With the use of tow-tank experiments, data may be generated for ships of various classes using comprehensive instrumentation. This data gives the ability to determine the response of ships to various sea-state conditions far in advanced of their construction and launch. However, this data does not indicate the effects of those sea-states to the individuals aboard that ship. In order to define these effects a sea-state simulator must be designed and built. Once construction is completed a series of test must be conducted to determine the response of the simulator. This response allows the comparison to actual tow-tank data to determine if the simulator is capable of performing the desired research. / Master of Science

Accurate predictions of the annual energy yield from wave energy converters are essential to the development of the wave industry. The current method based on power matrices uses only a small part of the data available from sea state estimations and it is consequently prone to inaccuracies. The research presented in this work investigates the issue of energy yield prediction and questions the power matrix method. This is accomplished by quantifying the influence of several directional sea states parameters on the performances of wave energy converters. The approach taken was to test several wave energy converters in the Edinburgh Curved tank with a large set of sea states. The selected wave energy converters are a fix OWC, a set of two OWCs acting as a weak directional device and the desalination duck model. Uni-modal and bi-modal sea states were used. For the uni-modal sea states, parameters related to the wave system shape were considered. For the bi-modal sea states, the relative position of the wave system peaks was investigated and the uni-modality index was introduced to quantify the degree to which sea states could be considered bi-modal. For all sea states, the significant wave height was kept constant. The experimental work required good spectral estimates. The MLM and MMLM were adapted to deterministic waves to improve their stability and accuracy. A routine to isolate wave systems was also developed in order to estimate parameters with respect to each wave systems. For uni-modal spectra, parametric models of the observed performances of the devices could be devised. The frequency spreading and its interaction with the energy period proved to be as important as the energy period itself, which suggests that the frequency spreading should be used for energy production prediction. For bi-modal spectra, evidence of the duck sensitivity to directionality was found while the OWCs were not affected.

Sea state is a subjective quantity whose accuracy depends on an observer’s ability to translate local wind waves into numerical scales. It provides an analytical tool for estimating the impact of the sea on data quality and operational safety. Tasks dependent on the characteristics of local sea surface conditions often require accurate and immediate assessment. An attempt to automate sea state classification using eleven years of ship motion and sea state observation data is made using parametric modeling of distribution-based confidence and tolerance intervals and a probabilistic model using sea state frequencies. Models utilizing distribution intervals are not able to exactly convert ship motion data into various sea states scales with significant accuracy. Model averages compared to sea state tolerances do provide improved statistical accuracy but the results are limited to trend assessment. The probabilistic model provides better prediction potential than interval-based models, but is spatially and temporally dependent.

The ocean ambient noise is one of the important parameters in sonar equation. The ocean ambient noise includes diverse and complex sources like waves, marine life, ships, and etc. Using different ways to analyze are needed to understand the complicated properties of ambient noise. Empirical equation obtained from linear regression of wind speed and ambient noise data is a common method to predict noise level. In this article, the ambient noise data were collected from experiments at northeastern sea of Taiwan in 2007, 2008 and 2009. Applying corresponding wind speed data to observed noise level the time series, coefficient of determination is used to estimate how noise fit with wind speed data of regression. The K-S test and Sea States are used to determine the wind speed threshold. Although it is the same sea area in three years, the ocean ambient noise still has variations due to time and variance of sound sources, so it is important to be investigated. This study compares the statistical properties and distribution in ambient noise level and frequencies with corresponding wind speed in same season.

Les surcotes de tempête sont souvent sous-estimées dans les modèles hydrodynamiques, ainsi que les grandes vagues dans les modèles de vagues. Les causes possibles sont une sous-estimation des vents dans les modèles atmosphériques et/ou une formulation incorrecte de la tension de vent. Les objectifs de cette thèse sont (1) d’estimer les biais par vents forts dans les modèles atmosphériques (2) de développer une nouvelle paramétrisation du coefficient de traı̂née permettant de réduire ce biais (3) d’étudier l’impact des vagues sur la tension de vent. La méthode consiste à étudier la réponse de l’atmosphère et de l’océan à la tension de vent. Dans une première partie, nous utilisons le modèle couplé vagues-atmosphère d’ECMWF. Nous montrons que les vents forts sont sous-estimés, avec un biais de l’ordre de -7 m/s à 30 m/s. Des écarts significatifs existent aussi entre les observations, les bouées et les vents issus de ASCAT-KNMI étant généralement inférieurs à ceux des plateformes et des autres données satellites utilisées dans cette étude (AMSR2, ASCAT-RSS, WindSat, SMOS et JASON-2). La nouvelle paramétrisation développée permet d’obtenir des vents plus forts qu’avec celle d’ECMWF par défaut. Dans une deuxième partie (réponse de l’océan), nous utilisons le modèle global océanique TUGO du LEGOS forcé par le modèle couplé vagues-atmosphère d’ECMWF. Nous montrons qu’une paramétrisation de la tension de vent dépendant des vagues plutôt que du vent est plus appropriée quand l’état de mer est jeune. Elle conduit à des surcotes plus proches des observations (marégraphes et traces altimétriques de JASON-2). L’impact des vagues sur la surcote est significatif, et peut atteindre 20 cm. / Storm surges may be underestimated in hydrodynamic models, as well as large wave heights in wave models. This could come from an underestimation of strong winds in atmospheric models and/or an inappropriate wind stress formulation. The objectives of the present work are (1) to estimate how strong are the biases for high winds in atmospheric models (2) to develop a new drag parameterization that could reduce this bias (3) to investigate the impact of the waves on the wind stress. The method consists of studying the response of the atmosphere and the ocean to the wind stress.In a first part, we use the coupled wave-atmosphere model from ECMWF. We show that strong winds may be underestimated, as much as -7 m/s at 30 m/s.Significant differences also exist between observations, with buoys and ASCAT-KNMI generally showing lower wind speeds than the platforms and other remote-sensing data used in this study(AMSR2, ASCAT-RSS, WindSat, SMOS and JASON-2).The newly empirically adjusted Charnock parameterization leads to higher winds compared to the default ECMWF parameterization. In a second part, we use the global ocean model TUGO fromLEGOS forced with ECMWF coupled wave-atmopshere model. We show that a wave-dependent rather than wind-dependent stress formulation is more appropriate, when the sea state is young and the sea rougher. It yields to simulated surges closer to observations (i.e. tide gauges and JASON-2 altimeter tracks). The wave impact on the surges is significant, and may reach 20 cm.

La connaissance de la climatologie des états de mer est primordiale pour le dimensionnement de structures marines, la gestion des zones côtières ou encore la récupération de l’énergie des vagues. L'estimation de la climatologie nécessite de disposer de données d'observation sur une longue durée, ce qui n'est pas le cas de l'Afrique de l'Ouest. Pour dépasser les limites en durée imposées par les observations, nous proposons dans ces travaux une approche stochastique pour estimer une climatologie de vagues en Afrique de l’Ouest, en s’appuyant sur une représentation “par événements” des données d’états de mer. Un “événement” désigne un système de vagues (houle ou mer du vent) en évolution au cours du temps, observable pendant une durée significative et que l’on peut relier à un unique phénomène météorologique source (e.g. dépressions, tempêtes, etc.). La représentation par événements permet de reproduire la cohérence temporelle des systèmes de vagues et de structurer les données d'états de mer avec une base physique. La démarche adoptée peut se décomposer suivant trois étapes. Nous avons d'abord extrait les événements à partir d’une série temporelle de spectres directionnels d’états de mer. Nous avons ensuite développé un modèle pour représenter chacun des événements par un nombre réduit de paramètres. Enfin, nous avons construit un générateur stochastique permettant la simulation d’événements individuels et la reconstitution de climatologies sur des durées de longueurs arbitraires. Les résultats ont montré un bon accord entre la climatologie reconstituée et celle de référence, permettant de conclure que le générateur peut valablement servir à la simulation de données d’états de mer en Afrique de l’Ouest pour les applications en génie océanique. / Accurate estimation of long-term sea conditions is a major issue in design of coastal and offshore structures, coastal zone management or wave energy harvesting. An estimation of long-term sea conditions requires long duration observational data while in West Africa, only a few (3 years) years of observational data are available. To overcome the limits in duration that observations impose, a stochastic approach, event-based representation of sea state data, is proposed to model the wave climate in West Africa. An “event” refers to a wave system (swell or wind sea) evolving over time, that can be observed for a finite, yet significant duration and that can be linked to a single meteorological source phenomenon (e.g. low pressure systems, storms, etc.). Event-based approach provides structures with physical meaning and temporal consistence for the representation of sea states data. The procedure we have used is decomposed into three following steps. First, we have extracted events from a time series of directional spectra. We have then developed a model to represent each event by a reduced number of parameters. In the last step, we have constructed the stochastic events generator which allows for simulation of individual events and for reconstruction of wave climate over durations of arbitrary lengths. Results showed good agreement between reconstructed climate and that of reference and allow to conclude that the stochastic events generator can reliably be used to simulate sea state data in West Africa for a ocean engineering applications.