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Evolving an Aircraft Using a Parametric Design System

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Part of the Lecture Notes in Computer Science book series (LNTCS,volume 8601)

Abstract

Traditional CAD tools generate a static solution to a design problem. Parametric systems allow the user to explore many variations on that design theme. Such systems make the computer a generative design tool and are already used extensively as a rapid prototyping technique in architecture and aeronautics. Combining a design generation tool with an evolutionary algorithm provides a methodology for optimising designs. This works uses NASA’s parametric aircraft design tool (OpenVSP) and an evolutionary algorithm to evolve a range of aircraft that maximise lift and reduce drag while remaining within the framework of the original design. Our approach allows the designer to automatically optimise their chosen design and to generate models with improved aerodynamic efficiency.

Keywords

  • Computational Fluid Dynamics
  • Evolutionary Algorithm
  • Pareto Front
  • Multidisciplinary Design Optimization
  • Computational Fluid Dynamics Analysis

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. Alpman, E.: Airfoil shape optimization using evolutionary algorithms. Aerospace Engineering Department, Pennstate University (2004)

    Google Scholar 

  2. Bensow, R.E., Bark, G.: Simulating cavitating flows with les in openfoam. In: V European Conference on Computational Fluid Dynamics, pp. 14–17 (2010)

    Google Scholar 

  3. Bollinger, K., Grohmann, M., Tessman, O.: Form, force, performance: Multi-parametric structural design. Architectural Design 78(2), 20–25 (2008)

    CrossRef  Google Scholar 

  4. Cardiff, P., Karač, A., Ivanković, A.: A large strain finite volume method for orthotropic bodies with general material orientations. In: Computer Methods in Applied Mechanics and Engineering (2013)

    Google Scholar 

  5. Day, M.: Grasshopper, generative modelling (2010), http://www.grasshopper3d.com/

  6. Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation 6(2), 182–197 (2002) ISSN 1089-778X

    CrossRef  Google Scholar 

  7. Dulikravich, G.S.: Aerodynamic shape design and optimization-status and trends. Journal of Aircraft 29(6), 1020–1026 (1992)

    CrossRef  Google Scholar 

  8. Gloudemans, J.R., Davis, P.C., Gelhausen, P.A.: A rapid geometry modeler for conceptual aircraft. In: 34th Aerospace Sciences Meeting and Exhibit, Reno, NV, January, pp. 15–18 (1996)

    Google Scholar 

  9. Holzer, D., Hough, R., Burry, M.: Parametric design and structural optimisation for early design exploration. International Journal of Architectural Computing 5(4), 625–643 (2007)

    CrossRef  Google Scholar 

  10. Jacobs, E.N., Ward, K.E., Pinkerton, R.M.: The characteristics of 78 related airfoil sections from tests in the variable-density wind tunnel. Technical report, DTIC Document (1933)

    Google Scholar 

  11. Kicinger, R., Arciszewski, T., De Jong, K.: Evolutionary computation and structural design: A survey of the state-of-the-art. Computers and Structures 83(23-24), 1943–1978 (2005), ISSN 0045-7949, doi: 10.1016/j.compstruc.2005.03.002

    Google Scholar 

  12. Andy Ko, Y.-Y.: The multidisciplinary design optimization of a distributed propulsion blended-wing-body aircraft. PhD thesis, Virginia Polytechnic Institute and State University (2003)

    Google Scholar 

  13. Lawson, B.: How designers think: the design process demystified. Elsevier/Architectural (2006), ISBN 9780750660778, http://books.google.ie/books?id=lPvqZJNAdG8C

  14. Naujoks, B., Willmes, L., Haase, W., Bäck, T., Schütz, M.: Multi-point airfoil optimization using evolution strategies. In: Proc. European Congress on Computational Methods in Applied Sciences and Engineering (ECCOMAS 2000)(CD-Rom and Book of Abstracts), p. 948 (2000)

    Google Scholar 

  15. Obayashi, S.: Multidisciplinary design optimization of aircraft wing planform based on evolutionary algorithms. In: 1998 IEEE International Conference on Systems, Man, and Cybernetics, vol. 4, pp. 3148–3153. IEEE (1998)

    Google Scholar 

  16. Parmee, I.C., Watson, A.H.: Preliminary airframe design using co-evolutionary multiobjective genetic algorithms. In: Proceedings of the Genetic and Evolutionary Computation Conference, vol. 2, pp. 1657–1665 (1999)

    Google Scholar 

  17. Patankar, S.V., Spalding, D.B.: A calculation procedure for heat, mass and momentum transfer in three-dimensional parabolic flows. International Journal of Heat and Mass Transfer 15(10), 1787–1806 (1972), http://www.sciencedirect.com/science/article/pii/0017931072900543 , doi: http://dx.doi.org/10.1016/0017-9310(72)90054-3, ISSN 0017-9310

  18. Quagliarella, D., D’ambrosio, D., Iollo, A.: Airfoil design using navier-stokes equations and hybrid evolutionary optimization techniques. Technical report, DTIC Document (2003)

    Google Scholar 

  19. Rogalsky, T., Derksen, R.W., Kocabiyik, S.: An aerodynamic design technique for optimizing fan blade spacing. In: Proceedings of the 7th Annual Conference of the Computational Fluid Dynamics Society of Canada, pp. 2–29. Citeseer (1999)

    Google Scholar 

  20. Shea, K., Aish, R., Gourtovaia, M.: Towards integrated performance-driven generative design tools. Automation in Construction 14(2), 253–264 (2005) ISSN 0926-5805

    Google Scholar 

  21. Simon, H.A.: The sciences of the artificial. The MIT Press (1996)

    Google Scholar 

  22. Oasys Software. GSA, structural analysis version 8.5 (2011), http://www.oasys-software.com/gsa-analysis.html

  23. Bentley Sytems. Generative components, v8i (2011), http://www.bentley.com/getgc/

  24. Weller, H.G., Tabor, G., Jasak, H., Fureby, C.: A tensorial approach to computational continuum mechanics using object-oriented techniques. Computers in physics 12, 620 (1998)

    CrossRef  Google Scholar 

  25. Wüthrich, B., Lee, Y.: Simulation and validation of compressible flow in nozzle geometries and validation of OpenFOAM for this application. PhD thesis, ETH, Swiss Federal Institute of Technology Zurich, Institute of Fluid Dynamics (2007)

    Google Scholar 

  26. Zitzler, E., Thiele, L.: Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach. IEEE Trans. Evolutionary Computation 3(4), 257–271 (1999)

    CrossRef  Google Scholar 

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Byrne, J., Cardiff, P., Brabazon, A., O’Neill, M. (2014). Evolving an Aircraft Using a Parametric Design System. In: Romero, J., McDermott, J., Correia, J. (eds) Evolutionary and Biologically Inspired Music, Sound, Art and Design. EvoMUSART 2014. Lecture Notes in Computer Science, vol 8601. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-44335-4_11

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  • DOI: https://doi.org/10.1007/978-3-662-44335-4_11

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-44334-7

  • Online ISBN: 978-3-662-44335-4

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