Methodology for tidal turbine representation in ocean circulation model

The present method proposes the use and adaptation of ocean circulation models as an assessment tool framework for tidal current turbine (TCT) array layout optimization. By adapting both momentum and turbulence transport equations of an existing model, the present TCT representation method is proposed to extend the actuator disc concept to 3-D large-scale ocean circulation models. Through the reproduction of experimental flume tests and grid dependency tests, this method has shown its numerical coherence as well as its ability to simulate accurately both momentum and turbulent turbine-induced perturbations in both near and far wakes in a relatively short period of computation time. Consequently the present TCT representation method is a very promising basis for the development of a TCT array layout optimization tool

Modelling Offshore Wave farms for Coastal Process Impact Assessment: Waves, Beach Morphology, and Water Users

The emerging global wave energy industry has the potential to contribute to the world’s energy needs, but careful consideration of potential impacts to coastal processes in the form of an impact assessment is required for each new wave energy site. Methods for conducting a coastal processes impact assessment for wave energy arrays vary considerably in the scientific literature, particularly with respect to characterising the energy absorption of a wave energy converter (WEC) array in a wave model. In this paper, modelling methods used in the scientific literature to study wave farm impacts on coastal processes are reviewed, with the aim of determining modelling guidance for impact assessments. Effects on wave climate, beach morphology, and the surfing resource for coastal water users are considered. A novel parameterisation for the WEC array transmission coefficient is presented that, for the first time, uses the permitted power rating of the wave farm, which is usually well defined at the impact assessment stage, to estimate the maximum likely absorption of a permitted WEC array. A coastal processes impact assessment case study from a wave farm in south-west Ireland is used to illustrate the application of the reviewed methods, and demonstrates that using the new ‘rated power transmission coefficient’ rather than a WEC-derived transmission coefficient or complete energy absorption scenario can make the difference between significant and non-significant levels of coastal impacts being predicted

Evaluation of turbulence closure models under spilling and plunging breakers in the surf zone

Turbulence closure models are evaluated for application to spilling and plunging breakers in the surf zone using open source computational fluid dynamics software. A new library of turbulence models for application to multiphase flows has been developed and is assessed for numerical efficiency and accuracy by comparing against existing laboratory data for surface elevation, velocity and turbulent kinetic energy profiles. Out of the models considered, it was found that, overall, the best model is the nonlinear k - ϵ model. The model is also shown to exhibit different turbulent characteristics between the different breaker types, consistent with experimental data

Comparison of HF Radar Fields of Directional Wave Spectra Against In Situ Measurements at Multiple Locations

The coastal zone hosts a great number of activities that require knowledge of the spatial characteristics of the wave field, which in coastal seas can be highly heterogeneous. Information of this type can be obtained from HF radars, which offer attractive performance characteristics in terms of temporal and spatial resolution. This paper presents the validation of radar-derived fields of directional wave spectra. These were retrieved from measurements collected with an HF radar system specifically deployed for wave measurement, using an established inversion algorithm. Overall, the algorithm reported accurate estimates of directional spectra, whose main distinctive characteristic was that the spectral energy was typically spread over a slightly broader range of frequencies and directions than in their in situ-measured counterparts. Two errors commonly reported in previous studies, namely the overestimation of wave heights and noise related to short measurement periods, were not observed in our results. The maximum wave height recorded by two in situ devices differed by 30 cm on average from the radar-measured values, and with the exception of the wave spreading, the standard deviations of the radar wave parameters were between 3% and 20% of those obtained with the in situ datasets, indicating the two were similarly grouped around their means. At present, the main drawback of the method is the high quality signal required to perform the inversion. This is in part responsible for a reduced data return, which did not exceed 55% at any grid cell over the eight-month period studied here.</jats:p

Using Artificial Neural Networks for the Estimation of Subsurface Tidal Currents from High-Frequency Radar Surface Current Measurements

An extensive record of current velocities at all levels in the water column is an indispensable requirement for a tidal resource assessment and is fully necessary for accurate determination of available energy throughout the water column as well as estimating likely energy capture for any particular device. Traditional tidal prediction using the least squares method requires a large number of harmonic parameters calculated from lengthy acoustic Doppler current profiler (ADCP) measurements, while long-term in situ ADCPs have the advantage of measuring the real current but are logistically expensive. This study aims to show how these issues can be overcome with the use of a neural network to predict current velocities throughout the water column, using surface currents measured by a high-frequency radar. Various structured neural networks were trained with the aim of finding the network which could best simulate unseen subsurface current velocities, compared to ADCP data. This study shows that a recurrent neural network, trained by the Bayesian regularisation algorithm, produces current velocities highly correlated with measured values: r2 (0.98), mean absolute error (0.05 ms−1), and the Nash–Sutcliffe efficiency (0.98). The method demonstrates its high prediction ability using only 2 weeks of training data to predict subsurface currents up to 6 months in the future, whilst a constant surface current input is available. The resulting current predictions can be used to calculate flow power, with only a 0.4% mean error. The method is shown to be as accurate as harmonic analysis whilst requiring comparatively few input data and outperforms harmonics by identifying non-celestial influences; however, the model remains site specific.</jats:p

Vertical structure of near-bed cross-shore flow velocities in the swash zone of a dissipative beach

Cross-shore velocity profiles are measured at 0.001m vertical resolution and at 100Hz over the lower 0.02-0.07m of the water column in the mid swash zone on a dissipative, macrotidal beach. Swash motion is predominantly at infragravity frequencies and forced by significant wave heights exceeding 1.5m and peak wave periods over 15s. Observations of long duration (> 14s) swashes during two rising tides are used to quantify the vertical structure of cross-shore flow velocities and estimate corresponding bed shear stress and friction coefficients. Analysis is performed on an individual swash event to an elevation of 0.07 m and an ensemble event made up of 24 individual swash events to an elevation of 0.02m. Cross-shore velocities exceed 2 m s-1 and are of a similar magnitude during both the uprush and the backwash. Changes in velocity with elevation indicate that the swash zone boundary layer extends to 0.07m during the strongest flows and is well-represented by the logarithmic model applied to this elevation, except near flow reversal. Maximum bed shear stresses estimated using the logarithmic model are 22 N m-2 and 10 N m-2 for the individual event and ensemble event respectively and mean values are larger during the backwash than the uprush. Mean friction coefficients estimated from equating the logarithmic model and the quadratic drag law are 0.018 and 0.019 for the individual event and ensemble event respectively. Bed shear stress may be underestimated if the logarithmic model is fit to a velocity profile that is only part boundary layer, emphasising the need for high resolution velocity profiles close to the bed for accurate bed shear stress predictions in the swash zone

The Impact of Waves and Tides on Residual Sand Transport on a Sediment‐Poor, Energetic, and Macrotidal Continental Shelf

©2019. The Authors. The energetic, macrotidal shelf off South West England was used to investigate the influence of different tide and wave conditions and their interactions on regional sand transport patterns using a coupled hydrodynamic, wave, and sediment transport model. Residual currents and sediment transport patterns are important for the transport and distribution of littoral and shelf-sea sediments, morphological evolution of the coastal and inner continental shelf zones, and coastal planning. Waves heavily influence sand transport across this macrotidal environment. Median (50% exceedance) waves enhance transport in the tidal direction. Extreme (1% exceedance) waves can reverse the dominant transport path, shift the dominant transport phase from flood to ebb, and activate sand transport below 120-m depth. Wave-tide interactions (encompassing radiation stresses, Stoke's drift, enhanced bottom-friction and bed shear stress, refraction, current-induced Doppler shift, and wave blocking) significantly and nonlinearly enhance sand transport, determined by differencing transport between coupled, wave-only, and tide-only simulations. A new continental shelf classification scheme is presented based on sand transport magnitude due to wave-forcing, tide-forcing, and nonlinear wave-tide interactions. Classification changes between different wave/tide conditions have implications for sand transport direction and distribution across the shelf. Nonlinear interactions dominate sand transport during extreme waves at springs across most of this macrotidal shelf. At neaps, nonlinear interactions drive a significant proportion of sand transport under median and extreme waves despite negligible tide-induced transport. This emphasizes the critical need to consider wave-tide interactions when considering sand transport in energetic environments globally, where previously tides alone or uncoupled waves have been considered