Interaction between Waves and Maritime Structures

This book is the result of a stimulating Special Issue of Water, focusing on the “Interaction between waves and Maritime Structures”. This broadly inclusive title allowed the gathering of articles on different topics of engineering concern, making the book appeal to both scientists and practical engineers. Original contributions on evergreen problems, such as wave overtopping at conventional and unconventional coastal structures, wave-induced pressures at vertical walls, hydraulic stability of rubble mound breakwaters and dynamics of crown-walls indeed represent the main core of the book; however, other intriguing research topics are also tackled, including the solution of the Navier–Stokes equations for biphase flows, the downscaling of large maritime structures in a physical lab, floating bodies mechanics and the numerical modeling of coastline evolution

Wave driven devices for the oxygenation of bottom layers

This thesis discusses the design of a system to use wave energy to pump oxygen-rich surface water towards the bottom of the sea. A simple device, called OXYFLUX, is proposed in a scale model and tested in a wave flume in order to validate its supposed theoretical functioning. Once its effectiveness has been demonstrated, a overset mesh, CFD model has been developed and validated by means of the physical model results. Both numerical and physical results show how wave height affects the behavior of the device. Wave heights lower than about 0.5 m overtop the floater and fall into it. As the wave height increases, phase shift between water surface and vertical displacement of the device also increases its influence on the functioning mechanism. In these situations, with wave heights between 0.5 and 0.9 m, the downward flux is due to the higher head established in the water column inside the device respect to the outside wave field. Furthermore, as the wave height grows over 0.9 m, water flux inverts the direction thanks to depression caused by the wave crest pass over the floater. In this situation the wave crest goes over the float but does not go into it and it draws water from the bottom to the surface through the device pipe. By virtue of these results a new shape of the floater has been designed and tested in CFD model. Such new geometry is based on the already known Lazzari’s profile and it aims to grab as much water as possible from the wave crest during the emergence of the floater from the wave field. Results coming from the new device are compared with the first ones in order to identify differences between the two shapes and their possible areas of application

Renewable Energies for Sustainable Development

In the current scenario in which climate change dominates our lives and in which we all need to combat and drastically reduce the emission of greenhouse gases, renewable energies play key roles as present and future energy sources. Renewable energies vary across a wide range, and therefore, there are related studies for each type of energy. This Special Issue is composed of studies integrating the latest research innovations and knowledge focused on all types of renewable energy: onshore and offshore wind, photovoltaic, solar, biomass, geothermal, waves, tides, hydro, etc. Authors were invited submit review and research papers focused on energy resource estimation, all types of TRL converters, civil infrastructure, electrical connection, environmental studies, licensing and development of facilities, construction, operation and maintenance, mechanical and structural analysis, new materials for these facilities, etc. Analyses of a combination of several renewable energies as well as storage systems to progress the development of these sustainable energies were welcomed

Proceedings of the XXVIIIth TELEMAC User Conference 18-19 October 2022

Hydrodynamic

Functional-input metamodeling: an application to coastal flood early warning

Les inondations en général affectent plus de personnes que tout autre catastrophe. Au cours de la dernière décennie du 20ème siècle, plus de 1.5 milliard de personnes ont été affectées. Afin d'atténuer l'impact de ce type de catastrophe, un effort scientifique significatif a été consacré à la constitution de codes de simulation numériques pour la gestion des risques. Les codes disponibles permettent désormais de modéliser correctement les événements d'inondation côtière à une résolution assez élevée. Malheureusement, leur utilisation est fortement limitée pour l'alerte précoce, avec une simulation de quelques heures de dynamique maritime prenant plusieurs heures à plusieurs jours de temps de calcul. Cette thèse fait partie du projet ANR RISCOPE, qui vise à remédier cette limitation en construisant des métamodèles pour substituer les codes hydrodynamiques coûteux en temps de calcul. En tant qu'exigence particulière de cette application, le métamodèle doit être capable de traiter des entrées fonctionnelles correspondant à des conditions maritimes variant dans le temps. À cette fin, nous nous sommes concentrés sur les métamodèles de processus Gaussiens, développés à l'origine pour des entrées scalaires, mais maintenant disponibles aussi pour des entrées fonctionnelles. La nature des entrées a donné lieu à un certain nombre de questions sur la bonne façon de les représenter dans le métamodèle: (i) quelles entrées fonctionnelles méritent d'être conservées en tant que prédicteurs, (ii) quelle méthode de réduction de dimension (e.g., B-splines, PCA, PLS) est idéale, (iii) quelle est une dimension de projection appropriée, et (iv) quelle est une distance adéquate pour mesurer les similitudes entre les points d'entrée fonctionnels dans la fonction de covariance. Certaines de ces caractéristiques - appelées ici paramètres structurels - du modèle et d'autres telles que la famille de covariance (e.g., Gaussien, Matérn 5/2) sont souvent arbitrairement choisies a priori. Comme nous l'avons montré à travers des expériences, ces décisions peuvent avoir un fort impact sur la capacité de prédiction du métamodèle. Ainsi, sans perdre de vue notre but de contribuer à l'amélioration de l'alerte précoce des inondations côtières, nous avons entrepris la construction d'une méthodologie efficace pour définir les paramètres structurels du modèle. Comme première solution, nous avons proposé une approche d'exploration basée sur la Méthodologie de Surface de Réponse. Elle a été utilisé efficacement pour configurer le métamodèle requis pour une fonction de test analytique, ainsi que pour une version simplifiée du code étudié dans RISCOPE. Bien que relativement simple, la méthodologie proposée a pu trouver des configurations de métamodèles de capacité de prédiction élevée avec des économies allant jusqu'à 76.7% et 38.7% du temps de calcul utilisé par une approche d'exploration exhaustive dans les deux cas étudiés. La solution trouvée par notre méthodologie était optimale dans la plupart des cas. Nous avons développé plus tard un deuxième prototype basé sur l'Optimisation par Colonies de Fourmis. Cette nouvelle approche est supérieure en termes de temps de solution et de flexibilité sur les configurations du modèle qu'elle permet d'explorer. Cette méthode explore intelligemment l'espace de solution et converge progressivement vers la configuration optimale. La collection d'outils statistiques utilisés dans cette thèse a motivé le développement d'un package R appelé funGp. Celui-ci est maintenant disponible dans GitHub et sera soumis prochainement au CRAN. Dans un travail indépendant, nous avons étudié l'estimation des paramètres de covariance d'un processus Gaussien transformé par Maximum de Vraisemblance (MV) et Validation Croisée. Nous avons montré la consistance et la normalité asymptotique des deux estimateurs. Dans le cas du MV, ces résultats peuvent être interprétés comme une preuve de robustesse du MV Gaussien dans le cas de processus non Gaussiens.Currently, floods in general affect more people than any other hazard. In just the last decade of the 20th century, more than 1.5 billion were affected. In the seek to mitigate the impact of this type of hazard, strong scientific effort has been devoted to the constitution of computer codes that could be used as risk management tools. Available computer models now allow properly modelling coastal flooding events at a fairly high resolution. Unfortunately, their use is strongly prohibitive for early warning, with a simulation of few hours of maritime dynamics taking several hours to days of processing time, even on multi-processor clusters. This thesis is part of the ANR RISCOPE project, which aims at addressing this limitation by means of surrogate modeling of the hydrodynamic computer codes. As a particular requirement of this application, the metamodel should be able to deal with functional inputs corresponding to time varying maritime conditions. To this end, we focused on Gaussian process metamodels, originally developed for scalar inputs, but now available also for functional inputs. The nature of the inputs gave rise to a number of questions about the proper way to represent them in the metamodel: (i) which functional inputs are worth keeping as predictors, (ii) which dimension reduction method (e.g., B-splines, PCA, PLS) is ideal, (iii) which is a suitable projection dimension, and given our choice to work with Gaussian process metamodels, also the question of (iv) which is a convenient distance to measure similarities between functional input points within the kernel function. Some of these characteristics - hereon called structural parameters - of the model and some others such as the family of kernel (e.g., Gaussian, Matérn 5/2) are often arbitrarily chosen a priori. Sometimes, those are selected based on other studies. As one may intuit and has been shown by us through experiments, those decisions could have a strong impact on the prediction capability of the resulting model. Thus, without losing sight of our final goal of contributing to the improvement of coastal flooding early warning, we undertook the construction of an efficient methodology to set up the structural parameters of the model. As a first solution, we proposed an exploration approach based on the Response Surface Methodology. It was effectively used to tune the metamodel for an analytic toy function, as well as for a simplified version of the code studied in RISCOPE. While relatively simple, the proposed methodology was able to find metamodel configurations of high prediction capability with savings of up to 76.7% and 38.7% of the time spent by an exhaustive search approach in the analytic case and coastal flooding case, respectively. The solution found by our methodology was optimal in most cases. We developed later a second prototype based on Ant Colony Optimization (ACO). This new approach is more powerful in terms of solution time and flexibility in the features of the model allowed to be explored. The ACO based method smartly samples the solution space and progressively converges towards the optimal configuration. The collection of statistical tools used for metamodeling in this thesis motivated the development of the funGp R package, which is now available in GitHub and about to be submitted to CRAN. In an independent work, we studied the estimation of the covariance parameters of a Transformed Gaussian Process by Maximum Likelihood (ML) and Cross Validation. We showed that both estimators are consistent and asymptotically normal. In the case of ML, these results can be interpreted as a proof of robustness of Gaussian ML in the case of non-Gaussian processes

Regional-Scale Forecasting for Coastal Storm Hazard Early Warning Systems

Sandy beach and dune systems often provide coastal communities with the first line of defence from the impacts of extreme storm events. During a storm’s approach, communities have a crucial opportunity to take preemptive actions to minimise the social, economic, and environmental impacts of the storm. Predicting coastal storm hazards, especially at the regional scale, however, is challenging due to the complexities of the hydrodynamic and morphodynamic processes occurring on erodible coastlines in high wave energy conditions. This thesis examines the nature and severity of coastal storm hazards and investigates approaches to effectively forecast these hazards for operational Early Warning Systems (EWSs). First, a conceptual framework for classifying coastal storm hazards is introduced. The Storm Hazard Matrix presents an integrated approach to categorising coastal flooding and beach erosion hazards. The Flooding Hazard Scale is based on the Storm Impact Scale first proposed by Sallenger (2000). The new Erosion Hazard Scale is based on several different morphological changes in beaches due to storms, including changes in beach width and dune erosion. The framework is demonstrated on two contrasting extreme storm events and successfully distinguishes between the severity of localised coastal flooding and/or beach erosion hazards. The enhanced insight provided by using the framework has the potential to be especially valuable for EWS applications. Next, a simple classification approach to forecast coastal storm erosion hazards based on the Erosion Hazard Scale is developed based on dune impact exposure (Larson et al., 2004), cumulative storm wave energy (Dolan and Davis, 1992; Harley et al., 2009) and the Dune Stability Factor (Armaroli et al., 2012). Two coastal change datasets are used to investigate the performance of the approach in terms of temporal variability and spatial variability. In each dataset, all events or locations of significant erosion impacts are correctly identified. The approach tended to be conservative with few false alarms (and no misses), demonstrating its predictive capability with limited input data and computational resources. Finally, machine learning techniques are investigated to leverage the increasing availability of coastal topographic and hydrodynamic data. A gradient boosted random forest ensemble model is trained using an extreme coastal storm erosion hazard dataset. The model is demonstrated by hindcasting regional-scale coastal storm erosion hazards over a period of 5 years. Additionally, an investigation of the training data requirements and interpretation of the model feature importance characteristics are also performed. The findings and insights discussed in this thesis represent the state-of-the-art approaches to forecasting coastal storm hazards at the regional scale and can serve to inform the implementation of future EWSs

Probabilistic approach of reservoir level depletion induced by drought

Droughts are one of the most complicated natural disasters on earth. The repetitive occurrence of droughts has enormous adverse impacts on different aspects of human lives and natural environment. Careful monitoring and early warning systems can assist in the development of effective drought management strategies. Therefore, it is of immense significance to have a full understanding of the characteristics of a developing drought (severity, frequency etc.) before planning any drought response measures. The main aim of this research is to develop a methodology to evaluate reservoir storage levels during drought periods in a probabilistic way. In doing so, a case study was conducted of the Upper Yarra reservoir, which is located in the upper part of the Yarra River catchment in Australia. In order to identify the impacts of drought on this reservoir, it is important to have detailed knowledge of the general drought conditions surrounding this reservoir, as major portions of its inflow are harvested from neighbouring areas. Therefore, a comprehensive investigation of drought characteristics over this area is essential. Six rainfall and six streamflow stations near the Upper Yarra reservoir were selected for evaluating meteorological and hydrological drought events using the Standardized Precipitation Index (SPI) and the Standardized Hydrological Drought Index (SHDI), respectively. Both of these indices detected drought events successfully when applied to the data. Univariate and bivariate frequency analysis of drought duration and severity were carried out using the Gumbel-Hougaard copula. A probabilistic assessment of the reservoir storage condition was carried out by joint consideration of probability of initial storage volume and probability of drought events affecting inflow to the reservoir. Therefore, frequency analysis of drought events of inflow to the reservoir with particular severity and duration were conducted before applying them to the reservoir system model with specific initial water levels. The quantitative exploration of trends of drought characteristics (e.g. severity, frequency) provides meaningful insight to water authorities for developing of drought management plans. This study employed basic and modified Mann-Kendall tests to detect monotonic trends in drought characteristics. Both tests identified significant decreasing trends for four stations in the study area. More specific results of trends were reported by Innovative Trend Analysis (ITA) method. The results indicate that extreme drought situations are more likely to appear at the Reefton, Warburton, Alderman Creek, Little Yarra and McMahons Creek stations. Using the Sequential Mann-Kendall test, it was observed that the starts of the abrupt change points for most stations were found during the Millennium Drought (1996 to 2009) in Victoria. The changing patterns of drought frequencies were also investigated using the Poisson regression method. All stations exhibited decreasing trends in inter-arrival times between successive drought events, indicating that droughts are becoming more frequent in this catchment. The integrated modelling software Source is used to construct a reservoir system model. The development of water demand function is an essential requirement for building of the reservoir system model by Source software. Multiple regression analysis (MRA) and principal component analysis (PCA) are used and, finally, PCA was selected for development of water demand function because PCA gives better results than MRA. This study determines a risk assessment of storage condition of the Upper Yarra reservoir due to impacts of drought events. A probabilistic approach is proposed, taking into account the variability of reservoir storage volume prior to a drought event and different drought scenarios. Both drought severity and durations are included in developing drought scenarios. All required inputs are used in Source software to determine the reservoir storage volume at the end of a drought event. The analysis is performed for Period 3 (June to August, the most critical time of a year in terms of availability of water in the reservoir) and Period 1 (December to February, the least critical time). Three prespecified storage conditions are studied: (1) when storage drops < 50% of its full supply volume (FSV) (CC1); (2) when storage drops < 40% of FSV (CC2); and (3) when storage drops < 30% of FSV (CC3). The main conclusions of these analyses are summarized as follows: 1) the probability of storage reduction below the prespecified conditions is higher in Period 3 than in Period 1; 2) the risk of storage reduction can be successfully evaluated based on two uncertain parameters (initial storage volume and drought severity) and the results show that the initial storage volume is a more dominant uncertain parameter in probability calculation than drought severity for long as well as short-duration droughts; 3) several drought zones are successfully constructed for each condition on plots of initial storage vs. drought severity. It should be noted that each zone is constructed for a specific drought duration and period. If needed, other zones can be developed for other periods and drought durations following the same approach; 4) the constructed zones will give indications to water authorities about the reduction of storage due to long- and short-duration drought events; 5) finally, the general form of the relationship between initial storage volume and probability of storage reduction below any particular level for any drought event of known duration and severity is developed. Results of this study provide a technical reference for the risk assessment of reservoirs due to drought events and will assist in the development of appropriate action plans

Renewable Energy in Marine Environment

The effects of human-caused global warming are obvious, requiring new strategies and approaches. The concept of business-as-usual is now no longer beneficial. Extraction of renewable energy in marine environments represents a viable solution and an important path for the future. These huge renewable energy resources in seas and oceans can be harvested, including wind, tide, and waves. Despite the initial difficulties related mostly to the elevated operational risks in the harsh marine environment, newly developed technologies are economically effective or promising. Simultaneously, many challenges remain to be faced. These are the main issues targeted by the present book, which is associated with the Special Issue of Energies Journal entitled “Renewable Energy in Marine Environment”. Papers on innovative technical developments, reviews, case studies, and analytics, as well as assessments, and papers from different disciplines that are relevant to the topic are included. From this perspective, we hope that the results presented are of interest to for scientists and those in related fields such as energy and marine environments, as well as for a wider audience