Not only for aircraft performance calculations and trajectory predictions but also for air traffic management simulation tools and operational equipment, accomplishment of an accurate propulsive model for transport aircraft has a crucial priority and is a remarkable topic for aircraft industry. In the literature, there are very few propulsive modeling studies; furthermore, the demand for an accurate thrust model still remains unfulfilled. In this study, a new turbofan engine propulsive model determining the relationship between thrust, flight altitude, and Mach number was developed by using genetic algorithms (GAs) method and multilayer feed-forward neural networks (FNNs) utilizing Levenberg-Marquardt (LM), delta-bar-delta (DBD), and conjugate gradient (CG) learning algorithms. Estimated thrust values by the derived models showed a good fitting with actual thrust data for both models, which validated each model’s accuracy.
Propulsive Modeling for TF-30 and JT9D-3 Turbofan Engines of Transport Aircraft by Using Genetic Algorithms and Neural Networks
Applied Mechanics and Materials ; 799-800 ; 885-889
2015-10-19
5 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
British Library Conference Proceedings | 2015
|Emerald Group Publishing | 2020
|DEVELOPMENT PROGRESS NEW TRANSPORT ENGINES JT9D
SAE Technical Papers | 1971
|Propulsive wing turbofan V/STOL
Engineering Index Backfile | 1965
|