The ability to capture and visualize information within the flow poses challenges for visualizing 3D flow fields. Stream surfaces are one of many useful integration based techniques for visualizing 3D flow. However seeding integral surfaces can be challenging. Previous research generally focuses on manual placement of stream surfaces. Little attention has been given to the problem of automatic stream surface seeding. This paper introduces a novel automatic stream surface seeding strategy based on vector field clustering. It is important that the user can define and target particular characteristics of the flow. Our framework provides this ability. The user is able to specify different vector clustering parameters enabling a range of abstraction for the density and placement of seeding curves and their associated stream surfaces. We demonstrate the effectiveness of this automatic stream surface approach on a range of flow simulations and incorporate illustrative visualization techniques. Domain expert evaluation of the results provides valuable insight into the users requirements and effectiveness of our approach.

Streamline seeding rakes are widely used in vector field visualization. We present new approaches for calculating similarity between integral curves (streamlines and pathlines). While others have used similarity distance measures, the computational expense involved with existing techniques is relatively high due to the vast number of euclidean distance tests, restricting interactivity and their use for streamline seeding rakes. We introduce the novel idea of computing streamline signatures based on a set of curve-based attributes. A signature produces a compact representation for describing a streamline. Similarity comparisons are performed by using a popular statistical measure on the derived signatures. We demonstrate that this novel scheme, including a hierarchical variant, produces good clustering results and is computed over two orders of magnitude faster than previous methods. Similarity-based clustering enables filtering of the streamlines to provide a nonuniform seeding distribution along the seeding object. We show that this method preserves the overall flow behavior while using only a small subset of the original streamline set. We apply focus + context rendering using the clusters which allows for faster and easier analysis in cases of high visual complexity and occlusion. The method provides a high level of interactivity and allows the user to easily fine tune the clustering results at runtime while avoiding any time-consuming recomputation. Our method maintains interactive rates even when hundreds of streamlines are used.

A coupled blade element momentum-computational fluid dynamics (BEM-CFD) model is used to conduct simulations of groups of tidal stream turbines. Simulations of single, double and triple turbine arrangements are conducted first to evaluate the effects of turbine spacing and arrangement on flow dynamics and rotor performance. Wake recovery to free-stream conditions was independent of flow velocity. Trends identified include significant improvement of performance for the downstream rotor where longitudinal spacing between a longitudinally aligned pair is maximised, whereas maintaining a lateral spacing between two devices of two diameters or greater increases the potential of benefitting from flow acceleration between them. This could significantly improve the performance of a downstream device, particularly where the longitudinal spacing between the two rows is two diameters or less. Due to the computational efficiency of this modelling approach, particularly when compared to transient computational fluid dynamics simulations of rotating blades, the BEM-CFD model can simulate larger numbers of devices. An example of how an understanding of the hydrodynamics around devices are affected by rotor spacing can be used to optimise the performance of a 14 turbine array is presented. Compared to a regular staggered configuration, the total power output of the array was increased by over 10%. Highlights  for a longitudinally spaced pair of turbines, the downstream device will have a lower performance even at 40 diameters' spacing because wake recovery is slow.  for a low lateral spacing between a pair of turbines (3 diameters or less), flow recovery is slow between them because of wake expansion.  flow acceleration between a pair of laterally spaced turbines can improve the performance of a third device positioned further downstream but laterally between them.  the most benefit from such flow acceleration is gained by minimising the longitudinal spacing between the two rows of devices.  for a small lateral spacing, performance of the downstream device is compromised.  based on the patterns observed for two and three turbine arrangements, the overall performance of a 14 turbine staggered array was improved by over 10%.

No abstract

No abstract