Dynamic modeling of exergy efficiency of turboprop engine components using hybrid genetic algorithm-artificial neural networks

BAKLACIOĞLU T., TURAN Ö., Aydin H.

Energy, cilt.86, ss.709-721, 2015 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 86
  • Basım Tarihi: 2015
  • Doi Numarası: 10.1016/j.energy.2015.04.025
  • Dergi Adı: Energy
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.709-721
  • Anahtar Kelimeler: Artificial neural networks, Genetic algorithms, Energy, Exergy, Turboprop, Optimization, MULTIOBJECTIVE OPTIMIZATION, ENERGY-CONSUMPTION, SEARCH ALGORITHM, TURBOJET ENGINE, AIRCRAFT, SUSTAINABILITY, PERFORMANCE, COST, DESIGN, PREDICTION
  • Anadolu Üniversitesi Adresli: Evet

Özet

© 2015 Elsevier Ltd.Genetic algorithm is utilized to design the optimum initial value of parameters and topology of the artificial neural network which is trained by applying the improved backpropagation algorithm using momentum factor so as to minimize the spent time and effort. In this study, a comprehensive dynamic modeling of turboprop engine components plant is accomplished using hybrid GA (genetic algorithm) ANN (artificial neural networks) strategy. The turboprop engine is equipped with main components such as compressor, combustor, gas turbine and power turbine. Newly derived GA-ANN model takes into account five independent engine variables (i.e., torque, power, gas generator speed, engine mass air flow and fuel flow). These dynamic variables are used as inputs of the ANN while exergy efficiencies of the components are considered as the output parameter of the network. The results show that the hybridization with the genetic algorithm has improved the accuracy even further compared to the trial-and-error case, and the estimated values of exergy efficiencies of the components obtained by the derived model provide a close fit with the reference data.