Dynamic aeroengine models have an important role in the design of real-time control systems. Modelling of aeroengines using dynamic performance simulations is a key step in the design process in order to reduce costs and the development period. A dynamic model can provide a numerical counterpart for the development of control systems and for the study of the engine behaviour in both steady and unsteady scenarios. The latter situation is particularly felt in the military field. The Viper 632-43 engine analysed in this work is a military turbojet, so it was necessary to develop a model that would replicate its behaviour as realistically as possible. The model was built using the Gas turbine Simulation Program (GSP) software and validated both in steady and transient conditions. Once the engine model was validated, different machine learning techniques were used to estimate (data mining) and predict an engine parameter; the Exhaust Gas Temperature (EGT) has been chosen as the key parameter. A MultiGene Genetic Programming (MGGP) technique has been used to derive simple mathematical relationships between different input parameters and the EGT. These, then, can be used to calculate the EGT value of a real Viper 632-43 engine knowing a priori the input parameters and in any operating condition. Finally, the EGT estimated by this algorithm has been added to the dataset used for the one-step-ahead EGT prediction by Artificial Neural Network (ANN). A time-series ANN was used for the EGT prediction, i.e. the Nonlinear AutoRegressive with eXogenous inputs (NARX) neural network. This network recognizes the input data as a real time series and is therefore able to predict the output in the next time step. It was chosen to use, as forecasting method, the one-step-ahead technique which allows to predict the EGT in the immediately next time step.

Hybrid MultiGene Genetic Programming - Artificial neural networks approach for dynamic performance prediction of an aeroengine

De Giorgi M. G.
Conceptualization
;
2020-01-01

Abstract

Dynamic aeroengine models have an important role in the design of real-time control systems. Modelling of aeroengines using dynamic performance simulations is a key step in the design process in order to reduce costs and the development period. A dynamic model can provide a numerical counterpart for the development of control systems and for the study of the engine behaviour in both steady and unsteady scenarios. The latter situation is particularly felt in the military field. The Viper 632-43 engine analysed in this work is a military turbojet, so it was necessary to develop a model that would replicate its behaviour as realistically as possible. The model was built using the Gas turbine Simulation Program (GSP) software and validated both in steady and transient conditions. Once the engine model was validated, different machine learning techniques were used to estimate (data mining) and predict an engine parameter; the Exhaust Gas Temperature (EGT) has been chosen as the key parameter. A MultiGene Genetic Programming (MGGP) technique has been used to derive simple mathematical relationships between different input parameters and the EGT. These, then, can be used to calculate the EGT value of a real Viper 632-43 engine knowing a priori the input parameters and in any operating condition. Finally, the EGT estimated by this algorithm has been added to the dataset used for the one-step-ahead EGT prediction by Artificial Neural Network (ANN). A time-series ANN was used for the EGT prediction, i.e. the Nonlinear AutoRegressive with eXogenous inputs (NARX) neural network. This network recognizes the input data as a real time series and is therefore able to predict the output in the next time step. It was chosen to use, as forecasting method, the one-step-ahead technique which allows to predict the EGT in the immediately next time step.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11587/440682
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