The general objective of sHYpS is to support the decarbonization of the shipping industry, by leveraging on previous and on-going work and investment made by Viking and some consortium members. It will develop a hydrogen-based solution, which can be adapted to multiple types of vessels and in some cases can already achieve IMO’s target for 2030 and 2050. The project will develop a (i) novel hydrogen storage intermodal 40’ ISO c-type container, (ii) the complete detailed design of modular containerized power train based on optimized PEM Fuel Cells and (iii) their dedicated logistics. On one hand the project will define a logistic based on swapping pre-filled containers, on the other hand it will define a perspective scale-up of the storage capacity and the supply applied to the Port of Bergen use case. This will allow to kick start a supply-chain without waiting for the full infrastructure to be in place. We show how this approach can already support a remarkable part of the vessels in the EU waters. The project will use the window of opportunity of 1 Viking’s new builds Ocean Cruise vessel to install the storage system onboard with the complete gas handling and energy management system and test it during the shakedown cruise by 2026, with a limited power Fuel Cell. When the 6MW will be in place (pendent investment decision by Viking) this will allow to cut 50% of emissions in a 14-day fjord cruise. The midterm outcomes are remarkable since Viking has a building program of 6 Ocean Cruise ships by 2030 and several river ships. With the right logistics in place the ISO container technology can develop in hundreds of units per year. In the meantime, the upscale design of the container from this project will approach more segments in sea and IWW application and look to hundreds of vessels in the order book of commercial fleets. The value-chain include LH2 suppliers, giving the opportunity to speed up a supply of thousands of tons of LH2 per year in the next 20 years.

CoolR represents a fundamental shift in engine technology that is expected to increase Internal Combustion Engine efficiency to 140% of current levels. The innovative CoolR Split Cycle engine achieves this remarkable headline efficiency by employing quasi-isothermal compression. The engine thus combines the high compression ratios preferred for efficient running, plus low emissions, of internal combustion engines with the heat recuperating capabilities associated with combined cycle gas turbines. The principles of this system have been deployed already by Ricardo in a power generation plant. The objective of this programme is to determine the technical feasibility and best route to market of a vehicle-centric concept variant within 10 years.

This Innovation project for impact will bring together policy and practice leaders concerned with how planning decisions affect urban air quality. The overarching aim is to make a software platform for the quantitative assessment of Green Infrastructure as an aid to the improvement of roadside air quality. We call this platform GI4RAQ. Our particular objectives can be summarised as: 1. to provide a consolidated, open-source, computer modelling code for roadside air pollution in urban settings based on our existing research code. 2. to co-design of a fit-for-purpose, simple, and attractive GI4RAQ platform for urban practitioners as a front-end to the consolidated model code. 3. to demonstrate that the GI4RAQ platform can unlock a critical impasse in current planning policy and so enable capacity-building on the regulatory and consultancy sides of the planning process. We will work with major influencers in the private and public sectors, which offers a rapid and cost-effective route to meaningful impact. Specifically, we will work with Transport for London and the Greater London Authority to influence the next issue of the London Plan. To be released towards the end of 2019, a proposed new policy requiring larger-scale developments to be 'Air Quality Positive' may be implemented, but only if tools exist to evidence such a result at planning. We will work with the UK's leading air quality consultants, Cambridge Environmental Research Consultants (CERC) and Ricardo Energy & Environment, to ensure that the GI4RAQ platform is fit for operational use and that it can be used alongside current Air Quality tools. London's 33 Local Authorities must ensure their Local Plans conform to the London Plan, and Authorities across the UK look to the London Plan in preparing their own Local Plans, both of which provide cascading impact for our proposed work. The project is designed to dovetail with 'WM Air', a large multi-partner programme focused on West Midlands' air quality led by the University of Birmingham. The GI4RAQ Principal Investigator leads the work stream on green infrastructure in WM Air alongside GI4RAQ partner Birmingham City Council, thereby ensuring rapid knowledge transfer between research and practice in London and Birmingham. The project will establish a robust approach to 'GI4RAQ' interventions to deliver reliable improvements in roadside air quality, based on quantitative computer modelling but avoiding the time and expense of full fluid flow simulations. The approach develops directly out of a NERC Innovation Pathfinder, which established that a strong demand for quantitative GI4RAQ exists, but also identified the policy impasse, and a placement of the researcher co-Investigator in Transport for London.

The project aims to create new fundamental knowledge and advanced numerical tools regarding the atomisation, heating and evaporation characteristics of liquefied gases, in order to significantly advance the technology required to efficiently control cryogenic injection. Liquid gases such as air, nitrogen or natural gas can serve as cost-effective energy vectors within power production units as well as transport "fuels" with zero emissions. For example, energy coming from renewables can be used in order to "cool" air or nitrogen, up to the point that they become liquids. Follow up injection of these liquids to a higher temperature environment causes rapid re-gasification and a 700-fold expansion in volume, which can drive a turbine or piston engine even without combustion. Most importantly, because of the low boiling point of cryogenic liquids, low-grade or ambient heat can be used as a heat source, which otherwise is wasted. A better understanding and control of the injection dynamics of the cryogenic fluids could boost the efficiency of hybrid combustion systems to 60% (Ricardo's Cryopowder split-cycle engine), and achieve zero emissions when used for work generation through isothermal expansion without the need of combustion (Dearman Engine and Libertine Free Piston Engine). Recently, there has been an increased interest towards cryogenic technologies, however this has been focused mostly on the liquefaction processes (such as the £6m EPSRC grant to the Birmingham Centre for Cryogenic Energy Storage). Within the suggested project the attention is shifted towords the injection process of the cryogenics in real life industrial applications. Dr Vogiatzaki with the support from two leading UK companies in the field of innovative energy system solutions (Ricardo Ltd and Libertine Ltd) aspires to provide new knowledge and robust modelling tools to unlock the dynamics of cryogenic energy carrier's atomisation and heat transfer dynamics.

Computational fluid dynamics (CFD) is fundamental to modern engineering design, from aircraft and cars to household appliances. It allows the behaviour of fluids to be computationally simulated and new designs to be evaluated. Finding the best design is nonetheless very challenging because of the vast number of designs that might be explored. Computational optimisation is a crucial technique for modern science, commerce and industry. It allows the parameters of a computational model to be automatically adjusted to maximise some benefit and can reveal truly innovative solutions. For example, the shape of an aircraft might be optimised to maximise the computed lift/drag ratio. A very successful suite of methods to tackle optimisation problems are known as evolutionary algorithms, so-called because they are inspired by the way evolutionary mechanisms in nature optimise the fitness of organisms. These algorithms work by iteratively proposing new solutions (shapes of the aircraft) for evaluation based upon recombinations and/or variations of previously evaluated solutions and, by retaining good solutions and discarding poorly performing solutions, a population of optimised solutions is evolved. An obstacle to the use of evolutionary algorithms on very complex problems with many parameters arises if each evaluation of a new solution takes a long time, possibly hours or days as is often the case with complex CFD simulations. The great number of solutions (typically several thousands) that must be evaluated in the course of an evolutionary optimisation renders the whole optimisation infeasible. This research aims to accelerate the optimisation process by substituting computationally simpler, dynamically generated "surrogate" models in place of full CFD evaluation. The challenge is to automatically learn appropriate surrogates from a relatively few well-chosen full evaluations. Our work aims to bridge the gap between the surrogate models that work well when there are only a few design parameters to be optimised, but which fail for large industry-sized problems. Our approach has several inter-related aspects. An attractive, but challenging, avenue is to speed up the computational model. The key here is that many of these models are iterative, repeating the same process over and over again until an accurate result is obtained. We will investigate exploiting partial information in the early iterations to predict the accurate result and also the use of rough early results in place of the accurate one for the evolutionary search. The other main thrust of this research is to use advanced machine learning methods to learn from the full evaluations how the design parameters relate to the objectives being evaluated. Here we will tackle the computational difficulties associated with many design parameters by investigating new machine learning methods to discover which of the many parameters are the relevant at any stage of the optimisation. Related to this is the development of "active learning" methods in which the surrogate model itself chooses which are the most informative solutions for full evaluation. A synergistic approach to integrate the use of partial information, advanced machine learning and active learning will be created to tackle large-scale optimisations. An important component of the work is our close collaboration with partners engaged in real-world CFD. We will work with the UK Aerospace Technology Institute and QinetiQ on complex aerodynamic optimisation, with Hydro International on cyclone separation and with Ricardo on diesel particle tracking. This diverse range of collaborations will ensure research is driven by realistic industrial problems and builds on existing industrial experience. The successful outcome of this work will be new surrogate-assisted evolutionary algorithms which are proven to speed up the optimisation of full-scale industrial CFD problems.