Prediction of temperature field distribution in a gas turbine using a higher order neural network
DOI:
https://doi.org/10.14311/AP.2023.63.0430Keywords:
gas turbine cooling, heat transfer, artificial neural network, natural convectionAbstract
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.
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Copyright (c) 2024 Jan Pařez, Patrik Kovář, Adam Tater
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