Harbour vessel emissions are growing concerns in the maritime industry regarding environmental sustainability. Accurate emissions prediction can stand in monitoring and addressing the issue. This study proposes a machine-learning approach using Artificial Neural Network (ANN) for predicting harbour vessel emissions. The approach shows superiority over the bottom-up method introduced by the 4th IMO GHG Study regarding prediction accuracy. Actual emissions data from onboard measurements are used for training ANN models and as references for evaluating the methods. Compared to the bottom-up method, the improvement in error reduction can be up to 30% for predicting nitrogen oxides and 54% for carbon monoxide when only using ship-related factors as input variables. By adding selected meteorological factors in the experiments, the prediction accuracy enhancement can achieve up to 48% for nitrogen oxides and 62% for carbon monoxide. The proposed ANN approach could assist relevant stakeholders in improving emissions prediction and operations optimisation.
Determining the key characteristics of a ship during the concept and preliminary design phases is a critical and intricate process. In this study, we propose an alternative to traditional empirical methods by introducing a model to estimate the main particulars of diesel-powered Z-Drive harbor tugboats. This prediction is performed to determine the main particulars of tugboats: length, beam, draft, and power concerning the required service speed and bollard pull values, employing Bayesian network and non-linear regression methods. We utilized a dataset comprising 476 samples from 68 distinct diesel-powered Z-Drive harbor tugboat series to construct this model. The case study results demonstrate that the established model accurately predicts the main parameters of a tugboat with the obtained average of mean absolute percentage error values; 6.574% for the Bayesian network and 5.795%, 9.955% for non-linear regression methods. This model, therefore, proves to be a practical and valuable tool for ship designers in determining the main particulars of ships during the concept design stage by reducing revision return possibilities in further stages of ship design.
Design waves have been used in the past for the probabilistic assessment of wave-induced loads and responses of offshore structures. Various response-conditioning techniques have been employed to determine suitable wave episodes, typically based on linear response transfer functions. Nevertheless, extreme events are not always driven by linear phenomena but can be triggered by near-resonant effects, as in the case of the slow-drift motions of moored floating bodies. Limited research has been devoted to addressing this class of responses using response-conditioned waves (RCW). This paper presents a new approach for deriving RCWs that accounts for combined wave- and low-frequency responses. Both the response amplitude operator (RAO) and the quadratic transfer function (QTF) are employed in an iterative response-conditioning procedure. That permits the identification of appropriate short-duration wave episodes that excite resonant slow-drift motions. These wave episodes are then used in a two-step multi-fidelity design wave methodology for the probabilistic evaluation of the fully nonlinear extreme responses. The proposed approach is validated experimentally for predicting the surge excursions of a moored container ship, and good agreement is found against Monte Carlo results in irregular waves.
Wind Propulsion Systems (WPS) have gained significant attention as a means of decarbonizing shipping. Limitations in available deck space, emissions reduction targets, and regulatory compliance have led to a wide array of potential WPS configurations, each exhibiting distinct aerodynamic performance and requiring unique optimum sail trims for each unit due to complex interactions. This variability challenges existing aerodynamic models and optimization efforts for maximizing fuel savings. To address this, we present a novel methodology that, for the first time in WPS aerodynamic performance prediction, combines Computational Fluid Dynamics (CFD), independent sail trim optimization, and Machine Learning (ML) to develop surrogate models — Gaussian Process Regression and Feedforward Neural Networks — that rapidly predict aerodynamic performance with CFD-equivalent accuracy. These surrogates capture aerodynamic interactions across various WPS configurations, including unit number, deck arrangement, independent sail trim, hull characteristics, and wind conditions. While employing established ML techniques, our approach is novel in its resource-efficient generation of a comprehensive aerodynamic database, derived from the first in-depth independent trim optimization of a DynaRig case study. Our approach enables the modeling of complex, non-linear interactions that traditional interpolation methods fail to capture. Results show that the developed surrogate models achieve CFD-level accuracy, with an average error below 1 while significantly reducing computational time. This ML-enhanced framework facilitates extensive, rapid WPS design optimizations, supporting efficient integration into performance prediction programs (PPPs) and maximizing fuel savings and emissions reductions tailored to specific routes and wind conditions.Machine Learning; CFD-Simulations; Aerodynamic Performance; Wind Propulsion Systems; Green Shipping; Independent Sail Trim Optimization.
This paper addresses the connection between added wave resistance and required propulsion power of ships, having focus on the early stage of new ship designs, notably tankers and bulk carriers. The paper investigates how mean added wave resistance affects the required torque of a fixed pitch propeller and thus also the operational conditions of a directly coupled main engine. The interest of the study has its background in the assessment of minimum propulsion power, and the study considers the prescriptive guidelines of the IMO as basis. Specifically, the study focuses on an assessment of the minimum forward speed attainable under consideration of the propeller light running margin and static load limits of engines in the early phase of new ship designs, where details of hull geometry are not available. The study considers three semi-empirical methods for predicting mean added wave resistance. All methods are known to be applied in the industry, emphasising that only methods relying solely on main particulars, together with information about sea state and advance speed, are of interest. The paper contains a case study used to illustrate the importance of the added wave resistance prediction with respect to the loading of the main engine. It is shown that, despite small absolute differences, the consequence in relation to the loading of the propeller and hereby the directly coupled main engine can be relatively large. Furthermore, the study illustrates that the propeller light running margin of a fixed pitch propeller directly coupled to the main engine has crucial influence on the attainable speed during adverse weather conditions.
Wind Propulsion Systems (WPS) for commercial ships are vital to achieving the IMO targets on energy efficiency and GHG emissions. Most WPS will operate in a hybrid mode alongside actual main propulsion units. This will affect the propeller and engine operating conditions and thus, their performance. The present paper discusses a preliminary assessment of commercial ship propellers and engine performance variation as a function of the wind power installed for two propeller plant types (Fixed Pitch Propeller, FPP, and Controllable Pitch Propeller, CPP) at constant speed operational mode. The contribution is based on empirical and analytical methods requiring minimal input data. It aims to provide general trends and contribute basic knowledge on this matter. A cost model is included for a cost-benefit assessment of both propeller types. This leads to advice on which systems to install as a function of WPS installation size.
Background: The provision of healthcare for seafarers is challenging due to their isolated working conditions and limited access to healthcare. Telemedical maritime assistance relies on pre-stocked medical chests aboard vessels. However, the contents of these medical chests are not globally standardized, and current data on prescription patterns is limited.
Material and methods: A retrospective observational cohort study was conducted, analyzing medical records from ships that received telemedical assistance between 2021 and 2023. Data were obtained from medical reports submitted by onboard medical officers and an internal medical record registry.
Results: In total, 9,458 individual patients received medical assistance, and 11,698 medications were prescribed. The mean patient age was 38.7 years (range 0–91 years), and patients aged 30–49 years were the most likely to receive medication. Merchant ships were the most common type of vessel requiring medical assistance, and they also had the highest probability for drug prescriptions. Approximately 80% of patients were prescribed medications, and patients required on average 1.24 prescriptions (range 0–6). A total of 266 individual types of medication were available for prescription. Overall, patient demographics and prescription patterns did not change over the three-year period.
Conclusions: The prescription patterns in the treatment of acutely ill seafarers are highly variable due to excessive availability of different drugs. The rare use of certain medications suggests potential inefficiencies in the stock management of medical chests, warranting further evaluation. These findings underscore the need for standardizing medical chests based on a data-driven approach. Medication use in telemedical care of seafarers is frequent but highly variable. A data-driven standardization of medical chests may improve efficiency and ensure availability of essential drugs.
International initiatives have successfully brought down the emissions, and hence also the related negative impacts on environment and human health, from shipping in Emission Control Areas (ECAs). However, the question remains as to whether increased shipping in the future will counteract these emission reductions. The overall goal of this study is to provide an up-to-date view on future ship emissions and provide a holistic view on atmospheric pollutants and their contribution to air quality in the Nordic (and Arctic) area. The first step has been to set up new and detailed scenarios for the potential developments in global shipping emissions, including different regulations and new routes in the Arctic. The scenarios include a Baseline scenario and two additional SOx Emission Control Areas (SE-CAs) and heavy fuel oil (HFO) ban scenarios. All three scenarios are calculated in two variants involving Business-AsUsual (BAU) and High-Growth (HiG) traffic scenarios. Additionally a Polar route scenario is included with new ship traffic routes in the future Arctic with less sea ice. This has been combined with existing Current Legislation scenarios for the land-based emissions (ECLIPSE V5a) and used as input for two Nordic chemistry transport models (DEHM and MATCH). Thereby, the current (2015) and future (2030, 2050) air pollution levels and the contribution from shipping have been simulated for the Nordic and Arctic areas. Population exposure and the number of premature deaths attributable to air pollution in the Nordic area have thereafter been assessed by using the health assessment model EVA (Economic Valuation of Air pollution). It is estimated that within the Nordic region approximately 9900 persons died prematurely due to air pollution in 2015 (corresponding to approximately 37 premature deaths for every 100 000 inhabitants). When including the projected development in both shipping and land-based emissions, this number is estimated to decrease to approximately 7900 in 2050. Shipping alone is associated with about 850 premature deaths during presentday conditions (as a mean over the two models), decreasing to approximately 600 cases in the 2050 BAU scenario. Introducing a HFO ban has the potential to lower the number of cases associated with emissions from shipping to approximately 550 in 2050, while the SECA scenario has a smaller impact. The "worst-case" scenario of no additional regulation of shipping emissions combined with a high growth in the shipping traffic will, on the other hand, lead to a small increase in the relative impact of shipping, and the number of premature deaths related to shipping is in that scenario projected to be around 900 in 2050. This scenario also leads to increased deposition of nitrogen and black carbon in the Arctic, with potential impacts on environment and climate.
Wind propulsion systems (WPS) for commercial ships can be a key ingredient to achieving the IMO green targets. Most WPS installations will operate in conjunction with propellers and marine engines in a hybrid mode, which will affect their performance. The present paper presents the development of a generic, fast, and easy tool to predict the propeller and engine performance variation, along with the cost, as a function of the wind power installed in two operation conditions: fixed ship speed and constant shaft speed. Specific focus is directed toward showing generic trends and trade-offs that inform economic decision-making. To this end, a key feature of the presented work is the ability to assess the cost–benefit of both controllable pitch propellers and fixed pitch propellers (CPPs and FPPs). This provides advice on when, in terms of WPS installation size, it is worthwhile to install which kind of propeller. CPPs are found to be more suitable for newly built wind-powered ships (>70% wind power), while a conventional FPP is satisfactory for wind-assisted ships (<70% wind power) and retrofitted installations. The results for a 91,373 GT bulk carrier showed that a WPS unloads the propeller and the engine, which leads to an increase in the propulsive efficiency and a detrimental rise of the engine specific fuel oil consumption. However, propeller gains are found to be greater than engine losses, which result in extra savings. Thus, not only does a WPS save fuel and corresponding pollutant emissions, but it also increases the entire propulsive efficiency.
This article contributes to the accelerating development of methods for sustainability assessment (SA) to support maritime spatial planning (MSP), by proposing an ecosystem services based framework for SA. MSP is generally seen as an important approach to promote a more sustainable use of sea space. However, so far all sustainability concerns are not equally well represented in planning practice; in particular, social sustainability aspects such as social justice and sociocultural values related to human-nature connectedness receive less attention. We first explored concepts and principles related to sustainability assessment and social sustainability in the scientific literature. Based on this, we analyzed how far the present approaches to assessments in Baltic Sea EU Member States have been extended from environmental concerns to towards broader sustainability concerns so far. Using current best practice in two pioneering countries (UK and Sweden), we illustrated how social sustainability principles could match with applied social impact criteria, and further, how such criteria can inform an ecosystem services-based impact assessment framework. Based on existing frameworks, including the DPSIR (driving forces, pressures, state, impact, response) environmental assessment framework and the ecosystem service cascade, we propose a sustainability impact assessment framework for MSP (MSP-SA) integrating across sustainability dimensions and including assessment of distributional aspects of marine ecosystem service benefits. Finally, we discuss the applicability and further development of the framework in relation to present day sustainability assessment practice in MSP.