Browse > Article
http://dx.doi.org/10.12989/aas.2016.3.2.149

Turbomachinery design by a swarm-based optimization method coupled with a CFD solver  

Ampellio, Enrico (Department of Mechanical and Aerospace Engineering, Politecnico di Torino)
Bertini, Francesco (GE Avio S.r.l.)
Ferrero, Andrea (Department of Mechanical and Aerospace Engineering, Politecnico di Torino)
Larocca, Francesco (Department of Mechanical and Aerospace Engineering, Politecnico di Torino)
Vassio, Luca (Department of Electronics and Telecommunications, Politecnico di Torino)
Publication Information
Advances in aircraft and spacecraft science / v.3, no.2, 2016 , pp. 149-170 More about this Journal
Abstract
Multi-Disciplinary Optimization (MDO) is widely used to handle the advanced design in several engineering applications. Such applications are commonly simulation-based, in order to capture the physics of the phenomena under study. This framework demands fast optimization algorithms as well as trustworthy numerical analyses, and a synergic integration between the two is required to obtain an efficient design process. In order to meet these needs, an adaptive Computational Fluid Dynamics (CFD) solver and a fast optimization algorithm have been developed and combined by the authors. The CFD solver is based on a high-order discontinuous Galerkin discretization while the optimization algorithm is a high-performance version of the Artificial Bee Colony method. In this work, they are used to address a typical aero-mechanical problem encountered in turbomachinery design. Interesting achievements in the considered test case are illustrated, highlighting the potential applicability of the proposed approach to other engineering problems.
Keywords
MDO; swarm intelligence; discontinuous Galerkin; turbomachinery; CFD;
Citations & Related Records
연도 인용수 순위
  • Reference
1 McCullagh, P. and Nelder, J. (1989), Generalized Linear Models, Second Edition, Chapman and Hall/CRC, Boca Raton, Florida, USA.
2 Martins, J.R.R.A., Alonso, J.J. and Reuthes, J.J. (2005), "A coupled-adjoint sensitivity analysis method for high-fidelity aero-structural design", Optim. Eng., 6(1), 33-6.   DOI
3 Martins, J.R.R.A. and Lambe, A.B. (2013), "Multidisciplinary design optimization: a survey of architectures", AIAA J., 51(9), 2049-2075.   DOI
4 Michalek, J., Monaldi, M. and Arts, T. (2010), "Aerodynamic performance of a very high lift low pressure turbine airfoil (T106C) at low Reynolds and high Mach number with effect of free stream turbulence intensity", J. Turbomach., 134(6), 061009.   DOI
5 Onate, E. (2009), Structural Analysis with the Finite Element Method: Linear Statics, Volume 1: Basis and Solids, Springer, Berlin, Germany.
6 Pacciani, R., Marconcini, M., Arnone, A. and Bertini, F. (2011), "An assessment of the laminar kinetic energy concept for the prediction of high-lift, low-Reynolds number cascade flows", Proc. Inst. Mech. Eng. A J. Power Energy, 225, 995-1003.   DOI
7 Pandolfi, M. (1984), "A contribution to the numerical prediction of unsteady flows", AIAA J., 22(5), 602-610.   DOI
8 Panigrahi, B.K., Shi, Y. and Lim, M.H. (2011), Handbook of Swarm Intelligence, Springer-Verlag, Berlin, Germany.
9 Rao, S.S. (2009), Engineering Optimization: Theory and Practice, John Wiley & Sons, Hoboken, New Jersey, USA.
10 Schabowski, Z., Hodson, H., Giacche, D., Power, B. and Stokes, M.R. (2010), "Aeromechanical optimisation of a winglet-squealer tip for an axial turbine", ASME Turbo Expo 2010, Glasgow, June.
11 Saad, Y. (2003), Iterative Methods for Sparse Linear Systems, 2nd Edition, SIAM, Philadelphia, USA.
12 Talbi, E.G. (2009), Metaheuristics: from design to implementation, John Wiley & Sons, Hoboken, New Jersey, USA.
13 Toffolo, A. and Benini, E. (2003), "Genetic diversity as an objective in multi-objective evolutionary algorithms", Evol. Comput., 11(2), 151-167.   DOI
14 Tizhoosh, H. (2005), "Opposition-based learning: a new scheme for machine intelligence", Proceedings of international Conference on Computational Intelligence for Modelling, Control and Automation, Vienna, Austria, November.
15 Vanderplaats, G.N. (2007), Multidiscipline Design Optimization, Vanderplaatz R&D, Inc., Colorado Springs, Colorado, USA.
16 Vestraete, T. and Periaux, J. (2012), Introduction to optimization and multidisciplinary design in Aeronautics and Turbomachinery, von Karman Institute for Fluid Dynamics, Rhode-Saint-Genese, Belgium.
17 Wilcox, D.C. (1998), Turbulence Modeling for CFD, 2nd edition, DCW Industries, La Canada, Canada.
18 Wilson, D.G. (1991), The design of high-efficiency turbomachinery and gas turbines, MIT press, Cambridge, Massachusetts, USA.
19 Yang, X.S. (2010), Nature-Inspired Metaheuristic Algorithms, Second Edition, Luniver Press, Frome, United Kingdom.
20 Anders, J.M. and Haarmeyer, J. (2010), "A parametric blade design system", von Karman Institute for Fluid Dynamics, Rhode-Saint-Genese, Belgium.
21 Bassi, F., Botti, L., Colombo, A., Di Pietro, D.A. and Tesini, P. (2012), "On the flexibility of agglomeration based physical space discontinuous Galerkin discretizations", J. Comput. Phys., 231(1), 45-65.   DOI
22 Bassi, F., Crivellini, A., Rebay, S. and Savini, M. (2005), "Discontinuous Galerkin solution of the Reynoldsaveraged Navier-Stokes and k-omega turbulence model equations", Comput. Fluid., 34, 507-540.   DOI
23 Bertini, F., Dal Mas, L., Vassio, L. and Ampellio, E. (2013), "Multidisciplinary optimization for gas turbines design", XXII AIDAA Conference, Naples, Italy, September.
24 Bonabeau, E., Dorigo, M. and Theraulaz, G. (1999), Swarm Intelligence: From Natural To Artificial Systems, Oxford University Press, New York, NY, USA.
25 Boyd, S. and Vandenberghe, L. (2004), Convex Optimization, Cambridge University Press, Cambridge, UK.
26 Binitha, S. and Siva Sathya, S. (2012), "A survey of bio inspired optimization algorithms", Int. J. Soft Comput. Eng., 2(2), 137-151.
27 Bolaji, A., Khader, A., Al-Betar, M. and Awadallah, M. (2013), "Artificial Bee Colony Algorithm, its variants and applications: a survey", J. Theor. Appl. Inform. Technol., 47(2), 434-459.
28 Colombo, A. (2011), "An agglomeration-based discontinuous Galerkin method for compressible flows", PhD Thesis, University of Bergamo, Italy.
29 Deb, K., Pratap, A., Agarwal, S. and Meyarivan, T. (2002), "A fast and elitist multi-objective genetic algorithm: NSGA-II", IEEE T. Evol. Comput., 6(2),182-197.   DOI
30 Deb, K. (2001), Multi-Objective Optimization Using Evolutionary Algorithms, John Wiley & Sons, New York, NY, USA.
31 Ferrero, A. and Larocca, F. (2013), "Test cases C1.1, C1.2 and C1.6", Second International Workshop on High-Order CFD Methods, Cologne, Germany, http://www.dlr.de/as/hiocfd.
32 Ferrero, A. and Larocca, F. (2015), "Test cases C1.2 and C1.3", Third International Workshop on High-Order CFD Methods, Orlando, Florida, USA, January, https://www.grc.nasa.gov/hiocfd/.
33 Ferrero, A., Larocca, F. and Puppo, G. (2015), "A robust and adaptive recovery-based discontinuous Galerkin method for the numerical solution of convection-diffusion equations", J. Numer. Meth. Fluid., 77(2), 63-91.   DOI
34 Forrester, A., Sobester, A. and Keane, A. (2008), Engineering Design Via Surrogate Modelling: A Practical Guide, John Wiley & Sons, UK.
35 Geuzaine, C. and Remacle, J.F. (2009), "Gmsh: a three-dimensional finite element mesh generator with built-in pre-and post-processing facilities", Int. J. Numer. Meth. Eng., 79(11), 1309-1331.   DOI
36 Glover, F. and Kochenberger, G.A. (2003), Handbook of Metaheuristics, International Series in Operations Research & Management Science, Springer, US.
37 Hillewaert, K., Carton de Wiart, C. and Arts, T. (2013), "Test cases C3.7", Second International Workshop on High-Order CFD Methods, Cologne, May, http://www.dlr.de/as/hiocfd.
38 Iollo, A., Ferlauto, M. and Zannetti, L. (2001), "An Aerodynamic Optimization Method based on the Inverse Problem Adjoint Equations", J. Comput. Phys., 173(1), 87-115.   DOI
39 Karaboga, D. (2005), "An idea based on honey bee swarm for numerical optimization", Technical Report TR06, Erciyes University, Turkey.
40 Jones, D.R. (2001), "A taxonomy of global optimization methods based on response surfaces", J. Global Opti., 21(4), 345-383.   DOI
41 Karaboga, D. (2007), "A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm", J. Global Opti., 39(3), 459-471.   DOI
42 Karaboga, D. and Akay, B. (2009), "A comparative study of artificial bee colony algorithm", Appl. Math. Comput., 214(1),108-132.   DOI
43 Karaboga, D., Gorkemli, B., Ozturk, C. and Karaboga, N. (2014), "A comprehensive survey: artificial bee colony (ABC) algorithm and applications", Artif. Intell. Rev., 42(1), 21-57.   DOI
44 Kennedy, J. and Eberhart, R. (1995), "Particle swarm optimization", Proceedings of IEEE International Conference on Neural Networks IV, 1942-1948.
45 Kennedy, J. (2010), Encyclopedia of Machine Learning: Particle Swarm Optimization, Springer, New York, USA.
46 Koiro, M.J., Myers, R.A. and Delaney, R.A. (1999), TADS-A CFD-Based Turbomachinery Analysis and Design System With GUI. NASA contract report CR-1999-206603.
47 Koziel, S. and Yang, X.S. (2011), Computational Optimization, Methods and Algorithms, Springer-Verlag, Berlin, Germany.
48 Koziel, S. and Leifsson, L. (2013), Surrogate-Based Modeling and Optimization: Applications in Engineering, Springer, New York, USA.
49 Larocca, F. (2008), "Multiple objective optimization and inverse design of axial turbomachinery blades", J. Prop. Power, 24(5), 1093-1099.   DOI