Izvorni znanstveni članak

https://doi.org/10.31217/p.34.2.13

Estimation of gas turbine shaft torque and fuel flow of a CODLAG propulsion system using genetic programming algorithm

Nikola Anđelić ; Sveučilište u Rijeci, Tehnički fakultet, Rijeka, Hrvatska
Sandi Baressi Šegota orcid.org/0000-0002-3015-1024 ; Sveučilište u Rijeci, Tehnički fakultet, Rijeka, Hrvatska
Ivan Lorencin ; Sveučilište u Rijeci, Tehnički fakultet, Rijeka, Hrvatska
Zlatan Car orcid.org/0000-0003-2817-9252 ; Sveučilište u Rijeci, Tehnički fakultet, Rijeka, Hrvatska

Sažetak

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.

Ključne riječi

Artificial Intelligence; Combined Diesel-Electric and Gas Porpulsion System; Genetic Programming Algorithm; Gas Turbine Saft Torque Estimation; Fuel Flow Estimation

Hrčak ID:

247799

URI

https://hrcak.srce.hr/247799

Datum izdavanja:

21.12.2020.

Posjeta: 1.579 *