Modelling an energetic tidal strait: investigating implications of common numerical configuration choices

Mackie, Lucas; Evans, Paul; Harrold, Magnus; O'Doherty, Timothy; Piggott, Matthew D.; Angeloudis, Athanasios

doi:10.1016/j.apor.2020.102494

“…which is used instead, following Mackie et al (2021). Additionally and when relevant, intertidal processes are treated using the wetting and drying formulation of Karna et al (2011).…”

Section: Shallow Water Equation Modelling With Thetismentioning

confidence: 99%

“…Increased refinement of a uniform element size ∧h = 5 m has been imposed to resolve individual turbines within the Meygen tidal site. The simulation results are produced using a variable Manning's n M across the domain based on bed classification data provided by the British Geological Survey, as described by Mackie et al (2021), and a timestep ∆t = 100 s.…”

Section: Application To the Pentland Firth Scotland Ukmentioning

confidence: 99%

Combining shallow-water and analytical wake models for tidal array micro-siting

Jordan¹,

Dundovic²,

Fragkou³

et al. 2022

Preprint

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Array optimisation is critical for improving power performance and reducing infrastructure costs thereby helping enable tidal-stream energy to become a competitive renewable energy source. However, ascertaining an optimal array layout is a highly complex problem, subject to the specific site hydrodynamics characterisation and multiple inter-disciplinary constrains. In this work, we present a novel optimisation approach that combines an analytical-based wake model, FLORIS, with an ocean model, Thetis. The approach is demonstrated with applications of increasing complexity. By utilising the method of analytical wake superposition, the addition or alteration of turbine position does not require re-calculation of the entire flow field, thus allowing the use of simple heuristic techniques to perform optimisation at a fraction of the computational cost of more sophisticated methods. Using a custom condition-based placement algorithm, this methodology is applied to the Pentland Firth for 24 turbines with a rated speed of 3.05 m/s, demonstrating practical implications whilst also considering the temporal variability of the tide. Micro-siting using this technique generated an array 12% more productive on average than a staggered layout, despite flow speeds regularly exceeding the rated value. Performance was further evaluated through assessment of the optimised layout within the ocean model that represents the turbines through a discrete turbine representation.Used iteratively, this methodology could be applied to deliver improved array configurations in a manner that accounts for local hydrodynamic effects.

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“…We employ a parameter estimation method in order to calibrate the model with respect to the spatially varying Manning coefficient, n. In order to constrain the parameter's spatial variation, we use sediment maps within the model domain. In an approach similar to [33],…”

Section: Parameterising the Manning Coefficientmentioning

confidence: 99%

“…3 for the spatial distribution of the sediment types. Based on [33]. In any regions where sediment data is unavailable, the default Manning coefficient of n = 0.025 s m −1/3 is applied.…”

Section: Observation Datamentioning

confidence: 99%

“…Alternatively, sedimentological data can be used for the purpose of constraining the spatial variation of the BFC, due to the underlying physical relationship between sediment type and the roughness of the sea bed, and hence the value of the friction coefficient. [33] directly apply Manning coefficients derived from sedimentological data within a model of the Irish Sea, supplemented by a localised BFC enhancement around a region of interest which they tune for optimal model performance. Similarly, [17] utilise sedimentological data to derive a spatially varying quadratic drag parameter for a tidal stream power application off the coast of Brittany, and subsequently perform a sensitivity analysis with respect to the roughness length assigned to one of the sediment types.…”

Section: Introductionmentioning

confidence: 99%

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Sedimentological data-driven bottom friction parameter estimation in modelling Bristol Channel tidal dynamics

Warder¹,

Angeloudis²,

Piggott³

2022

Preprint

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1

0

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Accurately representing the bottom friction effect is a significant challenge in numerical tidal models. Bottom friction effects are commonly defined via parameter estimation techniques. However, the bottom friction coefficient (BFC) can be related to the roughness of the sea bed. Therefore, sedimentological data can be beneficial in estimating BFCs. Taking the Bristol Channel and Severn Estuary as a case study, we perform a number of BFC parameter estimation experiments, utilising sedimentological data in a variety of ways. Model performance is explored through the results of each parameter estimation experiment, including applications to tidal range and tidal stream resource assessment. We find that theoretically derived sediment-based BFCs are in most cases detrimental to model performance. However, good performance is obtained by retaining the spatial information provided by the sedimentological data in the formulation of the parameter estimation experiment; the spatially varying BFC can be represented as a piecewise-constant field following the spatial distribution of the observed sediment types. By solving the resulting low-dimensional parameter estimation problem, we obtain good model performance as measured against tide gauge data. This approach appears well suited to modelling tidal range energy resource, which is of particular interest in the case study region. However, the applicability of this approach for tidal stream resource assessment is limited, since modelled tidal currents exhibit a strong localised response to the BFC; the use of piecewise-constant (and therefore discontinuous) BFCs is found to be detrimental to model performance for tidal currents.

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Sedimentological data-driven bottom friction parameter estimation in modelling Bristol Channel tidal dynamics

Warder

¹

,

Angeloudis

²

,

Piggott

³

2022

View full text Add to dashboard Cite

Accurately representing the bottom friction effect is a significant challenge in numerical tidal models. Bottom friction effects are commonly defined via parameter estimation techniques. However, the bottom friction coefficient (BFC) can be related to the roughness of the sea bed. Therefore, sedimentological data can be beneficial in estimating BFCs. Taking the Bristol Channel and Severn Estuary as a case study, we perform a number of BFC parameter estimation experiments, utilising sedimentological data in a variety of ways. Model performance is explored through the results of each parameter estimation experiment, including applications to tidal range and tidal stream resource assessment. We find that theoretically derived sediment-based BFCs are in most cases detrimental to model performance. However, good performance is obtained by retaining the spatial information provided by the sedimentological data in the formulation of the parameter estimation experiment; the spatially varying BFC can be represented as a piecewise-constant field following the spatial distribution of the observed sediment types. By solving the resulting low-dimensional parameter estimation problem, we obtain good model performance as measured against tide gauge data. This approach appears well suited to modelling tidal range energy resource, which is of particular interest in the case study region. However, the applicability of this approach for tidal stream resource assessment is limited, since modelled tidal currents exhibit a strong localised response to the BFC; the use of piecewise-constant (and therefore discontinuous) BFCs is found to be detrimental to model performance for tidal currents.

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Modelling an energetic tidal strait: investigating implications of common numerical configuration choices

Cited by 20 publications

References 45 publications

Combining shallow-water and analytical wake models for tidal array micro-siting

Combining shallow-water and analytical wake models for tidal array micro-siting

Sedimentological data-driven bottom friction parameter estimation in modelling Bristol Channel tidal dynamics

Sedimentological data-driven bottom friction parameter estimation in modelling Bristol Channel tidal dynamics

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