Launch and recovery of small vehicles from a large vessel is a common operation in maritime sectors, such as launching and recovering unmanned underwater vehicles from a patrol of research vessel or launching and recovering lifeboats from offshore platforms or ships. Such operations are often performed in harsh sea conditions. The recent User Inspired Academic Challenge Workshop on Maritime Launch and Recovery, held in July 2014 and coordinated by BAE systems, identified various challenges associated with safe launch and recovery of off-board, surface and sub-surface assets from vessels while underway in severe sea conditions. One of them is the lack of an accurate and efficient modelling tool for predicting the hydrodynamic loads on and the motion of two floating bodies, such as vessels of different size which may be coupled by a non-rigid link, in close proximity in harsh seas. Such a tool may be employed to minimise the risk of collisions and unacceptable motions, and to facilitate early testing of new concepts and systems. It may also be used to estimate hydrodynamic loads during the deployment of a smaller vessel (for example, a lifeboat) and during recovery of a smaller vessel from the deck of a larger vessel. The difficulties associated with development of such tools lie in the following aspects: (1) the water waves in harsh sea states have to be simulated; (2) the motion of the small vehicle and change in its wetted surface during launch or recovery can be very large, possibly moving from totally dry in air to becoming entirely submerged; (3) the viscous effects may play an important role and cannot be ignored, and will affect the coupling between ocean waves and motion of the vehicles. Existing methods and tools available to the industry cannot deal with all of these issues together and typically require very high computational resources. This project will develop an accurate and efficient numerical model that can be applied routinely for the analysis of the motion and loadings of two bodies in close proximity with or without physical connection in high sea-states, which of course can be employed to analyse the launch and recovery process of a small vehicle from a large vessel and to calculate the hydrodynamics during the process. This will be achieved building upon the recent developed numerical methods and computer codes by the project partners and also the success of the past and ongoing collaborative work between them. In addition, the project will involve several industrial partners to ensure the delivery of the project and to promote impact.

Currently many marine operations, such as the Launch and Recovery (L&R) from a mother ship of small craft, manned and unmanned air vehicles and submersibles, can only be attempted safely in sufficiently calm sea-states. As an example, the L&R of a small craft from a mothership typically involves the two vessels moving together in proximity (linked by a bow-line) before the main physical connection of the two via a crane/hoist mechanism. In many cases the wave-critical high risk elements of the overall task, i.e. the connection and subsequent hoist of the small craft to the parent vessel, only last for a few tens of seconds. Taking longer than this increases the operation at risk. Once the two craft are physically connected the operator is committed to initiate the hoisting process. In this context even the short term prediction of quiescent periods of vessel motion resulting from lower than average wave activity in otherwise large sea states, has considerable operational value and may allow L&R to be untaken safely in conditions which would currently be deemed unsuitable. Such enhanced L&R capabilities are very attractive to modern navies. In this project the research aim is to develop a novel approach to predicting a suitable time instant at which to initiate an L&R operation, together with a confidence measure (provided as advice to a human operator), and then to control the execution of the subsequent lift operation once initiated, using a novel form of Model Predictive Control (MPC). The key project deliverables are: (i) a prototype decision support system (DSS), running within a software simulator, which provides continuously updated short term predictive simulations over a finite-time horizon of all aspects of the recovery process; (ii) a controller for the actual physical hoist process. These two elements will exploit hydrodynamic vessel motion prediction models driven by wave predictions from a Deterministic Sea Wave Prediction (DSWP) system, and historical and real-time vessel motion sensor data. The DSS will initially be engaged as the small craft approaches the mothership and picks up a bow-line (a low risk activity), but is not yet attached to the hoist mechanism. The research will assume the presence on the mothership of a generic winch/crane lifting system with a single cable. The cable tension is a key controlled quantity and the maximum lifting force available is a major system specification parameter. The DSS will: (i) identify an appropriate moment to attach the hoist line and initiate hoisting during predicted quiescent periods; (ii) provide a confidence measure for the safety/success of that specific simulated lift. An appropriate time to attach and hoist will be identified by taking a snap-shot of the current state of both vessels (to use as initial conditions) together with short term predictions of the movement of the mothership to simulate whether it is possible to successfully recover the small craft using the MPC controller. The operator will then be presented with a current advice summary including confidence metrics. If as a result of this advice connection and hoisting is not initiated, the process repeats using a snapshot of the new current data. This cycle continues until the operator decides to engage the hoist (or the recovery is aborted). When connection/hoisting is actually initiated, the physical lifting phase will then employ the same MPC controller used in the simulation, exploiting predictions of the motion of the mothership, the actual real-time measured motions of both craft and a free body model of the small craft when suspended clear of the water.

This project is one of the small number of proposals selected by an industrial consortium in collaboration with EPSRC to go forward as full proposals to the EPSRC Launch and Recovery Co-Creation Initiative. It involves a collaboration between Exeter and Southampton Universities, Scripps Institution of Oceanography (USA), BAE Systems, MOD and OCEANWAVES (Germany). It is supported by a mentoring/dissemination group comprising: BAE Systems, MOD, SEA Ltd, Zenotech and ESI Group. The practical driver is to enable a wide range of wave limited maritime operations to be carried out safely at higher sea states than is presently feasible. Particularly important examples are launch and recovery operations from mother ships of small boats, manned and unmanned air vehicles, and submersibles. The research concerns the two coupled areas of: (a) predicting the actual shape of sea waves, termed Deterministic Sea Wave Prediction and the application of this to predicting calmer periods in otherwise large seas (Quiescent Period Prediction), and (b) a comprehensive investigation of the properties of such quiescent periods and the creation of a quiescence simulator. The research involves an integrated combination of challenging fundamental new theory, simulation, large scale data analysis and experimental testing. An applications oversight, designed to facilitate post project the optimum push through to higher technology readiness levels, is provided by the industrial mentoring panel. MOD and BAE Systems are also research partners. The research will provide the predicted wave environment information required by closely allied projects within this EPSRC Launch and Recovery Co-Creation Initiative which are aimed at (a) modelling the motion of small craft in the near wave/flow field of a parent vessel and (b) control of launch and recovery operations. An alternative application of the new science is in the optimal control of wave energy converters where large increases in performance per unit cost are possible (see the impact case).

Research Area: ART-AI is a multidisciplinary CDT, bringing together computer science, social science and engineering so that its graduates will be specialists in one subject, but have substantial training and experience in the others. The ART-AI management team brings together research in AI, HCI,politics/economics, and engineering, while the CDT as a whole has a team of >40 supervisors across seven departments in three faculties and the institutes for policy research (IPR) and for mathematical innovation (IMI). This is not a marriage of convenience: many CDT members have experience of interdisciplinary working and together with CDT cohorts and partners, we will create accessible, transparent and intelligible AI, driven by ethical and responsible principles, to address issues in, for example, policy design and political decision-making, development of trust in AI for humans and organisations, autonomous systems, sensing and data analysis, explanation of machine decision-making, public service design, social simulation and the ethics of socio-technical systems. Need: Hardly a day passes without a news article on the wonders and dangers of AI. But decisions - by individuals, organisations, society and government - on how to use or not use AI should be informed and ethical. We need policy experts to recognise both opportunities and threats, engineers to extend our technical capabilities, and scientists to establish what is tractable and to predict likely outcomes of policies and innovations. We need mutually informed decisions taking account of diverse needs and perspectives. This need is expressed in measured terms by a slew of major reports (see Case for Support) and Commons and Lords committees, all reflecting the UKCES Sector Insights (Evidence report #92, 2015) prediction of a need by 2022 for >0.5M additional workers in the digital sector against just a third of that number graduating annually. To realise the government vision for AI (White Paper), a critical fraction of those 0.5M workers need to be leaders and innovators with in-depth scientific and technical knowledge to make the right calls on what is possible, what is desirable, and how it can be most safely deployed. Beyond the UK, a 2018 PwC report indicates AI will impact ~10% of jobs, or ~326 million globally by 2030, with ~33% in high-skill jobs across most economic sectors. The clear conclusion is a need for a significant cadre of high-skill workers and leaders with a detailed knowledge of AI, an understanding of how to utilise it, and its political, social and economic implications. The ART-AI is designed to deliver these in collaboration and co-creation with stakeholders in these areas. Approach: ART-AI will produce interdisciplinary graduates and interdisciplinary research by (i) exposing its students to all three disciplines in the taught elements, (ii) fostering development of multi-discipline perspectives throughout the doctoral research process, and (iii) establishing international and stakeholder perspectives whilst contributing to immediate, real-world problems through a programme of visiting lecturers, research visits to leading institutions and internships. The CDT will use some conventional teaching, but the innovations in doctoral training are: (i) multi-disciplinary team projects; (ii) structured and facilitated horizontal (intra-cohort) peer learning and vertical (inter-cohort) mentoring, and in the interdisciplinary cross-cohort activities in years 2-4; (iii) demonstrated contextualisation of the primary discipline research in the other disciplines both at transfer (confirmation) at the end of year 2 and in the final dissertation. Each student will have a primary supervisor from their main discipline, a co-supervisor from at least one of the other two, and where appropriate, one from a CDT partner, reflecting the interdisciplinarity and co-creation that underpin the CDT.

The acoustics industry contributes £4.6 billion to the UK's economy annually, employing more than 16,000 people, each generating over £65,000 in gross value added across over 750 companies nationwide. The productivity of acoustics industry is similar to that of other enabling technologies, for example the UK photonics industry (£62k per employee in 2014). Innovation through research in acoustics is a key to its industry success. The UK's acoustics industry and research feeds into many major global markets, including the $10 billion market for sound insulation materials in construction, $7.6 billion ultrasound equipment market and $31 billion market for voice recognition. This is before the vital role of acoustics in automotive, aerospace, marine and defence is taken into consideration, or that of the major UK industries that leverage acoustics expertise, or the indirect environmental and societal value of acoustics is considered. All the four Grand Challenges identified in the 2017 UK Industrial Strategy require acoustics innovation. The Industrial Strategy Challenge Fund (ISCF, https://www.ukri.org/innovation/industrial-strategychallenge-fund/) focuses on areas all of which need support from acoustics as an enabling technology. The future of acoustics research in the UK depends on its ability to contribute to the Four Grand Challenges. Numerous examples are emerging to demonstrate the central role of acoustics in addressing the four Grand Challenges and particularly through more focused research. The acoustics-related research base in the UK is internationally competitive, but it is important to continue to link this research directly to the four Grand Challenges. In this process, the role of UK Acoustics Network (UKAN) is very important. The Network unites over 870 members organised in 15 Special Interest Groups (www.acoustics.ac.uk) who represent industry, academia and various non-academic organisations which success relies on the quality of acoustics related research in the UK. UKAN was funded by the EPSRC as a standard Network grant with the explicit aim of pulling together the formerly disparate and disjoint acoustics community in the UK, across both industry and academia. UKAN has been remarkably successful. Its success is manifested in the large number of its members, numerous network events it has run since its inception in November 2017 and contribution it has made to the acoustics research community. Unfortunately, UKAN has not been in the position to fund new, pilot adventurous or translational projects nor has it any funding support for on-going research or knowledge transfer (KT) activities. The purpose of UKAN+ is to move beyond UKAN, create strategic connections between acoustics challenges and the Grand Challenges and to tackle these challenges through pilot studies leading in turn to full-scale grant proposals and systematic research and KT projects involving a wider acoustics community. There is a great opportunity for the future of the UK's acoustics related research to move on beyond this point, build upon the assembled critical mass and explore the trans-disciplinary work initiated by UKAN. Therefore, this proposal is for UKAN+ to take this community to the next stage, connect this Network more widely in the UK and internationally to contribute through coordinated research to the solution of Grand Challenges set by the government. UKAN+ will develop a new roadmap for acoustics research in the UK related to Grand Challenges, award exploratory (pilot) cross-disciplinary research projects to the wider community to support adventure research and knowledge transfer activities agreed in the roadmap and support the development of develop full-scale bids to the government research funding bodies which are aligned with the Grand Challenges. UKAN+ will also set up a National Centre or Coordination of Acoustics Research, achieve full sustainability and support best Equality, Diversity and Inclusion practices.