Physical wave basin tests with a focus on uncertainty estimation have been conducted on a fixed sphere subjected to wave loads at Aalborg University as part of the effort of the OES Wave Energy Converters Modeling Verification and Validation (formerly, OES Task 10) working group to increase credibility of numerical modeling of WECs.
The present note defines an idealized test case formulated to accurately represent the physical tests in a simple way. The test case consists of a fixed, rigid sphere half submerged in water subjected to regular waves of three different levels of linearity. The objective of the present note is to allow for numerical tests of the idealized test case.
High-fidelity models become more and more used in the wave energy sector. They offer a fully nonlinear simulation tool that in theory should encompass all linear and nonlinear forces acting on a wave energy converter (WEC). Studies using high-fidelity models are usually focusing on validation of the model. However, a validated model does not necessarily provide reliable solutions. Solution verification is the methodology to estimate the numerical uncertainties related to a simulation. In this work we test four different approaches: the classical grid convergence index (GCI); a least-squares version (LS-GCI); a simplified version of the least-square method (SLS-GCI); and the ITTC recommended practice. The LS-GCI requires four or more solutions whereas the other three methods only need three solutions. We apply these methods to four different high-fidelity models for the case of a heaving sphere. We evaluate the numerical uncertainties for two parameters in the time domain and two parameters in the frequency domain. It was found that the GCI and ITTC were hard to use on the frequency domain parameters as they require monotonic convergence which sometimes does not happen due to the differences in the solutions being very small. The SLS-GCI performed almost as well as the SL-GCI method and will be further investigated.
Any ecosystem based fisheries management system is necessarily faced with the problem of multiple objectives that trade-off against one another. Typically, objectives such as the maximization of yield, employment or profit or minimizing environmental impacts will be optimized in different parts of the decision space, which is formed of the fishing mortality rates that can be applied to the various species, given the constraints imposed by the mixed species nature of many fishing fleets. Since objectives cannot be simultaneously achieved, managers need to consider how such objectives trade-off against one another in order to choose a balanced strategy. Normally, they also have to consider the views of different groupings of stakeholders, who often favour widely different and conflicting objectives. This is particularly difficult if stakeholders are reluctant to expose their negotiating positions. This article explores two possible approaches to developing a Decision Support Framework for the North Sea. The first is a classic Multi- Criteria Analysis (MCA) approach that was developed in cooperation with North Sea stakeholders. The implementation went smoothly for the definition of suitable scenarios, decision trees and criteria, but failed in facilitating consensus on how to set priorities at the stakeholder level. However, it remains a possible approach for higher level management to adopt. Consequently, to aid effective decision-making a simpler approach was designed to visualise stakeholders concerns both to themselves and to the managers in charge of actual decision-making. Rather than trying to achieve some joint optima of the objectives that stakeholders wish to achieve this approach seeks to avoid the solutions various stakeholder groups resent the most. This ‘N dimensional potato approach’ proposed here treats the decision space as analogous to a partially rotten potato that has to be prepared for the table: each group of stakeholders cut away those parts of the decision space that they consider unacceptable. Ideally, this would leave a decision space where somewhat acceptable compromise solutions exist. But, if no decision space is left after all have made their cuts, this approach will still inform managers about the consequences of different solutions in terms of which group will be disappointed and by how much. Making this approach operational requires both uncovering various stakeholders’ views of the unacceptable areas, and also displaying these areas in a convenient fashion together with areas of stakeholder consent. The article describes the steps taken to address these two tasks by the North Sea case study of the MareFrame research project.
The International Energy Agency Technology Collaboration Program for Ocean Energy Systems (OES) initiated the OES Wave Energy Conversion Modeling Task, which focused on the verification and validation of numerical models for simulating wave energy converters (WECs). The long-term goal is to assess the accuracy of and establish confidence in the use of numerical models used in design as well as power performance assessment of WECs. To establish this confidence, the authors used different existing computational modeling tools to simulate given tasks to identify uncertainties related to simulation methodologies: (i) linear potential flow methods; (ii) weakly nonlinear Froude–Krylov methods; and (iii) fully nonlinear methods (fully nonlinear potential flow and Navier–Stokes models). This article summarizes the code-to-code task and code-to-experiment task that have been performed so far in this project, with a focus on investigating the impact of different levels of nonlinearities in the numerical models. Two different WECs were studied and simulated. The first was a heaving semi-submerged sphere, where free-decay tests and both regular and irregular wave cases were investigated in a code-to-code comparison. The second case was a heaving float corresponding to a physical model tested in a wave tank. We considered radiation, diffraction, and regular wave cases and compared quantities, such as the WEC motion, power output and hydrodynamic loading.