Application of Neural Network Technology and High-performance Computing for Identification and Real-time Hardware-in-the-loop Simulation of Gas Turbine Engines

The engineering method for the recurrent neural network construction and identification of a mathematical model of gas turbine engines on a real data is proposed, describing the learning algorithm and the network structure. The complete process of modeling and experimental investigation – from desig...

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Bibliographic Details
Published inProcedia engineering Vol. 176; pp. 402 - 408
Main Authors Pogorelov, G.I., Kulikov, G.G., Abdulnagimov, A.I., Badamshin, B.I.
Format Journal Article
LanguageEnglish
Published Elsevier Ltd 2017
Subjects
FADEC
gas turbine engine
hardware-in-the-loop simulation
high-performance computing
identification
neural network
nonlinear dynamics
gas turbine engine
identification
neural network
nonlinear dynamics
hardware-in-the-loop simulation
FADEC
high-performance computing
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Summary:The engineering method for the recurrent neural network construction and identification of a mathematical model of gas turbine engines on a real data is proposed, describing the learning algorithm and the network structure. The complete process of modeling and experimental investigation – from designing of a gas turbines model in form of neural networks to its testing and debugging on the test-bed – are presented. The method was approved on a hardware-in-the-loop test-bed with a FADEC closed loop control for the start-up, ground and flight modes.
ISSN:1877-7058
1877-7058
DOI:10.1016/j.proeng.2017.02.338