Metaheuristic and Machine Learning Models for TFE-731-2, PW4056, and JT8D-9 Cruise Thrust


BAKLACIOĞLU T.

INTERNATIONAL JOURNAL OF TURBO & JET-ENGINES, cilt.34, sa.3, ss.221-232, 2017 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 34 Sayı: 3
  • Basım Tarihi: 2017
  • Doi Numarası: 10.1515/tjj-2016-0002
  • Dergi Adı: INTERNATIONAL JOURNAL OF TURBO & JET-ENGINES
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.221-232
  • Anahtar Kelimeler: aircraft, thrust modeling, genetic algorithms, neural network, AIRCRAFT
  • Anadolu Üniversitesi Adresli: Evet

Özet

The requirement for an accurate engine thrust model has a major antecedence in airline fuel saving programs, assessment of environmental effects of fuel consumption, emissions reduction studies, and air traffic management applications. In this study, utilizing engine manufacturers' real data, a metaheuristic model based on genetic algorithms (GAs) and a machine learning model based on neural networks (NNs) trained with Levenberg-Marquardt (LM), delta-bar-delta (DBD), and conjugate gradient (CG) algorithms were accomplished to incorporate the effect of both flight altitude and Mach number in the estimation of thrust. For the GA model, the analysis of population size impact on the model's accuracy and effect of number of data on model coefficients were also performed. For the NN model, design of optimum topology was searched for one-and two-hidden-layer networks. Predicted thrust values presented a close agreement with real thrust data for both models, among which LM trained NNs gave the best accuracies.