Neural Network Modeling of Helicopters Turboshaft Engines at Flight Modes Using an Approach Based on “Black Box” Models
Year: 2022
Type: conference-paper
Abstract: The work is devoted to further research in the field of creation and modernization of an on- board monitoring system for helicopters turboshaft engines. In this work, neural network modeling of helicopters turboshaft engines at flight modes was carried out using an approach based on “black box” models, and the main approaches to modeling complex dynamic systems are described. It is shown that the developed universal diagram for training a neural network model of helicopters turboshaft engines, as well as a universal mathematical model of helicopters turboshaft engines (gas turbine engines with a free turbine), which establishes the relation between all thermogas-dynamic parameters, are fully implemented in the described approaches to modeling complex dynamic systems. Having introduced the previously developed mathematical model of helicopters turboshaft engines (gas turbine engines with a free turbine) in the Matlab/Simulink program into a neural network of the NARX type (nonlinear autoregression model with exogenous inputs), its performance of the neural network model was assessed in relation to the TV3-117 turboshaft engine, which is part of the power installation of the Mi-8MTV helicopter. The work involved a computational experiment, the results of which were to obtain values degree of increase in the total pressure in the compressor, compressor turbine shaft power, compressor turbine operation, fuel consumption in the combustion chamber in dynamics. It is shown that the implementation error of the method for helicopters turboshaft engines working process thermogas-dynamic parameters identification using a neural network – NARX model, did not exceed 0.43 % when calculating individual engine parameters, while for the classical method (helicopters TE thermogas-dynamic model) it is about 1.96 % for considered engine parameters.
Institution: Kharkiv National University of Internal Affairs
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W7166300147