A2 - Improved modelling tools for resource and loading assessment
A - Resource and Environment Characterisation
Existing models for predicting ORE resources and extreme loading on ORE facilities can be unreliable, particularly when extrapolating to extreme conditions, new regions, or when modelling new types of device or system.
Existing models need to be improved or further developed to become suitable tools for designing ORE systems under the full range of conditions, particularly frontier developments and new devices.
Context And Need
Better and more reliable prediction of ORE performance will improve confidence in project financial projections. ORE performance predictions depend on modelling of the resource, the interaction of the resource with the ORE structures and the effect of the ORE farm on the environment. Existing models for wind resource modelling need to be adapted to include the motion responses of floating offshore wind structures and the presence of future very large farms. Existing models for wave resource modelling need to be expanded to consider extreme load conditions. Better representation of ordinary and extreme onsite conditions and wakes are needed in wind and tidal turbine modelling, and better understanding of the interaction and coupling between device, array and farm scales of models. Better ways to represent wind, wave and tidal farms will help to raise the survivability and reliability of ORE systems. These will also raise confidence in regional scale models for the performance of interacting ORE systems. Integration with ecological modelling and with sediment dynamics models is needed to understand the environmental impact.
Improved modelling tools are needed for wave, wind and tidal power resource and extreme loading assessment and for farm planning and project design. Models are also needed that include multi-scale farm-resource interaction and allow the effect of the environment on the ORE structures to be modelled as well as the effect of the ORE farm on the environment.
Impact on CAPEX as the interaction with resource and environmental conditions will be better understood and therefore design can have reduced uncertainty and potentially be less conservative.
Impact on OPEX as improved understanding of loading will better inform inspection and maintenance schedules, which together with improved prediction of available weather windows, will inform operations planning. Impact on all areas as a result of cost savings and better, more reliable predictions. Impact on ORE performance, survivability and reliability as fully-integrated and high-fidelity models will be built, leading to efficient predictions of the ORE structure responses under extreme and highly non-linear conditions associated with survivability and reliability issues.
ROMS, FAST, SWAN, WAMIT, SPH, RANS CFD etc. are used to model waves, currents and wind interactions between ORE structures and the environment. Regional scale models tend to have very simplistic representation of the turbine or wave energy device. Lacks the full coupled interaction with the environment. Conventional representations for responses of ORE structures and wind, wave farms are generally linear, which ignore the non-linearities that may be indispensable under extreme conditions or arrays.
Following ongoing projects are related:
- Wave Energy Transition to Future by Evolution of Engineering and Technology
- Modelling, Optimisation and Design of Conversion for Offshore Renewable Energy (UK-China MOD-CORE)
- Resilient Integrated-Coupled FOW platform design methodology (ResIn)
- CCP-WSIA Collaborative Computational Project in Wave Structure Interaction
- MAXFARM (MAXimizing wind Farm Aerodynamic Resource via advanced Modelling)
- FloWTurb: Response of Tidal Energy Converters to Combined Tidal Flow, Waves, and Turbulence
- Offshore Wind Innovation Hub - O&M and Windfarm Lifecycle innovation priorities
Supergen ORE Hub - Flexible Fund Research
- Accounting for Current in Wave Buoy Measurements
Lead Institution: University of Manchester
This project will develop a novel methodology to accurately quantify and describe the impact of current on wave measurement buoys. This work enables future measurements to more accurately account for the impact of current, provide a framework for estimating the current from wave buoy measurements, and reprocessing existing buoy datasets to provide historical current estimates. This means that offshore wind, tidal and wave energy technologies can be better designed considering the environmental conditions that they will be exposed to. Furthermore, the opportunity to use common, scaled, characterisation technology in the tank and field will aid the understanding of techniques used to translate site data into the laboratory.
- Veers Extension to Non-neutral Incoming Winds (VENTI)
Lead Institution: University of Surrey
As our society becomes ever-more dependent on wind power, it is increasingly important to gain a deeper understanding and more accurate predictability of the wind power availability, the aero-elastic fatigue loads on the wind turbine blades/drive train, and the associated issues of turbine control. The Sandia method proposes to numerically simulate the instantaneous three-dimensional wind field impacting on a wind turbine based solely on information from the frequency spectrum of the incoming wind (i.e. PSD) and its two-point velocity correlations in space across the turbine diameter. This method of prediction is very appealing for industrial applications as numerical predictions agree well with field measurements. This project will investigate whether the Sandia method can reliably be applied to flow with different stability properties, and thereby allow both better initial turbine design and better live prediction of loads and fatigue in service.
- Demonstrating a machine learning system to integrate metocean data, sensor networks, and model output for improved coverage and accuracy
Lead Institution: University of Exeter
Machine Learning for Low-Cost Offshore Modelling (MaLCOM) will develop a modelling methodology to provide rapid, accurate nowcasts and forecasts of the wave conditions at a regional scale using limited input data and requiring drastically reduced computational power. MaLCOM will use the historical outputs of a physics-based model and in-situ measurements to build a statistical representation, termed a surrogate model, between measurements and modelled conditions throughout a region using a machine learning approach. The developed surrogate model will provide two key benefits: (1) immediate spatial assessment of conditions with very little computational power required, such that it could be deployed on a mobile device or autonomous vessel and (2) improved accuracy of metocean forecasts through integrating in-situ measurements. This project will allow refinement and demonstration of this resource modelling and forecasting concept for marine energy sites.
- Improved Models for Multivariate Metocean Extremes (IMEX)
Lead Institution: University of Exeter
The design of offshore renewable energy (ORE) structures requires estimates of the joint extreme values of metocean variables. For example, the design of fixed or floating offshore wind turbines requires estimates of joint (concurrent) extremes of wave heights and wind speeds. Similarly, the design of tidal turbines requires estimates of the joint extremes of wave heights and current speeds, whilst for wave energy converters the joint extremes of wave heights and periods are important. The aim of the proposed research is to address the several research challenges in this area by (i) extending existing multivariate statistical models to create a single coherent and straightforward framework in which to estimate multivariate extremes, and (ii) developing open-source software for estimating multivariate metocean extremes, based on the methodologies developed in (i). The objectives of the proposed research are:
1. Extend the existing composite model approach to higher dimensions;
2. Develop a novel single-model approach for multivariate extremes;
3. Integrate the models into open-source software for estimation of multivariate extremes;
4. Demonstrate the application of models to extreme loading of ORE structures.
- FASTWATER: Freely-Available mesoScale simulation Tool for Wave, Tides and Eddy Replication
Lead Institution: University of Edinburgh
Offshore Renewable Energy (ORE), and multiple other marine technology interventions, are constrained by insufficient understanding, at the right scales and times, of the dynamic interactions of underlying ocean physical and environmental processes. These processes, including waves-current-turbulent atmosphere, are not adequately measured, statistically characterised or modelled to enable efficient design and operation of offshore infrastructure. For tidal energy sites in particular, spatial variability and the presence of waves result in significant uncertainties in device response and performance, with yield prediction errors of 30% reported. These effects are amplified across arrays. Further, recent research indicates that channel-scale three-dimensional (3D) flow features play a dominant role in the error of array yield predictions. Numerical simulation tools must replicate waves, spatial variability and 3D flow features robustly, with complexity built up in a modular way and with holistic and transparent spatial calibration and validation. Model calibration-validation in ORE lags other sectors e.g., climate modelling, epidemiological models of the covid pandemic and petrochemical exploration. Statistical techniques, commonly used in e.g., economics, neurology and seismology, are underused and could be readily adapted to help characterise spatial features of the flow. The project team have identified a large and costly gap between the development of increasingly complex and powerful models in academia and their adoption & exploitation by ORE organisations. FASTWATER is a targeted programme of research which - informed by industry - builds on multiple EPSRC academic and UK and EC industrial-academic projects, to bridge this gap. The primary aim is to reduce the cost of tidal energy arrays by developing powerful simulation tools that can be readily exploited by the sector.
Links to Industry Priorities:
We would also like to invite UK researchers and industry stakeholders within ORE to submit links to research projects, both past and present, for inclusion within the landscape.
Therefore, if you have a UK-based research project within an area of ORE that you feel is relevant to a specific research theme or challenge within the Research Landscape, click HERE to submit your research project to the research landscape.
PhD projects in Offshore Renewable Energy
In order to better understand the breadth of ORE research currently being conducted in the UK, the Supergen ORE Hub has collated from its academic network, UK Centres for Doctoral Training and Industrial partners, a list of PhDs currently being undertaken in ORE.