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Archives of Computational Methods in Engineering

, Volume 21, Issue 3, pp 189–271 | Cite as

Aerodynamic Shape Optimization in Aeronautics: A Fast and Effective Multi-Objective Approach

  • Claudio Comis Da RoncoEmail author
  • Rita Ponza
  • Ernesto Benini
Article

Abstract

The present work aims at introducing a fast and effective CFD-based automatic loop for optimization of rotorcraft components. The automatic loop presented here was strictly designed around an innovative Multi Objective Evolutionary Algorithm, developed at University of Padua, namely the GeDEA-II. Recent papers showed its excellent performance when tested on state-of-the-art problems. In order to test the performance of this algorithm two test cases are presented, each having its peculiar characteristics. The first problem regards the single-objective, multi-constraints aerodynamic optimization of the ERICA tilt-rotor cockpit region. The second one is a multi-point multi-constraint optimization of the left intake of the AgustaWestland AW101 helicopter. Results demonstrate the effectiveness of this automatic optimization loop in tackling real-word engineering problems.

Notes

Acknowledgments

The present research has been funded in the framework of the Joint Technology Initiatives: Clean Sky under grant agreement number 270609 “CODE-Tilt, Contribution to design optimization of tiltrotor components for drag reduction.” Authors acknowledge Antonio Saporiti and the Aerodynamic Office of AgustaWestland for the invaluable support they have given to this work.

References

  1. 1.
    Adelman HM, Mantay WR (1991) Integrated multidisciplinary design optimization of rotorcraft. J Aircr 28(1):22–28CrossRefGoogle Scholar
  2. 2.
    Agrawal RB, Deb K (1994) Simulated binary crossover for continuous search space. Technical report, Complex SystemsGoogle Scholar
  3. 3.
    Aircrew station vision requirements for military aircraft. Military standard mil-std-850b. Technical report, 1970Google Scholar
  4. 4.
    Altair Engineering. HyperMesh & BatchMesher User’s Guide. Altair, (2009)Google Scholar
  5. 5.
    Amir M (2008) Application of piezoelectric actuators at sub- sonic speeds. J Aircr 45(4):1419–1430CrossRefGoogle Scholar
  6. 6.
    Amitay M, Glezer A (2006) Aerodynamic flow control using synthetic jet actuators, vol 330., Lecture Notes in Control and Information. Sciences Springer, Berlin/HeidelbergGoogle Scholar
  7. 7.
    Anderson BH (1987) Inlets, ducts and nozzles. Technical report, NASA, CP-3049Google Scholar
  8. 8.
    Ansys Inc. FLUENT User’s Guide, release 14.0. ANSYS, (2010)Google Scholar
  9. 9.
    Ansys Inc. Tgrid User’s Guide, release 14.0. ANSYS, (2010)Google Scholar
  10. 10.
    Bäck T (1994) Selective pressure in evolutionary algorithms: a characterization of selection mechanisms. In: Proceedings of the first IEEE conference on evolutionary computation. IEEE Press, Piscataway, pp 57–62Google Scholar
  11. 11.
    Bäck T, Schwefel H-P (1993) An overview of evolutionary algorithms for parameter optimization. Evol Comput 1(1):1–23CrossRefGoogle Scholar
  12. 12.
    Bäck T (1996) Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming. genetic algorithms. Oxford University Press, Oxford, UKGoogle Scholar
  13. 13.
    Bandyopadhyay PM (1989) Viscous drag reduction of a nose body. AIAA J 27(3):274–282CrossRefGoogle Scholar
  14. 14.
    Bergamaschi L, Zilli G, Venturin M (2008) Metodi di line-search. Lecture Notes. Dipartimento di Metodi e Modelli Matematici Universitá di PadovaGoogle Scholar
  15. 15.
    Bergamaschi L, Zilli G, Venturin M (2008) Metodi di ottimizzazione. lecture notes. Dipartimento di Metodi e Modelli Matematici Universitá di PadovaGoogle Scholar
  16. 16.
    Bleuler S, Laumanns M, Thiele L, Zitzler E (2003) PISA - A platform and programming language independent interface for search algorithms. In: Fonseca CM et al (eds) Conference on evolutionary multi-criterion optimization (EMO 2003), vol 2632. LNCSSpringer, Berlin, pp 494–508Google Scholar
  17. 17.
    Calarese W, Pan Crisler W, Gustafson GL (1985) Afterbody drag reduction by vortex generatorsGoogle Scholar
  18. 18.
    Campanardi G, Zanotti A, Macchi C (2008) Final complete wind tunnel test database. technical report. Technical report, NICETRIP/POLIMI/WP4.TR007/4.0, Version 4.0Google Scholar
  19. 19.
    Catalano FM (2004) Afterbody drag reduction by vortex generators. Acta Polytechnica 44(3):274–282Google Scholar
  20. 20.
    Celi R (1999) Recent applications of design optimization to rotorcraft—a survey. J Aircr 36(1):176–189CrossRefGoogle Scholar
  21. 21.
    Chattopadhyay A, Narayan J (1993) Optimum design of high speed prop-rotors using a multidisciplinary approach. Eng Optim 22(1):1–17CrossRefGoogle Scholar
  22. 22.
    Coello Coello CA, Lamont GB, Van Veldhuizen DA (2006) Evolutionary algorithms for solving multi-objective problems (Genetic and evolutionary computation). Springer-Verlag New York Inc, SecaucusGoogle Scholar
  23. 23.
    Coello Coello CA (2002) Theoretical and numerical constraint-handling techniques used with evolutionary algorithms: a survey of the state of the art. Comput Methods Appl Mech Eng 191(11–2):1245–1287CrossRefzbMATHMathSciNetGoogle Scholar
  24. 24.
    Comis Da Ronco C, Benini E (2013) A simplex crossover based evolutionary algorithm including the genetic diversity as objective. Appl Soft Comput 13(4):2104–2123CrossRefGoogle Scholar
  25. 25.
    Comis Da Ronco C, Benini E (2012) Gedea-ii: A novel evolutionary algorithm for multi-objective optimization problems based on the simplex crossover and the shrink mutation. In:Lecture Notes in Engineering and Computer Science: Proceedings of The World Congress on Engineering and Computer Science 2012, WCECS 2012, 24–26 October, 2012, San Francisco, pp 1298–1303Google Scholar
  26. 26.
    Comis Da Ronco C, Benini E (2012) Gedea-ii: a simplex-crossover based multi objective evolutionary algorithm including the genetic diversity as objective. In: Proceedings of the fourteenth international conference on genetic and evolutionary computation conference companion, pp 619–620Google Scholar
  27. 27.
    Corne DW, Knowles JD, Oates MJ (2000) The pareto envelope-based selection algorithm for multiobjective optimization. In Proceedings of the Parallel Problem Solving from Nature VI Conference. Springer. Berlin, pp 839–848Google Scholar
  28. 28.
    Deb K, Goyal MA (1996) Combined genetic adaptive search (geneas) for engineering design. Comput Sci Inform 26:30–45Google Scholar
  29. 29.
    Deb K (1999) Multi-objective genetic algorithms: problem difficulties and construction of test problems. Evol Comput 7:205–230CrossRefGoogle Scholar
  30. 30.
    Deb K, Agrawal S, Pratap A, Meyarivan T (2000) fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: Nsga-ii. Springer, Berlin, pp 849–858Google Scholar
  31. 31.
    Deb K (2001) Multi-objective optimization using evolutionary algorithms. Wiley, New YorkzbMATHGoogle Scholar
  32. 32.
    Deb K, Thiele L, Laumanns M, Zitzler E (2001) Scalable test problems for evolutionary multi-objective optimization. Technical report, Computer Engineering and Networks Laboratory (TIK), Swiss Federal Institute of Technology (ETH)Google Scholar
  33. 33.
    di Milano P (2010) Green rotorcraft technical report. specification of geometrical constraints and of the design point for the optimisation task, together with the revised erica catia file. Technical reportGoogle Scholar
  34. 34.
    Eiben AE, Bäck T (1997) Empirical investigation of multiparent recombination operators in evolution strategies. Evol Comput 5(3):347–365CrossRefGoogle Scholar
  35. 35.
    Farokhi S (2008) Aircraft propulsion. Wiley, New YorkGoogle Scholar
  36. 36.
    Fletcher R, Reeves CM (1964) Function minimization by conjugate gradients. Comput J 7:149–154CrossRefzbMATHMathSciNetGoogle Scholar
  37. 37.
    Fogel DB (1995) Evolutionary computation: toward a new philosophy of machine intelligence. IEEE Press, PiscatawayGoogle Scholar
  38. 38.
    Fonseca CM, Fleming PJ (1993) Genetic algorithms for multiobjective optimization: Formulation, discussion and generalization. In: Proceedings of the fifth international conference on genetic algorithms. Morgan-Kaufmann, San FranciscoGoogle Scholar
  39. 39.
    Friedmann PP (1991) Helicopter vibration reduction using structural optimization with aeroelastic/multidisciplinary constraints—a survey. J Aircr 28(1):8–21CrossRefGoogle Scholar
  40. 40.
    Gabriel E (1968) Drag estimation of v/stol aircraft. Technical report, Boeing Vertol Report D8–2194-1Google Scholar
  41. 41.
    Gad el Hak M (2000) Flow control. Cambridge University Press, PiscatawayCrossRefzbMATHGoogle Scholar
  42. 42.
    Gates GC (1989) Aircraft propulsion systems technology and design. AIAA Education Series, pp 277–303Google Scholar
  43. 43.
    Gessow A, Meyers GC (1952) Aerodynamics of the helicopter. Macmillan, New YorkzbMATHGoogle Scholar
  44. 44.
    Giancamilli G, Nannoni F, Cicalè M (2001) Erica: the european advance tilt-rotor. in European Rotorcraft ForumGoogle Scholar
  45. 45.
    Gieseng J, Barthelemy JFM (1998) Summary of industry mdo applications and needs. AIAA Paper 98–4737Google Scholar
  46. 46.
    Godard G, Stanislas M (2006) Control of a decelerating boundary layer. part 3: Optimization of round jets vortex generators. Aerosp Sci Technol 10(6):455–464CrossRefGoogle Scholar
  47. 47.
    Goldberg DE (1989) Genetic algorithms in search. Optimization and machine learning. Addison-Wesley Longman Publishing Co.Inc, BostonzbMATHGoogle Scholar
  48. 48.
    Hajela P (1999) Nongradient methods in multidisciplinary design optimization: status and potential. J Aircr 36(1):255–265CrossRefGoogle Scholar
  49. 49.
    Hassan AA, Martin PB, Tung C, Cerchie D, Roth J (2005) Active flow control measurements and cfd on a transport helicopter fuselage. In: Proceedings of the 61st annual AHS international forum and airfoil. pp 349–369Google Scholar
  50. 50.
    Hefner J, Bushnell D (1977) An overview of concepts for aircraft drag reduction. technical report, special course on concepts for drag reduction. AGARD Special Course, AGARD Report No. 654Google Scholar
  51. 51.
    Heyes AL, Smith DAR (2005) Modification of a wing tip vortex by vortex generators. Aerosp Sci Technol 9(6):469–475CrossRefGoogle Scholar
  52. 52.
    Hill P, Peterson C (1992) Mechanics and thermodynamics of propulsion. Addison-Wesley Publishing Company, ReadingGoogle Scholar
  53. 53.
    Holmes BJ, Obara CJ (1982) Observations and implications of natural laminar flow on practical airplane surfaces. J Aircr 20(12):993–1006CrossRefGoogle Scholar
  54. 54.
    Holst TL, Pulliam TH (2003) Evaluation of genetic algorithm concepts using model problems, part ii: Multi-objective optimization. Technical reportGoogle Scholar
  55. 55.
    Hooke R, Jeeves TA (1961) Direct search solution of numerical and statistical problems. J ACM 8(2):221–229CrossRefGoogle Scholar
  56. 56.
    Houghton EL, Brock AE (1970) Aerodynamics for engineering students, 2nd edn. Edward Arnold, New YorkGoogle Scholar
  57. 57.
    Hunecke K (1997) Jet engines: fundamentals of theory, design, and operation. Publishers & Wholesaler, New YorkGoogle Scholar
  58. 58.
    Joslin RD (1998) Aircraft laminar flow control. Annu Rev Fluid Mech 30(1):1–29CrossRefGoogle Scholar
  59. 59.
    Kagambage E, Dimitriadis G (2008) Design, manufacture and instalation of a scaled model of the nicetrip engine nacelle in the ulg wind tunnel. Technical reportGoogle Scholar
  60. 60.
    Kennedy J, Eberhart R (1995) Particle swarm optimization. proceedings. In: IEEE International Conference on Neural Networks, pp 1942–1948Google Scholar
  61. 61.
    Kirkpatrick S, Gelatt CD, Vecchi MP (1983) Optimization by simulated annealing. Science 220(4598):671–680CrossRefzbMATHMathSciNetGoogle Scholar
  62. 62.
    Knowles J, Corne D, Deb K (2006) Multiobjective problem solving from nature: from concepts to applications. Natural computing series. Springer-Verlag New York Inc, SecaucusGoogle Scholar
  63. 63.
    Krishna Prasad K, Choi KS, Truong TV (1996) Emerging techniques in drag reduction. Wiley, New YorkGoogle Scholar
  64. 64.
    Kursawe F (1991) A variant of evolution strategies for vector optimization. In Schwefel HP, Manner R (eds) Parallel problem solving from nature. 1st Workshop, PPSN I, volume 496 of Lecture Notes in Computer Science. Springer-Verlag, Berlin, pp 193–197Google Scholar
  65. 65.
    Lamousin HJ, Waggenspack WN Jr (1994) Nurbs-based free-form deformations. IEEE Comput Graph Appl 14:59–65CrossRefGoogle Scholar
  66. 66.
    Lee J-W, Min B-Y, Byun Y-H, Kim S-J (2006) Multipoint nose shape optimization of space launcher using response surface method. J Spacecr Rocket 43(1):137–146CrossRefGoogle Scholar
  67. 67.
    Lee C, Hong G, Ha Q (2002) Effectiveness of synthetic jets enhanced by instability of tollmien-schlichting waves. AIAA PaperGoogle Scholar
  68. 68.
    Lin JC, Robinson SK, McGhee RJ, Valarezo WO (1994) Separation control on high-lift airfoils via micro-vortex generators. J Aircr 31(6):1317–1323CrossRefGoogle Scholar
  69. 69.
    Marco N, Desideri JA (1999) Multilevel parametrization for aerodynamical optimization of 3d shapes. Technical reportGoogle Scholar
  70. 70.
    Matlab (2010) Genetic algorithm and direct search toolbox, theory guide, 2nd ednGoogle Scholar
  71. 71.
    Matlab (2010) Optimization toolbox, theory guide, 2nd ednGoogle Scholar
  72. 72.
    S. Melone, Garcia-Duffy, and C. D’Andrea A (2010) Exploitation of active controls and morphing technologies to enhance rotor aerodynamic performance in hover conditions. American Helicopter Society Specialistís Conference on Aeromechanics, FishermanísGoogle Scholar
  73. 73.
    Menter FR, Langtry R, Völker S (2006) Transition modelling for general purpose CFD codes. Flow Turbul combust 77(1–4):277–303Google Scholar
  74. 74.
    Miura H (1985) Applications of numerical optimization methods to helicopter design problems—a survey. Vertica 9(2):141–154Google Scholar
  75. 75.
    Mossi K, Castro N, Bryant R, Mane P (2005) Boundary condition effects on piezo-synthetic jets. Integr Ferroelectr 71(1):257– 266Google Scholar
  76. 76.
    Nelder JA, Mead R (1965) A simplex method for function minimization. Comput J 7(4):308–313CrossRefzbMATHGoogle Scholar
  77. 77.
    North Atlantic Treaty Organization. Advisory Group for Aerospace Research, Development. Fluid Dynamics Panel, and Von Karman Institute for Fluid Dynamics. Aircraft Drag Prediction and Reduction. AGARD report. North Atlantic Treaty Organization, Advisory Group for Aerospace Research and Development, (1985)Google Scholar
  78. 78.
    Ono I, Kobayashi S (1997) A real-coded genetic algorithm for function optimization using unimodal normal distribution crossover. In: Proceedings of the seventh international conference on genetic algorithms, pp 246–253Google Scholar
  79. 79.
    Pagnano G (2005) Il convertiplano erica: evoluzione della ricerca nei programmi quadro europei. CRUI Viaggio della Ricerca in Italia, Milano, Agusta coordinamento R&T, p 2005Google Scholar
  80. 80.
    Parsons JS, Goodson RE, Goldschmied FR (1974) Shaping of axisymmetric bodies for minimum drag in incompressible flow. J Hydronaut 8(3):100–107CrossRefGoogle Scholar
  81. 81.
    Periaux J (2003) Genetic algorithms in aeronautics and turbomachinery. Blackwell Science, ChichesterGoogle Scholar
  82. 82.
    Politecnico di Milano (2008) Nicetrip: final complete wind tunnel test data base for aircraft sizing, suitable for use by the codes of the partners. Technical reportGoogle Scholar
  83. 83.
    Powell MJD (1964) An effcient method for finding the minimum of a function of several variables without calculating derivatives. Comput J 7(2):155–162CrossRefzbMATHMathSciNetGoogle Scholar
  84. 84.
    J. Reneaux and J. Preist (1996) Control of the attachment line contamination. In: 2nd European forum on laminar flow technology, Bordeaux, 1996Google Scholar
  85. 85.
    Robert JP (1992) Drag reduction: an industrial challenge. Technical reportGoogle Scholar
  86. 86.
    Rosenbrock HH (1960) An automatic method for finding the greatest or least value of a function. Comput J 3(3):175–184CrossRefMathSciNetGoogle Scholar
  87. 87.
    Samareh JA (1999) A survey of shape parameterization techniques. J Aircr 36:97–104CrossRefGoogle Scholar
  88. 88.
    Seddon J, Goldsmith EL (1999) Intake Aerodynamics. AIAA education series. Blackwell Science, OxfordGoogle Scholar
  89. 89.
    Sederberg TW, Parry SR (1986) Free-form deformation of solid geometric models. In: Proceedings of the 13th annual conference on computer graphics and interactive techniques. ACM, New York, USA, pp 151–160. doi: 10.1145/15922.15903
  90. 90.
    Seifert A (1993) Oscillatory blowing: a tool to delay boundary layer separation. AIAA J 31(11):2052–2060CrossRefGoogle Scholar
  91. 91.
    Simpson RL (2001) Junction flows. Annu Rev Fluid Mech 33:415–445CrossRefGoogle Scholar
  92. 92.
    Sobieszczanski-Sobieski J, Haftka RT (1997) Multidisciplinary aerospace design optimization: survey of recent developments. Struct Optim 14(1):1–23CrossRefGoogle Scholar
  93. 93.
    Spall JC (2004) Introduction to stochastic search and optimization: estimation, simulation, and control. J Am Stat Assoc 99:1204–1205Google Scholar
  94. 94.
    Spendley W, Hext GR, Himsworth FR (1962) Sequential application of simplex designs in optimization and evolutionary operation. Technometrics 4:441–461CrossRefzbMATHMathSciNetGoogle Scholar
  95. 95.
    Stanewsky E (2001) Adaptive wing and flow control technology. Progress Aerosp Sci 37(7):583–667CrossRefGoogle Scholar
  96. 96.
    Thomas SWA (1984) Aircraft drag reduction technology—a summaryGoogle Scholar
  97. 97.
    Toffolo A, Benini E (2002) Genetic diversity as an objective in multi-objective evolutionary algorithms. Evol Comput 11:583–667Google Scholar
  98. 98.
    Tsutsui S, ans Yamamura M, Higuchi T (1999) Multi-parent recombination with simplex crossover in real coded genetic algorithms. In: Proceedings of the GECCO-99, pp 657–644Google Scholar
  99. 99.
    Tsutsui S, Ghosh A (1998) A study on the effect of multiparent recombination in real coded genetic algorithms. In: IEEE world congress on computational intelligence. The 1998 IEEE international conference on 4–9 May 1998. IEEE, Anchorage, AK, pp 828–833. doi: 10.1109/ICEC.1998.700159
  100. 100.
    Vecchio A, Kurowski M, D’Alascio A (2008) Synthesis report on technology review of active devices for blunt fuselage drag reduction. (first draft version including technology review on implementation aspects of synthetic or pulsed jets). Technical reportGoogle Scholar
  101. 101.
    Viswanath P (2002) Aircraft viscous drag reduction using riblets. Progress Aerosp Sci 30(30):571–600CrossRefGoogle Scholar
  102. 102.
    Von Karman Institute for Fluid Dynamics (2009) Flow control: fundamentals, advances and applications. Lecture SeriesGoogle Scholar
  103. 103.
    Von Mises R (1959) Theory of flightGoogle Scholar
  104. 104.
    Walsh MJ (1983) Riblets as a viscous drag reduction technique. AIAA J 21(4):485–486 Google Scholar
  105. 105.
    Warsop C, Hucker M, Press AJ (2007) Pulsed air-jet actuators for flow separation control. Flow Turbul Combust 78(3–4):251–281Google Scholar
  106. 106.
    Wolpert DH, Macready WG (1997) No free lunch theorems for optimization. IEEE Transa Evol Comput 1(1):67–82CrossRefGoogle Scholar
  107. 107.
    You D, Moin P (2006) Large-eddy simulation of flow separation over an airfoil with synthetic jet control. Annual Research, Briefs, pp 337–346Google Scholar
  108. 108.
    Young T (1997) Investigation of hybrid laminar flow control (hlfc) surfaces. Aircr Des 2(2–3):127–146Google Scholar
  109. 109.
    Zitzler E, Deb K, Thiele L (2000) Comparison of multiobjective evolutionary algorithms: empirical results. Evol Comput 8:173–195CrossRefGoogle Scholar
  110. 110.
    Zitzler E, Deb K, Thiele L (1999) Comparison of multiobjective evolutionary algorithms on test functions of different difficulty. In: Genetic and evolutionary computation conference (GECCO 1999): bird-of-a-feather workshop on multi-criterion optimizationGoogle Scholar
  111. 111.
    Zitzler E, Künzli S (2004) Indicator-based selection in multiobjective search. In X. Yao et al. (eds) Conference on Parallel Problem Solving from Nature (PPSN VIII), vol 3242. Springer, Berlin, pp 832–842Google Scholar
  112. 112.
    Zitzler E, Laumanns M, Thiele L (2002) SPEA2: Improving the strength pareto evolutionary algorithm for multiobjective optimization. In K.C. Giannakoglou et al. (eds) Evolutionary methods for design, optimisation and control with application to industrial problems (EUROGEN 2001), International Center for Numerical Methods in Engineering (CIMNE), pp 95–100Google Scholar

Copyright information

© CIMNE, Barcelona, Spain 2014

Authors and Affiliations

  • Claudio Comis Da Ronco
    • 1
    Email author
  • Rita Ponza
    • 1
  • Ernesto Benini
    • 2
  1. 1.HIT09 S.r.l.PadovaItaly
  2. 2.Department of Industrial EngineeringUniversity of PadovaPadovaItaly

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