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Assessing shipping water vertical loads on a fixed structure by convolution model and wet dam-break tests

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Abstract Estimation of loads derived from shipping water events in naval and offshore structures is of importance to improve their structural design or to predict changes in their dynamics. For… Click to show full abstract

Abstract Estimation of loads derived from shipping water events in naval and offshore structures is of importance to improve their structural design or to predict changes in their dynamics. For the case of vertical loads on deck of a fixed structure, it is possible to estimate analytically their evolution in time by considering the distribution of shipping water elevations. However, the classical approach to estimate this distribution (i.e., dam-break method) tends to overestimate the amount of water on deck and does not follow the generated decay trends observed experimentally. In the present work, the time evolution of the vertical loads due to shipping water events was studied analytically and experimentally. The validation of the use of a convolution model to estimate the time evolution of vertical loads is presented, aiming to improve the results obtained with classical approaches. A systematic experimental study has been conducted using the wet dam-break method to generate isolated shipping water events, measuring the slow-varying vertical loads on a rectangular fixed structure. A force balance and a high-speed camera have been used at the same sampling rate to monitor the vertical loads and the shipping water evolution on the deck. Results demonstrated that the use of the convolution model improved the representation of the time series of loads compared with the traditional dam-break approach. With this new method, it was possible to capture the peaks and the decay tendencies observed in the experimental data in an approximated way.

Keywords: water; shipping water; vertical loads; fixed structure; dam break; convolution model

Journal Title: Applied Ocean Research
Year Published: 2019

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