Application of neural network technology and high-performance computing for identification and real-time hardware-in-the-loop simulation of gas turbine engines

摘要

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.