Modelling and simulation of cargo heating systems
Date of Issue2014
School of Mechanical and Aerospace Engineering
Cargo oil tankers are used in transporting crude oil to various parts of the world. Crude oil is used in various aspects of daily life. This crude oil while transported in cargo oil tankers demands some pre-requisites prior to conditioning the oil for ease of pumping in and out of crude oil tank in ports. While transporting the crude oil through low temperature seas, there is a risk of solidification of crude oil which may possibly damage the tank. Hence the crude oil is heated using cargo heating systems. Previously, the cargo heating was monitored by the captain of the ship and the heating process depended solely on the experiences of the captain. Heating is done by boilers and the increase in the amount of fuel used to heat up the oil leads to an increase in the cost incurred in heating the oil. As an industrial project in collaboration with the company American Eagle Tankers (AET) Singapore Pte. Ltd, the main objective of this dissertation is to numerically predict the amount of heat required for the crude oil to attain an optimum temperature via CFD simulation techniques, in order to reduce the cost incurred in the usage of fuel to produce steam from the boilers by reducing the consumption of fuel used in the process of heating. The present CFD simulation is performed for analysing the influences of various parameters on the heat transfer in cargo oil tankers, such as seawater temperature, air temperature, air velocity and ship speed by means of the commercial software - Ansys Fluent. This study proposes a standard database involving CFD simulations. In order to build this database, the studies of both mesh and time-step sensitivities are performed on various tank configurations. In addition, the original model with steel fins in the frames and the ballast tank and the approximated model without the frames and ballast tank are compared to deduce their relative error. The relationship between different ranges is established with respect to each parameter. On the basis of this database, a data pool is formulated. In future, the simulation results obtained from Ansys Fluent has to be validated with the test results and if exists any deviation correlation studies have to be performed to reduce the gap between simulation and test results. After minimising the gap between the simulation and test results, the database can be used to supplement the effective prediction of fuel consumption.