Abstract
The multilevel-multidisciplinary-multipoint optimization system developed at the von Kármán Institute and its applications to turboma-chinery design is presented. To speed up the convergence to the optimum geometry, the method combines an Artificial Neural Network, a Design Of Experiment technique and a Genetic Algorithm. The different components are described, the main requirements are outlined and the basic method is illustrated by the design of an axial turbine blade.
A procedure for multipoint optimization, aiming for optimal performance at more than one operating point, is outlined and applied to the optimization of a low solidity diffuser.
The extension to a multidisciplinary optimization, by combining a Navier-Stokes solver with a Finite Element Analysis, allows an efficient search for a compromise between the sometimes conflicting demands of high efficiency and respect of mechanical constraints. It is shown that a significant reduction of the stresses is possible with only a small penalty on the performance and that this approach may lead to geometries that would normally be excluded when using less sophisticated methods.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
Keywords
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.
References
Aarts, E.H.L., Korst, J.H.M.: Simulated annealing in Boltzmann machines. Wiley Chichester (1987)
Alsalihi, Z., Van den Braembussche, R.A.: Evaluation of a design method for radial impellers based on artificial neural network and genetic algorithm. In: Proc. of ESDA 2002, 6th Biennial Conference on Engineering Systems Design and Analysis. Istanbul (2002)
Arnone, A.: Viscous analysis of three-dimensional rotor flow using a multigrid method. ASME Journal of Turbomachinery 116, 435–445 (1994)
Bäck, T.: Evolutionary Algorithms in Theory and Practice. Oxford University Press, New York (1996)
Carroll, D.L.: FORTRAN genetic algorithm (GA) driver version 1.7.1a (2001). URL URL:http:/cuaerospace.com/carroll/ga.html
Cosentini, R., Alsalihi, Z., Van den Braembussche, R.A.: Expert system for radial compressor optimization. In: Proc. 4th European Conference on Turbomachinery. Firenze (2001)
Demeulenaere, A., Van den Braembussche, R.A.: Three-dimensional inverse design method for turbine and compressor blades. In: Design Principles and Methods for Aircraft Gas Turbine Engines, RTO MP-8 (1998)
Harinck, J., Alsalihi, Z., Van Buytenen, J.P., Van den Braembussche, R.A.: Optimization of a 3D radial turbine by means of an improved genetic algorithm. In: Proceedings of European Turbomachinery Conference. Lille (2005)
Kostrewa, K., Van den Braembussche, R.A., Alsalihi, Z.: Optimization of radial turbines by means of design of experiment. Tech. Rep. VKI-PR-2003-17, von Kármán Institute for Fluid Dynamics (2003)
Lichtfuss, H.J.: Customized profiles — the beginning of an area. In: ASME Turbo Expo 2004, Paper GT2004-53742 (2004)
Montgomery, D.C.: Design of Experiments. John Wiley & Sons, Inc. (1997)
Nursen, C., Van den Braembussche, R.A., Alsalihi, Z.: Analysis and multipoint optimization of low solidity vaned diffusers. Tech. Rep. VKI-SR-2002-31, von Kármán Institute for Fluid Dynamics (2002)
Pierret, S., Van den Braembussche, R.A.: Turbomachinery blading design using Navier Stokes solver and artificial neural network. ASME Journal of Turbomachinery 121, 326–332 (1999)
SAMTECH group: SAMCEF FEA code. URL http://www.samcef.com
Siamion, J., Coton, T., Van den Braembussche, R.A.: Design and evaluation of a highly loaded LP turbine blade. In: Proc. 5th ISAIF Conference (International Symposium on Experimental and Computational Aerothermodynamics of Internal Flows). Gdansk (2001)
Thilmany, J.: Walkabout in another world. Mechanical Engineering 122(11), 1–9 (2000)
Vanderplaats, G.N.: Numerical Optimization Techniques for Engineering Design. McGraw-Hill (1984)
Verstraete, T., Alsalihi, Z., Van den Braembussche, R.A.: Multidisciplinary optimization of a radial compressor for micro gas turbine applications. In: ASME Turbo Expo 2007, Paper GT2007-27484 (2007)
Verstraete, T., Alsalihi, Z., Van den Braembussche, R.A.: Numerical study of the heat transfer in micro gas turbines. Journal of Turbomachinery 129(4), 835–841 (2007)
Volpe, G.: Geometric and surface pressure restrictions in airfoil design. In: Special Course on Inverses Methods for Airfoil Design for Aeronautical and Turbomachinery Applications, AGARD-R-780 (1990)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Van den Braembussche, R.A. (2008). Numerical Optimization for Advanced Turbomachinery Design. In: Thévenin, D., Janiga, G. (eds) Optimization and Computational Fluid Dynamics. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72153-6_6
Download citation
DOI: https://doi.org/10.1007/978-3-540-72153-6_6
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-72152-9
Online ISBN: 978-3-540-72153-6
eBook Packages: EngineeringEngineering (R0)