Application of Neural Network Technology and High-performance Computing for Identification and Real-time Hardware-in-the-loop Simulation of Gas Turbine Engines. (2017)

• Record Type: Journal Article
• Authors: Pogorelov, G.I.; Kulikov, G.G.; Abdulnagimov, A.I.; Badamshin, B.I.
• Is Part Of: Procedia engineering. Volume 176(2017)
• Page Start: 402
• Page End: 408
• Publication Date: 2017
• Subjects: gas turbine engine -- FADEC -- neural network -- identification -- nonlinear dynamics -- hardware-in-the-loop simulation -- high-performance computing; Engineering -- Congresses; Engineering -- Periodicals; Engineering; Conference proceedings; Periodicals; 620.005
• DOI: 10.1016/j.proeng.2017.02.338
• Languages: English
• ISSNs: 1877-7058

Abstract

Abstract: 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.