Corrosive wear of cylinder liners in large two-stroke marine diesel engines that burn heavy fuel oil containing sulfur is coupled to the formation of gaseous sulfur trioxide (SO3) and subsequent combined condensation of sulfuric acid (H2SO4) and water (H2O) vapor. The present work seeks to address how fuel sulfur content, charge air humidity and liner temperature variations affects the deposition of water and sulfuric acid at low load operation. A phenomenological engine model is applied to simulate the formation of cylinder/bulk gas combustion products and dew points comply with H2O–H2SO4 vapor liquid equilibrium. By assuming homogenous cylinder gas mixtures condensation is modeled using a convective heat and mass transfer analogy combined with realistic liner temperature profiles. Condensation of water is significantly altered by the liner temperature and charge air humidity while sulfuric acid condensation (the order is a few mg per cylinder every cycle) is proportional to the fuel sulfur content. Condensation takes place primarily in the upper part of the cylinder liner where a reduction of the surface temperature or saturated charge air provides that the deposited acid can be highly diluted with water.
In this work, three-dimensional computational fluid dynamics (CFD) studies of sulphur oxides (SOx) and sulphuric acid (H2SO4) formation processes in a large, low speed two-stroke marine diesel engine are carried out. The current numerical study aims to investigate the conversion of sulphuric dioxide (SO2) to sulphuric trioxide (SO3) and the possibility of H2SO4 condensation which are the prerequisites to better understand the corrosion-induced wear phenomenon. This is achieved with the aid of the implementation of a multicomponent surrogate model, which comprises a skeletal n-heptane mechanism and a reduced sulphur subset mechanism. In the present work, performance of the coupled CFD-chemical kinetic model is evaluated using both qualitative and quantitative methods. The modelling results show that the temporal and spatial evolutions of SOx predicted by the skeletal model are similar to those by the base mechanism. Predictions of the variations of SOx and the associated SO2 to SO3 conversion in response to the change of fuel sulphur content, swirl velocity, start of injection, scavenge pressure and humidity qualitatively agree with numerical and experimental results from the literature. The model is further evaluated using the measured SO2 to SO3 conversion levels in a low load, low scavenge pressure case and a low load, high scavenge pressure case. The absolute values of simulated and measured conversion levels are close, although the former appear to be higher. The current results show that the flame impinges at the cylinder liner near top dead centre. The gas is cooled rapidly by the wall temperature and H2SO4 is produced in the region where the local temperature is less than 600 K. Based on the flue gas correlation, the acid dew point temperature is higher than the wall temperature, suggesting that acid condensation may begin early at the top part of the cylinder liner. The predicted distribution corresponds well with the distribution of corroded parts observed in service engines. The model is expected to serve as an important tool to simulate the rates of SO2 absorption into lubricating oil film and H2SO4 condensation in this combustion system.
An increasing number of disruptions in ports, plants and warehouses have generated ripple effects over supply networks impacting economic activity. We demonstrate how the spread of the pandemic geographically expands the ripple effect by reducing the workers' participation in production, so undermining the ability of firms and, as a result, the entire cross-border sup- ply chain network to satisfy customers' demands. Our model of the spatio-temporal dynamics of the propagation of Covid-19 infection for supply networks contributes toward ripple effect visualisation and quantification by combining the flow of goods and materials through a typical global supply chain with an epidemiological model. The model enables prospective analyses to be performed in what-if scenarios to simulate the impact on the workforce in each node. The outcome should be helpful tools for managers and scholars. Results from this research will help mitigate the impact and spread of a pandemic in a particular region and the ability of a supply network to overcome the ripple effect. A stylised case study of a cross-border supply chain illustrates the ripple effect by showing how waves with crests at varying dates impact the ability to serve demand showing how a supply chain manager can obtain a forward-looking picture.
This paper summarises the work conducted within the 1st FOWT (Floating Offshore Wind Turbine) Comparative Study organised by the EPSRC (UK) ‘Extreme loading on FOWTs under complex environmental conditions’ and ‘Collaborative computational project on wave structure interaction (CCP-WSI)’ projects. The hydrodynamic response of a FOWT support structure is simulated with a range of numerical models based on potential theory, Morison equation, Navier-Stokes solvers and hybrid methods coupling different flow solvers. A series of load cases including the static equilibrium tests, free decay tests, operational and extreme focused wave cases are considered for the UMaine VolturnUS-S semi-submersible platform, and the results from 17 contributions are analysed and compared with each other and against the experimental data from a 1:70 scale model test performed in the COAST Laboratory Ocean Basin at the University of Plymouth. It is shown that most numerical models can predict similar results for the heave response, but significant discrepancies exist in the prediction of the surge and pitch responses as well as the mooring line loads. For the extreme focused wave case, while both Navier–Stokes and potential flow base models tend to produce larger errors in terms of the root mean squared error than the operational focused wave case, the Navier-Stokes based models generally perform better. Given the fact that variations in the solutions (sometimes large) also present in the results based the same or similar numerical models, e.g., OpenFOAM, the study highlights uncertainties in setting up a numerical model for complex wave structure interaction simulations such as those involving a FOWT and therefore the importance of proper code validation and verification studies.
Modern slavery and human trafficking present an inherent risk to workers engaged in operations surrounding shipping activities. One of the first statutes addressing the problem was the UK Modern Slavery Act [MSA], which presents several statutory sanctions for shipping companies in case of non-compliance. The article examines the sanctions available under different provisions of MSA as applied specifically to shipping activities.
As a further step, the article conducts an empirical analysis of the reporting practices of liner shipping operators, eligible for compliance under MSA. In addition to the rates of compliance, it engages in content analysis of the modern slavery statements, seeking to identify the uniform patterns of reporting and industry best practices.
The last part of the article discusses whether corporate undertakings as reported in modern slavery statements may serve as a ground for tort liability, similarly to the supply chain or ‘production liability’ emerging from group policies and codes of conduct.
This paper explores the application of modular multi-level converters (MMC) as a means for harnessing the power from off-shore wind power plants. The MMC consists of a large number of simple voltage sourced converter (VSC) submodules
that can be easily assembled into a converter for high-voltage and high power. The paper shows that the MMC converter has a fast response and low harmonic content in comparison with a two-level VSC option. The paper discusses the modeling approach used, including a solution to the modeling challenge imposed by the very large number of switching devices in the MMC.
This study exploits service modularity in front-end logistics services in e-fulfillment, from a customer-centric approach, particularly in order management, delivery, and return. Through an online survey of UK customers, the service priorities of 494 respondents via AHP (Analytic Hierarchical Process) were analyzed. Extracting customers' service priorities, ordering behavior, and demographic information as input data, the clustering algorithm KAMILA (KAy-means for MIxed LArge data sets) was further applied. The three identified customer clusters (multichannel shoppers, infrequent shoppers, and online fans) provide preliminary evidence on how commonality and variability aspects of service modularity in front-end logistics services can optimize the number of service options and their performance levels. Therefore, our study, building on value co-creation and modularity, proposes a systematic way of exploiting service modularity for the customer segmentation process that addresses heterogeneous customer preferences cost-efficiently and uncomplicatedly. Furthermore, we provide a framework for the governance of front-end logistics services, guiding outsourcing decisions. Accordingly, it reveals the implications of customer priorities and service decomposition logic choices on value creation. Finally, the propositions formulated aim to develop theoretical foundations for explaining how the heterogeneity in customer priorities for logistics services can be managed with modularity, creating value both for customers and retailers.
An adaptive machine learning framework is established for an implicit determination of the performance degradation of a ship due to marine growth, i.e., biofouling. The framework is applied in a case study considering telemetry data of a cruise ship operating predominantly in the Caribbean Sea. The dataset encompasses seven years including three dry-docking intervals and several in-water cleaning events. The COVID-19 period receives special focus due to the drastic change in the operational profile. A main outcome of the study is a comparison of the derived performance estimate to the corresponding results of the industry standard ISO 19030. Additional aspects of the present study include the use of special regularization techniques for incremental machine learning and the increase of transparency through the implementation of prediction intervals indicating model uncertainty. Overall, it is found that the developed machine learning framework shows good agreement with the industry standard underlining its plausibility.
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.
Abstract: In recent years, the development of ground robots with human-like perception capabilities has led to the use of multiple sensors, including cameras, lidars, and radars, along with deep learning techniques for detecting and recognizing objects and estimating distances. This paper proposes a computer vision-based navigation system that integrates object detection, segmentation, and monocular depth estimation using deep neural networks to identify predefined target objects and navigate towards them with a single monocular camera as a sensor. Our experiments include different sensitivity analyses to evaluate the impact of monocular cues on distance estimation. We show that this system can provide a ground robot with the perception capabilities needed for autonomous navigation in unknown indoor environments without the need for prior mapping or external positioning systems. This technique provides an efficient and cost-effective means of navigation, overcoming the limitations of other navigation techniques such as GPS-based and SLAM-based navigation. Graphical Abstract: [Figure not available: see fulltext.]