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The paper presents regression formulas that allow us to estimate the final price of new container ships, based on TEU and deadweight capacity, service speed, length between perpendiculars and gross tonnage of container ships built from 2005 to 2015. The formulas were developed using the author’s own method based on curve fitting techniques and regression methods. The study shows that utilising the author’s method to predict the final price could offer greater accuracy solutions than any standard methods presented in literature. This method was implemented properly with ndCurveMaster curve fitting software which was developed by the author and was applied to develop regression equations presented in the article. The formulas presented in the article have practical application for estimation of container ship final price needed in transport studies or preliminary parametric container ship design. These equations refer to the most up to date vessels and offer the chance to advance ship design theory.

The paper presents mathematical relationships that allow us to forecast the newbuilding price of new bulk carriers, based on data concerning vessels built in 2005-2015. The presented approximations allow us to estimate the price based on a gross tonnage capacity and a main engine power The approximations were developed using linear regression and the theory of artificial neural networks. The presented relations have practical application for estimation of bulk carrier newbuilding price needed in preliminary parametric design of the ship. It follows from the above that the use of artificial neural networks to predict the price of a bulk carrier brings more accurate solutions than linear regression.

The article presents the mathematical function to calculate the added wave resistance transfer function for bulk carriers. Based on this function, the statistical mean added wave resistance generated by an irregular head wave with arbitrary statistical parameters can be forecasted. The input parameters are: waterplane area, waterplane coefficient, ship speed, and frequency of the regular wave. The model has been developed based on the theory of artificial neural networks. The presented function can be used in design analyses, and for planning shipping routes in situations when basic geometrical parameters of the hull are only available and not the full technical documentation. The article presents sample cases of use of this function to calculate the added wave resistance transfer function and the statistical mean added wave resistance. Another presented application refers to waterplane coefficient optimisation taking into account the added wave resistance at the stage of preliminary bulk carrier design.

The paper presents mathematical relationships that allow us to forecast the estimated main engine power of new container ships, based on data concerning vessels built in 2005-2015. The presented approximations allow us to estimate the engine power based on the length between perpendiculars and the number of containers the ship will carry. The approximations were developed using simple linear regression and multivariate linear regression analysis. The presented relations have practical application for estimation of container ship engine power needed in preliminary parametric design of the ship. It follows from the above that the use of multiple linear regression to predict the main engine power of a container ship brings more accurate solutions than simple linear regression.

On the modeling of car passenger ferryship design parameters with respect to selected sea-keeping qualities and additional resistance in waves This paper presents the modeling of car passenger ferryship design parameters with respect to such design criteria as selected sea-keeping qualities and additional resistance in waves. In the first part of the investigations approximations of selected statistical parameters of design criteria of ferryship were elaborated with respect to ship design parameters. The approximation functions were obtained with the use of artificial neural networks. In the second part of the investigations design solutions were searched for by applying the singleand multi-criterial optimization methods. The multi-criterial optimization was performed by using Pareto method. Such approach made it possible to present solutions in such form as to allow decision makers (shipowner, designer) to select solutions the most favourable in each individual case.

Modelling of green water ingress into holds of an open-top containership in its preliminary design phase In this paper a method is presented of modelling the green water ingress into holds of open-top containership, which can be useful in the preliminary ship design phase. As a result of the research a mathematical formula which makes it possible to determine a minimum freeboard height with a view of as- low- as- possible occurrence rate of green water ingress into holds at given ship design parameters, was obtained. The research was carried out under assumption of constant ship hull dimensions. The design formula was elaborated by using a method based on a goal-oriented conceptual approach to formulation of design criteria, proposed by IMO. On the basis of the concept a deterministic scenario describing operational conditions of the ship in question, was assumed, and for the conditions the research was performed.

Application of artificial neural networks to approximation and identification of sea-keeping performance of a bulk carrier in ballast loading condition This paper presents an application of artificial neural networks to approximation and identification of additional wave-generated resistance, slamming and internal forces depending on ship motion and wave parameters. The analysis was performed for a typical bulk carrier in ballast loading conditions. The investigations were carried out on the basis of ship response data calculated by means of exact numerical methods. Analytical functions presented in the form of artificial neural networks were analyzed with a view of their accuracy against standard values. Possible ways of application of the artificial neural networks were examined from the point of view of accuracy of approximation and identification of the assumed ship response parameters.

Approximation of the index for assessing ship sea-keeping performance on the basis of ship design parameters This paper presents a new approach which makes it possible to take into account seakeeping qualities of ship in the preliminary stage of its design. The presented concept is based on representing ship's behaviour in waves by means of the so called operational effectiveness index. Presented values of the index were calculated for a broad range of design parameters. On this basis were elaborated analytical functions which approximate the index depending on ship design parameters. Also, example approximations of the index calculated by using artificial neural networks, are attached. The presented approach may find application to ship preliminary design problems as well as in ship service stage to assess sea-keeping performance of a ship before its departure to sea.

In this paper are presented problems of modelling seakeeping qualities of open-top container carriers in the preliminary design phase. Approximations of accelerations and occurrence rate of green water ingress to holds are presented in function of main ship design and wave parameters. The approximations have been elaborated by applying theory of artificial neural networks in the wide range of ship hull dimensions and forms, while ship motion and wave parameters have been limited to real operational conditions described by means of the so called operational scenario. Such approach has made it possible to reach high accuracy of approximation and simple structure of mathematical model simulatneously.

The main scientific aim of this research was to elaborate design guidelines which could make it possible to improve sea-keeping qualities of passenger-car ferries. The searchedfor design guidelines were prepared in the form of regression functions as well as artificial neural networks on the basis of the results obtained from calculations with the use of numerical methods based on the theory of planar flow around a body. The guidelines make it possible to predict ship roll, sea-sickness index, lateral and vertical accelerations on the basis of quantities available in the preliminary stage of ship design.

This paper presents an analysis of a presently applied approach to accounting for seakeeping qualities of FPSO sea-going ships and possible using it in preliminary design stage. Approximations of heaving, pitching, green water ingress on the deck and slamming of FPSO ships, based on main ship design and wave parameters, are presented. The approximations were elaborated with the use of the linear regression method and theory of artificial neural networks for a very wide range of FPSO ship dimensions and hull forms. In the investigations ship operational conditions were limited to those occurring in real service of FPSO ships, described by means of the so called operational scenario. Such approach made it possible to reach simultaneously high approximation accuracy and simple structure of mathematical model.

This paper presents a method which makes it possible to determine optimum hull form of passenger car ferry with regard to selected sea-keeping qualities and additional resistance in waves. In the first phase of investigations a hull form characterized by the highest qualities was selected from the list of similar ships. Next, its optimum dimension ratios were determined. Design criteria were formulated by using a method based on deterministic scenarios, but objective functions of partial targets were determined in the form of artificial neural networks. To select the best design variants elements of fuzzy logic were used, that made it possible a.o. to show merits of the design by means of linguistic variables. Such approach made it possible to find the best hull form and its dimensions from the point of view of all considered criteria simultaneously.