Active monitoring of transverse stability in fishing vessels is paramount due to its significant incidence in operational accidents. Access to systems for automatic detection of changes in vessel’s stability related parameters would better support the crew during fishing and navigation operations. The paper presents an initial experimental validation of a signal-based transverse stability monitoring system, which consists of an estimator-detector kernel that solely uses measurements of roll motion to identify changes in vessel’s metacentric height by estimating the roll natural frequency. Its performance is evaluated based on experimental data from a towing tank scale model test campaign. The proposed transverse stability monitoring system well performs by identifying the potential risks and changes in loading condition.
This study presents a novel approach to forecast freight rates in container shipping by integrating soft facts in the form of measures originating from surveys among practitioners asked about their sentiment, confidence or perception about present and future market development. As a base case, an autoregressive integrated moving average (ARIMA) model was used and compared the results with multivariate modelling frameworks that could integrate exogenous variables, that is, ARIMAX and Vector Autoregressive (VAR). We find that incorporating the Logistics Confidence Index (LCI) provided by Transport Intelligence into the ARIMAX model improves forecast performance greatly. Hence, a sampling of sentiments, perceptions and/or confidence from a panel of practitioners active in the maritime shipping market contributes to an improved predictive power, even when compared to models that integrate hard facts in the sense of factual data collected by official statistical sources. While investigating the Far East to Northern Europe trade route only, we believe that the proposed approach of integrating such judgements by practitioners can improve forecast performance for other trade routes and shipping markets, too, and probably allows detection of market changes and/or economic development notably earlier than factual data available at that time.
Global climate change, which is largely attributed to human activity, is one of the foremost challenges of the 21st century. In recent times, there have been notable alterations in the Earth's climate, resulting in profound impacts on ecosystems and biodiversity. These alterations are caused by greenhouse gas, such as carbon dioxide, methane, and nitrous oxide. Greenhouse gas emissions are caused by practices such as deforestation, industrial operations, and the combustion of fossil fuels in vehicles, vessels, aircraft, and manufacturing facilities. The maritime and aviation industry is currently responsible for approximately 6% of global greenhouse gas emissions. Due to logistical and economic constraints, these industries are heavily reliant on liquid fuels, making direct electrification options unavailable for large parts of these sectors. As a result, these sectors are considered ‘hard to abate’. Understanding the future climate mitigation challenges associated with the maritime and aviation sectors is crucial in shaping effective policy measures, avoiding stranded assets, and preserving the chance to meet Paris Agreement-compatible emission reduction pathways.
This thesis identifies three main challenges and proposes modelling approaches to address them when modelling decarbonization pathways for the aviation and maritime sectors. From these challenges, research gaps have been identified that this PhD thesis aims to fill. Three models have been developed for the thesis: a maritime optimization model, a maritime demand model, and an aviation demand model. The modelling landscape and methodology vary across models, ranging from econometrics and data science to mathematical optimization.
To overcome the challenges and fill in the research gaps, three corresponding modelling approaches have been successfully applied:
1. Developing a holistic decarbonization modelling landscape. This includes life-cycle representations of technology costs and emissions, the upscaling of bottleneck technologies, the availability of sustainable biomass, and consideration of competing demand from other industries, as well as representations of policy levers such as carbon pricing or improvements to fuel efficiency.
2. Developing demand models that interpret the underlying scenario narrative consistently (SSP framework).
3. Improving the representation of technological learning for low-carbon technologies in energy system models.
The findings acquired by applying these three modelling approaches are valuable for energy modellers, climate scientists, and policymakers and offer unique insights into the inherent system dynamics associated with decarbonization of hard-to-abate sectors. Utilizing this modelling landscape reveals that current decarbonization efforts for hard-to-abate sectors are insufficient.
Closed-form expressions to estimate the energy absorption and damage extent for severe ship collision damages were initially developed in 1999 [1, 2], and further validated with experimental data in 2016 [3]. To gain further confidence for applications within design using the proposed analytical procedure, it is evident that more detailed and comprehensive comparisons and validations with experiments and numerical simulations are necessary. The purpose of the present paper is to use the analytical approach and finite element analyses to study in depth model-scale and full-scale collision tests so that to further quantify key calculation parameters and to verify the capability and accuracy of the proposed analytical method. In total 18 experimental tests and one full-scale collision accident are evaluated. The 18 experimental energy absorption-penetration and collision force-penetration curves, and the associated finite element simulations, are compared with results obtained from the analytical calculations. It can be concluded that the analytical method gives consistently good agreement with all experiments analysed here. Finally, an application of the analytical method is demonstrated by an example where speed restrictions are determined in a port to avoid LNG cargo leakage in an event of an LNG carrier being struck by another ship.
The estimation of the thrust deduction fraction is generally conducted in ideal weather conditions. However, the presence of waves considerably alters the magnitude of this propulsive coefficient. The increased load of the propeller could be the main cause for the variation of the thrust deduction fraction in realistic operating conditions. In this work, load-varying self-propulsion model-scale numerical simulations in calm water conditions for the same ship speed are performed to investigate the influence of the propeller loading on the thrust deduction fraction. The single screw model-scale KVLCC2 tanker is selected as the case study. The results reveal a non-linear inverse correlation between the thrust deduction fraction and the propeller loading. A comparison with model-testing conducted on the KVLCC2 tanker in regular head waves suggests that the propeller loading is the main factor influencing the magnitude of the thrust deduction fraction in waves for the considered case vessel.
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 PSWF-based 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.
Currently, the shipping industry is facing a great challenge of reducing emissions. Reducing ship speeds will reduce the emissions in the immediate future with no additional infrastructure. However, a detailed investigation is required to verify the claim that a 10% speed reduction would lead to 19% fuel savings (Faber et al., 2012).
This paper investigates fuel savings due to speed reduction using detailed modeling of ship performance. Three container ships, two bulk carriers, and one tanker, representative of the shipping fleet, have been designed. Voyages have been simulated by modeling calm water resistance, wave resistance, propulsion efficiency, and engine limits. Six ships have been simulated in various weather conditions at different speeds. Potential fuel savings have been estimated for a range of speed reductions in realistic weather.
It is concluded that the common assumption of cubic speed-power relation can cause a significant error in the estimation of bunker consumption. Simulations in different seasons have revealed that fuel savings due to speed reduction are highly weather dependent. Therefore, a simple way to include the effect of weather in shipping transport models has been proposed.
Speed reduction can lead to an increase in the number of ships to fulfill the transport demand. Therefore, the emission reduction potential of speed reduction strategy, after accounting for the additional ships, has been studied. Surprisingly, when the speed is reduced by 30%, fuel savings vary from 2% to 45% depending on ship type, size and weather conditions. Fuel savings further reduce when the auxiliary engines are considered.
We present a novel solution approach to the container pre-marshalling problem using the A* and IDA* algorithms combined with several novel branching and symmetry breaking rules that significantly increases the number of pre-marshalling instances that can be solved to optimality. A* and IDA* are graph search algorithms that use heuristics combined with a complete graph search to find optimal solutions to problems. The container pre-marshalling problem is a key problem for container terminals seeking to reduce delays of inter-modal container transports. The goal of the container pre-marshalling problem is to find the minimal sequence of container movements to shuffle containers in a set of stacks such that the resulting stacks are arranged according to the time each container must leave the stacks. We evaluate our approach on three well-known datasets of pre-marshalling problem instances, solving over 500 previously unsolved instances to optimality, which is nearly twice as many instances as the current state-of-the-art method solves.
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.
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.