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PhD Dissertation Defense: Saulo Mendes

July 13, 2020 @ 3:00 pm - 4:00 pm

photo of Saulo Mendes a graduate student within UNC's Department of Marine Sciences and a member of the Scotti labThe PhD Dissertation Defense of Saulo Mendes is presented by the University of North Carolina at Chapel Hill’s, Department of Marine Sciences. This event will be held on Monday, July 13th at 3:00 pm. This event will be held ONLINE ONLY and it will be available online via Zoom (Use Zoom Meeting ID: 921 0312 4559).

Title: On The Statistics of Oceanic Rogue Waves in Finite Depth: Exceeding Probabilities, Physical Constraints and Extreme Value Theory

Abstract: In recent decades, the appearance of unexpected extreme ocean waves have been confirmed, which due to its rarity and deviation from the linear theory of the ocean surface waves are currently known as rogue waves, e.g. waves of large amplitude that seemingly appear from nowhere and are by definition at least twice as tall as the significant wave height and much steeper than surrounding waves. Yet, no consensus in either deterministic or operational approaches on their prediction has been achieved. The drive to develop better tools to predict the occurrence of rogue waves goes beyond the academic realm, as they are linked with the majority of ship losses associated with unknown causes, hence, the complete understanding of all possible physical processes of generation, modeling of the exceeding probability as well as the creation of an accurate warning system for different sea states are essential for the design of both marine installations and ships. In order to advance the understanding of rogue waves, we developed physical bounds for the ensemble of waves and created a model that estimates their maximum height, thus, significantly improving the accuracy in comparison with the standard theories. Furthermore, we extended this approach to wave crest heights, as the need for crest probabilities is justified by the risk factor they represent for fixed structures, obtaining similar success. Finally, we create a new probability distribution that simultaneously improves the prediction accuracy and obeys the physical bounds.


July 13, 2020
3:00 pm - 4:00 pm
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