Dynamic drag modeling of submerged aquatic vegetation canopy flows

Vegetation has a profound effect on flow and sediment transport processes in natural rivers, by increasing both skin friction and form drag. The increase in drag introduces a drag discontinuity between the in-canopy flow and the flow above, which leads to the development of an inflection point in the velocity profile, resembling a free shear layer. Therefore, drag acts as the primary driver for the entire canopy system. Most current numerical hydraulic models which incorporate vegetation rely either on simple, static plant forms, or canopy-scaled drag terms. However, it is suggested that these are insufficient as vegetation canopies represent complex, dynamic, porous blockages within the flow, which are subject to spatially and temporally dynamic drag forces. Here we present a dynamic drag methodology within a CFD framework. Preliminary results for a benchmark cylinder case highlight the accuracy of the method, and suggest its applicability to more complex cases.

Extending the canopy flow model for natural, highly flexible macrophyte canopies

Flow structures above vegetation canopies have received much attention within terrestrial and aquatic literature. This research has led to a good process understanding of mean and turbulent canopy flow structure. However, much of this research has focused on rigid or semi-rigid vegetation with relatively simple morphology. Aquatic macrophytes differ from this form, exhibiting more complex morphologies, predominantly horizontal posture in the flow and a different force balance. While some recent studies have investigated such canopies, there is still the need to examine the relevance and applicability of general canopy layer theory to these types of vegetation. Here, we report on a range of numerical experiments, using both semi-rigid and highly flexible canopies. The results for the semi-rigid canopies support existing canopy layer theory. However, for the highly flexible vegetation, the flow pattern is much more complex and suggests that a new canopy model may be required.

Structured sparsity for spatially coherent fibre orientation estimation in diffusion MRI.

We propose a novel formulation to solve the problem of intra-voxel reconstruction of the fibre orientation distribution function (FOD) in each voxel of the white matter of the brain from diffusion MRI data. The majority of the state-of-the-art methods in the field perform the reconstruction on a voxel-by-voxel level, promoting sparsity of the orientation distribution. Recent methods have proposed a global denoising of the diffusion data using spatial information prior to reconstruction, while others promote spatial regularisation through an additional empirical prior on the diffusion image at each q-space point. Our approach reconciles voxelwise sparsity and spatial regularisation and defines a spatially structured FOD sparsity prior, where the structure originates from the spatial coherence of the fibre orientation between neighbour voxels. The method is shown, through both simulated and real data, to enable accurate FOD reconstruction from a much lower number of q-space samples than the state of the art, typically 15 samples, even for quite adverse noise conditions.