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

Abstract

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.

Artificial Neural Network Based Prediction of Energy Generation from Thermoelectric Generator with Environmental Parameters

“…Particularly, the ANN usage has been used widely in engineering applications such as heat transfer analysis, performance prediction and dynamic prediction [1], [2]. They have also been applied in many fields as a function approximation tool including time series prediction, regression analysis, interpolation and extrapolation; as a classification tool that include fault detection, pattern recognition and lastly as a data processing tool which include filtering and clustering [3].…”

“…Once a network is trained, parameters are fixed and any case can be executed within a short period without the need for re-programming and re-modeling [3].…”