Dr. Haifeng Wang has worked on two projects in the EPSRC SUPERGEN 1 / Future Network Technology Consortium on FACTS (Flexible AC Transmission Systems) and one project in the EPSRC SUPERGEN 3 / Energy Storage Consortium on EES (Energy Storage Systems) applied in power supply systems considering renewable generation. Those three projects have achieved significant step progress with novel ideas and theoretical methods proposed. However, the main tool he has used so far to evaluate and demonstrate his theoretical research results for these three SUPERGEN projects is computer simulation, as in the UK there is no laboratory of physical power systems available for him to conduct experiment. Because in power system research and development, physical experiment is a vitally essential step of work towards field trial and applications, this proposal seeks the support from the EPSRC Collaborating for Success through People (CSP) scheme for him and his Research Officer to carry out laboratory experiment of physical power systems for his SUPERGEN projects through international collaboration. The proposed experiment will be conducted at the Physical Power System Laboratory (PPSL), College of Electrical and Electronic Engineering, Huazhong University of Science and technology (HUST), Wuhan, China. The support required from the EPSRC CSP is to cover the cost of people-based activities for the proposed physical experiment, while the Chinese collaborator will cover the cost of equipment and consumables.

Marine mussels can survive the harsh marine environment at intertidal zones by anchoring themselves to various wet surfaces through adhesive plaques. Recent research progress has highlighted that, in addition to the interaction of protein-based chemistry at the adhesion sites, the unique adhesive structure of a mussel plaque plays an important role. Motivated by this natural phenomenon, the proposal aims to establish the knowledge on the underwater adhesive behaviours of mussel plaque-inspired anchoring systems for the applications of the offshore floating structures. The existing deep water anchoring systems such as drilled piles, suction anchors, and gravity anchors may be subject to various limitations with respect to the cost, the seabed conditions, and the installation; and can cause significant impact on the local marine environment. In addition, removal of these anchoring systems at the decommissioning phase could be difficult and expensive. In comparison, the plaque-like anchoring systems can potentially have the following ground-breaking features: (a) the adhesion at the anchoring systems can be switched on and off based on the requirement, which can lead to revolution in the design, construction, sustainability, and life cycle operation of the offshore floating structures, (b) by using advanced composite materials, the anchoring systems can be applied to a wide range of seabed conditions, i.e., rocky surfaces and soil surfaces, with minimum impact on the local marine environment ( i.e., no drilling or excavation on the seabed is required), and (c) the manufacturing and installation processes can be much more simplified, which leads to cost-effective solutions. The proposed research has the potential for substantial impact on various applications involving offshore floating structures such as offshore floating wind turbine (OFWT) systems, offshore oil rigs, tidal current turbine systems, and subsea infrastructure. Among these applications, it is worth noting that the requirement for developing novel OFWT systems has been highlighted by the offshore renewable energy sector and the recent governmental strategy- the UK Government has already committed to 1 GW of floating wind by 2030. The research will establish lab-scale prototypes of the mussel plaque-inspired anchoring systems. Using a combination of experimental techniques, adhesion theories and numerical modelling approaches, we will (1) evaluate the performance of the prototypes, and (2) examine the failure modes, detachment forces, traction force distributions and ductility under controlled external factors. The scaling up effect will be studied by examining the performance of the prototypes at different length scales. Investigation will also be conducted to examine the adhesion on different types of substrates, i.e., rock and soil. The optimised designs will be achieved via verified parameter studies, which can act as the guidance for engineering designs. Assessment in terms of likely cost and technical effectiveness will also be conducted based on the optimised designs.

Doubly-fed induction generator wind turbines (DFIG-WTs) have been widely adopted by the current wind power generation systems (WPGSs) due to their cost-effective provision of a high efficiency energy conversion via variable speed operation. Most of the installed DFIG-WTs utilise vector control (VC) for the power control of DFIG. To cope with the increasing demand of integrating the large capacity of wind power into the current power grid, grid operators require that the WPGSs should ride through grid faults and support grid stability. However, VCs are not capable of providing satisfied fault ride-through capability as they are mainly derived based on the steady-state operation of the DFIG. On the other hand, the time-varying nonlinearities and disturbances existing in the DFIG-WTs are needed to be tackled so as to improve the energy conversion efficiency. This proposal will investigate an advanced nonlinear adaptive control algorithm for the DFIG-WT to improve the energy conversion efficiency, the fault-ride through capability and the support of grid stability. The proposed controller will adaptively compensate unknown and time-varying disturbances such as intermittent wind power inputs, the nonlinear dynamics of the DFIG-WT and the power grid. Without relying on an accurate system model, the developed controller will have a relative simpler control algorithm compared to other advanced control methods and can be implemented based on the current hardware used by the vector control method. Due to the wide usage of the DFIG-WTs in the current WPGSs and in the fast growing offshore wind farms, designing a novel controller and upgrading the current used VCs will have great practical importances and help the integration of large capacity of wind power into power grid.

The threat posed by tick-borne diseases (TBD) in temperate regions such as the UK is growing rapidly. Human exposure is often linked to woodlands that support high densities of tick vectors and key wildlife hosts of these pathogens, and are intensively used by people. Climate change and government policies to increase woodland connectivity and improve human recreational access are highly likely to increase risks of TBD in the UK. To mitigate this threat we need to better understand effects of landscape structure on the movement and habitat use of those wildlife species which are key hosts for ticks and zoonotic pathogens. We also need to understand how humans use landscapes, where they are most at risk of exposure to tick bites and whether exposure could be prevented by habitat and host management. Given recent shifts across Europe in the distributions of TBD and tick populations, it is also critical to understand how longer term climate and land use changes may affect the introduction, establishment and spread of TBDs. Bringing together researchers from ecology, epidemiology, public health, and social science, TICKSOLVE aims to address these gaps. We will provide evidence for optimal greening and woodland restoration policies that will maximise benefits to biodiversity and human wellbeing while minimising human risks from current and future tick-borne diseases by: 1. Bringing together key national and regional level actors in health, land and biodiversity policy that interact with landscapes and TBD systems, to frame key risk scenarios and feasible environmental interventions for TBDs. 2. Better understanding how landscape structure shapes wildlife host distribution, habitat selection and movements and consequently impacts on ticks and TBD risk combining ecological surveys, pathogen genetics and computer modelling 3. Mapping how people use woodland landscapes and how this interacts with risk of encountering infected ticks to identify high risk areas for human exposure 4. Modelling how potential environmental barriers and interventions could reduce human exposure, integrating this knowledge of ecological interactions across the landscapes 5. Predicting how changes in woodland area and climate and patterns of bird migration may change TBD risks in the future 6. Co-developing interventions to minimise current and future TBD risks with stakeholders and policymakers that are locally appropriate. The research will focus on three emerging pathogens that pose a risk to the UK. Firstly Lyme disease (LD) which is currently present in the UK and can cause long-term debilitation. Reported cases of LD have increased 10-fold since 2000, probably linked to an expanding distribution of its main tick vector, Ixodes ricinus. Secondly, tick-borne encephalitis (TBE) which has been recently detected in ticks in the UK with evidence of suspected human cases in 2019. TBE uses the same tick vector and can cause severe neurological damage and death with some 5,000 to 12,000 reported cases each year in mainland Europe. Thirdly, Crimean Congo Haemorrhagic Fever (CCHF), caused by a WHO priority pathogen CCHF virus, with epidemic potential, is expanding north-westward in Europe. It's tick vector, Hyalomma spp., was found recently on migratory birds arriving in the UK. The TICKSOLVE project platform and approach of co-developing research, models and risk communication materials with stakeholders, accounting for diverse land management priorities, will enable formulation of future-proofed woodland and greening policies that minimise risks of these diverse TBDs. Furthermore, engagement with key global partners and networks through webinars and meetings will facilitate transfer of TICKSOLVE inter-disciplinary approaches to other rapidly changing tick-borne disease systems worldwide.

We are stepping into a new era of digitalisation. In this new era machines will communicate and exchange large amounts of data to ensure they can work harmoniously and collaboratively with little human intervention. Current machines use symbolic language to represent the data, but they cannot directly interpret its meaning. As a result, information loss and incorrect interpretation can often happen during communication. To improve manufacturing intelligence, we need the manufacturing system to "understand" the data, which we refer to as "semantics" of the data. If the manufacturing system can be represented at a semantic level, the data will become knowledge to the machine and enable it to be ready for exchange, interrogation and reuse. There is current work taking place to upgrade manufacturing systems to a semantic level but this is still at an early and enabling stage. This fellowship aims to effect a step change in manufacturing intelligence, to support rigorous semantic exchanges between different manufacturing phases, and to allow formalisation and reuse of new/existing knowledge from advanced manufacturing. The proposed research will build a novel semantic infrastructure for advanced manufacturing by supporting knowledge representation, interrogation, reasoning and exchange for smart design, manufacturing and measurement of advanced products. The focus will be on the development of a toolbox to formalise knowledge in/between design, manufacturing and measurement, especially for additive manufacturing (AM). The resulting semantic infrastructure will allow the machine to "interpret" the meaning of the data/information. To be more specific: how the design parameters (geometries, tolerances and materials) are related to each other; how the design parameters relate with the AM process/post process parameters (layer thickness, build orientation); and how the design and process parameters contribute to the measurement details (methods, calibration, etc.). The work will provide a new universal language for any data/information involved in a manufacturing value chain, and will permit a comprehensive infrastructure to digitalise the fast growing AM industry.