Due to the increasing impacts of ships pollutants on the environment and the preventive laws that are tightening every day, the utilization of all-electric ships is a recent emerging technology. Being a promising technology, the usage of fuel cells as the main energy resource of marine vessels is an interesting choice. In this article, an all-electric hybrid energy system with zero emission based on fuel cell, battery, and cold-ironing is proposed and analyzed. To this end, actual data of a ferry boat, including load profiles and paths, are considered to assess the feasibility of the proposed energy system. The configuration of the boat and energy resources as well as the problem constraints are modeled and analyzed. Finally, the boat's energy management in hourly form for a one-day period is implemented. The improved sine cosine algorithm is used for the power dispatch optimization, and all models are implemented in MATLAB software. Based on the analysis results, the proposed hybrid system and the energy management method have high performance as an applicable method for the marine vessels. In addition, to be a zero-emission ship, the proposed system has an acceptable energy cost.
All-electric ships, and especially the hybrid ones with diesel generators and batteries, have attracted the attention of maritime industry in the last years due to their less emission and higher efficiency. The variant emission policies in different sailing areas and the impact of physical and environmental phenomena on ships energy consumption are two interesting and serious concepts in the maritime issues. In this paper, an efficient energy management strategy is proposed for a hybrid vessel that can effectively consider the emission policies and apply the impacts of ship resistant, wind direction and sea state on the ships propulsion. In addition, the possibility and impact of charging and discharging the carried electrical vehicles’ batteries by the ship is investigated. All mentioned matters are mathematically formulated and a general model of the system is extracted. The resulted model and real data are utilized for the proposed energy management strategy. A genetic algorithm is used in MATLAB software to obtain the optimal solution for a specific trip of the ship. Simulation results confirm the effectiveness of the proposed energy management method in economical and reliable operation of the ship considering the different emission control policies and weather condition impacts.
Our recent experimental investigations of flexible side-by-side blades under both steady and unsteady flows have observed flutter in both scenarios. Flutter significantly impacts blade kinematics and the hydrodynamic drag experienced by the blades. Our numerical approach [1], utilizing the reactive force model, successfully reproduces flutter phenomena. In contrast, the traditional Morison’s equation fails to trigger flutter. In the static regime where flutter does not occur, the bulk drag coefficients calibrated from experiments in steady and unsteady flows can be unified through an effective Cauchy number, allowing for the use of analytical models developed for steady flows in unsteady flows. In the flutter regime, using the bulk drag coefficient from steady flows underestimates the drag load in oscillatory flow.
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.
The maritime industry is a crucial hard-to-abate sector that is expected to depend on high-energy density renewable liquid fuels in the future. Traditionally, decarbonization pathways have been assessed assuming exogenous cost trajectories for renewable liquid fuels based on an exogenous learning curve. While past studies have looked at the impact of endogenizing learning curves for a specific technology utilizing linear approximation, a fully endogenous direct non-linear implementation of learning curves in a detailed sectoral model (maritime industry) that explores dynamics concerning sensitive parameters does not yet exist. Here, we apply an open-source optimization model for decarbonizing the maritime industry and further develop the model by encompassing a nonconvex mixed-integer quadratically constrained programming approach to analyze the impact of endogenized learning curves for renewable fuel costs following an experience curve approach. We find that global greenhouse gas emissions are significantly lower (up to 25% over a 30 year horizon) when utilizing endogenously modeled prices for renewable fuels compared to commonly used exogenous learning frameworks. Furthermore, we find that conventional modeling approaches overestimate the cost of climate mitigation, which can have significant policy implication related to carbon pricing and fuel efficiency requirements. In a broader context, this emphasizes the potential opportunities that can be achieved if policymakers and companies accelerate investments that drive down the costs of renewable technologies efficiently and thus trigger endogenous experience-based learning in real life.
Strict limits on the maximum sulphur content in fuel used by ships have recently been imposed in some Emission Control Areas (ECAs). In order to comply with these regulations many ship operators will switch to more expensive low-sulphur fuel when sailing inside ECAs. Since they are concerned about minimizing their costs, it is likely that speed and routing decisions will change because of this. In this paper, we develop an optimization model to be applied by ship operators for determining sailing paths and speeds that minimize operating costs for a ship along a given sequence of ports. We perform a computational study on a number of realistic shipping routes in order to evaluate possible impacts on sailing paths and speeds, and hence fuel consumption and costs, from the ECA regulations. Moreover, the aim is to examine the implications for the society with regards to environmental effects. Comparisons of cases show that a likely effect of the regulations is that ship operators will often choose to sail longer distances to avoid sailing time within ECAs. Another effect is that they will sail at lower speeds within and higher speeds outside the ECAs in order to use less of the more expensive fuel. On some shipping routes, this might give a considerable increase in the total amount of fuel consumed and the CO2 emissions.
This study presents a detailed quantification of how flow orientation affects mass transfer and frictional resistance in periodically confined channels, offering novel insights into the physical similarity relations governing these phenomena. We constitute that the Sherwood number and friction factor adhere to universal scaling laws of the form Sh = A1+B sin(2α) Re1 2 and f = A1+B sin(2α) Re−1 2 , where α depicts the orientation of the periodically confined channel. It is found that the flow orientation and the cross flow velocity independently affect both, the Sherwood number and the friction factor. A key contribution of this work is the explicit characterization of the flow orientation: a 45° rotation of the flow relative to the spacer structure increases the Sherwood number by nearly 25%, while the friction factor rises by approximately 20%. These findings highlight a fundamental trade-off between mass transfer enhancement and flow resistance, suggesting that any process optimization must carefully balance the gains in mixing efficiency against the increased energy dissipation. This study provides a robust framework for further investigations into how periodic geometrical constraints influence transport processes in complex flow systems.
Maritime transportation is an essential pillar of modern societies, serving as the backbone of global trade. The shipping industry relies heavily on fossil fuels, significantly impacting the environment and contributing to climate change. The International Maritime Organization (IMO) has introduced a strategy to reduce greenhouse gas emissions from international shipping and decarbonize the industry to combat this issue. This strategy aims to accomplish energy efficiency gains, transition to alternative fuels, and implement market-based measures.
Various energy efficiency indicators are in use to monitor the performance of ships, both from technical and operational perspectives. Building upon previous research that identified shortcomings in these indicators, this thesis investigates alternative methods of assessing the energy efficiency of ships. Emphasizing the importance of a benchmarking tool, the primary objective of this thesis is to contribute to the policy debate on reducing emissions in international shipping by developing a comprehensive carbon intensity indicator.
The thesis comprises four articles addressing various approaches to monitoring ship carbon emissions. The first article focuses on the influence of weather conditions on a ship’s energy efficiency, thereby contributing to the ongoing discussion on weather correction factors. Using model-based machine learning techniques, this article illustrates the diverse sea conditions encountered, their impact on energy efficiency, and the necessity of accounting for this diversity through multiple correction factors.
The second and third articles introduce and develop the concept of operational cycles for maritime transportation, drawing inspiration from the driving cycles employed in the automotive industry. The second article describes the process of generating operational cycles for the maritime sector as a novel concept. It validates this concept using real-world data obtained from a fleet of container ships. Building upon this foundation, the third article extends the concept by elaborating more comprehensive cycles that better represent real-world indicators.
The fourth article explores voluntary reporting frameworks in the shipping industry. It focuses on the Clean Cargo case and investigates the needs and interests of its members regarding this private initiative and related reporting framework. The discussion revolves around the role of these voluntary frameworks as complementary approaches to regulatory frameworks towards maritime decarbonization.
Based on the methodology developments and analysis through the thesis, the following key findings and recommendations are presented:
• The weather impact on ships’ fuel consumption prevents an accurate and real assessment of ships’ efficiency. Multiple weather correction factors for energy efficiency indicators introduce a novel approach.
• Inspired by the automotive industry, maritime operational cycles improve the assessment of technical and operational aspects of a ship’s energy efficiency. The cycles reduce the variability inherent to energy
efficiency indicators and are suitable as benchmarking tools.
• Although the IMO regulatory framework remains at the core of the maritime decarbonization strategy, regional regulatory frameworks and private initiatives have demonstrated their capacity to enhance industry
practices and facilitate regulatory developments.
This thesis contributes to enhancing carbon emissions monitoring in the maritime industry by introducing new methodologies and assessments. The resulting proposals are designed to enrich ongoing discussions within the IMO and complement the existing regulatory frameworks.
Aiming at reducing CO2 emissions from shipping at the EU level, a system for monitoring, reporting, and verification (MRV) of CO2 emissions of ships was introduced in 2015 with the so-called ‘MRV Regulation’. Its stated objective was to produce accurate information on the CO2 emissions of large ships using EU ports and to incentivize energy efficiency improvements by making this information publicly available. On 1 July 2019, the European Commission published the relevant data for 10,880 ships that called at EU ports within 2018. This milestone marked the completion of the first annual cycle of the regulation’s implementation, enabling an early assessment of its effectiveness. To investigate the value of the published data, information was collected on all voyages performed within 2018 by a fleet of 1041 dry bulk carriers operated by a leading Danish shipping company. The MRV indicators were then recalculated on a global basis. The results indicate that the geographic coverage restrictions of the MRV Regulation introduce a significant bias, thus prohibiting their intended use. Nevertheless, the MRV Regulation has played a role in prompting the IMO to adopt its Data Collection System that monitors ship carbon emissions albeit on a global basis.
Multi-phase flow meters are of huge importance to the offshore oil and gas industry. Unreliable measurements can lead to many disadvantages and even wrong decision-making. It is especially important for mature reservoirs as the gas volume fraction and water cut is increasing during the lifetime of a well. Hence, it is essential to accurately monitor the multi-phase flow of oil, water and gas inside the transportation pipelines. The objective of this review paper is to present the current trends and technologies within multi-phase flow measurements and to introduce the most promising methods based on parameters such as accuracy, footprint, safety, maintenance and calibration. Typical meters, such as tomography, gamma densitometry and virtual flow meters are described and compared based on their performance with respect to multi-phase flow measurements. Both experimental prototypes and commercial solutions are presented and evaluated. For a non-intrusive, non-invasive and inexpensive meter solution, this review paper predicts a progress for virtual flow meters in the near future. The application of multi-phase flows meters are expected to further expand in the future as fields are maturing, thus, efficient utilization of existing fields are in focus, to decide if a field is still financially profitable.