Critical maritime infrastructure protection has become a priority in ocean governance, particularly in Europe. Increased geopolitical tensions, regional conflicts, and the Nord Stream pipeline attacks in the Baltic Sea of September 2022 have been the main catalysts for this development. Calls for enhancing critical maritime infrastructure protection have multiplied, yet, what this implies in practice is less clear. This is partially a question of engineering and risk analysis. It also concerns how the multitude of actors involved can act concertedly. Dialogue, information sharing, and coordination are required, but there is a lack of discussion about which institutional set ups would lend themselves. In this article, we argue that the maritime counter-piracy operations off Somalia, as well as maritime cybersecurity governance hold valuable lessons to provide new answers for the institutional question in the critical maritime infrastructure protection agenda. We start by clarifying what is at stake in the CMIP agenda and why it is a major contemporary governance challenge. We then examine and assess the instruments found in maritime counter-piracy and maritime cybersecurity governance, including why and how they provide effective solutions for enhancing critical maritime infrastructure protection. Finally, we assess the ongoing institution building for CMIP in Europe. While we focus on the European experience, our discussion on designing institutions carries forward lessons for CMIP in other regions, too.
This review article presents a summary of the main categories of models developed for modeling cavitation, a multiphase phenomenon in which a fluid locally experiences phase change due to a drop in ambient pressure. The most common approaches to modeling cavitation along with the most common modifications to said approaches due to other effects of cavitating flows are identified and categorized. The application of said categorization is demonstrated through an analysis of selected cavitation models. For each of the models presented, the various assumptions and simplifications made by the authors of the model are discussed, and applications of the model to simulating various aspects of cavitating flow are also presented. The result of the analysis is demonstrated via a visualization of the categorizations of the highlighted models. Using the preceding discussion of the various cavitation models presented, the review concludes with an outlook toward future improvements in the modeling of cavitation.
Wind-assisted ship propulsion (WASP) technology seems to be a promising solution toward accelerating the shipping industry’s decarbonization efforts as it uses wind to replace part of the propulsive power generated from fossil fuels. This article discusses the status quo of the WASP technological growth within the maritime transport sector by means of a secondary data review analysis, presents the potential fuel-saving implications, and identifies key factors that shape the operational efficiency of the technology. The analysis reveals three key considerations. Firstly, despite the existing limited number of WASP installations, there is a promising trend of diffusion of the technology within the industry. Secondly, companies can achieve fuel savings, which vary depending on the technology installed. Thirdly, these bunker savings are influenced by environmental, on-board, and commercial factors, which presents both opportunities and challenges to decision makers.
In this study, a method for predicting the extreme value distribution of the Vertical Bending Moment (VBM) in a flexible ship under a given short-term sea state is presented. The First Order Reliability Method (FORM) is introduced to evaluate the Probability of Exceedances (PoEs) of extreme VBM levels. The Karhunen-Loeve (KL) representation of stochastic ocean wave is adopted in lieu of the normal wave representation using the trigonometric components, by introducing the Prolate Spheroidal Wave Functions (PSWFs) to formulate the wave elevations. By this means, reduction of the number of stochastic variables to reproduce ocean wave is expected, which in turn the number of computations required during FORM based prediction phases is significantly reduced. In this study, the Reduced Order Model (ROM), which was developed in our previous studies, is used to yield the time-domain VBMs along with the hydroelastic (whipping) component in a ship. Two different short-term sea states, moderate and severe ones, are assumed. The FORM based predictions using PSWF for normal wave-induced VBM are then validated by comparing with those using the normal trigonometric wave representation and Monte Carlo Simulations (MCSs). Through a series of numerical demonstrations, the computational efficiency of the FORM based prediction using PSWF is presented. Then, the validation is extended to the severe sea state where the whipping vibration contributes to the extreme VBM level to a large degree, and finally the conclusions are given.
An efficient extreme ship response prediction approach in a given short-term sea state is devised in the paper. The present approach employs an active learning reliability method, named as the active learning Kriging + Markov Chain Monte Carlo (AK-MCMC), to predict the exceedance probability of extreme ship response. Apart from that, the Karhunen-Loève (KL) expansion of stochastic ocean wave is adopted to reduce the number of stochastic variables and to expedite the AK-MCMC computations. Weakly and strongly nonlinear vertical bending moments (VBMs) in a container ship, where the former only accounts for the nonlinearities in the hydrostatic and Froude-Krylov forces, while the latter also accounts for the nonlinearities in the radiation and diffraction forces together with slamming and hydroelastic effects, are studied to demonstrate the efficiency and accuracy of the present approach. The nonlinear strip theory is used for time domain VBM computations. Validation and comparison against the crude Monte Carlo Simulation (MCS) and the First Order Reliability Method (FORM) are made. The present approach demonstrates superior efficiency and accuracy compared to FORM. Moreover, methods for estimating the Mean-out-crossing rate of VBM based on reliability indices derived from the present approach are proposed and are validated against long-time numerical simulations.
This paper presents a new approach to attain estimates of the sea state based on short-time sequences of wave-induced ship responses. The present sea state estimation method aims at reconstructing the incident wave profiles in time domain. In order to identify phase components of the incident waves, the Prolate Spheroidal Wave Functions (PSWF) are employed. The use of PSWF offers an explicit expression of phase components in the measured responses and incident waves, indicating that estimations can be efficiently attained. A method to estimate the relative wave heading angle based on the response measurements and pre-computed transfer functions of the responses is also proposed. The method is tested with numerical simulations and experimental measurements of ship motions, i.e. heave, pitch, and roll, together with vertical bending moment and local pressure in a post-panamax size containership. Validation is made by comparing the reconstructed wave profiles with the incident waves. The accuracy and efficiency of the present approach are promising. At the same time, it is shown that the use of responses, which are more broad-banded in their frequency characteristics, is an effective means to cope with high frequency noise in reconstructed waves.
This paper studies real-time deterministic prediction of wave-induced ship motions using the autocorrelation functions (ACFs) from short-time measurements, namely the instantaneous ACFs. The Prolate Spheroidal Wave Functions (PSWF) are introduced to correct the large lag time errors in the instantaneous sample ACF, together with a modification of the autocorrelation (AC) matrix for ensuring its positive definiteness. The validity of the PSWF-based ACFs is first examined by using the ship motion measurements from model experiment under stationary wave excitations. It is shown that the use of PSWFbased ACFs leads to better prediction accuracy than direct use of sample ACFs. The validation is then extended to ship motion prediction using in-service data from a container ship, and an improvement of the prediction accuracy by PSWF-based ACFs is again found. Finally, the effectiveness of use of the instantaneous ACFs for non-stationary wave-induced responses is highlighted by comparing with the prediction results based on the ACFs from long-time measurements.
This paper presents a new approach for estimating encountered wave elevation sequences by use of measured ship responses, where wind-waves and swell may come from different directions, i.e. bi-directional waves. The main assumption of the approach, making use of Prolate Spheroidal Wave Functions (PSWF), is that the wave field is represented by multi-directional irregular waves. Thus, combining available measured responses, the phases and amplitudes of the multi-directional irregular waves are derived as the solution, by which the wave profiles can be estimated. Numerical investigations using artificially generated response measurements (sway, heave, pitch, vertical bending moment) of a bulk carrier in uni-directional and bi-directional long-crested as well as short-crested sea states are made. It is shown that the present approach can accurately estimate wave elevation sequences in such sea states.
An efficient extreme ship response prediction approach in a given short-term sea state is devised in the paper. The present approach employs an active learning reliability method, named as the active learning Kriging + Markov Chain Monte Carlo (AK-MCMC), to predict the exceedance probability of extreme ship response. Apart from that, the Karhunen-Loève (KL) expansion of stochastic ocean wave is adopted to reduce the number of stochastic variables and to expedite the AK-MCMC computations. Weakly and strongly nonlinear vertical bending moments (VBMs) in a container ship, where the former only accounts for the nonlinearities in the hydrostatic and Froude-Krylov forces, while the latter also accounts for the nonlinearities in the radiation and diffraction forces together with slamming and hydroelastic effects, are studied to demonstrate the efficiency and accuracy of the present approach. The nonlinear strip theory is used for time domain VBM computations. Validation and comparison against the crude Monte Carlo Simulation (MCS) and the First Order Reliability Method (FORM) are made. The present approach demonstrates superior efficiency and accuracy compared to FORM. Moreover, methods for estimating the Mean-out-crossing rate of VBM based on reliability indices derived from the present approach are proposed and are validated against long-time numerical simulations.
Accurate estimation of the roll damping of a ship is important for reliable prediction of roll motions. In particular, characterization and prediction of parametric roll incidence and other events associated with large roll angles require detailed knowledge about the damping terms. In the present paper, an approach to identify the stability parameters, i.e. linear and nonlinear roll damping coefficients in conjunction with the natural roll frequency, based on onboard response measurements is proposed. The method starts by estimating the encountered wave profile using wave-induced response measurements other than roll, e.g., heave, pitch, and sway motions. The estimated wave profile is then fed into a physic-based nonlinear roll estimator, and then the stability parameters that best reproduce the measured roll motion are identified by optimization. In turn, in-situ identification can be achieved while simultaneously collecting the response measurements. A numerical investigation using synthetic response measurements is made first, then follows an experimental investigation using a scaled model ship. Good results have been obtained in both long-crested and short-crested irregular waves.