JH
J. Hill
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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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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.
...
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.
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.
Integration of Geographic Information System frameworks into domain discretisation and meshing processes for geophysical models
Enabled by unstructured mesh approaches
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.
...
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.