996 resultados para Ship model


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Parametric roll is a critical phenomenon for ships, whose onset may cause roll oscillations up to +-40 degrees, leading to very dangerous situations and possibly capsizing. Container ships have been shown to be particularly prone to parametric roll resonance when they are sailing in moderate to heavy head seas. A Matlab/Simulink parametric roll benchmark model for a large container ship has been implemented and validated against a wide set of experimental data. The model is a part of a Matlab/Simulink Toolbox (MSS, 2007). The benchmark implements a 3rd-order nonlinear model where the dynamics of roll is strongly coupled with the heave and pitch dynamics. The implemented model has shown good accuracy in predicting the container ship motions, both in the vertical plane and in the transversal one. Parametric roll has been reproduced for all the data sets in which it happened, and the model provides realistic results which are in good agreement with the model tank experiments.

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The objective of this work is to formulate a nonlinear, coupled model of a container ship during parametric roll resonance, and to validate the model using experimental data.

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Mode of access: Internet.

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This paper presents a discussion on the use of MIMO and SISO techniques for identification of the radiation force terms in models for surface vessels. We compare and discuss two techniques recently proposed in literature for this application: time domain identification and frequency domain identification. We compare the methods in terms of estimates model order, accuracy of the fit, use of the available information, and ease of use and implementation.

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Sediments in Arctic sea ice are important for erosion and redistribution and consequently a factor for the sediment budget of the Arctic Ocean. The processes leading to the incorporation of sediments into the ice are not understood in detail yet. In the present study, experiments on the incorporation of sediments were therefore conducted in ice tanks of The Hamburg Ship Model Basin (HSVA) in winter 1996/1997, These experiments showed that on average 75 % of the artificial sea-ice sediments were located in the brine-channel system. The sediments were scavenged from the water column by frazil ice. Sediments functioning as a nucleus for the formation of frazil ice were less important for the incorporation. Filtration in grease ice during relatively calm hydrodynamic conditions was probably an effective process to enrich sediments in the ice. Wave fields did not play an important role for the incorporation of sediments into the artificial sea ice. During the expedition TRANSDRIFT III (TDIII, October 1995), different types of natural, newly-formed sea ice (grease ice, nilas and young ice) were sampled in the inner Laptev Sea at the time of freeze-up. The incorporation of sediments took place during calm meteorological conditions then. The characteristics of the clay mineral assemblages of these sedirnents served as references for sea-ice sediments which were sampled from first-year drift ice in the outer Laptev Sea and the adjacent Arctic Ocean during the POLARSTERN expedition ARK-XI/1 (July-September 1995). Based on the clay mineral assemblages, probable incorporation areas for the sedirnents in first-year drift ice could be statistically reconstructed in the inner Laptev Sea (eastern, central, and Western Laptev Sea) as well as in adjacent regions. Comparing the amounts of particulate organic carbon (POC) in sea-ice sediments and in surface sediments from the shelves of potential incorporation areas often reveals higher values in sea-ice sediments (TDIII: 3.6 %DM; ARK-XI/1: 2.3 %DM). This enrichment of POC is probably due to the incorporation process into the sea ice, as could be deducted from maceral analysis and Rock-Eval pyrolysis. Both methods were applied in the present study to particulate organic material (POM) from sea-ice sediments for the first time. It was shown that the POM of the sea-ice sediments from the Laptev Sea and the adjacent Arctic Ocean was dominated by reworked, strongly fragmented, allochthonous (terrigenous) material. This terrigenous component accounted for more than 75 % of all counted macerals. The autochthonous (marine) component was also strongly fragmented, and higher in the sediments from newly-formed sea ice (24 % of all counted macerals) as compared to first-year drift ice (17 % of all counted macerals). Average hydroge indices confirmed this pattern and were in the transition zone between kerogen types II and III (TDIII: 275 mg KW/g POC; ARK-XI/1: 200 mg KW/g POC). The sediment loads quantified in natural sea ice (TDIII: 33.6 mg/l, ARK-XI/1: 49.0 mg/l) indicated that sea-ice sediments are an important factor for the sediment budget in the Laptev Sea. In particular during the incorporation phase in autumn and early winter, about 12 % of the sediment load imported annually by rivers into the Laptev Sea can be incorporated into sea ice and redistributed during calm meteorological conditions. Single entrainment events can incorporate about 35 % of the river input into the sea ice (ca. 9 x 10**6 t) and export it via the Transpolar Drift from the Eurasian shelf to the Fram Strait.

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A network of ship-mounted real-time Automatic Weather Stations integrated with Indian geosynchronous satellites Indian National Satellites (INSATs)] 3A and 3C, named Indian National Centre for Ocean Information Services Real-Time Automatic Weather Stations (I-RAWS), is established. The purpose of I-RAWS is to measure the surface meteorological-ocean parameters and transmit the data in real time in order to validate and refine the forcing parameters (obtained from different meteorological agencies) of the Indian Ocean Forecasting System (INDOFOS). Preliminary validation and intercomparison of analyzed products obtained from the National Centre for Medium Range Weather Forecasting and the European Centre for Medium-Range Weather Forecasts using the data collected from I-RAWS were carried out. This I-RAWS was mounted on board oceanographic research vessel Sagar Nidhi during a cruise across three oceanic regimes, namely, the tropical Indian Ocean, the extratropical Indian Ocean, and the Southern Ocean. The results obtained from such a validation and intercomparison, and its implications with special reference to the usage of atmospheric model data for forcing ocean model, are discussed in detail. It is noticed that the performance of analysis products from both atmospheric models is similar and good; however, European Centre for Medium-Range Weather Forecasts air temperature over the extratropical Indian Ocean and wind speed in the Southern Ocean are marginally better.

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See errata at end of item for cover, foreward, pages 26 and 29, and Form 1473.

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Mode of access: Internet.

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Mode of access: Internet.

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