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

TL;DR

This study uses genetic programming to derive symbolic models that accurately estimate gas turbine shaft torque and fuel flow in a ship's CODLAG propulsion system, based on a large publicly available dataset.

Contribution

It introduces a novel application of genetic programming to develop symbolic expressions for key propulsion parameters using real-world ship data.

Findings

High accuracy in torque estimation with R^2 scores above 0.999

Fuel flow estimation achieved R^2 scores above 0.995

Demonstrates effectiveness of genetic programming for predictive modeling in marine systems

Abstract

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 $R^2$ 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 $R^2$ scores of 0.999201, 0.999296, and 0.999374, respectively. The three best symbolic expressions obtained for fuel flow estimation generated $R^2$ scores of 0.995495, 0.996465, and 0.996487, respectively.