Figure 4 - uploaded by Mariana Bernardino
Content may be subject to copyright.
Context in source publication
Similar publications
The present work compares the performance of the added-weight and lost buoyancy methods for simulating the damaged ship condition in steady state wave induced load calculations. The wave loads are evaluated by a three-dimensional linear panel method. Horizontal and vertical bending moment and shear force are calculated as the main induced wave load...
SHIP ENERGY MANAGEMENT AND DEVELOPMENT OF A METHODOLOGY AND SOFTWARE FOR
VESSELS OPTIMUM ECONOMIC SPEED.
Citations
... The attempts to use different wave databases to determine the most probable wave loads a ship will be subjected, show a significant scatter in the predictions [16,18,4], and also show differences from the values along different routes [17,39]. However the use of the various databases normally leads to higher values than prescribed in the design rules and one explanation can be that ships tend to avoid the roughest weather and thus are exposed to somewhat lower sea states. ...
The quantitative assessment of the influence of rough weather avoidance on the wave climate ships experience in their oceanic routes is presented. The only meteorological source that naturally includes this effect is the database derived from the Voluntary Observing Ships’ programme, which is here compared with the state-of-the-art ERA-interim numerical re-analysis. The results of this work allow quantifying the reduction of the mean significant wave height encountered by ships in their operations for a more aware planning of the missions and better assessment of the environmental conditions.
... In Queirós et al. (2012) the Lagrangian methodology is applied to the 10 years of Significant Wave Height (SWH) data to assess the vulnerability of two routes to storms in the north Atlantic. In this study a seasonal evaluation of the storm was performed, as it was concluded that most storms occur during the winter December to March. ...
A Lagrangian approach is used to study the characterization of the situations of extreme storms, monitoring the characteristics of each storm since their initiation to the dissipation, contrasting with the more usual Eulerian approach that only assesses the local conditions of waves. In order to assess the characteristics of the ocean storms of the 2013/2014 winter, this methodology was applied to three months of Era-interim significant wave height data. Ninety nine storms were identified and characterized regarding the maximum significant wave height in the track, the storm area associated with the maximum significant wave height, storm life time, time of maximum significant wave height arising, maximum area in the track, time of maximum area arising, length of the track, mean direction of track, arise length of track mean direction of track arising, decay length of the track, mean direction of track decay, in the North Atlantic, during this period. The characteristics of these storms are compared with the corresponding ones from a previous study that includes a ten year data set. The Hercules storm, that affected the western coast of Europe during early January is also analysed. location. The analyzed data can be both from observations or model results. The type of information that can be obtained in this approach is essentially the frequency of events, their duration and intensity, and an estimate of extreme conditions , at given locations.
The wave climate along the main transoceanic routes of the North Atlantic sub basin is determined using three different databases: two derived by numerical models in the HIPOCAS and ERA40 databases and one from Voluntary Observing Ships. For each route the distribution of the mean significant wave height along the path is computed as well as the specific scatter diagram. In addition an assessment of the relative wave heading probability is provided. The results highlight a bias in the visual observations especially in the summer and, more in general, for low sea states. The correction of this bias allows better understanding of rough weather avoidance by ships and to determine a storm avoidance correction.
