Prediction of temperature field distribution in a gas turbine using a higher order neural network

Abstract

This paper presents the prediction of temperature field distribution in a single annular section using an artificial neural network (ANN). This temperature distribution is non-uniform on the outer tube due to continuous natural convection and radiation caused by the homogeneous steady-state heating of the inner tube, which represents the hot gas flow path through the turbine. The outer tube represents the case of a gas turbine. This temperature is important for the electronic components attached to the engine or the overall engine deformation. The presented approach allows for a quick estimation of the temperature distribution without the need to perform time consuming computational fluid dynamics (CFD) simulations. This can greatly accelerate the design and development of gas turbines. A machine learning approach is applied to an extensive set of CFD simulations under different operating conditions and geometry setups.