Effect of Shaped-Hole on Film Cooling Effectiveness of Gas Turbine Blade

Film cooling is one of the cooling techniques to cool the hot section components of a gas turbine engines. The gas turbine blade leading edges are the vital parts in the turbines as they are directly hit by the hot gases, hence the optimized cooling of gas turbine blade surfaces is essential. This study aims at investigating the film cooling effectiveness and heat transfer coefficient experimentally and numerically for the three different gas turbine blade leading edge models each having the one row of film cooling holes at 15, 30 and 45 degrees hole orientation angle respectively from stagnation line. Each row has the five holes with the hole diameter of 3mm, pitch of 20mm and has the hole inclination angle of 20deg. in spanwise direction. Experiments are carried out using the subsonic cascade tunnel facility of National Aerospace Laboratories, Bangalore at a nominal flow Reynolds number of 1,00,000 based on the leading edge diameter, varying the blowing ratios of 1.2, 1.50, 1.75 and 2.0. In addition, an attempt has been made for the film cooling effectiveness using CFD simulation, using k-€ realizable turbulence model to solve the flow field. Among the considered 15, 30 and 45 deg. models, both the cooling effectiveness and heat transfer coefficient shown the increase with the increase in hole orientation angle from stagnation line. The film cooling effectiveness increases with the increase in blowing ratio upto 1.5 for the 15 and 30 deg. models, whereas on the 45 deg. model the increase in effectiveness shown upto the blowing ratio of 1.75. The heat transfer coefficient values showed the increase with the increase in blowing ratio for all the considered three models. The CFD results in the form of temperature, velocity contours and film cooling effectiveness values have shown the meaningful results with the experimental values.

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Effect of a Cutback Squealer and Cavity Depth on Film-Cooling Effectiveness for a Gas Turbine Blade Tip

9th AIAA/ASME Joint Thermophysics and Heat Transfer Conference ◽

10.2514/6.2006-3404 ◽

2006 ◽

Cited By ~ 3

Author(s):

Shantanu Mhetras ◽

Diganta Narzary ◽

Zhihong Gao ◽

Je-Chin Han

Keyword(s):

Gas Turbine ◽

Turbine Blade ◽

Film Cooling ◽

Gas Turbine Blade ◽

Cavity Depth ◽

Cooling Effectiveness ◽

Film Cooling Effectiveness ◽

Blade Tip

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Experimental investigation of film cooling effectiveness on a gas turbine blade pressure surface with diffusion slot holes

Applied Thermal Engineering ◽

10.1016/j.applthermaleng.2019.114851 ◽

2020 ◽

Vol 168 ◽

pp. 114851 ◽

Cited By ~ 3

Author(s):

Zhiqiang Yu ◽

Chen Li ◽

Baitao An ◽

Jianjun Liu ◽

Guangyao Xu

Keyword(s):

Experimental Investigation ◽

Gas Turbine ◽

Turbine Blade ◽

Film Cooling ◽

Gas Turbine Blade ◽

Cooling Effectiveness ◽

Film Cooling Effectiveness ◽

Pressure Surface

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Prediction of Film Cooling Effectiveness on a Gas Turbine Blade Leading Edge Using ANN and CFD

International Journal of Turbo and Jet Engines ◽

10.1515/tjj-2016-0034 ◽

2018 ◽

Vol 35 (2) ◽

pp. 101-111 ◽

Cited By ~ 5

Author(s):

J. O. Dávalos ◽

J. C. García ◽

G. Urquiza ◽

A. Huicochea ◽

O. De Santiago

Keyword(s):

Gas Turbine ◽

Turbine Blade ◽

Film Cooling ◽

Leading Edge ◽

Hole Diameter ◽

Relative Importance ◽

Gas Turbine Blade ◽

Cooling Effectiveness ◽

Film Cooling Effectiveness ◽

Blowing Ratio

Abstract In this work, the area-averaged film cooling effectiveness (AAFCE) on a gas turbine blade leading edge was predicted by employing an artificial neural network (ANN) using as input variables: hole diameter, injection angle, blowing ratio, hole and columns pitch. The database used to train the network was built using computational fluid dynamics (CFD) based on a two level full factorial design of experiments. The CFD numerical model was validated with an experimental rig, where a first stage blade of a gas turbine was represented by a cylindrical specimen. The ANN architecture was composed of three layers with four neurons in hidden layer and Levenberg-Marquardt was selected as ANN optimization algorithm. The AAFCE was successfully predicted by the ANN with a regression coefficient R2<0.99 and a root mean square error RMSE=0.0038. The ANN weight coefficients were used to estimate the relative importance of the input parameters. Blowing ratio was the most influential parameter with relative importance of 40.36 % followed by hole diameter. Additionally, by using the ANN model, the relationship between input parameters was analyzed.

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