Estimation of Gas Turbine Shaft Torque and Fuel Flow of a CODLAG Propulsion System Using Genetic Programming Algorithm

In this paper, the publicly available dataset of condition based maintenance of combined diesel-electric and gas (CODLAG) propulsion system for ships has been utilized to obtain symbolic expressions which could estimate gas turbine shaft torque and fuel flow using genetic programming (GP) algorithm. The entire dataset consists of 11934 samples that was divided into training and testing portions of dataset in an 80:20 ratio. The training dataset used to train the GP algorithm to obtain symbolic expressions for gas turbine shaft torque and fuel flow estimation consisted of 9548 samples. The best symbolic expressions obtained for gas turbine shaft torque and fuel flow estimation were obtained based on their R2 score generated as a result of the application of the testing portion of the dataset on the aforementioned symbolic expressions. The testing portion of the dataset consisted of 2386 samples. The three best symbolic expressions obtained for gas turbine shaft torque estimation generated R2 scores of 0.999201, 0.999296, and 0.999374, respectively. The three best symbolic expressions obtained for fuel flow estimation generated R2 scores of 0.995495, 0.996465, and 0.996487, respectively.

• Record URL:
  • https://doi.org/10.31217/p.34.2.13
• Record URL:
  • https://hrcak.srce.hr/247799
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/13320718
• Supplemental Notes:
  • Abstract reprinted with permission from the Faculty of Maritime Studies of the University of Rijeka.
• Authors:
  • Anđelić, Nikola
  • Šegota, Sandi Baressi
  • 0000-0002-3015-1024
  • Lorencin, Ivan
  • Car, Zlatan
  • 0000-0003-2817-9252
• Publication Date: 2020

Language

• English

Media Info

• Media Type: Web
• Features: Figures; References; Tables;
• Pagination: pp 323-337
• Serial:
  • Pomorstvo, Scientific Journal of Maritime Research
  • Volume: 34
  • Issue Number: 2
  • Publisher: University of Rijeka, Croatia
  • ISSN: 1332-0718
  • EISSN: 1846-8438
  • Serial URL: http://hrcak.srce.hr/pomorstvo?lang=en
• Publication flags:

  Open Access (libre)

Subject/Index Terms

• TRT Terms: Artificial intelligence; Fuel mixtures; Gas turbines; Genetic algorithms; Propulsion; Ships
• Subject Areas: Energy; Marine Transportation; Vehicles and Equipment;

Filing Info

• Accession Number: 01783922
• Record Type: Publication
• Files: TRIS
• Created Date: Sep 30 2021 9:30AM