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Hamburg, Germany

Schroder C.,Hamburg Ship Model Basin
Proceedings of the International Offshore and Polar Engineering Conference | Year: 2015

To better understand the environmental impact of shipping in arctic areas, this paper will focus on the development of a tool to determine the traveling time, consumed type of fuel and corresponding exhaust emissions. This tool has been developed during the European Union funded research project ACCESS (Arctic Climate Change, Economy and Society) (HSVA, 2014). The results depend on ship parameters like hull shape, propulsion system, engine characteristic and consumed type of fuel as well as the environmental conditions which are mainly influenced by the climate change. Copyright © 2015 by the International Society of Offshore and Polar Engineers (ISOPE). Source


Myland D.,Hamburg Ship Model Basin
International Journal of Offshore and Polar Engineering | Year: 2014

Within the research project IRO-2 (Ice Forecast and Route Optimization), the process of ships breaking through first-year sea ice ridges was analyzed. To understand the process of a ship breaking through ridges, six ridge ramming model tests with systematically varied keel depths were performed in HSVA’s large ice model basin. Based on the model test results, a general method to predict the ship’s average transit velocity in first-year sea ice ridges was established. The method serves as a precondition for developing an efficient route optimization tool for ice-covered seas and will be embedded in this tool. In conclusion, the model test results were compared with ice resistance measurements in the Baltic Sea. © by The International Society of Offshore and Polar Engineers. Source


Ehle D.,Hamburg Ship Model Basin
RINA, Royal Institution of Naval Architects - International Conference on the Ice Class Ships 2012 | Year: 2012

Within the research project IRO-2 (Ice Forecast and Route Optimization) the process of ships breaking through first-year sea ice ridges was analysed. Therefore ridge ramming model tests with systematically varied keel depths were carried out in HSVA's Large Ice Model Basin. Based on the model test results a general method to predict the average transit velocity of ships breaking through first year sea ice ridges was established. The method serves as a precondition for developing an efficient route optimization tool for ice covered seas and will be embedded in this tool. © 2012: The Royal Institution of Naval Architects. Source


Repetto-Llamazares A.H.V.,Norwegian University of Science and Technology | Hoyland K.V.,Norwegian University of Science and Technology | Evers K.-U.,Hamburg Ship Model Basin
Cold Regions Science and Technology | Year: 2011

The strength of freeze-bonds in thin saline ice has been investigated through two series (in 2008 and 2009) of experiments in the Hamburg Ship Model Basin (HSVA) as a function of the normal confinement (σ), the submersion time (δt) and the initial ice temperature (Ti). The freeze-bonds were mostly formed in a submerged state, but some were also formed in air. The experimental set-up was improved in the 2009 experiments. In 2008 a ductile-like failure mode dominated (78%), whereas in 2009 the brittle-like dominated (93%). We suggest that this is a combined ice and test set-up effect. The 2009 experimental procedures allowed for careful sample handling giving higher strength and it was softer. Both these things should provoke a more brittle-like force-time response. The average freeze-bond strength in brittle-like samples was around 9kPa while in ductile-like samples was around 2kPa. The maximum freeze-bonds strength were measured for short submersion times, from 1 to 20min, and reached a maximum value of 30kPa. A Mohr-Coulomb like failure model was found appropriate to represent the freeze-bond shear strength as function of the normal confinement. Saline freeze-bonds in saline water had cohesion/friction angle around 4 and 1.4. kPa/25° for the brittle- and ductile-like samples respectively, which fitted well with previously published data.A bell-shape dependence for τc vs δt was found, which agreed with the predictions by Shafrova and Høyland (2007). We suggest that this is essentially a freeze-bond porosity effect and propose three phases in time with subsequent cooling, heating and equilibrium to account for this trend. Qualitative experiments showed that the submersion time and the initial ice temperature were strongly coupled. To account for the connection between contact time, block dimensions and ice properties and the freeze-bond strength, dimensionless number were used. Fourier scaling was more appropriate than Froude scaling to scale freeze-bonds. The freeze-bonding made in air developed fast (in less than 30. s) when the ice was cold and dry, but no freeze-bonding occurred for the same contact times when the ice was warm and wet. © 2010 Elsevier B.V. Source


Reimer N.,Hamburg Ship Model Basin
Proceedings of the International Conference on Port and Ocean Engineering under Arctic Conditions, POAC | Year: 2011

Today the prediction of resistance and propulsion in ice covered waters is usually carried out using well established semi empiric methods. These methods are based on a subdivision of resistance into several components with regard to the physical origin (e.g. breaking, submersion or friction). For each component different parameters (hull shape data and ice properties) are included. Nevertheless, the range of ships of reference these methods are based on is naturally restricted and today many ships operating in ice covered waters differ significantly regarding their hull shape parameters. A second inadequacy of the existing methods is that due to the subdivision of resistance into components, simultaneous effects are hardly taken account. To offer a prediction method including the correlation between different parameters (e.g. ice thickness and speed) and at the same time enlarge the range of validity concerning hull shape parameters, a prediction method based on artificial neural networks (ANN) was developed in scope of a master thesis in cooperation between Hamburg Ship Model Basin and Hamburg University of Technology. Neural networks offer the possibility of learning multiple functional relations without using explicit approaches and are therefore able to include correlation of simultaneous influences. To learn the relations the networks are trained by gradient descent methods on a data set including both input and output parameters. After successful training the networks are then able to generalize the relation to predict the output parameters for an unknown input data set. For prediction on resistance and propulsion of ships in ice covered waters the networks were trained with data collected at Hamburg Ship Model Basin including data from model tests and full scale trials. The results turned out to have an acceptable accuracy. Copyright © (2011) by Port and Ocean Engineering under Arctic Conditions (POAC 2011). Source

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