Friday, 11 June 2021

IEM HQ WEBINAR - Talk on Ship Resistance Powering (Digital Platform)

 WEBINAR - Talk on Ship Resistance Powering (Digital Platform)


By Marine Engineering & Naval Architecture Technical Division, IEM

Date: 28 June 2021
Time: 03:00 PM - 05:00 PM
CPD/PDP: 2

SYNOPSIS

The knowledge of ship resistance and powering is an important aspect to be considered in ship design. Ship powering requirement is often requested by the ship owner and a contract is usually written before the construction of the vessel by the ship builder. Ship resistance is mainly from the drag of the ship’s hull and the resultant wave making due to the interaction of the hull with the surrounding water. Whilst ship powering is the power required by the propulsion device to drive the ship’s hull at the required speed. With all these considerations, the ship designer could determine the appropriate propulsion system for the particular ship design. Ultimately, an efficient hull and propulsion system will result in an environmentally friendly ship hull with minimum possible emissions of the exhaust gases.

In this presentation, firstly, a brief overview of the ship resistance and powering is given. This is then followed by presenting some experiences gained through the research work and model tests carried out at the Towing Tank of Universiti Teknologi Malaysia since its operation in the year 1997. Work on various ship hulls like Displacement hull, SWATH, Trimaran and Amphibious boat are given. Some aspects of shallow water and bank effects are also provided. In the age of computerisati on, the use of numerical computation in CFD is also highlighted. The presentation ends with the current development of ship resistance prediction utilising the AI approach. In this approach, graph theory and machine learning methods are proposed in conjunction with Concurrent Engineering application in Preliminary Ship Design. It is applied to estimate the passenger ship preliminary powering requirements. Based on the identified hull parameter variables, data analysis is carried out to investigate their significance and interrelationships. It is then used to develop the machine learning model to predict the powering requirements.