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.

SUMMARYA computational procedure is presented for solving complex variably saturated ows in porous media, that may easily be implemented into existing conventional ÿnite-volume-based computational uid dynamics codes, so that their functionality might be geared upon to readily enable the modelling of a complex suite of interacting uid, thermal and chemical reaction process physics. This procedure has been integrated within a multi-physics ÿnite volume unstructured mesh framework, allowing arbitrarily complex three-dimensional geometries to be modelled. The model is particularly targeted at ore heap-leaching processes, which encounter complex ow problems, such as inÿltration into dry soil, drainage, perched water tables and ow through heterogeneous materials, but is equally applicable to any process involving ow through porous media, such as in environmental recovery processes. The computational procedure is based on the mixed form of the classical Richards equation, employing an adaptive transformed mixed algorithm that is numerically robust and signiÿcantly reduces compute (or CPU) time. The computational procedure is accurate (compares well with other methods and analytical data), comprehensive (representing any kind of porous ow model), and is computationally e cient. As such, this procedure provides a suitable basis for the implementation of large-scale industrial heap-leach models.

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%.

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