Sail-assisted motor vessels weather routing using a fuzzy logic model

In this study sail-assisted motor vessels weather routing is investigated to establish the most economical route by applying available information of the ship's behavior regarding the encountered sea-conditions. To derive the vessel’s fuel use on a route, a fuzzy logic model is constructed through an automated identification process. Only data collected from actual integrated bridge measurements systems is used. Fuzzy modeling is a framework providing a flexible and transparent mathematical structure to describe the physical relationships in a vessel's behavior. This consumption model is integrated into a determinist weather-routing optimization workflow based on a systematic meshing scheme of the sailing area. Pareto-optimization with a multi-objective genetic algorithm is used to maximize fuel economy in a limited or optimum time. The benefits of the developed decision helping tool in sail-assisted motor vessel routing are highlighted on a westbound north Transatlantic journey.     

Air–sea gas exchange at extreme wind speeds measured by autonomous oceanographic floats

Measurements of the air–sea fluxes of N₂ and O₂ were made in winds of 15–57 m s^− 1 beneath Hurricane Frances using two types of air-deployed neutrally buoyant and profiling underwater floats. Two “Lagrangian floats” measured O₂ and total gas tension (GT) in pre-storm and post-storm profiles and in the actively turbulent mixed layer during the storm. A single “EM-APEX float” profiled continuously from 30 to 200 m before, during and after the storm. All floats measured temperature and salinity. N₂ concentrations were computed from GT and O₂ after correcting for instrumental effects. Gas fluxes were computed by three methods. First, a one-dimensional mixed layer budget diagnosed the changes in mixed layer concentrations given the pre-storm profile and a time varying mixed layer depth. This model was calibrated using temperature and salinity data. The difference between the predicted mixed layer concentrations of O₂ and N₂ and those measured was attributed to air–sea gas fluxes F_BO and F_BN. Second, the covariance flux F_CO(z) = wO₂′(z) was computed, where w is the vertical motion of the water-following Lagrangian floats, O₂′ is a high-pass filtered O₂ concentration and (z) is an average over covariance pairs as a function of depth. The profile F_CO(z) was extrapolated to the surface to yield the surface O₂ flux F_CO(0). Third, a deficit of O₂ was found in the upper few meters of the ocean at the height of the storm. A flux F_SO, moving O₂ out of the ocean, was calculated by dividing this deficit by the residence time of the water in this layer, inferred from the Lagrangian floats. The three methods gave generally consistent results. At the highest winds, gas transfer is dominated by bubbles created by surface wave breaking, injected into the ocean by large-scale turbulent eddies and dissolving near 10-m depth. This conclusion is supported by observations of fluxes into the ocean despite its supersaturation; by the molar flux ratio F_BO/F_BN, which is closer to that of air rather than that appropriate for Schmidt number scaling; by O₂ increases at about 10-m depth along the water trajectories accompanied by a reduction in void fraction as measured by conductivity; and from the profile of F_CO(z), which peaks near 10 m instead of at the surface.At the highest winds O₂ and N₂ are injected into the ocean by bubbles dissolving at depth. This, plus entrainment of gas-rich water from below, supersaturates the mixed layer causing gas to flux out of the near-surface ocean. A net influx of gas results from the balance of these two competing processes. At lower speeds, the total gas fluxes, F_BO, F_BN and F_CO(0), are out of the ocean and downgradient.     

From user equilibrium to system optimum: a literature review on the role of travel information,bounded rationality and non-selfish behaviour at the network and individual levels

Travel information continues to receive significant attention in the field of travel behaviour research, as it is expected to help reduce congestion by directing the network state from a user equilibrium towards a more efficient system optimum. This literature review contributes to the existing literature in at least two ways. First, it considers both the individual perspective and the network perspective when assessing the potential effects of travel information, in contrast to earlier studies. Secondly, it highlights the role of bounded rationality as well as that of non-selfish behaviour in route choice and in response to information, complementing earlier reviews that mostly focused on bounded rationality only. It is concluded that information strategies should be tailor-made to an individual's level of rationality as well as level of selfishness in order to approach system-optimal conditions on the network level. Moreover, initial ideas and future research directions are provided for assessing the potential of travel information in order to improve network efficiency of existing road networks.     

Consumer preferences for electric vehicles: a literature review

Widespread adoption of electric vehicles (EVs) may contribute to the alleviation of problems such as environmental pollution, global warming and oil dependency. However, the current market penetration of EV is relatively low in spite of many governments implementing strong promotion policies. This paper presents a comprehensive review of studies on consumer preferences for EV, aiming to better inform policy-makers and give direction to further research. First, we compare the economic and psychological approach towards this topic, followed by a conceptual framework of EV preferences which is then implemented to organise our review. We also briefly review the modelling techniques applied in the selected studies. Estimates of consumer preferences for financial, technical, infrastructure and policy attributes are then reviewed. A categorisation of influential factors for consumer preferences into groups such as socio-economic variables, psychological factors, mobility condition, social influence, etc. is then made and their effects are elaborated. Finally, we discuss a research agenda to improve EV consumer preference studies and give recommendations for further research.

Abbreviations: AFV: alternative fuel vehicle; BEV: battery electric vehicle; CVs: conventional vehicles; EVs: electric vehicles; FCV: fuel cell vehicle; HCM: hybrid choice model; HEV: hybrid electric vehicle (non plug-in); HOV: high occupancy vehicle; MNL: MultiNomial logit; MXL: MiXed logit model; PHEV: plug-in hybrid electric vehicle; RP: revealed preference; SP: stated preference.     

Missing the tide? Workplace cultural differences as a barrier to seafarer mobility in the U.S. workboat and sail training sectors

ABSTRACT

While previous research details the psychological and social benefits of sail training programs for amateur ‘trainees,’ much less is known about the value and transferability of sail training work experience in terms of professional development for seafaring careers. This article reviews the current state of scientific knowledge on sail training and presents the findings of a qualitative study of maritime recruiter perceptions at 10 U.S. flag workboat companies. Results suggest that the maritime industry may benefit from an improved understanding of the role that sail training programs and the sail training community can play in training and inspiring the next generation of motivated and professional seafarers. Workboat recruiters see positive and negative aspects of sail training experience and perceive cultural divides within the maritime industry that may affect a seafarers’ job mobility, whether or not such divides actually exist.     

Lagrangian data in a high-resolution numerical simulation of the North Atlantic: I. Comparison with in situ drifter data

A model/data comparison was performed between simulated drifters from a high-resolution numerical simulation of the North Atlantic and a data set from in situ surface drifters. The comparison makes use of pseudo-Eulerian statistics such as mean velocity and eddy kinetic energy, and Lagrangian statistics such as integral time scales. The space and time distribution of the two data sets differ in the sense that the in situ drifters were released inhomogeneously in space and time while the simulated drifters were homogeneously seeded at the same time over a regular 1° grid. Despite this difference, the total data distributions computed over the complete data sets show some similarities that are mostly related to the large-scale pattern of Ekman divergence/convergence.Comparisons of eddy kinetic energy and root mean square velocity indicate that the numerical model underestimates the eddy kinetic energy in the Gulf Stream extension and in the ocean interior. In addition, the model Lagrangian time scales are longer in the interior than the in situ time scales by approximately a factor of 2. It is suggested that this is primarily due to the lack of high-frequency winds in the model forcing, which causes an underestimation of the directly forced eddy variability. Regarding the mean flow, the comparison has been performed both qualitatively and quantitatively using James' statistical test. The results indicate that over most of the domain, the differences between model and in situ estimates are not significant. However, some areas of significant differences exist, close to high-energy regions, notably around the Gulf Stream path, which in the model lies slightly north of the observed path, although its strength and structure are well represented overall. Mean currents close to the buffer zones, primarily the Azores Current, also exhibit significant differences between model results and in situ estimates. Possibilities for model improvement are discussed in terms of forcings, buffer zone implementations, turbulence and mixed layer parameterizations, in light of our model/data comparison.     

On the residual advection of passive constituents

In this paper, the different approaches used to simulate the long-term advection of passive constituents on tidal shelves are discussed in the framework of large scale hydrodynamic modelling. The direct approach in which the unsteady mesoscale (tides and storm surges) and macroscale (monthly or seasonal mean processes) currents are used is very demanding in computer resources (CPU and memory). On the other hand, the use of residual velocities gives a larger understanding of the long-term transport processes and simplifies numerical treatments. However, the definition of such appropriate velocities is still an open question. In the context of large scale models, Lagrangian residuals are not applicable. Eulerian residual transport velocities fail to represent long-term motions when tidal non-linearities are important. The first order Lagrangian residual velocity introduced by Feng et al. (1986) and generalized here is shown to be a very good solution. The North-Western European Continental Shelf (NWECS) is used as an example to compare the numerical solutions obtained with the different approaches.