To fully understand the performance of tidal stream turbines for the development of ocean renewable energy, a range of computational models is required. We review and compare results from several models of horizontal axis turbines at different spatial scales. Models under review include blade element momentum theory (BEMT), blade element actuator disk, Reynolds averaged Navier Stokes (RANS) CFD (BEM-CFD), blade-resolved moving reference frame and coastal models based on the shallow water equations. To evaluate the BEMT, a comparison is made to experiments with three different rotors. We demonstrate that, apart from the near-field wake, there are similarities in the results between the BEM-CFD approach and a coastal area model using a simplified turbine fence at a headland case.

Flow characteristics in coastal regions are strongly influenced by the topography of the seabed and understanding the fluid dynamics is necessary before installation of tidal stream turbines (TST). In this paper, the bathymetry of a potential TST deployment site is used in the development of the a CFD (Computational Fluid Dynamics) model. The steady state k-ϵ and transient Large Eddy Simulation (LES) turbulence methods are employed and compared. The simulations are conducted with a fixed representation of the ocean surface, i.e., a rigid lid representation. In the vicinity of Horse Rock a study of the pressure difference shows that the small change in height of the water column is negligible, providing confidence in the simulation results. The stream surface method employed to visualise the results has important inherent characteristics that can enhance the visual perception of complex flow structures. The results of all cases are compared with the flow data transect gathered by an Acoustic Doppler Current Profiler (ADCP). It has been understood that the k-ϵ method can predict the flow pattern relatively well near the main features of the domain and the LES model has the ability to simulate some important flow patterns caused by the bathymetry.

This application paper describes a novel, cluster-based, semi-automatic, stream surface placement strategy for structured and unstructured computational fluid dynamics (CFD) data, tailored towards a specific application: The BLOODHOUND jet and rocket propelled land speed record vehicle. An existing automatic stream surface placement algorithm⁽⁸⁾, is extensively modified to cater for large unstructured CFD simulation data. The existing algorithm uses hierarchical clustering of velocity and distance vectors to find potential stream surface seeding locations. This work replaces the hierarchical clustering algorithm, designed to work with small regular grids, with a K-means clustering approach suitable for large unstructured grids. Modifications are made to the seeding curve construction algorithm, improving the smoothness and distribution of the discretised curve in complex cases. A new distance function is described which allows the user to target particular characteristics of simulation data. The proposed algorithm reduces the required memory footprint and computational requirement compared to previous work⁽⁸⁾. The performance and effectiveness of the proposed algorithm is demonstrated, and CFD domain expert evaluation is provided describing the value of this approach.

The ability of a CFD engineer to study, capture, and visualise 3D flow simulation data is a challenge. Stream surfaces are a useful tool for visualising 3D flow because of their ability to convey many field attributes from their structure. It is important that the CFD engineer can interact with, and examine specific characteristics of the CFD data. We introduce an interactive, cluster based stream surface placement strategy for structured and unstructured CFD data. A two-phase hybrid clustering algorithm is used to visualise interesting subsets of the flow. An interactive tree map interface provides a visual overview and enables interactive selection of cluster details corresponding to interesting features of the data at which to place stream surfaces. We demonstrate the performance and effectiveness of our interactive framework on a range of flow simulations and provide domain expert evaluation of the results, providing valuable insight for the CFD engineers.