R. Santjer
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4 records found
1
This dissertation explores the applicability of probabilistic models to explicitly capture such dependencies and to demonstrates how such models can enhance decision-making across different offshore technologies, with a particular focus on aquaculture and floating photovoltaic systems. Two copula-based multivariate modelling approaches with different complexity are applied. On the one hand, the Gaussian copula-based Bayesian Network (GCBN) offers a comparatively accessible and interpretable framework, as its graphical structure can be derived from the underlying physical processes. On the other hand, vine copula models are used to represent complex dependence patterns more flexibly. Unlike GCBNs, they are not restricted to a single copula family, instead, each pair of variables can be modelled using the copula that best captures their dependence. This flexibility, however, comes at the cost of higher model complexity and increased computational and practical challenges.... ...
This dissertation explores the applicability of probabilistic models to explicitly capture such dependencies and to demonstrates how such models can enhance decision-making across different offshore technologies, with a particular focus on aquaculture and floating photovoltaic systems. Two copula-based multivariate modelling approaches with different complexity are applied. On the one hand, the Gaussian copula-based Bayesian Network (GCBN) offers a comparatively accessible and interpretable framework, as its graphical structure can be derived from the underlying physical processes. On the other hand, vine copula models are used to represent complex dependence patterns more flexibly. Unlike GCBNs, they are not restricted to a single copula family, instead, each pair of variables can be modelled using the copula that best captures their dependence. This flexibility, however, comes at the cost of higher model complexity and increased computational and practical challenges....
Offshore floating structures are experiencing harsh environmental conditions risking their safety. Therefore, mooring lines are crucial for ensuring structures’ stability. Sudden increases in tensions after temporarily slack of the mooring line are called snap loads and are the most critical load states. These snap loads and their dependence to various factors are investigated in the present study. 12 study locations in the south-eastern North Sea are selected. For each location, wave and current variables are extracted from a three-dimensional large-scale numerical model covering the European Shelf. Mooring tensions at different rope positions are calculated via a Finite Element model for flexible mooring lines for different hydrodynamic conditions and used subsequently to obtain tension rates as indicator for snap loads. The dependence among 13 variables per study location is modelled via Gaussian copula-based Bayesian Networks (GCBN). This allows for spatial analysis of the relationships between hydrodynamic variables and tension rates, but also to determine the influence of hydrodynamic variables on expected tension rates. Furthermore, distributions of tension rates are obtained under specific constant hydrodynamic conditions. The results indicate that conditionalising on certain hydrodynamic variables can reduce the expected tension rates, as their marginal distributions are characterised by heavy tails. Still, mooring systems should be designed conservatively. However, once specific hydrodynamic information is available, uncertainties can be minimised, enhancing safety and reliability. Thus, accounting for the dependence among hydrodynamic variables and tension rates is crucial for improving the safety of structures under varying environmental conditions.
Aquaculture at sea is gaining increasing importance, not only as a (local) food source but also due to its potential of being combined with other offshore activities such as wind parks. Nevertheless, experience of offshore aquaculture is limited. This study aims to provide a framework to evaluate offshore aquaculture suitability accounting for the probabilistic dependence between relevant variables. This framework is applied to obtain suitability maps of aquaculture for the North Sea for the blue mussel Mytilus edulis and the sugar kelp Saccharina latissima. For each of these species, three ecological variables are selected and the optimal growth and critical survival limits are defined. Here, suitability is defined as the probability of meeting these conditions. Data on the selected variables is extracted from a large-scale 3D hydrodynamic and ecological model of the northwest European Shelf, of which daily extremes are sampled. The probabilistic model is developed using bivariate copula models, which are fitted to each variable pair to describe their joint distribution function at each studied location. Empirical distribution functions are used to describe the univariate distribution function of each variable and location. Using Monte-Carlo simulations, the probability of meeting the optimal and critical limits is estimated and suitability maps accounting for the probabilistic dependence between the variables are generated. In addition, suitability maps disregarding the dependence are generated and compared to those accounting for the probabilistic dependence. It was found that considering the dependence between variables significantly improves the accuracy of the results for optimal and critical growth conditions for both species. The presented method allows to identify potential areas where blue mussel and sugar kelp cultivation is the most suitable. For instance, in this study, a north-south elongated area west of the German and Danish coast appears to be most suitable for blue mussels, while estuaries and rivers are found the most suitable for the sugar kelp.