficient direct numerical optimisation approach for aerofoil shape design, Aeronaut J, 2010, 114, pp 611627.CrossRefGoogle Scholar
11.McRoberts, R., Early, J.M., Spence, S. and Medina, H.Investigation of Utilizing a Single Surface Depression in the Optimisation of NLF Aerofoil Design, AIAA 2010-4679, 28th AIAA Applied Aerodynamics Conference, Chicago, Illinois, USA, June 2010.Google Scholar
12.Drela, M.XFOIL: An Analysis and Design System for Low Reynolds Number Airfoils, MIT Department of Aeronautics and Astronautics, Cambridge, Massachusetts, USA, 1989.Google Scholar
13.Deb, K., Pratap, A., Agarwal, S. and Meyarivian, T.A Fast and elitist multiobjective genetic algorithm: NSGA-II, IEEE Transactions on Evolutionary Computing, 6, (2), 2002, pp 182197.CrossRefGoogle Scholar
14.Kulfan, B.M.Universal parametric geometry representation method, J Aircraft, 2008, 45, (1), pp 142158.CrossRefGoogle Scholar
15.Keane, A.J.Statistical improvement criteria for use in multiobjective design optimisation, AIAA J. 2006, 44, (4), pp 879891.CrossRefGoogle Scholar
16.Forrester, A.I.J. and Keane, A.J.Recent advances in surrogate-based optimization, Progress In Aerospace Sciences, 2009, 45, pp 5079.CrossRefGoogle Scholar
17.Jones, D.R.A Taxonomy of global optimization methods based on response surfaces, J Global optimization, 21, pp 345383, 2001.CrossRefGoogle Scholar
18.Drela, M.A User’s Guide to MSES 3.00, MIT Department of Aeronautics and Astronautics, Cambridge, Massachusetts, 2004.Google Scholar
19.Smith, A.Transition, Pressure Gradient, and Stability Theory, In the Proceedings of the 9th International Congress of Applied Mechanics, 4, pp 234244, 1956.Google Scholar
20.van Ingen, V.A.Suggested Semi-Empirical Method for the Calculation of the Boundary-Layer Region, Technical Report, University of Delft, The Netherlands, 1956. Report VTH71, VTH74.Google Scholar
21.Mattson, C.A., Mullur, A.A. and Messac, A.Minimal Representation of Multiobjective Design Space Using A Smart Pareto Filter, 9th AIAA/ISSMO Symposium on Multidisciplinary Analysis and Optimization, Georgia, USA, September 2002.CrossRefGoogle Scholar
22.Srinivas, N. and Deb, K.Multiobjective optimization using nondominated sorting genetic algorithms, Evolutionary Computing, 1994, 2-3, pp 221248.CrossRefGoogle Scholar
23.Deb, K. and Goyal, M.A Combined Genetic Adaptive Search (GeneAS) for engineering design, Computer Science and Informatics, 1996, 26, pp 3045.Google Scholar
24.Deb, K. and Agrawal, R.B.Simulated binary crossover for continuous search space, Complex Systems, 1995, 9, pp 115148.Google Scholar
25.Fujino, M., Yoshizaki, Y. and Kawamura, Y.Natural-laminar-flow airfoil development for a lightweight business jet, J Aircr, 2003, 40, (4), pp 609615.CrossRefGoogle Scholar
26.Collier, F.NASA Sponsored Activities in Laminar Flow Technologies for Advanced Transport Aircraft 2nd UTIAS-MITAS International Workshop on Aviation and Climate Change, 27-28 May, 2010, Toronto, Canada.Google Scholar