K. DEVECİ, H. MARAL, C. B. ŞENEL, E. ALPMAN, L. KAVURMACIOĞLU, C. CAMCI

AEROTHERMAL OPTIMIZATION OF SQUEALER GEOMETRY IN AXIAL FLOW TURBINES USING GENETIC ALGORITHM

In turbomachines, a tip gap is required in order to allow the relative motion of the blade and to prevent the blade tip surface from rubbing. This gap which lay out between the blade tip surface and the casing, results in fluid leakage due to the pressure difference between the pressure side and the suction side of the blade. The tip leakage flow causes almost one third of the aerodynamic loss and unsteady thermal loads over the blade tip. Previous experimental and numerical studies revealed that the squealer blade tip arrangements are one of the effective solutions in increasing the aerothermal performance of the axial flow turbines. In this paper the tip leakage flow is examined and optimized with the squealer geometry as a means to control those losses related with the tip clearance. The squealer height and width have been selected as design parameters and the corresponding computational domain was obtained parametrically. Numerical experiments with such parametrically generated multizone structured grid topologies paved the way for the aerothermal optimization of the high pressure turbine blade tip region. Flow within the linear cascade model has been numerically simulated by solving Reynolds Averaged Navier-Stokes (RANS) equations in order to produce a database. For the numerical validation a well-known test case, Durham cascade is investigated in end wall profiling studies has been used. Sixteen different squealer tip geometries have been modeled parametrically and their performance have been compared in terms of both aerodynamic loss and convective heat transfer coefficient at blade tip. Also, these two values have been introduced as objective functions in the optimization studies. A state of the art multi-objective optimization algorithm, NSGA-II, coupled with an Artificial Neural Network is used to obtain the optimized squealer blade tip geometries for reduced aerodynamic loss and minimum heat transfer coefficient. Optimization results are verified using CFD.

Keywords:

Multi-objective Optimization, Squealer, Tip Leakage Flow,

PDF

___

[1] Mischo, B., Behr, T., & Abhari, R. S. (2008). Flow physics and profiling of recessed blade tips: impact on performance and heat load. Journal of Turbomachinery, 130(2), 021008.
[2] Schabowski, Z., & Hodson, H. (2014). The reduction of over tip leakage loss in unshrouded axial turbines using winglets and squealers. Journal of Turbomachinery, 136(4), 041001.
[3] Heyes, F. J. G., Hodson, H. P., & Dailey, G. M. (1991, June). The effect of blade tip geometry on the tip leakage flow in axial turbine cascades. In ASME 1991 International Gas Turbine and Aeroengine Congress and Exposition (pp. V001T01A052-V001T01A052). American Society of Mechanical Engineers.
[4] Krishnababu, S. K., Newton, P. J., Dawes, W. N., Lock, G. D., Hodson, H. P., Hannis, J., & Whitney, C. (2009). Aerothermal investigations of tip leakage flow in axial flow turbines—part I: effect of tip geometry and tip clearance gap. Journal of Turbomachinery, 131(1), 011006.
[5]Azad, G. S., Han, J. C., Bunker, R. S., & Lee, C. P. (2002). Effect of squealer geometry arrangement on a gas turbine blade tip heat transfer. Journal of heat transfer, 124(3), 452-459.
[6] Key, N. L., & Arts, T. (2006). Comparison of turbine tip leakage flow for flat tip and squealer tip geometries at high-speed conditions. Journal of Turbomachinery, 128(2), 213-220.
[7] Denton, J. D. , (1993). Loss mechanisms in turbomachines. ASME Journal of Turbomachinery, 115, 621-656.
[8] Moore, J. O. H. N., & Tilton, J. S. (1988). Tip leakage flow in a linear turbine cascade. Journal of turbomachinery, 110(1), 18-26.
[9] Bindon, J. P. (1989). The measurement and formation of tip clearance loss. Journal of turbomachinery, 111(3), 257-263.
[10] Yaras, M. I.; Sjolander, S. A. (1992). Prediction of tip leakage losses in axial turbines. ASME Journal of Turbomachinery, 114(1), 204-210.
[11] Tallman, J.; Lakshminarayana, B. (2001). Numerical simulation of tip leakage flows in axial flow turbines, with emphasis on flow physics: part 1 – Effect of tip clearance height. ASME Journal of Turbomachinery, 123, 314-323.
[12] Lakshminarayana, B. (1996). Fluid dynamics and heat transfer of turbomachinery; Wiley: New York.
[13] Yaras, M. I., & Sjolander, S. A. (1992). Effects of simulated rotation on tip leakage in a planar cascade of turbine blades: part I—tip gap flow. Journal of Turbomachinery, 114(3), 652-659.
[14] Ameri, A. A., Steinthorsson, E., & Rigby, D. L. (1998). Effect of squealer tip on rotor heat transfer and efficiency. Journal of Turbomachinery, 120(4), 753-759.
[15] Camci, C., Dey, D., & Kavurmacioglu, L. (2005). Aerodynamics of tip leakage flows near partial squealer rims in an axial flow turbine stage. Journal of Turbomachinery, 127(1), 14-24.
[16] Kavurmacioglu, L., Dey, D., & Camci, C. (2007). Aerodynamic character of partial squealer tip arrangements in an axial flow turbine. Part II: Detailed numerical aerodynamic field visualisations via three dimensional viscous flow simulations around a partial squealer tip. Progress in Computational Fluid Dynamics, an International Journal, 7(7), 374-386.
[17] Lee, S. W.; Kim, S. U. (2010). Tip gap height effects on the aerodynamic performance of a cavity squealer tip in turbine cascade in comparison with plane tip results: part I – tip gap flow structure. Experiments in Fluids, 49, 1039-1051.
[18] Zhou, C., & Hodson, H. (2012). Squealer geometry effects on aerothermal performance of tip-leakage flow of cavity tips. Journal of Propulsion and Power, 28(3), 556-567.
[19] De Maesschalck, C., Lavagnoli, S., & Paniagua, G. (2015). Blade tip carving effects on the aerothermal performance of a transonic turbine. Journal of Turbomachinery, 137(2), 021005.
[20] De Maesschalck, C., Lavagnoli, S., Paniagua, G., Verstraete, T., Olive, R., & Picot, P. (2016). Heterogeneous optimization strategies for carved and squealer-like turbine blade tips. Journal of Turbomachinery, 138(12), 121011.
[21] Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. A. M. T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE transactions on evolutionary computation, 6(2), 182-197.
[22] Datta, R., & Regis, R. G. (2016). A surrogate-assisted evolution strategy for constrained multi-objective optimization. Expert Systems with Applications, 57, 270-284.
[23] Zhou, A., Qu, B. Y., Li, H., Zhao, S. Z., Suganthan, P. N., & Zhang, Q. (2011). Multiobjective evolutionary algorithms: A survey of the state of the art. Swarm and Evolutionary Computation, 1(1), 32-49.
[24] Lwin, K., Qu, R., & Kendall, G. (2014). A learning-guided multi-objective evolutionary algorithm for constrained portfolio optimization. Applied Soft Computing, 24, 757-772.
[25]nsga-ii--a-multi-objective-optimization-algorithm (Accessed March 2017). http://www.mathworks.com/matlabcentral/fileexchange /10429.
[26] Yan, J. (1999). The effect of end wall profiling on secondary flow in nozzle guide vanes. PhD Thesis, Durham University, England.
[27] Tehlah, N., Kaewpradit, P., & Mujtaba, I. M. (2016). Artificial neural network based modeling and optimization of refined palm oil process. Neurocomputing, 216, 489-501.
[28] Bagheri, M., Mirbagheri, S. A., Bagheri, Z., & Kamarkhani, A. M. (2015). Modeling and optimization of activated sludge bulking for a real wastewater treatment plant using hybrid artificial neural networks-genetic algorithm approach. Process Safety and Environmental Protection, 95, 12-25.
[29] Avcı, H., Kumlutaş, D., Özer, Ö., & Özşen, M. (2016). Optimisation of the design parameters of a domestic refrigerator using CFD and artificial neural networks. International Journal of Refrigeration, 67, 227-238.
[30] Tao, Y., Wang, P., Wang, J., Wu, Y., Han, Y., & Zhou, J. (2017). Combining various wall materials for encapsulation of blueberry anthocyanin extracts: Optimization by artificial neural network and genetic algorithm and a comprehensive analysis of anthocyanin powder properties. Powder Technology, 311, 77-87.
[31] Gossard, D., Lartigue, B., & Thellier, F. (2013). Multi-objective optimization of a building envelope for thermal performance using genetic algorithms and artificial neural network. Energy and Buildings, 67, 253-260.
[32] Asadi, E., da Silva, M. G., Antunes, C. H., Dias, L., & Glicksman, L. (2014). Multi-objective optimization for building retrofit: A model using genetic algorithm and artificial neural network and an application. Energy and Buildings, 81, 444-456.
[33] Magnier, L., & Haghighat, F. (2010). Multiobjective optimization of building design using TRNSYS simulations, genetic algorithm, and Artificial Neural Network. Building and Environment, 45(3), 739-746.

Başlangıç: 2015
Yayıncı: YILDIZ TEKNİK ÜNİVERSİTESİ

Arşiv