Journal of Aeronautics & Aerospace Engineering
Open Access
ISSN: 2168-9792
ISSN: 2168-9792
Department of Technology and Engineering, Astara Branch, Islamic Azad University, Astara, Iran
Research Article
Predictive Analytics of Fuel Consumption for the Boeing 787-9 Trent 1000 Dreamliner Engine Propulsion System Using Data- Driven Regression Learner Algorithms
Author(s): Nima Hajimirza Amin, Armita Firoozi Fard, Reza Javadi and Ashkan Abdalisousan*
In the aviation industry, fuel efficiency and emissions reductions are critical, but safety and performance must also be maintained. Using predictive analytics, we can identify trends, patterns and inefficiencies in fuel consumption and optimize engine performance. Using machine learning algorithms, we develop an analytical model to predict fuel consumption on a Boeing 787-9 Dreamliner. To predict fuel consumption, we evaluate several machine learning algorithms, including General Linear Regression (GLR), random forest, gradient boosting and Artificial Neural Networks (ANN). A stepwise linear regression algorithm provided the best performance with a Root Mean Squared Error (RMSE) of 1.0532. Temperature, thrust, altitude and Mach number affect the Trent 1000 engine's fuel consumption. It is possible to identify inefficiencies and opportunities for improvement by predicting fuel con.. View More»