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Challenges to overcome for routine usage of automatic optimisation in the propulsion industry

Published online by Cambridge University Press:  27 January 2016

S. Shahpar
S. Shahpar*
Affiliation:
CFD Methods, Design System Engineering, Rolls-Royce, Derby, UK
*
[email protected]
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Abstract

In industry, there is an ever-increasing requirement not only to design high performance new products but also to deliver them at lower cost and in shorter time. To meet these demanding engineering challenges, it is not sufficient to treat the different disciplines involved in a product design in isolation; rather they must be considered together as an integrated system that reflects the dependencies and interactions of the different disciplines. The design process must be automated to meet the stringent design time-lines. In spite of promising forays for over a decade, automatic design optimisation (ADO) and multidisciplinary optimisation (MDO) has not been widely adapted by the Turbomachinery design practitioners. This presentation will explore some of the technical and nontechnical barriers such as cultural and organisational issues that must be addressed if ADO/MDO is to be used routinely in industry. Some recent, successful application of automatic optimisation is also reported herein.

Type
Research Article
Information
The Aeronautical Journal , Volume 115 , Issue 1172 , October 2011 , pp. 615 - 625
Copyright
Copyright © Royal Aeronautical Society 2011 

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References

1. http://en.wikipedia.org/wiki/Technology_Readiness_Levels). Accessed March 2010.Google Scholar
2. Shahpar, S. Optimisation strategies used in turbomachinery design from an industrial perspective, 2010, Von Karman Institute For Fluid Dynamics (VKI) Lecture Series, Introduction to Optimization Methods and Tools for Multidisciplinary Design in Aeronautics and Turbomachinery, 21 May-4 June 2010, Brussels, Belgium.Google Scholar
3. Milli, A. and Shahpar, S. Full-parametric design system to improve the stage efficiency of a high-fidelity HP turbine configuration, 2008, ASME GT-2008-51348, Berlin, Germany.Google Scholar
4. Shahpar, S. Automatic aerodynamic design optimisation of turboma-chinery components — an industrial prospective, 2005, Invited lecture at VKI, Belgium.Google Scholar
5. Samareh, J.A. Survey of shape parameterization techniques for high-fidelity multidisciplinary shape optimization, AIAA J, May 2001, 39, (5).Google Scholar
6. Piegl, L. and Tiller, W. The NURBS Book, Second edition, 1997, Springer-Verlag Berlin Hwidelberg, New York.Google Scholar
7. Siemens PLM Software tech note: NX Programming and Customization, http://www.plm.automation.sciemens.com/en_us/ Images/4988_tcm1023-4564.pdf, visited May 2010.Google Scholar
8. NACA airfoil, http://en.wikepedia.org/wiki/NACA_airfoil#References, visited May 2010.Google Scholar
9. Harris, C.D. NASA supercritical airfoils — a matrix of family-related airfoils, March 1990, NASA technical paper 2969.Google Scholar
10. Robinson, G.M. and Keane, A.J. Concise orthogonal representation of supercritical aerofoils, J Aircr, 38, pp 580–583, 2001.Google Scholar
11. Shahpar, S. and Lapworth, L. PADRAM: Parametric design and rapid meshing system for turbomachinery optimisation, ASME Turbo Expo, Atlanta, Georgia, GT2003-38698.Google Scholar
12. Hicks, R.M. and Henne, P.A. Wing design by numerical optimization, J Aircr, 15, (7), pp 407–412, 1978.Google Scholar
13. Sederberg, T.W. and Parry, S.R. Free-form deformation of solid geometric models, Computer Graphics, 20, (4), pp 151–160, 1986.Google Scholar
14. Dawes, W.N. Building blocks towards VR-based flow sculpting, 2005, AIAA paper accepted for the presentation at the 43rd AIAA Aerospace Sciences Meeting & Exhibition, January 2005, Reno, Nevada, USA.Google Scholar
15. Dawes, W.N. Application of topology-free optimization to manage cooled turbine tip heat load, GT 2009-59817, Proceeding of ASME turbo Expo, June 2009, Orlando, Florida, USA.Google Scholar
16. Bloor, M.I.G. and Wilson, M.J. Using partial differential equations to generate free-form surfaces, CAD, 22, (4), pp 202–212, 1990.Google Scholar
17. Harvey, S.A., Dawes, W.N. and Gallimore, S.J. An automatic design optimisation system for axial compressors Part I: Software Development, ASME Paper GT2003-38115.Google Scholar
18. Jolliffe, I.T. Principal Component Analysis, Second edition, 2002, Springer Series in Statistics.Google Scholar
19. Swoboda, M., Huppertz, A., Keskin, A., Dierk, O. and Bestle, D. Multidiscipilinary compressor blading design process using automation and multi-objective optimization, 2006, ICAS-2006 Proceedings, Hamburg, Germany.Google Scholar
20. http://www.Rhino3D.com Google Scholar
21. http://www.CADfix.com Google Scholar
22. Cedar, R. The intelligent master model process for achieving functional design, 2003, Keynote lecture to Product Lifecycle Management Road Map 2003 conference, www.cpd-associates.com.Google Scholar
23. Bailey, M.W. and Verduin, W.H. FIPER: an intelligent system for the optimal design of highly engineered products, 2000, NIST Performance Metrics for Intelligent Systems Workshop, Gaitherburg, MD, USA.Google Scholar
24. Haimes, R. Control of boundary representation topology in multidisci-plinary analysis and design, 2010, 48th AIAA Aerospace Science meeting, AIAA-2010-1504, Orlando, Florida, USA.Google Scholar
25. Rendall, T.C.S. and Allen, C. Unified CFD-CSD interpolation and mesh motion using radial basis functions, Int J Numerical Methods in Engineering, 2008, 74, pp 1519–1559.Google Scholar
26. De Boer, A., van der Schoot, M.S. and Bijl, H. Mesh deformation based on radial basis function interpolation, Comput & Struct, 2007, 85, pp 784–795.Google Scholar
27. Jameson, A. Aerodynamic design via control theory, J Scientific Computing, September 1988, 3, (3), pp 233–260.Google Scholar
28. Jameson, A. Aerodynamic shape optimization using the adjoint method, 2003, Von Karman Series Lecture Notes for Fluid Dynamics, Brussels, Belgium.Google Scholar
29. Frey, P.J. and George, P-L. Mesh Generation, Second edition, ISBN 9781848210295, Wiley International, January 2010.Google Scholar
30. www.simulia.com/products/isight.html., visited May 2010.Google Scholar
31. www.phoenix-int,com/software/phx_modelcenter.php, visited May 2010.Google Scholar
32. www.vrand.com/DOT.html, visited May 2010.Google Scholar
33. Shahpar, S. SOFT: A new design and optimisation tool for turboma-chinery, Evolutionary Methods for Design, Optimisation and Control, 2002, Ginnakoglou, K. (Ed) et al, CIMNE.Google Scholar
34. www.ansys.com/products/workbench/default.asp, visited May 2010.Google Scholar
35. www.itk.org/Wiki/ParaView, visited May 2010.Google Scholar
36. See www.cnn.com/TECH/space/9909/30/mars.metric.02 Google Scholar
37. Private communication with Dr Helen Hughes, May 2010, Centre for Socio-Technical Systems Design, Leeds University Business School, Rolls-Royce UTC at Leeds.Google Scholar
38. Salas, E., Goodwin, F. and Burke, C.S. Team Effectiveness in Complex Organizations, Cross Disciplinary Perspectives and Approaches, 2008, Routledge, Taylor & Francis Group, New York, USA.Google Scholar
39. Cohen, S.G. and Bailey, D.E., What makes teams work: Group effectiveness research from the shop floor to the executive suite, J Management, 1997, 23, (3), pp 239–290.Google Scholar
40. Robbins, S. Essentials of Organizational Behavior, Eighth edition, 2005, Prentice Hall, Ohio, USA.Google Scholar
41. West, M.A., Borrill, C.S. and Unsworth, K.L. Team effectiveness in organisations, Cooper, C.L. and Robertson, I.T. (Eds), In International Review of Industrial and Organisational Psychology, 1998, 13, pp 1–48, John Wiley, London, UK.Google Scholar
42. Lawrence, P. and Scanlan, J. Planning in the dark: Why major engineering projects fail to achieve key goals, Technology Analysis & Strategic Management, July 2007, 19, (4), pp 509–525.Google Scholar
43. Milli, A. and Bron, O. Fully parametric High-fidelity CFD model for the design optimisation of the cyclic stagger pattern of a set of fan outlet guide vanes, 2009, GT2009-59416, ASME Turbo Expo 2009, Orlando, USA.Google Scholar
44. Westrum, R. Cultures with requisite imagination, in Verification and Validation in Complex Man-Machine Systems, Wise, J. (Ed) et al, 1993, Springer, New York.Google Scholar
45. Belie, R. Non-technical barriers to multi-disciplinary optimization in the aerospace industry, 2002, Ninth AIAA/ISSMO Symposium on Multidisciplinary Analysis and Optimization, Atlanta, Georgia, AIAA 2002-5439.Google Scholar
46. Shahpar, S. Three-dimensional design and optimisation of turboma-chinery blades using the Navier-Stokes equations, Proceeding of the ISABE Conference, Bangalore, ISABE-2001-1053.Google Scholar
47. Brown, D. The Da Vinci Code, 2003, Anchorbooks.Google Scholar
48. Keane, A. and Nair, P.B. Computational Approaches for Aerospace Design: The Pursuit of Excellence, 2005, John Wiley and Sons.Google Scholar
49. Harvey, S.A., Dawes, W.N. and Gallimore, S.J. An automatic design optimisation system for axial compressors, Part I: Software Development, ASME Paper GT2003-38115.Google Scholar
50. Shahpar, S. Towards robust CFD based design optimisation of virtual engine, Invited lecture at plenary 2 of the NATO/PFP RTO-MP-AVT-147 symposium on Computational Uncertainties, Athens, Greece, 1-4 October 2007.Google Scholar