The Aura CDT has already recruited 70 PhD researchers in five cohorts, between 2019 and 2023, supported by UKRI funding. Subject to a funding decision in December 2023, we hope to take on a further five cohorts of PhD researchers on a scholarship basis, from September 2024.
Following consultation with the offshore wind industry, the Aura CDT academic partners have worked together to develop seven PhD clusters. We will shortly list each individual project, with a view to opening the application process in January 2024.
Each cluster of PhDs will support two or more of the major challenges facing the offshore wind sector. This will strengthen cross-cohort research collaboration between our postgraduate students as well as connecting our academic supervisors cross-institution.
This Cluster is focused on addressing the challenges related to dynamic cables used in floating offshore wind turbines. Cables are a critical part of a wind farm and damage is a major cause of repair costs and lost production. As floating wind turbines become more common, these problems are likely to increase due to the complex loads on the cables.
Our research encompasses various activities to address this, including but not limited to:
Advancing numerical modelling techniques to enhance the accuracy of predicting the shape and motion characteristics of dynamic cables, accounting for the impact of underwater conditions such as tides and waves, as well as their interactions with nearby structures like the touch-down point and the floater. Developing cost-effective fibre-optical sensors designed to monitor the curvature and strain loads experienced by the cable during complex motion conditions. Conducting experimental validations to test and refine these concepts and technologies.
This cluster of projects revolves around advancing the field of offshore floating wind energy systems through a multidimensional approach.
The first project requires development of innovative probabilistic models for reliability and maintainability assessment of floating wind turbines with the aim to ensure optimal turbine availability. The second project aims to pioneer the development of the next generation of floating offshore wind turbine structures, and the third project employs cutting-edge reinforcement learning techniques for adaptive control in floating wind turbines, aiming to optimise their performance under dynamic and varying conditions.
Together, these projects contribute to the broader goal of pushing the boundaries of innovation in offshore wind energy, addressing both the structural and operational aspects of floating wind turbine systems.
The cluster seeks to pave the way for sustainable and effective utilisation of offshore wind resources, playing a pivotal role in the evolution of renewable energy technologies.
Developing the next generation of floating offshore wind turbine structures
Performance of offshore floating wind farms
Reinforcement Learning-Based Adaptive Control for Floating Wind Turbines
This research cluster is focused on offshore geotechnics with a particular emphasis on solid-structure interaction problems. The seabed represents one of the greatest areas of uncertainty within the offshore wind sector and is at the core of the majority of insurance claims. As the wind energy industry grows, wind farms are being pushed further offshore into deeper waters and new solutions must be considered for attaching offshore wind turbines to the seabed. This includes mooring methods for floating installations, where is essential that both the installation process and the long-term capacity are understood.
In addition to fixing the turbine, it is essential that cables transferring power to the national grid are protected from external aggression and this requires tools to understand the penetration risk of anchors on the seabed as well as assessment of the cable installation methods, all of which involve soil-structure interaction, and deep understanding of the complex behaviour of offshore soils under cyclic and dynamic loading. This cluster focuses on the development of numerical, analytical and empirical methods to tackle the above problems and provide new predictive techniques for the offshore wind industry.
The role of fabric anisotropy on cyclic loading of offshore soils: a grain-scale
Numerical modelling of deep penetrating anchors for floating wind installations
The offshore wind sector is rapidly expanding to meet net-zero energy demands, with individual turbines and farms getting larger and further from shore. Atmospheric wakes produced by these turbines are a key control on their power output and dictate wind farm layouts. In addition, wind farms can produce large-scale flow structures which affect atmospheric and sea-surface conditions, leading to poorly understood impacts on the environment.
The POWER cluster addresses this knowledge gap by investigating offshore wind wakes across all scales, from an improved understanding of turbine-scale wake turbulence, to predictions of wind farm wake impacts on regional-scale oceanography. The cluster will employ analytical, numerical, and data-driven techniques to address these challenges, with a supervisory team spanning the Universities of Hull, Sheffield, Loughborough, Durham, and the National Oceanography Centre, with additional support from the University of Minnesota and Imperial College London.
Single-turbine scale quantification of wake turbulence
Data assimilation for wake-wake interactions
Parametrising wakes for oceanographic models
Development and validation of physics-based models for wakes of large offshore wind farms
This cluster aims to understand and enhance the overall health and productivity of workers in the offshore windfarm sector by focusing on the interplay between physical, psychosocial, and cognitive wellbeing. Research in this cluster will investigate how current work practices and environmental factors impact the wellbeing triad, as well as the health and safety of workers during task execution.
This research will also seek to develop and evaluate novel interventions that aim to improve physical fitness, mental resilience, and cognitive functioning. As such, this cluster seeks to address the holistic wellbeing needs of the industry’s workforce.
Building psychosocial and physical resilience using self-management skills for offshore workers
Impact of “blue space” as a working condition on the health and wellbeing of offshore windfarm workers
Implications of Extreme Temperatures on Physiological and Cognitive Functioning in Offshore Wind Technicians
A structured health intervention to improve physical activity, nutrition and sleep in offshore windfarm workers
The aim of this cluster is to generate evidence-based practices that can enhance the safety and competence of workers involved in offshore wind energy projects. Specifically, projects in this cluster will development and evaluate novel interventions that can equip these professionals with the necessary skills and knowledge to navigate the unique challenges of working in offshore environments, ultimately ensuring the sustainable growth and success of the offshore wind industry.
These projects will provide a crucial step-change towards mitigating risks and promoting safety in this rapidly expanding sector.
Undertaking and activating transfer of non-technical skills safety training to workers in the offshore windfarm sector
Undertaking and activating transfer of technical skills safety training to workers in the offshore windfarm sector
Within the Energy Economics research cluster, we focus on a comprehensive examination of various aspects related to offshore wind farm development and its impacts on both the environment and society.
Our projects delve into the economic, environmental, and social dimensions of offshore wind energy, contributing valuable insights to the field.
Economic and environmental assessment of energy systems integration for increased utilisation and reduced curtailment of offshore wind farms
Unintended consequences of Offshore Wind farms: a socio-economic impact evaluation on wellbeing and community dynamics
Economic analysis of trade-offs and welfare implications in Offshore Wind farm development
Energy security vs. energy import costs: assessing the role of Offshore Wind Power
At the heart of sustainability is the recycling and upcycling of waste materials. This cluster will accommodate waste streams from offshore wind production including plastic transition pieces as well as whole blades post decommissioning.
Here, we will expand the circularity of offshore wind by producing a zero-carbon fuel and green chemicals through catalytic pathways using low carbon emitting techniques. We will also augment waste streams to prolong the operational life of ancestor offshore wind turbines through anti-corrosion coatings that can be applied to the wind turbine structure.
Transforming Non-Recyclable Waste into Sustainable Solutions: Novel Anti-corrosion Coatings for Offshore Wind Turbines
Passing the torch: Utilising full blade waste for the production, catalytic conversion and storage of H2 in methanol
Breaking forever: Capturing and converting toxic PFAS molecules using coral structured biochars from blade waste
Projects within this area will aim to develop new tools and processes that enable better monitoring and understanding of the environmental impacts of changing marine environment due to anthropogenic changes.
Through the development of efficient screening and assessment tools large volumes of data can be collected to inform an understanding of the ecological consequences and benefits due to wind turbines and inform future development.
Understanding the impacts and benefits of offshore wind on fish in the Greater North Sea
Sustainable methods for gross proximate composition of North Sea species as a key component of ecological models.
Streamlining the Environmental Impact Assessment Process using Artificial Intelligence and Machine Learning
The growth in wind energy production has been one of the UK’s success stories for this decade. This uptake comes with its challenges; turbines need to operate in an aggressive environment, and maintenance in offshore or remote areas is costly.
Reliability and optimum performance of the wind turbine power train, from blades through transmissions and power converter to distribution is essential to keep the cost of electricity generation to a minimum. The nation’s energy landscape is rapidly changing and to maintain the pace will need skilled research engineers and scientists.
Low maintenance reliable transmissions for large wind turbines
Multiscale design optimisation of wind turbine blade composite for tuneable mechanical properties
Protection Devices for MVDC Offshore wind Integration
This cluster is centred around integration of multiple renewable energy technologies integrated within a single offshore wind energy project. This integration aims at optimise energy production, enhance grid stability, and boost the efficiency of the overall offshore wind form.
There are several components that could be part of a hybrid offshore wind farm including photovoltaic arrays (to provide an additional renewable energy source), batteries (to ensure a consistent power supply), electrolysers (to produce green hydrogen necessary to decarbonise hard-to-abate sectors).
Innovating Interdisciplinary Airborne WindEnergy through Rigorous Wind Tunnel Testing and Collaborative Design
Integrated Engineering and Environmental Optimisation of Offshore Wind and Floating Solar Hybrid Systems
Efficient and Sustainable Offshore Wind Turbine-driven Green Hydrogen production
Risk-informed Planning and Reliability Evaluation of Hybrid Wind Power Plants
Reliability and availability are two key requirements in offshore wind where faults can result in catastrophic failures. Furthermore, any downtime caused by faults or maintenance results in significant loss of revenues. With the aim of reducing faults and increasing availability, we will investigate advanced solutions for real-time monitoring of the condition and health of off-shore wind assets during their lifetime.
We will use advanced tools including Machine Learnings and Digital Twins to monitor electrical equipment, generators, converters, mechanical and structural components such as blades etc. The aims are not only to avoid the risk of catastrophic failures but also to replace costly periodic routine maintenance with condition-based maintenance to be performed only when the remaining useful life decreases below a predefined threshold.
Use of probabilistic modelling to investigate optimal maintenance strategies in the life extension period of offshore wind turbines
Digital twins for health monitoring and fault
SafeML-based Confidence Generation and Explainability for UAV-based Anomality Detection of Blades Surface in Offshore Wind Turbines
This cluster aims to develop and deploy innovative AI-powered technologies to optimise the operation of wind turbine farms, improve safety, and reduce costs. The cluster will focus on developing AI-powered forecasting and optimisation algorithms to predict wind conditions and optimise turbine performance in real time, as well as AI-powered safety systems to detect and prevent potential hazards.
The cluster is expected to have a significant impact on the wind energy industry by making wind farms more efficient, reliable, and competitive, and accelerating the transition to a clean energy future.
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