The Lightship Mass Calculation Model Of A Merchant Ship By Empirical Methods

Estimating the lightship mass is an important factor in the early stage of ship design given its impact on shipbuilding costs. The mass of the hull and equipment, as well as its distribution, affects the hull strength, deadweight, and stability of the ship. Furthermore, a good estimate of the lightship mass in the preliminary phase is very important for defining the construction costs as accurately as possible. As the time available to the designer in the conceptual or preliminary phase of ship design is often limited, the methods used to determine the lightship mass must be reliable, fast, and effective. The paper presents and analyzes methods for estimating the lightship mass that are based on empirical data of built ships. Several empirical methods were combined and calibrated to establish the least-squares method which combination gives the value of the lightship mass closest to the one given in the collected database for three types of merchant ships: tankers, bulk carriers, and container ships. The paper presents the results of the calculations of the masses of hull steel, outfitting, machinery, and superstructure. It can be concluded that the presented model for all three types of merchant ships gives satisfactory results.

Computational model of hull steel potential values in sea water

The article provides an analysis of existing neural network models. The features of constructing a neural network using several parameters that affect the output value are described. The advantages of using neural networks and computing systems based on them are revealed. The task was solved using the Python programming language. The computing model of the potential values of steel with an oxide film and the potential of steel without an oxide film has been developed for various salinity of sea water and different types of hull steels used for hulls of sea vessels and underwater structures of ocean engineering structures designed for the Black Sea basin of the Sevastopol region and operated in this region. The obtained results will improve the accuracy of predicting potentials for various hull steel grades.