Neural Networks Modelling of Aero-derivative Gas Turbine Engine: A Comparison Study

Ibrahem Ibrahem, Ouassima Akhrif, Hany Moustapha, Martin Staniszewski

Abstract

In this paper, the modelling of aero derivative gas turbine engine with six inputs and five outputs using two types of neural network is presented. Siemens three-spool dry low emission aero derivative gas turbine engine used for power generation (SGT-A65) was used as a case study in this paper. Data sets for training and validation were collected from a high fidelity transient simulation program. These data sets represent the engines operation above its idle status. Different neural network configurations were developed by using of a comprehensive computer code, which changes the neural networks parameters, namely, the number of neurons, the activation function and the training algorithm. Next, a comparative study was done among different neural models to find the most appropriate neural network structure in terms of computation time of neural network training operation and accuracy. The results show that on one hand, the dynamic neural network has a higher capability than the static neural network in representation of the engine dynamics. On the other hand however, it requires a much longer training time.

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Paper Citation


in Harvard Style

Ibrahem I., Akhrif O., Moustapha H. and Staniszewski M. (2019). Neural Networks Modelling of Aero-derivative Gas Turbine Engine: A Comparison Study.In Proceedings of the 16th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO, ISBN 978-989-758-380-3, pages 738-745. DOI: 10.5220/0007928907380745


in Bibtex Style

@conference{icinco19,
author={Ibrahem Ibrahem and Ouassima Akhrif and Hany Moustapha and Martin Staniszewski},
title={Neural Networks Modelling of Aero-derivative Gas Turbine Engine: A Comparison Study},
booktitle={Proceedings of the 16th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,},
year={2019},
pages={738-745},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007928907380745},
isbn={978-989-758-380-3},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,
TI - Neural Networks Modelling of Aero-derivative Gas Turbine Engine: A Comparison Study
SN - 978-989-758-380-3
AU - Ibrahem I.
AU - Akhrif O.
AU - Moustapha H.
AU - Staniszewski M.
PY - 2019
SP - 738
EP - 745
DO - 10.5220/0007928907380745