Candy, Adam S., Avdis, Alexandros, Hill, Jonathan orcid.org/0000-0003-1340-4373 et al. (2 more authors) (2017) Efficient unstructured mesh generation for marine renewable energy applications. Renewable Energy. pp. 1-40. ISSN 0960-1481
Abstract
Renewable energy is the cornerstone of preventing dangerous climate change whilst maintaining 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 order to mitigate these effects, 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 efficiency 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 flow 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.
Metadata
Item Type: | Article |
---|---|
Authors/Creators: |
|
Copyright, Publisher and Additional Information: | © 2017 The Authors. Published by Elsevier Ltd. |
Dates: |
|
Institution: | The University of York |
Academic Units: | The University of York > Faculty of Sciences (York) > Environment and Geography (York) |
Depositing User: | Pure (York) |
Date Deposited: | 10 Oct 2017 14:30 |
Last Modified: | 16 Oct 2024 14:06 |
Published Version: | https://doi.org/10.1016/j.renene.2017.09.058 |
Status: | Published online |
Refereed: | Yes |
Identification Number: | 10.1016/j.renene.2017.09.058 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:122344 |
Download
Filename: 1_s2.0_S0960148117309205_main.pdf
Description: 1-s2.0-S0960148117309205-main
Licence: CC-BY-NC-ND 2.5