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Descripción
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| Motion based wave inference allows the estimation of thedirectional sea spectrum from the measured motions of a vessel.Solving the resulting inverse problem is challenging as it is oftenill-posed; as a matter of fact, statistical errors of the estimatedplatform response functions (RAOs) may lead to misleading esti-mations of the sea states as many noise values are severely am-plified in the mathematical process. Hence, in order to obtainreliable estimations of the sea conditions some hypothesis mustbe included by means of regularization parameters. This workdiscusses how these errors affect the regularization parametersand the accuracy of the sea state estimations. For this purpose, astatistical quantification of the errors associated to the estimatedtransfer functions has been included in an expanded Bayesian in-ference approach. Then, the resulting statistical inference modelhas been verified by means of a comparison between the outputsof this approach and those obtained without considering the sta-tistical errors in the Bayesian inference. The assessment of theimpact on the accuracy of the estimations is based on the re-sults of a dedicated model-scale experimental campaign, whichincludes more than 150 different test conditions. | |
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Internacional
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Si |
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Nombre congreso
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39th International Conference on Ocean, Offshore & Arctic Engineering (OMAE 2019) |
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Tipo de participación
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960 |
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Lugar del congreso
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Glasgow, Reino Unido |
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Revisores
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Si |
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ISBN o ISSN
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978-0-7918-5884-4 |
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DOI
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Fecha inicio congreso
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09/06/2019 |
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Fecha fin congreso
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14/06/2019 |
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Desde la página
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1 |
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Hasta la página
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12 |
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Título de las actas
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Proceedings of the 39th International Conference on Ocean, Offshore & Arctic Engineering (OMAE2019) |