This project change request is being raised to agree a change to the delivery dates for milestones 2 and 3. The original milestone dates as agreed in the contract were: Milestone 1 - Stage 2 core system development - 01/05/21 Milestone 2 - Stage 3 field testing and validation - 01/07/21 Milestone 3 - Stage 4 Final project, exploitation plan, project close out meeting and demonstration 1/08/21 The following revised delivery dates are requested: Milestone 1 - complete no change Milestone 2 - not fully completed revised date 01/09/21 Milestone 3 - revised date 31/10/21

A major design consideration for offshore wave energy devices is survivability under extreme wave loading. The aim of this project is to predict loading and response of two floating wave energy devices in extreme waves using CFD (computational fluid dynamics), in which fluid viscosity, wave breaking and the full non-linearity of Navier-Stokes and continuity equations are included. Two classes of device will be considered: Pelamis (of Ocean Power Delivery Ltd.), the prototype having already successfully generated electricity into the grid, and a floating buoy device responding in heave, known as the Manchester Bobber (Manchester University), which is being tested at 1/10th scale. Both classes of device are thought to be competitive with other renewable energy sources, being economically roughly equivalent to onshore wind energy. The CFD simulations will be undertaken in three ways: by commercial codes, CFX and COMET (STAR-CD); by recent advanced surface-capturing codes; and by the novel SPH (smoothed particle hydrodynamics) method. In order to address the uncertainties in the CFD approaches, such as the accuracy of prediction and the magnitude of computer resources required, a staged hierarchical approach of increasing computer demand will be taken in: mathematical formulation (from an inviscid single fluid to a two-fluid viscous/turbulence approach); wave description (from regular periodic to focussed wave groups including NewWave); and complexity of structure (from a fixed horizontal cylinder parallel to wave crests to the six degrees of freedom of Pelamis). At each stage, numerical results will be compared with experimental data. The significance of the inviscid v. viscous formulations, wave nonlinearity, non-breaking v. breaking conditions, and the dynamic response of the body will thus be assessed for extreme conditions. Designs for survivability should thus be better evaluated. The resulting CFD methodology will also benefit analysis of extreme wave interaction with ships, other marine vehicles and structures in general. For example interaction with freak waves and the 'green' water problem have yet to be resolved.

Electricity infrastructure provides a vital services to consumers. Across the UK there are thousands of miles of overhead lines and other assets that are vulnerable to a number of environmental risks. Wind risks have caused more disruptions to power supplies in the UK than any other environmental risks. Despite their importance, the future risks associated with windstorm disruption are currently highly uncertain as the coarse spatial resolution of climate models makes them unable to properly represent wind storm processes. STRAIN will address two challenges for infrastructure operators and stakeholders who are urgently seeking to understand and mitigate wind related risks in their pursuit to deliver more reliable services: (i) Build upon state-of-the-art modelling and analysis capabilities to assess the vulnerability of electricity networks and their engineering assets to high winds. This will consider the impact of different extreme wind events, over different parts of the electricity network, the households and businesses connected, and also apply a model representing infrastructure inter-connections to understand the potential impact on other infrastructures that require electricity such as road, rail and water systems. (ii) Climate models provide very uncertain wind projections, yet infrastructure operators require an understanding of future climate change to develop long term asset management strategies. To provide the necessary information we shall work with the Met Office and benefit from new high resolution simulations of future wind climate using a 1.5km climate model. These simulations have proven capable of representing convective storm processes, that drive many storms across the UK, and have already proven that they better capture extreme rainfall events. These methods will be applied to a case study of an electricity distribution network. These are more vulnerable to windstorms than the high voltage national transmission network. STRAIN will therefore, by synthesising and translating cutting-edge research, provide electricity distribution network operators with a significantly improved understanding of wind risks both now and in the longer term. This will improve the reliability of electricity supply to UK consumers including other infrastructure providers reliant on electricity distribution networks, and reduce costs by enabling more effective allocation of investments in adaptation and asset management. Furthermore, it will help other infrastructure service providers better understand the impacts of electricity disruption on their own systems, and plan accordingly. The improved understanding of future extreme wind storms will provide benefits across an even wider group of infrastructure and built environment stakeholders.

The world faces two pressing challenges. Congestion in cities, the lifeblood of national economies, is rising to unacceptable levels. This causes poor health outcomes and strangles economic growth. At the same time, humanity must confront the threat of climate change and reduce its dependence on fossil fuels. The Advanced Mobility Ecosystem Consortium (AMEC) is aiming to demonstrate the commercial and operational viability of Advanced Aerial Mobility (AAM) in the UK. This is an efficient, electric mode of aerial transport complementary to existing transport infrastructure, helping to deliver both increased connectivity and net zero emission targets. In doing so we will deliver cost-effective and convenient inter-regional and intra-regional travel to the British public. AMEC will demonstrate three first-of-kind air mobility services using Vertical Aerospace's emission-free VA-X4 eVTOL aircraft, operated by Virgin Atlantic. The first mission will take place between Bristol Airport and South-West node. The second will take place between Heathrow Airport and Skyports' Elstree vertiport. A third will digitally simulate a mission between Bristol Airport and London City Airport. These missions will explore and prove all aspects of the passenger journey, vehicle operation, airspace navigation, ground charging, security provision and local stakeholder management. Various technologies and methods are being proven. Vertical Aerospace is exploring novel means of compliance with civil aviation regulators as it prepares an airworthy vehicle for demonstration. Skyports is building a "living lab" vertiport at Elstree Airport to allow UK AAM stakeholders to trial technologies and operational concepts, facilitating commercial operations. Atkins and Skyports are deploying innovative digital infrastructure to modernise airspace and ensure compliance with national aviation safety regulations and border security. The consortium also involves the cooperation of world-leading public and academic institutions that are bringing their expertise to enable an economically viable AAM ecosystem. Cranfield are undertaking vertiport network and scheduling optimisation. Warwick Manufacturing Group (WMG) are developing open hardware and software standards for rapid eVTOL charging solutions that are essential to achieve fast turnarounds and high aircraft utilisation. Connected Places Catapult will manage delivery of the project and address, from a neutral perspective, the many public acceptance challenges surrounding the introduction of AAM services. The potential benefits to the UK are vast. Greater convenience for the travelling British public, substantial export earnings from the domestic manufacture of aircraft with associated products and services, enhanced connectivity driving GDP multiplier effects and levelling-up opportunities, and fewer harmful emissions.

A robot mounted on a mobile modular system that can be deployed to identify, sort and segregate radioactive waste for safe recycling or disposal. The robot will confront a mass of radioactive waste ranging from metals, to plastics, electrical equipment, soil and more. In order to process it, the robot will first identify an individual waste item using its vision system. The robot will recognise some waste, but otherwise it can be trained by an operative to identify a new waste type by sight. Through machine learning, the more the vision system is used, the more autonomous the process becomes. The system will also measure each item's weight, size, shape, surface area and composition for efficient sorting and packing. Visual recognition of waste is combined with radiometric and chemical characterisation to classify the waste for sorting. After visual identification, each item is picked up by the robotic arm and its level of radioactivity is monitored and it is chemically analysed. The information on the item's physical characteristics, material type and radioactivity level is used to sort the item into the correct waste-stream for safe recycling or storage. Records of this information, together with images of the items will be stored as a record of what items have been placed into each waste-stream. This project innovates on current state-of-the-art by removing the person from the process. This means that there is less risk to the operators from working in hazardous environments. There is less risk of human error in this repetitive task. The process will also be quicker and cheaper than a manual system, offering savings to the UK taxpayer on the cost of decommissioning redundant nuclear equipment and facilities. Combining a robot arm with vision systems, machine learning, nuclear and chemical characterisation systems will mark a new development for nuclear decommissioning. The robot arm can be of any model and size to suit the waste type. A further key innovation comes via the intelligent vision system, which automates the recognition of different forms of waste through machine learning. Human interaction is minimised, creating an efficient, waste-minimising workflow that can adapt to location, segregate waste by various measurable criteria, and will improve the more it is deployed. Waste generated by nuclear decommissioning is therefore dealt with safely, quickly and cheaply, with minimal human interaction, efficiently packing waste containers, and with a diligent recycling process.