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Container ship length was estimated using artificial neural networks (ANN), as well as a random search based on Multiple Nonlinear Regression (MNLR). Two alternative equations were developed to estimate the length between perpendiculars based on container number and ship velocity using the aforementioned methods and an up-to-date container ship database. These equations could have practical applications during the preliminary design stage of a container ship. The application of heuristic techniques for the development of a MNLR model by variable and function randomisation leads to the automatic discovery of equation sets. It has been shown that an equation elaborated using this method, based on a random search, is more accurate and has a simpler mathematical form than an equation derived using ANN.

Nowadays, when we try to automatize all activities, there is a growing demand for energy in all forms. Increasingly we reach for new energy sources that can be problematic to store or to transport, owing to their toxicity or explosive propensity. The article examines the issues of determining danger zones occurring as a result of liquefied natural gas (LNG) release. The range of danger zones caused through LNG release depends on a multitude of factors. The basic parameter that needs to be considered is a type of the released substance as well as the manner of its release. The range of a danger zone is determined by, inter alia, the concentration of a released substance and the atmospheric conditions existing at the time when depressurization occurs. The article analyses the problem of the range of danger zones in a function of wind speed and surface roughness with a defined value of Pasquill stability for various LNG types, starting with pure methane, and ending with the so-called LNG-heavy. The difficulty of the task becomes more complicated when the analysed surface over which a depressurization incident takes place involves water. The problem deepens even further when the analysed substance possesses explosive properties. Then, apart from regular substance concentration, upper and lower flammability limit ought to be considered. Calculations were conducted with DNV-Phast software, version 7.11.

This article presents regression formulas for the preliminary design of tankers, bulk carriers and container vessels, based on the data of ships built from 2000 to 2018. The formulas could have practical application for the estimation of total engine power by using ship’s deadweight or TEU capacity and speed. The regressions presented in this article are based on the most recent data and were developed for individual sub-types of tankers, bulk carriers and container ships. The presented regressions comply with trends found in the literature and offer greater accuracy for characteristics of new-built ships.

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.

This article presents regression equations to estimate container ship design characteristics based on the most up-to-date data and deadweight capacity, the number of containers and their combination at the preliminary design stage. These design formulas could have application for the estimation of key container ship characteristics such as: main ship dimensions, geometric parameters, main engine total power, ship velocity, final price and others. Regression equations were performed on the basis of IHS Maritime & Trade main container ship data built from 2005-2015. All equations presented in this paper could have practical application at the preliminary design stage and increase ship design theory development.

The article presents the use of multiple regression method to identify added wave resistance. Added wave resistance was expressed in the form of a four-state nominal function of: “thrust”, “zero”, “minor” and “major” resistance values. Three regression models were developed for this purpose: a regression model with linear variables, nonlinear variables and a large number of nonlinear variables. The nonlinear models were developed using the author's algorithm based on heuristic techniques. The three models were compared with a model based on an artificial neural network. This study shows that non-linear equations developed through a multiple linear regression method using the author’s algorithm are relatively accurate, and in some respects, are more effective than artificial neural networks.

This article presents design formulas for the preliminary design of tankers built from 2000 to 2018. These formulas could have practical application for the estimation of main tanker dimens...

This article presents preliminary design formulas developed using a database of container ships built since 2015. Artificial neural networks and multiple nonlinear regressions with randomly searched functions were used to develop these formulas. The use of random search for nonlinear functions in a Multiple Nonlinear Regression model gave estimates which were just as precise as estimates created by the artificial neural network. All equations presented in this paper could have practical application for the estimation of dimensions, such as: length between perpendiculars, breadth, draught moulded and side depth. The equations were developed in relation to deadweight, TEU capacity and ship speed. These kinds of relationships have not been demonstrated before in ship theory. A statistical analysis showed that the main dimensions of the container ships can be estimated highly accurately by using the equations presented in the paper. The study showed that taking into account deadweight, TEU capacity and ship speed as three input parameters can improve the accuracy of an estimation by up to 44 percent, than when compared to the estimate accuracy of the design equations which are based on one input parameter.

The article presents equations for the estimation of steel motor yacht light displacement based on selected design parameters, such as length overall, draught and breadth. The data of 240 build steel motor yachts from 2000 to 2020 with lengths from 30 m to 86 m were used to develop these equations. The complex design equations based on all design parameters were developed through the use of computational intelligence techniques, such as random search and Artificial Neural Networks. The simple relationships based on only one input design parameter were developed using common linear and non-linear regression. The study showed that the random search method, which utilizes a Multiple Nonlinear Regression model, offers good results that are as accurate as those developed through the use of artificial neural networks. The developed equations may have practical application for the parametric design of yachts.

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.

This article focuses on the use of an artificial neural network to estimate added resistance in regular head waves while using ship design parameters, such as length, breadth, draught or Froude number. In order to create a reliable model, only experimental data determined through model test measurements was used to train the neural network. This study showed that added wave resistance values predicted by the neural network soundly correlated with measured data and had good generalization ability. The developed neural network was presented in the form of mathematical function. This article presents examples of the use of this function to calculate added wave resistance. Functions presented here could have practical application in ship resistance analysis at the preliminary design stage.

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.