Multi-objective and multi-disciplinary design using evolutionary methods applied to aerospace design problems

Abstract

The design of real complex objects or systems is characterised by a multi-disciplinary approach. This article presents the methodology of multi-objective and multi-disciplinary design of aerodynamic objects, using optimisation. The in-house software ParaDes using Non-Uniform Rational B-Splines is a key element for the parametric modelling of aerodynamic objects. A multi-objective Genetic Algorithm is used for optimisation. Selected examples of a successful design solution are: (i) the design of a forward swept wing with natural laminar flow; (ii) the design of wings for low-speed turbo-prop small aircraft and small business jet aircraft; (iii) the design of a helicopter fuselage with and without rotor influence. In each case, the definition of the design problem, the objectives and constraints and the basic properties of the evaluation tools are discussed.

Keywords

Multi-criterial design multi-disciplinary design optimisation

Get full access to this article

View all access options for this article.

References

Sobieszczanski-Sobieski J and Haftka RT. Multidisciplinary aerospace design optimization: Survey of recent developments. AIAA Paper 96-0711, 1996.

Perez RE, Liu HHT and Behdinan K. Evaluation of multidisciplinary optimization approaches for aircraft conceptual design. AIAA Paper 2004-4537, 2004.

Defoort

Balesdent

Klotz

. Multidisciplinary aerospace system design: Principles, issues and ONERA experience. Aerospace Lab J 2012; 12(4): 1–17.

Iwaniuk

Wiśniowski

Żółtak

. Multi-disciplinary optimisation approach for a light turboprop aircraft-engine integration and improvement. Aircr Eng Aerosp Tec 2016; 88(2): 348–355.

Liu B, Haftka R and Akgun M. Composite wing structural optimization using genetic algorithms and response surfaces. AIAA Paper 19984854, 1998.

Oyama A. Multidisciplinary optimization of transonic wing design based on evolutionary algorithms coupled with CFD solver. In: Oñate E, Bugeda G and Suárez B (eds) Proceedings of European congress on computational methods in applied sciences and engineering, 2000, pp.1–17. CIMNE.

Jones

Crossley

Lyrintzis

. Aerodynamic and aeroacoustic optimization of rotorcraft airfoils via a parallel genetic algorithm. J Aircr 2012; 37(6): 1088–1096.

Perez R, Chung J and Behdinan K. Aircraft conceptual design using genetic algorithms. AIAA Paper 2000-4938, 2000.

Holst T and Pulliam T. Aerodynamic shape optimization using a real-number-encoded genetic algorithm. AIAA Paper 2001-2473, 2004.

10.

D’Souza BS and Simpson TW. A genetic algorithm based method for product family design optimization. Eng Optimiz 2003; 35(1): 1–18.

11.

Lampinen

. Cam shape optimisation by genetic algorithm. Comput Aided Des 2003; 35(8): 727–737.

12.

Gonzalez

Whitney

Srinivas

et al.

Optimum multidisciplinary and multi-objective wing design in CFD using evolutionary techniques. In: Clinton

David

(eds). Computational fluid dynamics, Berlin, Heidelberg: Springer Berlin Heidelberg, 2006, pp. 681–686.

13.

Liebeck

. Design of the blended wing body subsonic transport. J Aircr 2004; 41(1): 10–25.

14.

TTH

Leng

GSB

. Application of genetic algorithms to conceptual design of a micro-air vehicle. Eng Appl Artif Intell 2002; 15(5): 439–445.

15.

Jun

Jeon

Y-H

Rho

et al.

Application of collaborative optimization using genetic algorithm and response surface method to an aircraft wing design. J Mech Sci Technol 2006; 20: 133–133.

16.

Kim

Jeon

Lee

. Multi-objective and multidisciplinary design optimization of supersonic fighter wing. J Aircr 2006; 43(3): 817–824.

17.

Shahrokhi

Jahangirian

. Airfoil shape parameterization for optimum Navier–Stokes design with genetic algorithm. Aerosp Sci Technol 2007; 11(6): 443–450.

18.

Hartfield

Jenkins

Burkhalter

. Ramjet powered missile design using a genetic algorithm. J Comput Inf Sci Eng. 7(2): 167–173.

19.

Lee DS, Gonzalez LF, Srinivas K, et al. Multi-objective / multidisciplinary design optimisation of blended wing body UAV via advanced evolutionary algorithms. AIAA Paper 2007-0036, 2007.

20.

Wloch K and Bentley PJ. Optimising the performance of a Formula One car using a genetic algorithm. In: Xin Y, et al (eds) Parallel problem solving from nature – PPSN VII (Lecture Notes in Computer Science, vol. 3242). Berlin: Springer Berlin Heidelberg, pp.702–711.

21.

Lee

Gonzalezc

Srinivasa

et al.

Robust evolutionary algorithms for UAV/UCAV aerodynamic and RCS design optimisation. J Comput Fluids 2008; 37(5): 547–564.

22.

Parka

Leeb

Hanc

et al.

Improved genetic algorithm for multidisciplinary optimization of composite laminates. J Comput Struct 2008; 86(19–20): 1894–1903.

23.

Boria

Stanford

Bowman

et al.

Evolutionary optimization of a morphing wing with wind-tunnel hardware in the loop. AIAA J 2009; 47(2): 399–409.

24.

Jahangirian

Shahrokhi

. Inverse design of transonic airfoils using genetic algorithm and a new parametric shape method. Inverse Probl Sci Eng 2009; 17(5): 681–699.

25.

Park

Lee

. Optimal design of two-dimensional wings in ground effect using multi-objective genetic algorithm. J Ocean Eng. 37(10): 902–912.

26.

Dyera

Hartfielda

Dozierb

et al.

Aerospace design optimization using a steady state real-coded genetic algorithm. Appl Math Comput 2012; 218(9): 4710–4730.

27.

Garcia MJ, Boulanger P and Giraldo S. CFD based wing shape optimization through gradient-based method. In: EngOpt-2008: Proceedings of international conference on engineering optimization, Rio de Janeiro, Brazil, 1–5 June 2008.

28.

Stalewski W. Aerodyanmic optimisation of joined-wing aeroplane. In: IC-EpsMsO-2015: Proceedings of 6th international conference on experiments/process/system modelling/simulation/optimisation, Athens, Greece, 8–11 July 2015.

29.

Leoviriyakit K. Wing planform optimization via an adjoint method. PhD Dissertation, Stanford University, USA, 2005.

30.

Tashnizi

Taheri

Hekma

. Investigation of the adjoint method in aerodynamic optimization using various shape parameterization techniques. J Braz Soc Mech Sci 2010; 32(2): 176–186.

31.

Stalewski

. Numerical optimisation of rotorcraft airfoils based on genetic algorithm and unsteady aerodynamic criteria. Trans Inst Aviat 2006; 184–185(1–2): 54–64. (in Polish).

32.

Stalewski W. Parametric modelling of aerodynamic objects - The key to successful design and optimisation. In: CEAS 2011: Proceedings of the 3rd CEAS air & space conference, 21st AIDAA congress, Venice, Italy, 24–28 October 2011, pp.588–596.

33.

Rokicki

Stalewski

Żółtak

Multi-disciplinary optimisation of the forward swept wing. In: Burczynski

Périaux

(eds). Evolutionary and deterministic methods for design, optimatization and control with applications to industrial and societal problems, Barcelona: CIMNE, 2009, pp. 253–259.

34.

Stalewski

Żółtak

. Design of a turbulent wing for small aircraft using multidisciplinary optimisation. Arch Mech 2014; 66(3): 185–201.

35.

Stalewski W and Żółtak J. Multi-objective and multidisciplinary optimization of wing for small aircraft. In: CEAS 2011: Proceedings of the 3rd CEAS air & space conference, 21st AIDAA congress, Venice, Italy, 24–28 October 2011, pp.1015–1024.

36.

Stalewski

Żółtak

Multicriteria design and optimisation of helicopter fuselage. In: Poloni

Quagliarella

Périaux

et al. (eds). Evolutionary and deterministic methods for design, optimatization and control with applications to industrial and societal problems, Capua, Italy: CIRA, 2011, pp. 518–529.

37.

Stalewski W and Żółtak, J. Optimisation of the helicopter fuselage with simulation of main and tail rotor influence. In: ICAS 2012: Proceedings of 28th congress of the international council of the aeronautical sciences, 2012, Paper-0811, pp.1–11.

38.

Stalewski

Żółtak

. The preliminary design of the air-intake system and the nacelle in the small aircraft-engine integration process. Aircr Eng Aerosp Tec 2014; 86(3): 250–258.

39.

NACRE Project. New aircraft concepts research. R&D Project Sixth Framework Programme (FP6), http://cordis.europa.eu/project/rcn/75773_en.html (2010, accessed March 2016).

40.

CESAR Project. The cost-effective small aircraft. R&D Project Sixth Framework Programme (FP6), http://cordis.europa.eu/project/rcn/79963_en.html (2003, accessed March 2016).

41.

Sznajder

Stalewski

. Determination of non-linear aerodynamic characteristics of an aircraft using a potential flow model and viscous airfoil characteristics. Trans Inst Aviat 2010; 207(5): 63–83.

42.

Sznajder

. Determination of non-linear aerodynamic characteristics of an aircraft using a potential flow model and viscous airfoil characteristics. Trans Inst Aviat 2010; 207(5): 55–62.

43.

Wysocki Z, Kania W, Suchożebrska A, et al. Airfoil design and analysis AC1, Turbulent AFT, HLT. CESAR Deliverable Report D1.2.1-07, 2007.

44.

Stalewski W, Żółtak J, Wysocki Z, et al. Airfoil design and analysis AC2. CESAR Deliverable Report D1.2.1-9, 2007.

45.

Simpson B. Virtual blade model (PDF presentation), 2005, http://www.fluvius.com.au/Fluent_ugn05/ugm05_Virtual_Blade_Model_VBM.pdf (accessed February 2006).