Data regarding dynamic performance predictions of an aeroengine

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@Article{DEGIORGI:2020:DB,

• author = "Maria Grazia {De Giorgi} and Marco Quarta",
• title = "Data regarding dynamic performance predictions of an aeroengine",
• journal = "Data in Brief",
• volume = "31",
• pages = "105977",
• year = "2020",
• ISSN = "2352-3409",
• DOI = "doi:10.1016/j.dib.2020.105977",
• URL = "http://www.sciencedirect.com/science/article/pii/S2352340920308714",
• keywords = "genetic algorithms, genetic programming, Aeroengine, Turbojet modelling, Artificial neural network, Machine learning",
• abstract = "The design of aeroengine real-time control systems needs the implementation of machine learning based techniques. The lack of in-flight aeroengine performance data is a limit for the researchers interested in the development of these prediction algorithms. Dynamic aeroengine models can be used to overcome this lack. This data article presents data regarding the performance of a turbojet that were predicted by the dynamic engine model that was built using the Gas turbine Simulation Program (GSP) software. The data were also used to implement an Artificial Neural Network (ANN) that predicts the in-flight aeroengine performance, such as the Exhaust Gas Temperature (EGT). The Nonlinear AutoRegressive with eXogenous inputs (NARX) neural network was used. The neural network predictions have been also given as dataset of the present article. The data presented here are related to the article entitled {"}MultiGene Genetic Programming - Artificial Neural Networks approach for dynamic performance prediction of an aeroengine{"} [1]",

}

Genetic Programming entries for Maria Grazia De Giorgi Marco Quarta

Citations