Publications
210 results found
Zhang C, Zhang J, Angeloudis A, et al., 2023, Physical Modelling of Tidal Stream Turbine Wake Structures under Yaw Conditions, Energies, Vol: 16
Tidal stream turbines may operate under yawed conditions due to variability in ocean current directions. Insight into the wake structure of yawed turbines can be essential to ensure efficient tidal stream energy extraction, especially for turbine arrays where wake interactions emerge. We studied experimentally the effects of turbines operating under varying yaw conditions. Two scenarios, including a single turbine and a set of two turbines in alignment, were configured and compared. The turbine thrust force results confirmed that an increasing yaw angle results in a decrease in the turbine streamwise force and an increase in the turbine spanwise force. The velocity distributionfrom the single turbine scenario showed that the wake deflection and velocity deficit recovery rate increased at a rate proportional to the yaw angle. The two-turbine scenario results indicated that the deployment of an upstream non-yawed turbine significantly limited the downstream wake steering (i.e., the wake area behind the downstream turbine). Interestingly, a yawed downstream turbine was seen to influence the steering of both the upstream and the downstream wakes. These systematically derived data could be regarded as useful references for the numerical modelling and optimisation of large arrays.
Clare MCA, Piggott MD, 2023, Bayesian neural networks for the probabilistic forecasting of wind direction and speed using ocean data, Trends in Renewable Energies Offshore - Proceedings of the 5th International Conference on Renewable Energies Offshore, RENEW 2022, Pages: 533-540
Neural networks are increasingly used to predict wind direction and speed, two important factors for estimating a wind farm’s potential power output. However classical neural networks lack the ability to express uncertainty and hence here we apply Bayesian Neural Networks (BNNs) to the problem of offshore wind resource prediction. For BNNs, the weights and outputs are distributions leading to well-calibrated probabilistic forecasts which add considerable value to the results. In particular, probabilistic forecasts inform on the network’s ability to make predictions of out-of-sample datapoints. We use this property to conclude that the accuracy and uncertainty of our BNN is unaffected by the construction of a nearby wind farm. For our dataset, we use observations from the FINO1 research platform and ocean data as the predictors. We thus show that at this site, networks trained on pre-farm ocean data can accurately predict wind field information post construction of a wind farm.
Clare MCA, Wallwork JG, Kramer SC, et al., 2022, Multi-scale hydro-morphodynamic modelling using mesh movement methods, GEM: International Journal on Geomathematics, Vol: 13, ISSN: 1869-2672
Hydro-morphodynamic modelling is an important tool that can be used in the protection of coastal zones. The models can be required to resolve spatial scales ranging from sub-metre to hundreds of kilometres and are computationally expensive. In this work, we apply mesh movement methods to a depth-averaged hydro-morphodynamic model for the first time, in order to tackle both these issues. Mesh movement methods are particularly well-suited to coastal problems as they allow the mesh to move in response to evolving flow and morphology structures. This new capability is demonstrated using test cases that exhibit complex evolving bathymetries and have moving wet-dry interfaces. In order to be able to simulate sediment transport in wet-dry domains, a new conservative discretisation approach has been developed as part of this work, as well as a sediment slide mechanism. For all test cases, we demonstrate how mesh movement methods can be used to reduce discretisation error and computational cost. We also show that the optimum parameter choices in the mesh movement monitor functions are fairly predictable based upon the physical characteristics of the test case, facilitating the use of mesh movement methods on further problems.
Warder SC, Piggott MD, 2022, Optimal experiment design for a bottom friction parameter estimation problem, GEM-INTERNATIONAL JOURNAL ON GEOMATHEMATICS, Vol: 13, ISSN: 1869-2672
Shadrick J, Rood D, Hurst M, et al., 2022, Sea level rise will likely accelerate rock coast cliff retreat rates, Nature Communications, Vol: 13, ISSN: 2041-1723
Coastal response to anthropogenic climate change is of central importance to the infrastructure and inhabitants in these areas. Despite being globally ubiquitous, the stability of rock coasts has been largely neglected, and the expected acceleration of cliff erosion following sea-level rise has not been tested with empirical data, until now. We have optimised a coastal evolution model to topographic and cosmogenic radionuclide data to quantify cliff retreat rates for the past 8000 years and forecast rates for the next century. Here we show that rates of cliff retreat will increase by up to an order of magnitude by 2100 according to current predictions of sea-level rise: an increase much greater than previously predicted. This study challenges conventional coastal management practices by revealing that even historically stable rock coasts are highly sensitive to sea-level rise and should be included in future planning for global climate change response.
Little A, Piggott MD, Buchan AG, 2022, Authors reply to comment by Michio Aoyama on "Development of a gamma ray dose rate calculation and mapping tool for Lagrangian marine nuclear emergency response models" by Little et al., MARINE POLLUTION BULLETIN, Vol: 184, ISSN: 0025-326X
Halilovic S, Böttcher F, Kramer S, et al., 2022, Well layout optimization for groundwater heat pump systems using the adjoint approach, Energy Conversion and Management, Vol: 268, ISSN: 0196-8904
Groundwater heat pump systems cause thermal anomalies in the aquifer that can impact upon downstream systems and reduce their efficiency. Therefore, it is important to optimally position the extraction and injection wells of such systems to avoid negative interactions and maximize the thermal potential of the aquifer. This paper presents a new method to determine optimal well layouts of groundwater heat pumps using the adjoint approach, which is an efficient way to solve the underlying PDE-constrained optimization problem. An integral part of the method is the numerical groundwater simulation, which here is based on the finite element method. In addition, a multi-start initialization strategy is introduced in an attempt tobetter reach the global optimum. The method is applied to a real case study with 10 groundwater heat pumps, i.e. 20 wells, and two optimization scenarios with different natural groundwater temperatures. In both scenarios, the optimization method successfully determines a well layout that maximizes groundwater temperatures at all extraction wells. Comparing the results from these scenarios demonstrates that hydro-geological conditions can have a significant impact on the optimal well layout. The proposed method is equally applicable to systems with multiple extraction and injection wells and can be extended to various other shallow geothermal applications, such as combined heating and cooling systems.
Clare MCA, Leijnse TWB, McCall RT, et al., 2022, Multilevel multifidelity Monte Carlo methods for assessing uncertainty in coastal flooding, Natural Hazards and Earth System Sciences, Vol: 22, Pages: 2491-2515, ISSN: 1561-8633
When choosing an appropriate hydrodynamic model, there is always a compromise between accuracy and computational cost, with high-fidelity models being more expensive than low-fidelity ones. However, when assessing uncertainty, we can use a multifidelity approach to take advantage of the accuracy of high-fidelity models and the computational efficiency of low-fidelity models. Here, we apply the multilevel multifidelity Monte Carlo method (MLMF) to quantify uncertainty by computing statistical estimators of key output variables with respect to uncertain input data, using the high-fidelity hydrodynamic model XBeach and the lower-fidelity coastal flooding model SFINCS (Super-Fast INundation of CoastS). The multilevel aspect opens up the further advantageous possibility of applying each of these models at multiple resolutions. This work represents the first application of MLMF in the coastal zone and one of its first applications in any field. For both idealised and real-world test cases, MLMF can significantly reduce computational cost for the same accuracy compared to both the standard Monte Carlo method and to a multilevel approach utilising only a single model (the multilevel Monte Carlo method). In particular, here we demonstrate using the case of Myrtle Beach, South Carolina, USA, that this improvement in computational efficiency allows for in-depth uncertainty analysis to be conducted in the case of real-world coastal environments – a task that would previously have been practically unfeasible. Moreover, for the first time, we show how an inverse transform sampling technique can be used to accurately estimate the cumulative distribution function (CDF) of variables from the MLMF outputs. MLMF-based estimates of the expectations and the CDFs of the variables of interest are of significant value to decision makers when assessing uncertainty in predictions.
Piggott M, 2022, Multilevel multifidelity Monte Carlo methods for assessing coastal flood risk, Natural Hazards and Earth System Sciences, Vol: 22, Pages: 2491-2515, ISSN: 1561-8633
Abstract. When choosing an appropriate hydrodynamic model, there is always a compromise between accuracy and computational cost, with high fidelity models being more expensive than low fidelity ones. However, when assessing uncertainty, wecan use a multifidelity approach to take advantage of the accuracy of high fidelity models and the computational efficiencyof low fidelity models. Here, we apply the multilevel multifidelity Monte Carlo method (MLMF) to quantify uncertainty by computing statistical estimators of key output variables with respect to uncertain inputs, using the high fidelity hydrodynamicmodel XBeach and the lower fidelity coastal flooding model SFINCS. The multilevel aspect opens up the further advantageouspossibility of applying each of these models at multiple resolutions. This work represents the first application of MLMF in thecoastal zone and one of its first applications in any field. For both idealised and real-world test cases, MLMF can significantlyreduce computational cost for the same accuracy compared to both the standard Monte Carlo method and to a multilevel approach utilising only a single model (the multilevel Monte Carlo method). In particular, here we demonstrate using the case ofMyrtle Beach, USA, that this improvement in computational efficiency allows in-depth uncertainty analysis to be conducted inthe case of real-world coastal environments – a task that would previously have been practically unfeasible. Moreover, for thefirst time, we show how an inverse transform sampling technique can be used to accurately estimate the cumulative distributionfunction (CDF) of variables from the MLMF outputs. MLMF based estimates of the expectations and the CDFs of the variables of interest are of significant value to decision makers when assessing risk.
Pan W, Kramer SC, Piggott MD, et al., 2022, Modeling landslide generated waves using the discontinuous finite element method, International Journal for Numerical Methods in Fluids, Vol: 94, Pages: 1-33, ISSN: 0271-2091
A new two-layer model for impulsive wave generation by deformable granular landslides is developed based upon a discontinuous Galerkin finite element discretization. Landslide motion is modeled using a depth-averaged formulation for a shallow subaerial debris flow, which considers the bed curvature represented by the local slope angle variable and accounts for inter-granular stresses using Coulomb friction. Wave generation and propagation are simulated with the three-dimensional non-hydrostatic coastal ocean model Thetis to accurately capture key features such as wave dispersion. Two different techniques are used in treating wetting and drying (WD) processes during the landslide displacement and wave generation, respectively. For the lower-layer landslide motion across the dry bed a classical thin-layer explicit WD method is implemented, while for the resulting free-surface waves interacted with the moving landslide an implicit WD scheme is utilized to naturally circumvent the artificial pressure gradient problem which may appear in the dynamic interaction between the landslide and water if using the thin-layer method. The two-layer model is validated using a suite of test cases, with the resulting good agreement demonstrating its capability in describing both the complex behaviors of granular landslides from initiation to deposition, and the consequent wave generation and propagation.
Little A, Piggott MD, Buchan AG, 2022, Development of a gamma ray dose rate calculation and mapping tool for Lagrangian marine nuclear emergency response models., Marine Pollution Bulletin, Vol: 181, Pages: 1-12, ISSN: 0025-326X
This paper presents the development and testing of a gamma radiation dose rate calculation model for the marine environment, and evaluates the potential use for such a model in both short term nuclear emergency response management and emergency response planning. This is believed to be the first implementation of a full field gamma radiation mapping model (including air attenuation and buildup) to be incorporated within a Lagrangian marine dispersion model. Calculated surface gamma ray dose rates for nine generic release scenarios are presented and used to undertake an emergency countermeasure optioneering assessment.
Christensen AK, Piggott MD, van Sebille E, et al., 2022, Investigating microscale patchiness of motile microbes under turbulence in a simulated convective mixed layer., PLoS Comput Biol, Vol: 18
Microbes play a primary role in aquatic ecosystems and biogeochemical cycles. Spatial patchiness is a critical factor underlying these activities, influencing biological productivity, nutrient cycling and dynamics across trophic levels. Incorporating spatial dynamics into microbial models is a long-standing challenge, particularly where small-scale turbulence is involved. Here, we combine a fully 3D direct numerical simulation of convective mixed layer turbulence, with an individual-based microbial model to test the key hypothesis that the coupling of gyrotactic motility and turbulence drives intense microscale patchiness. The fluid model simulates turbulent convection caused by heat loss through the fluid surface, for example during the night, during autumnal or winter cooling or during a cold-air outbreak. We find that under such conditions, turbulence-driven patchiness is depth-structured and requires high motility: Near the fluid surface, intense convective turbulence overpowers motility, homogenising motile and non-motile microbes approximately equally. At greater depth, in conditions analogous to a thermocline, highly motile microbes can be over twice as patch-concentrated as non-motile microbes, and can substantially amplify their swimming velocity by efficiently exploiting fast-moving packets of fluid. Our results substantiate the predictions of earlier studies, and demonstrate that turbulence-driven patchiness is not a ubiquitous consequence of motility but rather a delicate balance of motility and turbulent intensity.
Song W, Zhang M, Wallwork JG, et al., 2022, M2N: mesh movement networks for PDE solvers
Mainstream numerical Partial Differential Equation (PDE) solvers requirediscretizing the physical domain using a mesh. Mesh movement methods aim toimprove the accuracy of the numerical solution by increasing mesh resolutionwhere the solution is not well-resolved, whilst reducing unnecessary resolutionelsewhere. However, mesh movement methods, such as the Monge-Ampere method,require the solution of auxiliary equations, which can be extremely expensiveespecially when the mesh is adapted frequently. In this paper, we propose toour best knowledge the first learning-based end-to-end mesh movement frameworkfor PDE solvers. Key requirements of learning-based mesh movement methods arealleviating mesh tangling, boundary consistency, and generalization to meshwith different resolutions. To achieve these goals, we introduce the neuralspline model and the graph attention network (GAT) into our modelsrespectively. While the Neural-Spline based model provides more flexibility forlarge deformation, the GAT based model can handle domains with more complicatedshapes and is better at performing delicate local deformation. We validate ourmethods on stationary and time-dependent, linear and non-linear equations, aswell as regularly and irregularly shaped domains. Compared to the traditionalMonge-Ampere method, our approach can greatly accelerate the mesh adaptationprocess, whilst achieving comparable numerical error reduction.
Clare MCA, Piggott MD, Cotter CJ, 2022, Assessing erosion and flood risk in the coastal zone through the application of multilevel Monte Carlo methods, Coastal Engineering, Vol: 174, ISSN: 0378-3839
Coastal zones are vulnerable to both erosion and flood risk, which can be assessed using coupled hydro-morphodynamic models. However, the use of such models as decision support tools suffers from a high degreeof uncertainty, due to both incomplete knowledge and natural variability in the system. In this work, we showfor the first time how the multilevel Monte Carlo method (MLMC) can be applied in hydro-morphodynamiccoastal ocean modelling, here using the popular model XBeach, to quantify uncertainty by computing statisticsof key output variables given uncertain input parameters. MLMC accelerates the Monte Carlo approach throughthe use of a hierarchy of models with different levels of resolution. Several theoretical and real-world coastalzone case studies are considered here, for which output variables that are key to the assessment of flood anderosion risk, such as wave run-up height and total eroded volume, are estimated. We show that MLMC cansignificantly reduce computational cost, resulting in speed up factors of 40 or greater compared to a standardMonte Carlo approach, whilst keeping the same level of accuracy. Furthermore, a sophisticated ensemblegenerating technique is used to estimate the cumulative distribution of output variables from the MLMC output.This allows for the probability of a variable exceeding a certain value to be estimated, such as the probabilityof a wave run-up height exceeding the height of a seawall. This is a valuable capability that can be used toinform decision-making under uncertainty
Clare MCA, Kramer SC, Cotter CJ, et al., 2022, Calibration, inversion and sensitivity analysis for hydro-morphodynamic models through the application of adjoint methods, Computers and Geosciences, Vol: 163, Pages: 1-13, ISSN: 0098-3004
The development of reliable, sophisticated hydro-morphodynamic models is essential for protecting the coastal environment against hazards such as flooding and erosion. There exists a high degree of uncertainty associated with the application of these models, in part due to incomplete knowledge of various physical, empirical and numerical closure related parameters in both the hydrodynamic and morphodynamic solvers. This uncertainty can be addressed through the application of adjoint methods. These have the notable advantage that the number and/or dimension of the uncertain parameters has almost no effect on the computational cost associated with calculating the model sensitivities. Here, we develop the first freely available and fully flexible adjoint hydro-morphodynamic model framework. This flexibility is achieved through using the pyadjoint library, which allows us to assess the uncertainty of any parameter with respect to any model functional, without further code implementation. The model is developed within the coastal ocean model Thetis constructed using the finite element code-generation library Firedrake. We present examples of how this framework can perform sensitivity analysis, inversion and calibration for a range of uncertain parameters based on the final bedlevel. These results are verified using so-called dual-twin experiments, where the ‘correct’ parameter value is used in the generation of synthetic model test data, but is unknown to the model in subsequent testing. Moreover, we show that inversion and calibration with experimental data using our framework produces physically sensible optimum parameters and that these parameters always lead to more accurate results. In particular, we demonstrate how our adjoint framework can be applied to a tsunami-like event to invert for the tsunami wave from sediment deposits.
Jordan C, Dundovic D, Fragkou AK, et al., 2022, Combining shallow-water and analytical wake models for tidal-array micro-siting, Journal of Ocean Engineering and Marine Energy, Vol: 8, Pages: 193-215, ISSN: 2198-6444
For tidal-stream energy to become a competitive renewable energy source, clustering multiple turbines into arrays is paramount. Array optimisation is thus critical for achieving maximum power performance and reducing cost of energy. However, ascertaining an optimal array layout is a complex problem, subject to specific site hydrodynamics and multiple inter-disciplinary constraints. 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 through 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 arrays with turbines of 3.05m/s rated speed, demonstrating practical implications whilst considering the temporal variability of the tide. For a 24-turbine array case, micro-siting using this technique delivered an array 15.8% more productive on average than a staggered layout, despite flow speeds regularly exceeding the rated value. Performance was evaluated through assessment of the optimised layout within the ocean model that treats turbines through a discrete turbine representation. Used iteratively, this methodology could deliver improved array configurations in a manner that accounts for local hydrodynamic effects.
Warder SC, Angeloudis A, Piggott MD, 2022, Sedimentological data-driven bottom friction parameter estimation in modelling Bristol Channel tidal dynamics, OCEAN DYNAMICS, Vol: 72, Pages: 361-382, ISSN: 1616-7341
Wallwork JG, Barral N, Ham DA, et al., 2022, Goal-oriented error estimation and mesh adaptation for tracer transport modelling, Computer-Aided Design, Vol: 145, Pages: 1-21, ISSN: 0010-4485
This paper applies metric-based mesh adaptation methods to advection-dominated tracer transport modelling problems in two and three dimensions, using the finite element package Firedrake. In particular, the mesh adaptation methods considered are built upon goal-oriented estimates for the error incurred in evaluating a diagnostic quantity of interest (QoI). In the motivating example of modelling to support desalination plant outfall design, such a QoI could be the salinity at the plant inlet, which could be negatively impacted by the transport of brine from the plant’s outfall. Four approaches are considered, one of which yields isotropic meshes. The focus on advection-dominated problems means that flows are often anisotropic; thus, three anisotropic approaches are also considered. Meshes resulting from each of the four approaches yield solutions to the tracer transport problem which give better approximations to QoI values than uniform meshing, for a given mesh size. The methodology is validated using an existing 2D tracer transport test case with a known analytical solution. Goal-oriented meshes for an idealised time-dependent desalination outfall scenario are also presented.
Zhang J, Zhang C, Angeloudis A, et al., 2022, Interactions between tidal stream turbine arrays and their hydrodynamic impact around Zhoushan Island, China, Ocean Engineering, Vol: 246, Pages: 1-13, ISSN: 0029-8018
Tidal currents represent an attractive renewable energy source particularly because of their predictability. Prospective tidal stream development sites are often co-located in close proximity. Under such circumstances, in order to maximise the exploitation of the resource, multiple tidal stream turbine arrays working in tandem would be needed. In this paper, a continuous array optimisation approach based on the open source coastal ocean modelling framework Thetis is applied to derive optimal configurations for four turbine arrays around Zhoushan Islands, Zhejiang Province, China. Alternative optimisation scenarios are tested to investigate interactions between the turbine arrays and their hydrodynamic footprint. Results show that there are no obvious competition effects between these four arrays around Hulu and Taohua Island. However, significant interactions could arise among the three turbine arrays situated around Hulu Island, with a maximum decrease in average power of 42.2%. By optimising all turbine arrays simultaneously, the competition effects can be minimised and the cost of energy reduced as less turbines are required to deliver an equivalent energy output. As for the potential environmental impact, it is found that the turbine array around Taohua Island would affect a larger area than turbine arrays around Hulu Island.
Anagnostopoulos S, Piggott M, 2022, Offshore wind farm wake modelling using deep feed forward neural networks for active yaw control and layout optimisation, WindEurope Electric City Conference, Publisher: IOP PUBLISHING LTD, ISSN: 1742-6588
Zhang M, Wang J, Tlhomole J, et al., 2022, Learning to Estimate and Refine Fluid Motion with Physical Dynamics, INTERNATIONAL CONFERENCE ON MACHINE LEARNING, VOL 162, ISSN: 2640-3498
Shadrick JR, Hurst MD, Piggott MD, et al., 2021, Multi-objective optimisation of a rock coast evolution model with cosmogenic Be-10 analysis for the quantification of long-term cliff retreat rates, Earth Surface Dynamics, Vol: 9, Pages: 1505-1529, ISSN: 2196-6311
This paper presents a methodology that uses site-specific topographic and cosmogenic 10Be data to perform multi-objective model optimisation of a coupled coastal evolution and cosmogenic radionuclide production model. Optimal parameter estimation of the coupled model minimises discrepancies between model simulations and measured data to reveal the most likely history of rock coast development. This new capability allows a time series of cliff retreat rates to be quantified for rock coast sites over millennial timescales. Without such methods, long-term cliff retreat cannot be understood well, as historical records only cover the past ∼150 years. This is the first study that has (1) applied a process-based coastal evolution model to quantify long-term cliff retreat rates for real rock coast sites and (2) coupled cosmogenic radionuclide analysis with a process-based model. The Dakota optimisation software toolkit is used as an interface between the coupled coastal evolution and cosmogenic radionuclide production model and optimisation libraries. This framework enables future applications of datasets associated with a range of rock coast settings to be explored. Process-based coastal evolution models simplify erosional processes and, as a result, often have equifinality properties, for example that similar topography develops via different evolutionary trajectories. Our results show that coupling modelled topography with modelled 10Be concentrations can reduce equifinality in model outputs. Furthermore, our results reveal that multi-objective optimisation is essential in limiting model equifinality caused by parameter correlation to constrain best-fit model results for real-world sites. Results from two UK sites indicate that the rates of cliff retreat over millennial timescales are primarily driven by the rates of relative sea level rise. These findings provide strong motivation for further studies that investigate the effect of past and future relative sea level
Collins DS, Avdis A, Wells MR, et al., 2021, Prediction of shoreline–shelf depositional process regime guided by palaeotidal modelling, Earth-Science Reviews, Vol: 223, ISSN: 0012-8252
Ancient shoreline–shelf depositional systems are influenced by an unusually wide array of geological, biological and hydrodynamic processes, with sediment transport and deposition primarily determined by the interaction of river, wave (including storm) and tidal processes, and changes in relative sea level. Understanding the impact of these processes on shoreline–shelf morphodynamics and stratigraphic preservation remains challenging. Numerical modelling integrated with traditional facies analysis provides an increasingly viable approach, with the potential to quantify, and thereby improve understanding of, the impact of these complex coastal sedimentary processes. An integrated approach is presented here that focuses on palaeotidal modelling to investigate the controls on ancient tides and their influence on sedimentary deposition and preservation – one of the three cornerstones of the ternary process classification scheme of shoreline-shelf systems. Numerical tidal modelling methodology is reviewed and illustrated in three palaeotidal model case studies of different scales and focus. The results are synthesised in the context of shoreline–shelf processes, including a critique and modification of the process-based classification scheme.The emphasis on tidal processes reflects their global importance throughout Earth’s history. Ancient palaeotidal models are able to highlight and quantify the following four controls on tidal processes: (1) the physiography (shape and depth) of oceans (1000s km scale) determines the degree of tidal resonance; (2) the physiography of ocean connections to partly enclosed water bodies (100–1000s km scale) determines the regional-scale flux of tidal energy (inflow versus outflow); (3) the physiography of continental shelves influences shelf tidal resonance potential; and (4) tides in relatively local-scale embayments (typically 1–10s km scale) are influenced by the balance of tidal amplification
Coles D, Angeloudis A, Greaves D, et al., 2021, A review of the UK and British Channel Islands practical tidal stream energy resource, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, Vol: 477, Pages: 1-33, ISSN: 1364-5021
This review provides a critical, multi-faceted assessment of the practical contribution tidal stream energy can make to the UK and British Channel Islands future energy mix. Evidence is presented that broadly supports the latest national-scale practical resource estimate, of 34 TWh/year, equivalent to 11% of the UK’s current annual electricity demand. The size of the practical resource depends in part on the economic competitiveness of projects. In the UK, 124 MW of prospective tidal stream capacity is currently eligible to bid for subsidy support (MeyGen 1C, 80 MW; PTEC, 30 MW; and Morlais, 14 MW). It is estimated that the installation of this 124 MW would serve to drive down the levelized cost of energy (LCoE), through learning, from its current level of around 240 £/MWh to below 150 £/MWh, based on a mid-range technology learning rate of 17%. Doing so would make tidal stream cost competitive with technologies such as combined cycle gas turbines, biomass and anaerobic digestion. Installing this 124 MW by 2031 would put tidal stream on a trajectory to install the estimated 11.5 GW needed to generate 34 TWh/year by 2050. The cyclic, predictable nature of tidal stream power shows potential to provide additional, whole-system cost benefits. These include reductions in balancing expenditure that are not considered in conventional LCoE estimates. The practical resource is also dependent on environmental constraints. To date, no collisions between animals and turbines have been detected, and only small changes in habitat have been measured. The impacts of large arrays on stratification and predator–prey interaction are projected to be an order of magnitude less than those from climate change, highlighting opportunities for risk retirement. Ongoing field measurements will be important as arrays scale up, given the uncertainty in some environmental and ecological impact models. Ba
Mackie L, Kramer SC, Piggott MD, et al., 2021, Assessing impacts of tidal power lagoons of a consistent design, OCEAN ENGINEERING, Vol: 240, ISSN: 0029-8018
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Avalos Patino J, Dargaville S, Neethling S, et al., 2021, Impact of inhomogeneous unsteady participating media in a coupled convection-radiation system using finite element based methods, International Journal of Heat and Mass Transfer, Vol: 176, Pages: 1-16, ISSN: 0017-9310
Combined convection–radiation is a common phenomenon in many engineering problems. A differentially–heated rectangular enclosure is a widely–used benchmark for testing numerical techniques developed for solving the coupled momentum and energy equations related to combined convection–radiation. Previous studies have tended to describe the phenomenon in cases using simplified characteristics for the participating media including the assumptions of: (i) uniform distribution, (ii) homogeneous cross section, (iii) grey gas radiation and (iv) under steady state conditions. The effects of an inhomogeneous unsteady participating media, e.g. composed of a mixture of gases, are arguably understudied. In this work the effect of an inhomogeneous unsteady participating media on combined convection–radiation inside a rectangular enclosure is considered, under both grey and non-grey gas modelling approaches involving a mixture of gases. A key novelty in this work is the inclusion of the ability to handle inhomogeneous participating media which change in space, time and absorption cross section values as a result of the convection–radiation coupling, allowing us to assess different gas modelling approaches. A global gas radiation model is used and a new non–uniform discretisation method for the absorption distribution function is introduced; this method allows a better handling of those energy groups in which the Planck absorption coefficient is low, improving the performance of the spherical harmonics method and mitigating ray–effects on finite elements in angle discretisation. The momentum and energy equations are solved numerically using finite element based discretisation methods. The radiative transfer equation is solved numerically using both spherical harmonics and finite elements for the angular discretisation, with their relative performance compared. The results highlight the importance that the characteristics of the partic
Zhang C, Kramer SC, Angeloudis A, et al., 2021, Improving tidal turbine array performance through the optimisation of layout and yaw angles, European Wave and Tidal Energy Conference, Pages: 2205-1-2205-7-2205-1-2205-7, ISSN: 2706-6932
Tidal stream currents change in magnitude and direction during flood and ebb tides. Setting the most appropriate yaw angles for a tidal turbine is not only important to account for the performance of a single turbine, but can also be significant for the interactions between the turbines within an array. In this paper, a partial differentiation equation (PDE) constrained optimisation approach is established based on the Thetis coastal ocean modelling framework. The PDE constraint takes the form here of the two-dimensional, depth-averaged shallow water equations which are used to simulate tidal elevations and currents in the presence of tidal stream turbine arrays. The Sequential Least Squares Programming (SLSQP) algorithm is applied with a gradient obtained via the adjoint method in order to perform design optimisation. An idealised rectangular channel test case is studied to demonstrate this optimisation framework. Located in the centre of the computational domain, turbine arrays comprised of 12 turbines are tested in aligned and staggered layouts. The setups are initially optimised based on their yaw angles alone; their locations and yaw angles are also optimized simultaneously to improve the array overall performance. Results indicate that for the aligned turbine array case, the energy output can be increased by approximately 80% when considering yaw angle optimisation alone. For the staggered turbine array, the increase is approximately 30%. The yaw optimised staggered array is able to outperform the yaw optimised aligned array by approximately 8%. If both layout and the yaw angles of the turbines are considered within the optimisation then the increase is more significant compared with optimising yaw angle alone.
Goss ZL, Coles DS, Kramer SC, et al., 2021, Efficient economic optimisation of large-scale tidal stream arrays, Applied Energy, Vol: 295, Pages: 1-17, ISSN: 0306-2619
As the tidal energy industry moves from demonstrator arrays comprising just a few turbines to large-scale arrays made up of potentially hundreds of turbines, there is a need to optimise both the number of turbines and their spatial distribution in order to minimise cost of energy. Optimising array design manually may be feasible for small arrays, but becomes an impractically large approach when the number of devices is high, especially if taking into account both the cost effectiveness of each turbine and also the coupled nature of the turbine locations and the local as well as far-field hydrodynamics.Previous work has largely focused on producing computational tools to automatically design the size and layout of large-scale tidal turbine arrays to optimise power. There has been some limited preliminary work to incorporate costs into these models, in order to improve the economic viability of tidal arrays. This paper provides the first in depth implementation and analysis of economic functionals, based upon metrics such as break even power and levelised cost of energy, used for design of explicit array sizing and spatial variation.The addition of these new economic functionals introduces complexity by increasing the number of inputs to the model, each of which are subject to their own uncertainty in value. For this reason, sensitivity analysis becomes both more important as well as more difficult to undertake. This paper presents a novel rapid methodology for deriving the optimal array design (number of turbines and their spatial distribution throughout the farm area) to minimise cost functionals, and its sensitivity to variations in the economic inputs. Importantly, the new aspects of this method introduced here do not rely on repeated model runs and iterative optimisation, two aspects that typically prove to be impractically expensive computationally. This more readily allows for the impact of changes in investor priorities to be investigated. It is also shown tha
Rasheed S, Warder SC, Plancherel Y, et al., 2021, An improved gridded bathymetric data set and tidal model for the Maldives Archipelago, Earth and Space Science, Vol: 8, Pages: 1-15, ISSN: 2333-5084
The Maldives faces a unique range of environmental challenges. While the country is almost entirely dependent upon oceanic resources with more than 99% of the area covered by ocean, the absence of a suitable bathymetric map of the seafloor of the Maldives severely limits the adoption and application of modern scientific methods for the prediction of both physical and biological oceanic processes across the country. Here, we present a new bathymetric data set for the country based upon accumulating data from various sources and demonstrate that the synthesis of these provides a far more accurate representation of the shallow water areas of the region than currently available products. We also show that the new bathymetric data set is of sufficiently high resolution to model tidal flows across the archipelago for the first time. The new bathymetric data set provides numerous opportunities to better understand oceanic flow, associated physical and biogeochemical processes, and their correlation to one another across the Maldives archipelago.
Warder SC, Horsburgh KJ, Piggott MD, 2021, Adjoint-based sensitivity analysis for a numerical storm surge model, Ocean Modelling, Vol: 160, Pages: 1-13, ISSN: 1463-5003
Numerical storm surge models are essential to forecasting coastal flood hazard and informing the design of coastal defences. However, such models rely on a variety of inputs, many of which carry uncertainty. An awareness and understanding of the sensitivity of model outputs with respect to those uncertain inputs is therefore essential when interpreting model results. Here, we use an unstructured-mesh numerical coastal ocean model, Thetis, and its adjoint, to perform a sensitivity analysis for a hindcast of the 5th/6th December 2013 North Sea surge event, with respect to the bottom friction coefficient, bathymetry and wind stress forcing. The results reveal spatial and temporal patterns of sensitivity, providing physical insight into the mechanisms of surge generation and propagation. For example, the sensitivity of the skew surge to the bathymetry reveals the protective effect of a sand bank off the UK east coast. The results can also be used to propagate uncertainties through the numerical model; based on estimates of model input uncertainties, we estimate that modelled skew surges carry uncertainties of around 5 cm and 15 cm due to bathymetry and bottom friction, respectively. While these uncertainties are small compared with the typical spread in an ensemble storm surge forecast due to uncertain meteorological inputs, the adjoint-derived model sensitivities can nevertheless be used to inform future model calibration and data acquisition efforts in order to reduce uncertainty. Our results demonstrate the power of adjoint methods to gain insight into a storm surge model, providing information complementary to traditional ensemble uncertainty quantification methods.
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