Dataset for System Identification of an Advanced Geared Turbofan Model Using Neural Networks

System Identification of an Advanced Geared Turbofan Model Using Neural Networks This repository contains the data, code (also available in GitHub) and models for the paper "System Identification of an Advanced Geared Turbofan Model Using Neural Networks," presented at the AIAA Scitech 2025 Forum. The project focuses on modeling the dynamics of an advanced geared turbofan engine using both classical system identification techniques and various deep learning architectures. The data are the .mat files. The res.zip contains the prediction results and models.zip contains the weights of the networks. Overview The primary goal of this research is to develop a high-fidelity simulation model of a turbofan engine using only input-output data. We explore and compare two main approaches: Classical System Identification: A "gray-box" Hammerstein model structure, where a nonlinear static model (quasi-steady) is identified using multivariate orthogonal functions, followed by the estimation of dynamic parameters for the unsteady behavior. Deep Learning: A "black-box" approach using several neural network architectures to model the engine's dynamic response. The performance of these models is evaluated on their ability to predict five key engine outputs: Fuel flow (W_f) Net thrust (F_net) Low-pressure shaft speed (N_1) High-pressure shaft speed (N_2) Turbine temperature (T_45) The results demonstrate that a novel neural differential architecture significantly outperforms both the classical system identification method and other neural network approaches.