Conference article

Recurrent Neural Network Based Simulation of a Single Shaft Gas Turbine

Hamid Asgari
VTT Technical Research Centre of Finland Ltd., Espoo, Finland

Emmanuel Ory
VTT Technical Research Centre of Finland Ltd., Espoo, Finland

Jari Lappalainen
Semantum Oy, Finland

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Published in: Proceedings of The 61st SIMS Conference on Simulation and Modelling SIMS 2020, September 22-24, Virtual Conference, Finland

Linköping Electronic Conference Proceedings 176:14, p. 99-106

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Published: 2021-03-03

ISBN: 978-91-7929-731-2

ISSN: 1650-3686 (print), 1650-3740 (online)


In this study, a model of a single shaft gas turbine (GT) is developed by using artificial intelligence (AI). A recurrent neural network (RNN) is employed to train the datasets of the GT variables in Python programming environment by using Pyrenn Toolbox. The resulting model is validated against the Test datasets. Thirteen significant variables of the gas turbine are considered for the modelling process. The results show that the RNN model developed in this study is capable of performance prediction of the system with a high reliability and accuracy. This methodology provides a simple and effective approach in dynamic simulation of gas turbines, especially when real datasets are only available over a limited operational range and using simulated datasets for modelling and simulation purposes is unavoidable.


gas turbine, modelling, simulation, artificial intelligence, recurrent neural network, black-box model


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