Goal-Oriented Metric-Based Mesh Adaptive Tidal Farm Modelling

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Joseph Gregory Wallwork , Lucas Mackie , Stephan C Kramer, Nicolas Barral , Athanasios Angeloudis, Matthew Piggott


The modelling of a tidal array farm is an inherently multi-scale endeavour. It requires the simultaneous resolution of tidal processes across tens or hundreds of kilometres of coastal ocean (including estuaries, or even entire seas), the hydrodynamics in the neighbourhood of the farm (hundreds of metres), the wakes of individual turbines (metres, or tens of metres) and device hydrodynamics (sub-metre). As such, the construction of an accurate, computationally efficient numerical model requires careful consideration of the underlying discretisation.

In this paper, we apply time-dependent mesh adaptation techniques based on the Riemannian metric framework to an idealised tidal array and assess the quality of the resulting approximations. Whilst classical hierarchical mesh adaptation methods modify mesh element/cell size in order to improve resolution locally, the metric-based approach also allows for control of element shape and orientation, which can be especially advantageous for advection-dominated problems. Metrics are normalised in such a way that the resulting discretisation is multi-scale in both space and time as per Alauzet and Olivier (2010). Typically, metrics are constructed from recovered derivatives of solution fields, such as fluid vorticity. Alternatively, metrics may be derived from goal-oriented error estimates, enabling accurate estimation of a diagnostic quantity of interest (QoI). In the context of tidal farm modelling, one clear QoI is the power output. Building upon the idealised steady-state test case considered in Wallwork et al. (2020), which represents turbines using a drag parametrisation in a depth-averaged shallow water model, we demonstrate here that goal-oriented mesh adaptation can be used to obtain an accurate approximation of tidal farm power output using relatively few overall degrees of freedom.




Physical Sciences and Mathematics


Riemannian metric, tidal power, Thetis


Published: 2021-05-19 09:29


CC BY Attribution 4.0 International