CFD-Powered Ship Trim Optimization: Integrating ANN for User-Friendly Software Tool Development

Abstract

This study presents a comprehensive approach to trim optimization as an energy efficiency improvement measure, focusing on reducing fuel consumption for one RO-RO car carrier. Utilizing Computational Fluid Dynamics (CFD) software, the methodology incorporates artificial neural networks (ANNs) to develop a mathematical model for estimating key parameters such as the brake power, daily fuel oil consumption (DFOC) and propeller speed. The complex ANN model is then integrated into a user-friendly software tool for practical engineering applications. The research outlines a seven-phase trim optimization process and discusses its potential extension to other types of ships, aiming to establish a universal methodology for CFD-based engineering analyses. Based on the trim optimization results, the biggest DFOC goes up to 10.5% at 7.5 m draft and up to 8% for higher drafts. Generally, in every considered case, it is recommended to sail with the trim towards the bow, meaning that the ship’s longitudinal center of gravity should be adjusted to tilt slightly forward.