Improved modelling of low-pressure rotor speed in commercial turbofan engines: A comprehensive analysis of machine learning approaches

Abstract

Accurate engine thrust modelling offers notable opportunities for reducing fuel consumption, mitigating emission effects, managing air traffic, and optimizing the preliminary design of aircraft. This study focuses on the development of machine learning models to predict the low-pressure rotor speed (N1) parameter, which is closely related to the thrust of turbofan engines, for commercial aircraft during the cruise phase. The analysis utilizes a Flight Data Recorder (FDR) dataset from 1086 actual flights involving twin-engine, narrow-body, short-to-medium haul commercial aircraft equipped with CFM56-7B turbofan engines. To achieve accurate N1 parameter predictions, the study employs machine learning models, including Deep Learning (DL), Random Forest (RF), Gradient Boosted Machines (GBM), and Artificial Neural Networks (ANN). These models are evaluated using statistically significant indicators, demonstrating that machine learning models yield highly accurate results. Among the models tested, the DL model provides the most precise estimates of the N1 parameter. This study not only advances the understanding of engine thrust modelling through machine learning but also provides practical insights for the aerospace industry. The findings underline the potential of machine learning techniques in delivering superior prediction accuracy, which can be integrated into real-time flight management systems, ultimately contributing to more sustainable and efficient aviation operations.