Abstract
Surrogate models are used to approximate complex problems in order to reduce the final cost of the design process. This study has evaluated the potential for employing surrogate modelling methods in turbo-machinery component design optimization. Specifically four types of surrogate models are assessed and compared, namely: neural networks, Radial Basis Function (RBF) Networks, polynomial models and Kriging models. Guidelines and automated setting procedures are proposed to set the surrogate models, which are applied to two turbo-machinery application case studies.
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References
Badjan G (2013) Evaluation of surrogate modelling methods for turbo-machinery component design optimization. MSc Thesis in Mechanical Engineering, University of Trieste, Italy
Haykin S (2005) Neural networks: a comprehensive foundation. Pearson Education, Hamilton
Hassibi B, Stork DG (1993) Optimal brain surgeon and general network pruning. IEEE Int Conf Neural Netw 169:293–299
Noorgard M (2000) Neural network based system identification toolbox. Technical Report 00-E-891, Technical University of Denmark
Chiang CC, Fu HC (1994) The classification capability of a dynamic threshold neural network. Pattern Recogn Lett 15:409–418
Pediroda V (2001) Utilizzo e sviluppo di tecniche “Soft-Computing” per lo studio e la progettazione di macchine a fluido. Ph.D. thesis in Chemical and Energy Technologies, University of Udine, Italy
Poloni C, Giurgevich A, Onesti L, Pediroda V (2000) Hybridization of a multi-objective genetic algorithm, a neural network and a classical optimizer for a complex design problem in fluid dynamics. Comput Methods Appl Mech Eng 186:403–420
Rippa S (1999) An algorithm for selecting a good value for the parameter \(c\) in radial basis function interpolation. Adv Comput Math 11:193–210
Forrester A, Sóbester A, Keane A (2008) Engineering design via surrogate modelling. Wiley, University of Southampton
Keane A, Nair P (2005) Computational approaches for aerospace design: the pursuit of excellence. Wiley, New York
Hastie T, Tibshirani R, Friedman J (2001) The elements of statistical learning. Springer, New York
Goldberg DE (1989) Genetic algorithm in search, optimization and machine learning. Addisons-Wesley
MATLAB (2008) User guide. The MathWorks Inc.
Perdichizzi A, Dossena V (1993) Incidence angle and pitch-chord effects on secondary flows downstream of a turbine cascade. J Turbomach 115:383–391
ANSYS FLUENT (2008) User guide. ANSYS, Inc.
Demuth H, Beale M (2000) Neural network toolbox: for use with MATLAB. User’s guide, Version 4, The MathWorks Inc.
Pediroda V, Poloni C (2006) Approximation methods and self organizing map techniques for MDO problems. Department of Mechanical Engineering, University of Trieste, Italy
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Badjan, G., Poloni, C., Pike, A., Ince, N. (2015). Evaluation of Surrogate Modelling Methods for Turbo-Machinery Component Design Optimization. In: Greiner, D., Galván, B., Périaux, J., Gauger, N., Giannakoglou, K., Winter, G. (eds) Advances in Evolutionary and Deterministic Methods for Design, Optimization and Control in Engineering and Sciences. Computational Methods in Applied Sciences, vol 36. Springer, Cham. https://doi.org/10.1007/978-3-319-11541-2_13
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DOI: https://doi.org/10.1007/978-3-319-11541-2_13
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