E1 - Higher and more consistent reliability through risk-based design
E - Survivability, Reliability and Design
Existing design methods can limit scope for innovation and cost reduction, particularly for arrays of devices.
Rational, whole-life models coupling resource to device/structure loading and response accounting for control and interaction with station-keeping infrastructure.
Context And Need
Many of the methods employed for design of offshore renewable energy systems are inherited, with some modification, from the oil and gas sector. Employing these methods can constrain design of offshore renewable energy systems to relatively high-cost solutions, limit design to conservative solutions and can result in low survivability of trial systems that integrate novel components or concepts.
When applied to multiple components or systems, probabilistic design methods provide the opportunity to design for target levels of reliability accounting for variability of the resource, device and component design tolerances, array configuration and device or array control parameters.
Demonstrable cost reduction for multiple units relative to multiple of single unit design.
Establish risk-based and/or probabilistic design approaches that span resource, device, control strategy and array. Develop rational, coupled whole-life models for resource - device/structure (and control) - foundation interaction to allow consistent target reliability. Unpack existing practices inherited from oil and gas, to remove conservatism, and counter current issues of low survivability of some trial systems
Potential for significant:
- reduction of CAPEX and OPEX
- increased reliability
- improved survivability (or equivalent for reduced expenditure)
- reduced risk to offshore operations and negative environmental impact
Can enable increased performance of control strategies and condition monitoring strategies.
- MONITOR (Atlantic Area project ID#: EAPA_333/2016): The cost of operations and maintenance is the single biggest obstacle to commercial-scale deployment of tidal stream energy. MONITOR uses a range of methods (at-sea measurements, lab testing & simulation) to improve reliability of tidal turbines. This will help developers lower their cost of energy while increasing capacity factor.
- Innovative floating offshore wind energy - Lifes50plus: Proving cost effective technology for floating substructures for 10MW wind turbines at water depths greater than 50m.
- WEC Design Response Toolbox (WDRT): The WDRT was developed by Sandia National Laboratories and the National Renewable Energy Laboratory (NREL) to provide extreme response and fatigue analysis tools, specifically for design analysis of ocean structures such as wave energy converters (WECs).
- Wave Energy Scotland – Knowledge Capture: Wave Energy Scotland is managing the most extensive technology programme of its kind in the wave energy sector. The Knowledge Library provides access to key information and documents generated through this world leading commercial and academic research & development.
Supergen ORE Hub - Flexible Funding Research
Lead Institution: University of Edinburgh
This project will directly solve the challenge of measuring the fatigue performance of tidal turbine blades by generating, for the first time globally, statistically robust accelerated cyclic loading data for the lifetime of a fullscale tidal blade. This will be carried out at economic cost over a short timescale that will enable developer designs to be more quickly refined than is currently possible. Tidal turbines operate in a harsh marine environment, characterised by significant levels of flow unsteadiness, with tidal blades needing to withstand both deterministic (e.g. shear profile, tidal fluctuations) and stochastic (e.g. waves, turbulence) induced loads. The resulting fatigue loading is a significant cause of blade failure. Understanding these loads and their impact on blade structural performance is crucial in order to avoid premature failure and to increase confidence in tidal blade design, leading to reduced cost of energy. This project will model, define and apply these fatigue loads to develop a process for full-scale tidal blade testing.
- WTIMTS - Wave-Turbulence Interaction and Measurement for Tidal Stream
Lead Institution: Swansea University
WTIMTS proposes a novel combined approach to measurement of turbulence and waves at tidal energy sites. If successful, this will allow an unprecedented level of confidence in decoupling these deeply entangled phenomena using only standard instrumentation (i.e., bed-mounted ADCPs and wavebuoys/WaveNet equivalent) – a limitation that is particularly relevant at the highly energetic sites of interest to the tidal stream industry, where more sophisticated instrument arrays are often impracticable. This will also permit the project to characterise, for the first time, the ways in which wave action enhances turbulence at such sites, and how far into the water column its influence extends.
- 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.
- iDRIVE: Intelligent Driveability Forecasting for Offshore Wind Turbine Monopile Foundations
Lead Institution: Oxford University
The UK government has set a new ambitious target of 40GW of offshore wind energy by 2030, aiming to produce sufficient wind capacity to power every UK home. The size of offshore wind turbines is increasing rapidly and continued optimisation in installation and design is key to the sustained expansion of the industry. Impact-driven monopiles remain the foundation of choice and accurate prediction of monopile driving behaviour is key to (i) choosing the optimal hammer size, (ii) ensuring that driving does not induce excessive fatigue stresses in the pile and (iii) avoiding an installation failure from refusal or free fall under self-weight. Industry currently relies on empirical methods to estimate soil resistance to driving (SRD) combined with soil rheological models built into wave equation software. Prediction of monopile installation behaviour has been shown to be uncertain using currently available empirical methods developed from a small database of long slender piles for the oil and gas industry. Given the large-scale nature of next-generation offshore wind farms (OWF), considerable savings (installation failures lead to multi-million pound remediation costs) can be realised if a more optimal, automated and adaptive approach to driveability prediction is adopted. The proposed framework uses Bayesian machine learning fused with conventional wave equation analysis to develop an up-to-date ‘uncertainty-quantified’ pile installation forecasting model. The tool will be rigorously validated using driving data from real-world OWF sites provided by industry partners. The research will develop new reliable tools for use by practitioners to predict the safe installation of monopile foundations.
- Physics-informed machine learning for rapid fatigue assessments in offshore wind farms
Lead Institution: University of Hull
Offshore wind energy is key in the UK’s plan to deliver the legally binding Net Zero 2050 targets, quadrupling the capacity by 2030. First-generation offshore wind monopiles are rapidly approaching their end of designed life. The next-generation of wind turbines are significantly larger, yet still monopile support structures dominate. Accurate estimation of accumulated monopile fatigue is essential now, to inform decommissioning decisions, and optimise future design and maintenance. Due to unpredictable offshore environments, and the difficulty of taking structural measurements, fatigue predictions are subject to significant error. This project proposes an industry-compatible step-change advance in accumulated fatigue assessment via novel integration of physical modelling and machine learning. The proposed model provides intuitive prediction of the level of fatigue for any turbine within the farm, at any point of its lifetime from distinct operational and environmental conditions, verifiable against physical models, yet with increased efficiency and fidelity of lifetime fatigue estimation
Links to Industry Priorities:
- Offshore Wind Innovation Hub - Substructures innovation priorities
- Offshore Wind Innovation Hub Roadmap Data: Total system design
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.