Application of Artificial Neural Network for the Heat Transfer Investigation Around a High-Pressure Gas Turbine Rotor Blade

2011 ◽  
Author(s):  
Ibrahim Eryilmaz ◽  
Sinan Inanli ◽  
Baris Gumusel ◽  
Suha Toprak ◽  
Cengiz Camci
Keyword(s):  
Neural Network ◽  
Heat Transfer ◽  
Artificial Neural Network ◽  
High Pressure ◽  
Rotor Blade ◽  
Incidence Angle ◽  
Transfer Coefficients ◽  
Experimental Conditions ◽  
Heat Transfer Coefficients ◽  
Artificial Neural

This paper presents the preliminary results of using artificial neural networks in the prediction of gas side convective heat transfer coefficients on a high pressure turbine blade. The artificial neural network approach which has three hidden layers was developed and trained by nine inputs and it generates one output. Input and output data were taken from an experimental research program performed at the von Karman Institute for Fluid Dynamics by Camci and Arts [5,6] and Camci [7]. Inlet total pressure, inlet total temperature, inlet turbulence intensity, inlet and exit Mach numbers, blade wall temperature, incidence angle, specific location of measurement and suction/pressure side specification of the blade were used as input parameters and calculated heat transfer coefficient around a rotor blade used as output. After the network is trained with experimental data, heat transfer coefficients are interpolated for similar experimental conditions and compared with both experimental measurements and CFD solutions. CFD analysis was carried out to validate the algorithm and to determine heat transfer coefficients for a closely related test case. Good agreement was obtained between CFD results and neural network predictions.

A Simple Heat Transfer Correlation for Three Inlet Configurations Using Artificial Neural Network in the Complex Transition Flow Regime

2003 ◽  
Author(s):  
A. J. Ghajar ◽  
L. M. Tam ◽  
S. C. Tam
Keyword(s):  
Neural Network ◽  
Heat Transfer ◽  
Artificial Neural Network ◽  
Least Squares Method ◽  
Transition Flow ◽  
Transfer Coefficients ◽  
Transition Flow Regime ◽  
Bell Mouth ◽  
Data Points ◽  
Artificial Neural

Local forced and mixed heat transfer coefficients were measured by Ghajar and Tam (1994) along a stainless steel horizontal circular tube fitted with reentrant, square-edged, and bell-mouth inlets under uniform wall heat flux condition. For the experiments the Reynolds, Prandtl, and Grashof numbers varied from about 280 to 49000, 4 to 158, and 1000 to 2.5×105, respectively. The heat transfer transition regions were established by observing the change in the heat transfer behavior. The data in the transition region were correlated by using the traditional least squares method. The correlation predicted the transitional data with an average absolute deviation of about 8%. However, 30% of the data were predicted with 10 to 20% deviation. The reason is due to the abrupt change in the heat transfer characteristic and its intermittent behavior. Since the value of heat transfer coefficient has a direct impact on the size of the heat exchanger, a more accurate correlation has been developed using the artificial neural network (ANN). A total of 1290 data points (441 for reentrant, 416 for square-edged, and 433 for bell mouth) were used. The accuracy of the new correlation is excellent with the majority of the data points predicted with less than 10% deviation.

Heat Transfer Coefficients and Film Cooling Effectiveness on the Squealer Tip of a Gas Turbine Blade

2003 ◽  
Vol 125 (4) ◽  
pp. 648-657 ◽  
Author(s):  
Jae Su Kwak ◽  
Je-Chin Han
Keyword(s):  
Heat Transfer ◽  
Gas Turbine ◽  
Turbine Blade ◽  
Film Cooling ◽  
Rotor Blade ◽  
Transfer Coefficients ◽  
Cooling Effectiveness ◽  
Film Cooling Effectiveness ◽  
Heat Transfer Coefficients ◽  
Blowing Ratio

Experimental investigations were performed to measure the detailed heat transfer coefficients and film cooling effectiveness on the squealer tip of a gas turbine blade in a five-bladed linear cascade. The blade was a two-dimensional model of a first stage gas turbine rotor blade with a profile of the GE-E3 aircraft gas turbine engine rotor blade. The test blade had a squealer (recessed) tip with a 4.22% recess. The blade model was equipped with a single row of film cooling holes on the pressure side near the tip region and the tip surface along the camber line. Hue detection based transient liquid crystals technique was used to measure heat transfer coefficients and film cooling effectiveness. All measurements were done for the three tip gap clearances of 1.0%, 1.5%, and 2.5% of blade span at the two blowing ratios of 1.0 and 2.0. The Reynolds number based on cascade exit velocity and axial chord length was 1.1×106 and the total turning angle of the blade was 97.9 deg. The overall pressure ratio was 1.2 and the inlet and exit Mach numbers were 0.25 and 0.59, respectively. The turbulence intensity level at the cascade inlet was 9.7%. Results showed that the overall heat transfer coefficients increased with increasing tip gap clearance, but decreased with increasing blowing ratio. However, the overall film cooling effectiveness increased with increasing blowing ratio. Results also showed that the overall film cooling effectiveness increased but heat transfer coefficients decreased for the squealer tip when compared to the plane tip at the same tip gap clearance and blowing ratio conditions.

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