DOI QR코드

DOI QR Code

인공 신경망을 이용한 프리피스톤 리니어 엔진의 연구

The Research About Free Piston Linear Engine with Artificial Neural Network

  • AHMED, TUSHAR (Graduate of Mechanical Engineering, University of Ulsan) ;
  • HUNG, NGUYEN BA (Graduate of Mechanical Engineering, University of Ulsan) ;
  • LIM, OCKTAECK (School of Mechanical Engineering, University of Ulsan)
  • 투고 : 2015.05.08
  • 심사 : 2015.06.30
  • 발행 : 2015.06.30

초록

Free piston linear engine (FPLE) is a promising concept being explored in the mid-20th century. On the other hand, Arficial neural networks (ANNs) are non-linear computer algorithms and can model the behavior of complicated non-linear processes. Some researchers already studied this method to predict internal combustion engine characteristics. However, no investigation to predict the performance of a FPLE using ANN approach appears to have been published in the literature to date. In this study, the ability of an artificial neural network model, using a back propagation learning algorithm has been used to predict the in-cylinder pressure, frequency, maximum stroke length of a free piston linear engine. It is advised that, well-trained neural network models can provide fast and consistent results, making it an easy-to-use tool in preliminary studies for such thermal engineering problems.

키워드

참고문헌

  1. R. Mikalsen, and A. P. Roskilly, "A review of free-piston engine history and application; applied thermal engineering", Vol. 27, 2007, pp. 2339-2352.
  2. P. Van Blarigan, N. Paradiso, and S. Goldsborough, "Homogeneous Charge Compression Ignition with a Free Piston: A New Approach to Ideal Otto Cycle Performance", SAE Technical Paper 982484, 1998, doi: 10.4271/982484.
  3. Yongil Oh, and Ocktaeck Lim, "A Study for Generating Power on Operating Parameters of Power pack Utilizing Linear Engine", SAE 2012-32-0061, doi: 10.4271/2012-32-0061.
  4. Erol Arcaklioglu, and Ismet Celikten, "A diesel engine's performance and exhaust emissions", applied enegy, Vol. 80, 2005, pp. 11-22. https://doi.org/10.1016/j.apenergy.2004.03.004
  5. S. Haykin, "Neural networks: A comprehensive Foundation", Macmillan, New Yourk (1994).
  6. Cenk Sayin et al., "Performance and exhaust emissions of a gasoline engine using artificial neural network", applied thermal energy, Vol. 27, 2007, pp. 46-54. https://doi.org/10.1016/j.applthermaleng.2006.05.016
  7. M. T. Hagan, and H. B. Demuth, "Neural network design, PWS Publishing company", Boston, USA, 1995.
  8. F. M. Ham, and I. Kostanic, "Principles of neurocomputing for science and engineering", Mc Graw-Hill, Singapore, 2001.