Renewable energy is the cornerstone of preventing dangerous climate change whilst main- taining a robust energy supply. Tidal energy will arguably play a critical role in the renewable energy portfolio as it is both predictable and reliable, and can be put in place across the globe. However, installation may impact the local and regional ecology via changes in tidal dynamics, sediment transport pathways or bathymetric changes. In or- der to mitigate these e
ects, tidal energy devices need to be modelled in order to predict hydrodynamic changes. Robust mesh generation is a fundamental component required for developing simulations with high accuracy. However, mesh generation for coastal domains can be an elaborate procedure. Here, we describe an approach combining mesh generators with Geographical Information Systems. We demonstrate robustness and e
- ciency by constructing a mesh with which to examine the potential environmental impact of a tidal turbine farm installation in the Orkney Islands. The mesh is then used with two well-validated ocean models, to compare their ow predictions with and without a turbine array. The results demonstrate that it is possible to create an easy-to-use tool to generate high-quality meshes for combined coastal engineering, here tidal turbines, and coastal ocean simulations.
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Computational simulations of physical phenomena rely on an accurate discretisation of model domains. Numerical models have increased in sophistication to a level where it is possible to support terrain-following boundaries that conform accurately to real physical interfaces, and resolve a multi-scale of spatial resolutions. Whilst simulation codes are maturing in this area, pre-processing tools have not developed significantly enough to competently initialise these problems in a rigorous, efficient and recomputable manner. In the relatively disjoint field of Geographic Information Systems (GIS) however, techniques and tools for mapping and analysis of geographical data have matured significantly. In order to ensure model simulations are reproducible and the provenance of the data required is recorded, the manipulation and agglomeration of initialisation data needs to be standardised and automated. With the typical constraints on simulation domains for geophysical models consisting of bounding paths and surface scalar fields in a two dimensional plane, GIS frameworks potentially offer exactly what is required to formalise this processing. A new approach to the discretisation of geophysical domains is presented (Candy et al. [1], and [2] — [3]), and introduced with verified implementations. A complex domain example with a multi scale discretisation will be presented with scales ranging from 2000 km down to 5m details of the manmade structures of Portland harbour (Figure 1) and lagoon flats behind Chesil beach on the south coast (Figure 2). This brings together the fields of geospatial analysis, meshing and numerical simulation models. This platform enables us to combine and built up features, quickly drafting and updating mesh descriptions with the rigour that established GIS tools provide. This, combined with the systematic workflow, supports a strong provenance for model initialisation and encourages the convergence of standards. @en

Embedding tidal turbines within simulations of realistic large-scale tidal flows is a highly multi-scale problem that poses significant computational challenges. Here this problem is tackled using actuator disc momentum (ADM) theory and Reynolds-averaged Navier–Stokes (RANS) with, for the first time, dynamically adaptive mesh optimisation techniques. Both k−ωk−ω and k−ωk−ω SST RANS models have been developed within the Fluidity framework, an adaptive mesh CFD solver, and the model is validated against two sets of experimental flume test results. A brief comparison against a similar OpenFOAM model is presented to portray the benefits of the finite element discretisation scheme employed in the Fluidity ADM model. This model has been developed with the aim that it will be seamlessly combined with larger numerical models simulating tidal flows in realistic domains. This is where the mesh optimisation capability is a major advantage as it enables the mesh to be refined dynamically in time and only in the locations required, thus making optimal use of limited computational resources.@en

Recent observational studies (Jenkins et al. 2010 [1] and Dutrieux et al. 2013 [2]) have helped to constrain estimates of the melt behaviour underneath Pine Island Glacier (PIG) ice shelf, in western Antarctica. Generally however, observations are limited, due to the relatively inaccessible and inhospitable environment. A solid ice cover, up to many kilometres thick, bars access to the water column, so that observational data can only be obtained by inference from above, drilling holes through, or launching autonomous vehicles beneath the ice. This is further exacerbated by the fact that results of these recent studies have implied a significant proportion of the melting (~80%) occurs in networks of sub-kilometre scale basal channels close to the grounding line, some of the most inaccessible parts of sub-ice shelf ocean cavities. Accurately representing these small-scale processes in conventional ocean models is a huge challenge even in focused regional studies, and will not be possible in global coupled climate simulations in the near future. I will present the development of a new model of PIG that is capable of resolving the range of scales necessary to evaluate the melt distribution and forming processes that dominate. This is built on the Fluidity model (Piggott et al. 2008 [8]) that simulates non-hydrostatic dynamics on meshes that, like the FESOM model of Timmermann et al. 2012 [4], can be unstructured. In this case, the grid can be unstructured in all three dimensions and use an anisotropic adaptive-in-time resolution to optimise the mesh and calculation in response to evolving solution dynamics. The parameterisation of melting in this model has been validated in idealised cavity domains (Kimura et al. 2013 [5]) and a validation is underway for the dynamic treatment of the ice-ocean interface (Candy et al. 2016 [7]). Additionally, the model is not limited to a specific vertical coordinate system and can capture purely vertical features, which enables it to accurately represent ice fronts, and small shallow features. I will discuss the development of this model of PIG; including the cavity domain, conforming to appropriately filtered boundaries generated from data collected during the British Antarctic Survey Autosub 2009 expedition, and the simulation of non-hydrostatic dynamics to date (see Figure 1). This will include validation to observations and MITgcm model results in a Circumpolar Deep Water forcing scenario from measurements in 2012, as recently presented in Science (Dutrieux et al. 2014 [3]). The unstructured nature of the developed model (Candy et al. 2016 [6]) captures the high spatial variation seen in melt rates in the small-scale channels, that its difficult to resolve in other fixed-mesh state-of-the-art models @en