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Modern cargo vessel transport constitutes an important part of global economy; hence it is of paramount importance to develop novel, more efficient reliability methods for cargo ships, especially if onboard recorded data is available. Classic reliability methods, dealing with timeseries, do not have the advantage of dealing efficiently with system high dimensionality and cross-correlation between different dimensions. This study validates novel structural reliability method suitable for multi-dimensional structural systems versus a well-established bivariate statistical method. An example of this reliability study was a chosen container ship subjected to large deck panel stresses during sailing. Risk of losing containers, due to extreme motions is the primary concern for ship cargo transport. Due to non-stationarity and complicated nonlinearities of both waves and ship motions, it is challenging to model such a phenomenon. In the case of extreme motions, the role of nonlinearities dramatically increases, activating effects of second and higher order. Moreover, laboratory tests may also be questioned. Therefore, data measured on actual ships during their voyages in harsh weather provides a unique insight into statistics of ship motions. This study aimed at benchmarking and validation of the state-of-the-art method, which enables extraction of the necessary information about the extreme system dynamics from onboard measured time histories. The method proposed in this study opens up broad possibilities of predicting simply, yet efficiently potential failure or structural damage risks for the nonlinear multi-dimensional cargo vessel dynamic systems as a whole. Note that advocated novel reliability method can be used for a wide range of complex engineering systems, thus not limited to cargo ship only.

The International Maritime Organization (IMO) has concerned significant care to the reduction of ship emissions and improvement of energy efficiency through operational measures. One of those measures is ship speed reduction, which is classified as a short-term measure; in which the speed is reduced below its designed value. The present paper aims at evaluating the potential energy efficiency, and environmental and economic benefits because of applying speed reduction measures. The research methodology depends on establishing a simple mathematical model for technical, environmental, and economical aspects because of this concept. As a case study, container ships from different categories in a range of 2500–15,000 twenty-foot equivalent units (TEU) are investigated. The results show that a 2500 TEU ship can comply with the energy efficiency existing ship index (EEXI) by reducing the service speed to 19 knots. While for the bigger ships, the service speed must be 21.5 knots or below. Furthermore, the operational carbon intensity indicator (CII) has been evaluated for the case studies and found that the CII rating will keep its score between A and C levels if the service speed is equal to or below 19.5 knots. Moreover, the annual profit margin of the ship will be calculated based on applying speed reduction measures. Based on the economical results, the annual profit margin value, and its corresponding optimum speed change with the size of the vessel and the applicable status of carbon taxes.

For the correct execution of the preliminary design of a transport ship, among other things, approximate formulas enabling the calculation of the weight of the unladen ship and the location of the center of gravity are necessary. The aim of the conducted research was to develop approximate formulas for calculating the weight and center of gravity of an empty container ship with a size ranging from 270 TEU to 3100 TEU, depending on the basic design parameters: ship speed V, deadweight DWT, and number of TEU containers. Since the weight of an unladen container ship has a very large impact on the ship’s operating parameters, an additional aim was to obtain regression formulas with greater accuracy than similar formulas published in the literature. Simple and multiple regression methods were used to develop regression formulas. The obtained results were verified on the basis of experimentally measured parameters obtained from built ships. The regression formulas presented in this article are characterized by high accuracy, greater than that of similar formulas published in the literature, and were developed for container ships currently under construction. A novelty of this study is the development of regression formulas for weight classes, which make up the total weight of an unladen ship.

In recent years, the size of container ships has become larger, thus requiring a more evident assurance of the hull structural safety. In order to evaluate the structural safety in operation, it is necessary to grasp the encountered sea state. The aim of this study is to estimate the encountered sea state using machine learning from measurement data of ocean-going 14,000TEU container ships. In this paper, as a first step in the study, considerable amounts of virtual sea state data and corresponding ship motion and structural response data are prepared. A convolutional neural network (CNN) is developed using these data to estimate the directional wave spectrum of encountered sea based on the hull responses. The input parameters of the formulated CNN include the spectral values of ship motion and structural response spectrum. The output of the CNN includes the sea state parameters of the Ochi-Hubble spectrum, specifically, significant wave height, modal wave frequency, mean wave direction, kurtosis, and concentration of wave energy directional distribution. It is found from the performance examination that the developed CNN is capable of accurately estimating the sea state parameters, although the level of accuracy decreases when the hull response is low. However, the decrease in accuracy when the hull response is low has a weak influence on the evaluation of the structural response to the estimated sea state.

Slow steaming is an operational measure in ocean-going vessels sailing at slow speeds. It can help climate mitigation efforts by cutting down marine fuel consumption and consequently reducing CO2 and other Greenhouse Gas Emissions (GHG). Due to climate change both the European Union (EU) and the International Maritime Organization (IMO) are analysing the inclusion of international shipping in the EU Emissions Trading System (ETS) in the near future or alternatively implementing a carbon tax. The paper proposes a methodology to decide the optimal speed of a vessel taking into account its characteristics and the factors that determine its economic results. The calculated cash flow can be used in valuation models. The methodology is applied for a case study for any container ship in a range from 2000 to 20,000 Twenty-foot Equivalent Units (TEU) on a leg of a round trip from Shanghai to Rotterdam. We calculate how speed reduction, CO2 emissions and ship owner’s earnings per year may vary between a business-as-usual scenario and a scenario in which shipping is included in the ETS. The analysis reveals that the optimal speed varies with the size of the vessel and depends on several variables such as marine fuel prices, cargo freight rates and other voyage costs. Results show that the highest optimal speed is in the range of 5500–13,000 TEUs whether or not the ETS is applied. As the number of TEUs transported in a vessel increases emissions per TEU decrease. In an established freight rate market, the optimal speed fluctuates by 1.8 knots. Finally, the medium- and long-term expectations for slow steaming are analysed based on future market prices.

Resistance is one of the important performance indicators of ships. In this paper, a prediction method based on the Radial Basis Function neural network (RBFNN) is proposed to predict the resistance of a 13500 transmission extension unit (13500TEU) container ship at different drafts. The predicted draft state in the known range is called interpolation prediction; otherwise, it is extrapolation prediction. First, ship features are extracted to make the resistance Rt prediction. The resistance prediction results show that the performance of the RBFNN is significantly better than the other four machine learning models, backpropagation neural network (BPNN), support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGBoost). Then, the ship data is processed in a dimensionless manner, and the models mentioned above are used to predict the total resistance coefficient Ct of the container ship. The prediction results show that the RBFNN prediction model still performs well. Good results can be obtained by RBFNN in interpolation prediction, even when using part of dimensionless features. Finally, the accuracy of the prediction method based on RBFNN is greatly improved compared with the modified admiralty coefficient.

The wind resistance of a model of a large container ship was investigated experimentally and numerically under various conditions to investigate the aerodynamic performance and identify approaches for reducing wind resistance. The container ship model with and without six types of forecastle fairing was tested in a wind tunnel under conditions of full load and various uneven loads in different wind directions. The model test found that a specially designed forecastle fairing and certain container configurations are effective in reducing wind resistance. Then, a numerical simulation was performed to investigate the characteristics of the flow field around the test model. The comparisons and analyzes of the pressure distribution, streamlines, and vortex structure illuminate the mechanism of load reduction caused by the forecastle fairing and container configurations that are useful for reducing wind resistance. The optimization of a container ship for wind resistance is dominated by the effect of the container configuration; the contributions of forecastle fairing are secondary. When the overall effects of forecastle fairing and container configuration are considered, the streamlined load is the variant most optimized for wind resistance.

Shipping is a significant contributor to global greenhouse gas (GHG) and air pollutant emissions. These emissions mainly come from using diesel fuel for power generation. In this paper, the natural gas is proposed as an alternative marine fuel to be used instead of conventional marine diesel oil. Numerical analysis of environmental and economic benefits of the natural gas-diesel dual-fuel engine is carried out. As a case study, a container ship of class A7 owned by Hapag-Lloyd has been investigated. The results show that the proposed dual-fuel engine achieves environmental benefits for reducing carbon dioxide (CO 2 ), nitrogen oxides (NOx), sulfur oxides (SOx), particulate matter (PM), and carbon monoxide (CO) emissions by 20.1%, 85.5%, 98%, 99%, and 55.7% with cost effectiveness of 109, 840, 9864, 27761, and 4307 US$/ton, respectively. The results show that the conversion process to the dual-fuel engine will comply with the current and future IMO regulations regarding air pollutant emissions. On the other hand, using the proposed dual-fuel engine on the container ship will improve the ship energy efficiency index by 29.6 % with annual fuel cost saving of 4.77 million US dollars.

Container ships are becoming larger and larger in recent years, requiring more evident assurance of the structural safety. To achieve this, it is essential to grasp actual stress history experienced by the ship structures to facilitate efficient design and maintenance, and to use them for optimal operation of the ship. To perform accurate estimation of these stress histories, it is important to precisely estimate the sea state which the ship is actually encountering. In this study, the authors studied a new method to estimate directional wave spectra using measured ship responses and discussed the following three cases. The first one is the combination of two components, vertical bending stress and horizontal bending stress. The second one is the combination of three components, vertical bending stress, horizontal bending stress and double bottom bending stress. The last one is the combination of three components of ship motion (pitch, roll and heave). The estimated sea states are compared with the ocean wave hindcast database and radar data, and then, accuracy and selection of appropriate combination of the responses are discussed.

The container ship stowage planning problem (CSPP) is a very complex and challenging issue concerning the interests of shipping companies and ports. This article has developed a many-objective CSPP solution that optimizes ship stability and reduces the number of shifts over the whole route while at the same time considering realistic constraints such as the physical structure of the ship and the layout of the container yard. Use the initial metacentric height (GM) along with the ship’s heeling angle and trim to measure its stability. Meanwhile, use the total amount of relocation in the container terminal yard, the voluntary shift in the container ship’s bay, and the necessary shift of the future unloading port to measure the number of shifts on the whole route. This article proposes a variant of the nondominated sorting genetic algorithm III (NSGA-III) combined with local search components to solve this problem. The algorithm can produce a set of non-dominated solutions, then decision-makers can choose the best practical implementation based on their experience and preferences. After carrying out a large number of experiments on 48 examples, our calculation results show that the algorithm is effective compared with NSGA-II and random weighted genetic algorithms, especially when applied to solve many-objective CSPPs.