Advertisement

Stochastic Optimisation in Computational Engineering Design

  • Timoleon Kipouros
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 175)

Abstract

It is evident that the requirements and specifications for engineering products, as well as the demand for these products, have increased substantially over the last couple of decades. As a result, various engineering design tasks have become considerably more complex. These observations and facts have necessitated the development of new design approaches that offer alternatives to the traditional ways of exploring design spaces and performing engineering design. At the same time, the Mathematical Sciences have produced a number of advanced search and optimisation algorithms that can explore and assess challenging and complicated models and functions. Furthermore, significant advances have been made in the field of Information Technology in terms of utilising massively parallel computational power. It has been shown that all these advancements can be exploited in complementary and synergistic ways when combined appropriately, producing a complete computational engineering design system. In this paper, a guide for deploying all these available technologies in efficient and appropriate ways is presented, illustrated with applications to real-world engineering problems in which not only are innovative solutions produced but also previously unidentified avenues of research are revealed.

Keywords

Wind Turbine Design Space Pareto Front Stochastic Optimisation Entropy Generation Rate 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Abramson, D., Lewis, A., Peachey, T., Fletcher, C.: An automatic design optimization tool and its application to computational fluid dynamics. In: Proceedings of Supercomputing (2001)Google Scholar
  2. 2.
    Adra, S.F., Dodd, T.J., Griffin, I.A., Fleming, P.J.: A convergence acceleration operator for multiobjective optimisation. IEEE Transactions on Ev. Comp. 13(4), 825–847 (2009)CrossRefGoogle Scholar
  3. 3.
    Bloor, M.I.G., Wilson, M.J.: Efficient parametrization of generic aircraft geometry. Journal of Aircraft 32(6), 1269–1275 (1995)CrossRefGoogle Scholar
  4. 4.
    Buhmann, M.D.: Radial basis functions: theory and implementations. Cambridge University Press (2003)Google Scholar
  5. 5.
    Clean sky at a glance: Bringing sustainable air transport closer (2012), http://www.cleansky.eu/lists/documents (cited April 17, 2012)
  6. 6.
    Deb, K., Pratap, A., Agrawal, S., Meyarivan, T.: A fast and elitist multi-objective Genetic Algorithm: NSGA-II. IEEE Transactions on Ev. Comp. 6, 182–197 (2002)CrossRefGoogle Scholar
  7. 7.
    Eftang, J.L., Huynh, D.B.P., Knezevic, D.J., Patera, A.T.: A two-step certified reduced basis method. Journal of Scientific Computing (2011), doi:10.1007/s1091501194942Google Scholar
  8. 8.
    Fischer, G.R., Kipouros, T., Savill, A.M.: Multi-objective shape optimisation for horizontal-axis wind turbine blades. AIAA-2012-1353 (2012)Google Scholar
  9. 9.
    Forrester, A.I.J., Sobester, A., Keane, A.J.: Engineering design via surrogate modelling: A practical guide. John-Wiley and Sons, Chichester (2008)CrossRefGoogle Scholar
  10. 10.
    Ghiasi, H., Pasini, D., Lessard, L.: A non-dominated sorting hybrid algorithm for multi-objective optimization of engineering problems. Eng. Opt. 43(1), 39–59 (2011)CrossRefGoogle Scholar
  11. 11.
    Ghisu, T., Parks, G.T., Jaeggi, D.M., Jarrett, J.P., Clarkson, P.J.: The benefits of adaptive parametrization in multi-objective Tabu Search optimization. Eng. Opt. 42, 959–981 (2010)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Ghisu, T., Parks, G.T., Jarrett, J.P., Clarkson, P.J.: An integrated system for the aerodynamic design of compression systems - Part I: Development. ASME Journal of Turbomachinery 133(1), 011011–1–011011–10 (2011)CrossRefGoogle Scholar
  13. 13.
    Ghisu, T., Parks, G.T., Jarrett, J.P., Clarkson, P.J.: An integrated system for the aerodynamic design of compression systems - Part II: Application. ASME Journal of Turbomachinery 133(1), 011012–1–011012–8 (2011)Google Scholar
  14. 14.
    Glover, F., Laguna, M.: Tabu Search. Kluwer Academic Publishers, Boston (1997)zbMATHCrossRefGoogle Scholar
  15. 15.
    Harvey, S.A., Dawes, W.N., Gallimore, S.J.: An automatic design optimisation system for axial compressors, Part I: Software development. ASME GT2003-38115 (2003)Google Scholar
  16. 16.
    Hettenhausen, J., Lewis, A., Mostaghim, S.: Interactive multi-objective particle swarm optimization with heatmap-visualization-based user interface. Eng. Opt. 42(2), 119–139 (2010)CrossRefGoogle Scholar
  17. 17.
    Inselberg, A.: Parallel Coordinates: Visual multidimensional geometry and its applications. Springer, New York (2009)zbMATHGoogle Scholar
  18. 18.
    Jaeggi, D.M., Parks, G.T., Kipouros, T., Clarkson, P.J.: The development of a multi-objective Tabu Search algorithm for continuous optimisation problems. European Journal of Operational Research 185, 1192–1212 (2008)MathSciNetzbMATHCrossRefGoogle Scholar
  19. 19.
    Keane, A.J., Nair, P.B.: Computational approaches for aerospace design: The pursuit of excellence. John-Wiley and Sons, Chichester (2005)CrossRefGoogle Scholar
  20. 20.
    Kellar, W.P.: Geometry modelling in computational fluid dynamics and design optimisation. PhD Thesis, Cambridge University, Department of Engineering (2003)Google Scholar
  21. 21.
    Kipouros, T., Jaeggi, D.M., Dawes, W.N., Parks, G.T., Savill, A.M.: Multi-criteria optimisation of turbomachinery blades: investigating the trade-off surface. AIAA-2005-4023 (2005)Google Scholar
  22. 22.
    Kipouros, T., Molinari, M., Dawes, W.N., Parks, G.T., Savill, A.M., Jenkins, K.W.: An investigation of the potential for enhancing the computational turbomachinery design cycle using surrogate models and high performance parallelisation. ASME GT2007-28106 (2007)Google Scholar
  23. 23.
    Kipouros, T., Ghisu, T., Parks, G.T., Savill, A.M.: Using post-analyses of optimisation processes as an active computational design tool. ICCES 7(4), 151–157 (2008)Google Scholar
  24. 24.
    Kipouros, T., Jaeggi, D.M., Dawes, W.N., Parks, G.T., Savill, A.M., Clarkson, P.J.: Insight into high-quality aerodynamic design spaces through multi-objective optimization. CMES 37(1), 1–23 (2008)Google Scholar
  25. 25.
    Kipouros, T., Jaeggi, D.M., Dawes, W.N., Parks, G.T., Savill, A.M., Clarkson, P.J.: Biobjective design optimization for axial compressors using Tabu Search. AIAA Journal 46(3), 701–711 (2008)CrossRefGoogle Scholar
  26. 26.
    Kipouros, T., Peachey, T., Abramson, D., Savill, A.M.: Enhancing and developing the practical optimisation capabilities and intelligence of automatic design software. AIAA-2012-1677 (2012)Google Scholar
  27. 27.
    Martínez, S.Z., Montaño, A.A., Coello Coello, C.A.: A nonlinear simplex search approach for multi-objective optimization. IEEE Congress on Ev. Comp., 2367–2374 (2011)Google Scholar
  28. 28.
    Mazlan, N.M., Savill, A.M., Kipouros, T., Li, Y.-G.: A numerical study into the effects of bio-synthetic paraffinic kerosine blends with jet-A fuel for civil aircraft engine. ASME GT2012-68754 (2012)Google Scholar
  29. 29.
    Molina-Cristóbal, A., Palmer, P.R., Skinner, B.A., Parks, G.T.: Multi-fidelity simulation modelling in optimization of a submarine propulsion system. In: IEEE Vehicle Power and Propulsion Conference (2011)Google Scholar
  30. 30.
    Pandya, B., D’Souza, N., Kipouros, T., Savill, A.M.: Structural design optimisation for helical gear pairs. In: NAFEMS-UK Conference, Engineering Simulation: Contributing to Business Success (2010)Google Scholar
  31. 31.
    Saddawi, S.D., Kipouros, T., Savill, A.M.: Computational engineering design for micro-scale combustors. ASME GT2012-69522 (2012)Google Scholar
  32. 32.
    Samareh, J.A.: Survey of shape parameterization techniques for high-fidelity multidisciplinary shape optimization. AIAA Journal 39(5), 877–884 (2001)CrossRefGoogle Scholar
  33. 33.
    Sederberg, T.W., Parry, S.R.: Free-form deformation of solid geometric models. SIGGRAPH 20(4), 151–160 (1986)CrossRefGoogle Scholar
  34. 34.
    Shahpar, S.: Automatic aerodynamic design optimisation of turbomachinery components - An industrial perspective. VKI Lecture series on Optimisation methods and tools for multicriteria/multidisciplinary design, pp. 1–40 (2004)Google Scholar
  35. 35.
    Shahpar, S.: Design of experiment, screening and response surface modelling to minimise the design cycle time. VKI Lecture series on Optimisation methods and tools for multicriteria/multidisciplinary design, pp. 1–49 (2004)Google Scholar
  36. 36.
    Siirtola, H., Räihä, K-J.: Interacting with parallel coordinates. Interacting with Computers 18(6), 1278–1309 (2006)Google Scholar
  37. 37.
    Trapani, G., Kipouros, T., Savill, A.M.: Computational aerodynamic design for 2D high-lift airfoil configurations. In: Pegasus AIAA (2010)Google Scholar
  38. 38.
    Van den Braembussche, R.A.: Tuning on optimization strategies. NATO RTO-EN-AVT 167 (2011)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Department of EngineeringUniversity of CambridgeCambridgeUK
  2. 2.School of Engineering, Department of Power and PropulsionCranfield UniversityCranfieldUK

Personalised recommendations