IMPACT is a Centre of Excellence supporting the global engineering industry with collaborative, fundamental research.
The distinct research infrastructure and ethos of IMPACT will enable a step change in research and innovation in the College’s already outstanding Materials, Processing and Numerical Technologies research groups.
Our research will focus on six core research themes:
1. Future Manufacturing Technologies
Theme Leader: Professor Nicholas Lavery
2. Next Generation Materials Property Measurement
Theme Leader: Professor Steve Brown
3. Advanced Structural Mechanics
Theme Leader: Dr Hamed Haddad Khodaparast
4. Thin Films and Coatings
Theme Leader: Dr Matthew Carnie
5. Data Centric Engineering
Theme Leader: Professor Paul Rees
6. Green Recovery
Theme Leader: Professor Perumal Nithiarasu
Our research programme is led by IMPACT Director, Professor Johann Sienz, and will be delivered through an internationally competitive team of in-house academics and researchers working in high-quality research infrastructure facilities.
Their expertise will lead to enhanced academic and industrial collaboration in fundamental research, thereby creating multi-disciplinary teams that are able to attract £24.6m of additional funding to carry out fundamental frontiers research.
IMPACT Research Themes
For a full overview of IMPACT themes please click here.
IMPACT Researchers
For a full list of IMPACT academics and researchers please click here.
Further Information
For general information about IMPACT, please download the IMPACT Leaflet here.
Below is a list of previous and current IMPACT related research (A to Z):
Start Date: November 2019
End Date: June 2020
Funder(s): ASEM-DUO
PI: Dr Mokarram Hossain, Swansea University
Project Partner(s): Indian Institute of Technology Patna
Research Area(s): Materials
The purpose of this exchange program is to strengthen the ongoing collaborating work on characterization of electroactive elastomers and development of particle filled elastomer composite. The developed material is going to be applied for developing an energy harvesting prototype of high performance for generating electricity from marine wave. The objectives of the exchange program are as follows:
Lecture/Research plan of the Indian Professors:
During the first week of the visit, at least two lectures will delivered on the development of elastomer composite and characterization of properties relevant to its application for energy harvesting applications. During the remaining weeks of the visit, following works are planned:
Start Date: January 2018
End Date: December 2018
Funder(s): Innovate UK
PI: Dr Cinzia Giannetti, Swansea University
Project Partner(s): Sunscape Systems Limited
Research Area(s):
Welsh slate, and Natural slate, is a highly desirable and premium product in domestic housing markets across the world. Installed well, by a skilled craftsman it is a beautiful addition to almost any house. However, resource and skills shortages across the globe have led to challenges in its ongoing use in the house building and renovation markets. Carapace is a new and innovative roofing solution that utilises waste slate material from local sources to produce a modular, composite roofing tile that can be installed in a fraction of the time of a tradition slate tile. Our innovation uses a unique interlocking system that allows the user to simply clip the tiles together to form a safe and secure slate roof installation. Our system will reduce the time taken to install a slate tile roof by up to 90%; we have demonstrated that installation of 1sqm of Carapace is possible in just 1 minute. This project will develop a SMaRT and future proof manufacturing process to produce the Carapace roofing system on a commercial scale, enabling our innovative product to become publically available on a large scale for the first time.
Start Date: November 2020
End Date: May 2021
Funder(s): UKRI Made Smarter
PI: Professor Johann Sienz
Project Partner(s): University of Warwick and Manufacturing Technology Centre (MTC)
Research Area(s): 3D printing,
The project aim is the development of a marketplace test bed: a digital supply chain marketplace (DSCM) that is digitally enabled, attractive to buyer and suppliers, and uses insight gained from other marketplace examples to address known barriers and issues. This Made Smarter testbed will act as a template for other marketplaces to be created to serve other sectors, geographies and verticals, for instance exclusive communities for highly regulated industries, where trust in the access, integrity, and security of information is critical.
Start Date: 22 March 2021
End Date: 21 September 2023
Funder(s): EPSRC
PI: Dr Dunhui Xiao
Project Partner(s):
Research Area(s): Artificial Intelligence, Fluid Dynamics, Numerical Analysis
Simulating large area urban turbulence flows in real time accurately remains a pressing challenge, yet to be addressed. Reduce Order Modelling (ROM) provides a means of real time simulation; traditional model reduction methods such as balanced truncation, the reduced-basis method, and (balanced) proper orthogonal decomposition (POD), have been developed. However challenges remain: the traditional methods restrict the state to evolve in a linear subspace; in addition, big area simulation cannot be conducted in real time. In addition, it is hard to define how accuracy the derived ROM is since there is no appropriate error estimator derived for the ROM. Also, existing ROM is either physically or data-driven and there is no physically informed data-driven ROM. Advancing in machine learning and in observational and simulation capabilities offer an opportunity to integrate simulation and data science approaches more intensively.
The proposed research offers scientific advances within the field of fluid flow modelling in order to underpin the establishment of novel sophisticated tools that will allow real-time simulations and prediction of air flows (and subsequently personal exposure to air pollutants); principal features include real-time air pollution predictions for next few hours. This can help minimise exposure to the population, especially vulnerable young/elderly patients. Individuals will be able to decide whether to go outside or not, exercise or make their daily plans.
The proposed new model combines physical models and data sciences technologies, in particular, the deep learning for predicting the non-linearity of turbulence flows, thus making the air flows predictions more reliable and accurate. An autoencoder deep neural network will potentially capture the non-linearity of urban turbulence flows and more accurate than the traditional model reduction methods (e.g. POD). Commercialisation of the research outputs will be undertaken in partnership with VortexIoT, Looker Tech and Spire Global. These are international leading companies in the fields of sensor technology, software company and provision of technical professional services.
The approach is based on an advanced fast-running computational model to manage and predict the airflow, air quality in a city, guide effective responses in emergencies and help people reduce the air pollution exposure time. The development of novel deep learning ROM, reducing computational times by several orders of magnitude, will make currently unsurmountable problems tractable:- e.g. detailed air flows through a big area or an entire city. The specific technology that distinguishes this project are the potential use of deep learning reduced order modelling, new computational domain decomposition, new error analysis for DLROM and data assimilation method.
The research objectives are: to develop deep learning based ROM framework with domain decomposition methods; to develop ROM framework with Autoencoder network that will project the system into a non-linear subspace, thus increasing accuracy; to develop ROMs with variable material properties such as variable initial or boundary conditions (different wind direction for example); ROM based data assimilation and optimisation methods; to perform an error analysis and optimal improvement for ROMs using machine learning methods.
The research has substantive health, environmental and economic impacts. Beneficiaries may include the general public through fast response to avoid exposure to air pollution technical professional services consultancy companies, local and national government environment bodies (e.g. NRW in Wales), computational engineering companies who will benefit from more efficient fluid flow model outcomes. In addition, public sector (spend budget savings in health) and air quality sensor device manufacturers incorporating the proposed modelling approach to enhance their offering (increased profits from sales).
https://gow.epsrc.ukri.org/NGBOViewGrant.aspx?GrantRef=EP/V000756/1
Start Date: August 2019
End Date: August 2022
Funder(s): EPSRC
PI: Dr Francesco Del Giudice, Swansea University
Project Partner(s): Dolomite Ltd
Research Area(s): Complex fluids & soft solids; Microsystems; Healthcare
New techniques which afford the rapid screening of cells to determine the presence of diseases are essential to provide efficient and effective future treatments and to improve patient health outcomes across a broad spectrum of disease states. In the last 30 years, the introduction of micro electro mechanical systems (MEMS) has prompted substantial research to develop miniaturised disease screening equipment based on microfluidic technologies. Such technologies aim to enhance point of care (POC) diagnosis, as a rapid clinical alternative to biopsy or blood tests (results typically between 2-10 days).
The encapsulation of cells together with expensive functionalised particles, called Drop-seq, is currently regarded as the most effective existing single diagnostic platform approach to derive the genome of cells affected by a variety of diseases down to the single-cell level. Although this Drop-seq technology represents the current state of the art, it suffers from a serious drawback as only approximately 10% of the cells involved are encapsulated together with a single functionalised expensive particle. This loss of over 90% of cells and expensive particles in the sensing process is a serious limitation for the screening of rare cells such as circulating tumour cells of which there are only 1/1000 in a typical blood sample. Earlier and more accurate detection of potentially fatal diseases would represent a remarkable advance in healthcare, with substantive reductions in the ongoing health cost burden and significant improvements to the quality of life of each affected individual.
The research proposed aims to exploit advances in viscoelastic flow technology to increase the efficiency of the Drop-seq technique to an unprecedented 100%. To achieve this transformative result the planned work will establish a means to ensure the equal-spacing of cells and functionalised particles before they approach the encapsulation area. This ensures that a single cell is encapsulated with a single particle, in a single droplet (a process referred to herein as deterministic encapsulation). Thus, the Drop-seq becomes deterministic when the frequency of droplet formation is synchronised with the frequency of particles and cells approaching the encapsulation area. Henceforth, the efficiency of the Drop-seq technology becomes 100%, rather than the mere 10% currently obtained.
The project will be carried out in conjunction with an industry partner that is recognised as the world leader in the design and manufacture of pioneering microfluidic products.
The objectives of the research are as follows:
(1) Achieving cell ordering in straight channel; (2) Achieving continuous formation of viscoelastic droplets with uniform sizes and shapes; (3) Encapsulation of single particles in viscoelastic droplets with uniform size distribution; (4) Encapsulation of single cells in viscoelastic droplets.
The research, which will achieve unprecedented disease detection capability, has substantive potential impacts, both in terms of healthcare outcomes and economic benefits. It will provide a basis for transforming the accuracy of detection for diseases such as lung, prostate, breast and bowel cancers, which currently account for more than half the types of cancer and lead to premature death. Beneficiaries will therefore include patients and healthcare professionals. Specifically, the POC testing approach has special relevance to healthcare professional working in remote locations. Examples include GP surgeries, clinics in remote locations or even within pharmacies. Our project partner is well placed to drive the research outputs to commercialisation and economic gain- on a global basis. Furthermore, widespread technical benefits in diverse fields including Process Engineering and Advanced Manufacturing may be anticipated to arise from enhanced knowledge of the innovative uses of viscoelastic flows within microfluidic settings.
https://gow.epsrc.ukri.org/NGBOViewGrant.aspx?GrantRef=EP/S036490/1
Start Date: February 2018
End Date: January 2023
Funder(s): EPSRC
PI: Professor David Wagg, The University of Sheffield
Project Partner(s): Airbus Group Limited, EDF Energy, Leonardo (UK), LOC Group (London Offshore Consultants), Romax Technology Limited, Schlumberger, Siemens, Stirling Dynamics Ltd, Ultra Electronics Ltd
Research Area(s): Aerospace, Defence and Marine; Energy
The aim of this proposal is to create a robustly-validated virtual prediction tool called a "digital twin". This is urgently needed to overcome limitations in current industrial practice that increasingly rely on large computer-based models to make critical design and operational decisions for systems such as wind farms, nuclear power stations and aircraft. The digital twin is much more than just a numerical model: It is a "virtualised" proxy version of the physical system built from a fusion of data with models of differing fidelity, using novel techniques in uncertainty analysis, model reduction, and experimental validation. In this project, we will deliver the transformative new science required to generate digital twin technology for key sectors of UK industry: specifically power generation, automotive and aerospace. The results from the project will empower industry with the ability to create digital twins as predictive tools for real-world problems that (i) radically improve design methodology leading to significant cost savings, and (ii) transform uncertainty management of key industrial assets, enabling a step change reduction in the associated operation and management costs. Ultimately, we envisage that the scientific advancements proposed here will revolutionise the engineering design-to-decommission cycle for a wide range of engineering applications of value to the UK.
https://gow.epsrc.ukri.org/NGBOViewGrant.aspx?GrantRef=EP/R006768/1
Start Date: April 2019
End Date: September 2027
Funder(s): EPSRC
PI: Professor M Jones
Project Partner(s): ABM University NHS Trust; Admiral Group Plc; Airbus Defence and Space GmbH; Amazon Web Services (Not UK); CPR Global Technology Ltd; Crown Packaging Plc; Digital Catapult; DST Innovations Ltd; Facebook (International); Ford Motor Co; Fujitsu; GeoLang; GFaI tech GmbH; GoFore UK; Google; IBM; McAfee Labs; Microsoft; Mishcon de Reya; Ordnance Survey; Oyster Bay Systems ltd; P A International Consulting Group Ltd; Pfizer; QinetiQ;
SPECIFIC (Innovation and Knowledge Ctr); Tata Steel UK Limited; Traydstream; University of Cagliari; Vizolution Ltd; Vortex IoT; ZeSys e.V.
Research Area(s): Artificial Intelligence; Human-Computer Interactions; Information & Knowledge Mgmt; Software Engineering; Information Technologies
The Centre's themes align with the 'Towards A Data Driven Future' and 'Enabling Intelligence' priority areas, meeting the needs identified by UKRI to provide a highly skilled - and in demand - workforce focused on ensuring positive, human-centred benefits accrued from innovations in data driven and intelligence-based systems.
The Centre has a distinct and methodologically challenging "people-first" perspective: unlike an application-orientated approach (where techniques are applied to neatly or simplistically defined problems, sometimes called "solutionism"), this lens will ensure that intense, multi-faceted and iterative explorations of the needs, capabilities and values of people, and wider societal views, challenge and disrupt computational science. In a world of big data and artificial intelligence, the precious smallness of real individuals with their values and aspirations are easily overlooked. Even though the impact of data-driven approaches and intelligence are only beginning to be felt at a human scale, there are already signs of concern over what these will mean for life, with governments and others worldwide addressing implications for education, jobs, safety and indeed even what is unique in being human. Sociologists, economists and policy makers of course have a role in ensuring positive outcomes for people and society of data-driven and intelligence systems; but, computational scientists have a pivotal duty too. Our viewpoint, then, will always see the human as a first-class citizen in the future physical-digital world, not perceiving themselves as outwitted, devalued or marginalised by the expanding capabilities of machine computation, automation and communication.
Swansea and the wider region of Wales is a place and community where new understandings of data science and machine intelligence are being formed within four challenging contexts defined in the Internet Coast City Deal: Life Science and Well-being; Smart Manufacturing; Smart and Sustainable Energy; and Economic Acceleration. Studies commissioned by the City Deal and BEIS evidence the science and innovation strengths in Swansea and region in these areas and indicate how transformational investments in these areas will be for the region and the UK. Our Centre will, then, immerse cohorts in these contexts to challenge them methodologically and scientifically. The use of data-driven and intelligence systems in each of the four contexts gives rise to security, privacy and wider ethical, legal, governance and regulatory issues and our Centre also has a cross-cutting theme to train students to understand, accommodate and shape current and future developments in these regards.
Cohort members will work to consider how the Centre's challenge themes direct and drive their thinking about data and intelligence, benefitting from both the multidisciplinary team that have built strong research agendas and connections with each of the contexts and the rich set of stakeholders that are our Centre has assembled. Importantly, a process of pivoting between challenge themes will be applied: insights, methods and challenges from one theme and its research projects will be tested and extended in others with the aim of enriching all. These, along with several other mechanisms (such as intra- and inter-cohort sandpits and side projects) are designed to develop a powerful bonding and shaping "cohort effect".
https://gow.epsrc.ukri.org/NGBOViewGrant.aspx?GrantRef=EP/S021892/1
Start Date: November 2017
End Date: October 2020
Funder(s): EPSRC
Project Partner(s): Luxembourg Inst for Sci and Tech (LIST)
Research Area(s): Materials Characterisation; Materials Synthesis & Growth; Numerical Analysis; Energy
This is a proposal for a joint research programme involving Swansea University (SU) and the Luxembourg Institute of Science and Technology (LIST)
The development of sustainable energy is one of the key scientific challenges of the 21st century. Environmental concerns have led to an increased interest in the use of solar energy as an alternative to fossil fuel based energy sources. In the solar energy field, significant research attention is being given to the processes of collection and storage, either in the form of heat or by direct conversion to electricity. The most important part of solar collectors is the solar selective coatings, which directly affect the efficiency of the system.
New nano-structured metamaterial based absorbers have many benefits over conventional absorbers, such as miniaturisation, adaptability and frequency tuning. However, there are two major challenges in producing these new metamaterial based absorbers. The first is to find the optimal nanostructure design, while the second is its synthesis within current nano-technological limits.
The project address the first challenge via a new multi-scale computational framework. Advanced computational techniques will be develop to perform the simulations at the nano-scale and explore the vast design space for the nano-structure. A novel methodology to couple the nano and micro scales will be developed and implemented and an optimisation technique applied at the micro scale to find the optimal nano-structure.
The second challenge will be addressed by employing the unique world prototype machine at LIST capable of fabricating/replicating nano/micro metallic structures by electrochemical deposition. The chemical stability of the materials employed at the nano-scale will also be considered with the objective to guarantee chemical stability at high temperatures, with the aim of boosting light absorption efficiency.
https://gow.epsrc.ukri.org/NGBOViewGrant.aspx?GrantRef=EP/P033997/1
Start Date: July 2019
End Date: June 2022
Funder(s): EPSRC
PI: Dr Bjornar Sandnes, Swansea University
Project Partner(s): University of Oslo
Research Area(s): Biophysics; Fluid Dynamics
Flow and mixing of fluids and granular materials occur in a wide range of processes. In nature, methane venting from ocean sediments represents a significant source of greenhouse gas, and the emission rate is intimately linked with complex interactions between the sediment and the rising methane gas in the form of bubbles or channels. Another example is the migration of gas through volcanic magma - a mixture of solid crystals and liquid melt - where the specifics of the complex flow patterning of the gas transport is thought to influence volcanic eruption behaviour. In the engineering sector, handling of granular suspensions and deformable porous materials are key in a wide range of industries from oil and gas, to food processing and pharmaceuticals. Despite the abundance and importance of such processes, fundamental knowledge of the physics that control them is lacking in many areas. These systems are inherently difficult to predict and control because of the very complex interactions taking place between the granular materials, liquids and gases during flow.
The main aim of the project is to uncover the physics of two unknown mechanisms, namely the role of fluid viscosity and grain-fluid interactions in controlling the flow behaviour of frictional fluids and deformable materials. Using both experiments and computer simulations, we will explore the full range of viscosity ratio; high viscosity fluids injected into low viscosity host fluids and vice versa, where the host fluid contains granular materials of a range of concentrations, shapes and sizes. Where the two fluids meet, the meniscus will push or pull on the grains depending on the wetting properties, e.g. whether the grains are hydrophilic ("water-loving") or hydrophobic ("water-hating"). Through finely controlled experiments matched with theory and simulations we will reveal the effect of wetting on the flow behaviour. The new insight will be incorporated into models that will allow a much more accurate prediction of frictional flow behaviour, and ultimately to improving forecasting of natural events such as volcanic eruptions, and to optimize industrial processing of granular suspensions and deformable materials.
https://gow.epsrc.ukri.org/NGBOViewGrant.aspx?GrantRef=EP/S034587/1
Start Date: September 2019
End Date: March 2022
Funder(s): Western Power - Network Innovation Allowance (NIA)
PI: Dr Grazia Todeschini, Swansea University
Project Partner(s): Western Power
Research Area(s): Network Improvements and System Operability
This work delivered by Swansea University includes the development of an algorithm which can improve the harmonic levels in the network by controlling the inverters of Distributed Generation. This will involve among other tasks a number of power system studies.
Objective(s)
The main objectives of the project are:
Problem(s)
It is expected that due to the increasing number of non-linear devices being connected to the distribution network, the harmonics in the network can become a challenge for Distribution Network Operators (DNOs). Existing solutions for managing harmonics are not suitable for dynamic networks with varying operating conditions or can be very expensive. Therefore, it is important to find alternative solutions in order to be able to manage harmonic levels in the network in a cost effective way.
Method(s)
The aim of this project is to develop an algorithm that can improve the network’s harmonic levels by controlling existing Distributed Generation inverters and will be delivered by Swansea University. As part of this, a number of power system studies will be performed in order to develop, implement the algorithm and test its operation. The algorithm will also be tested in a simulated environment at a university laboratory. This project will provide recommendations as to whether a further project is recommended for the trial of the algorithm in the network. The technical work produced will be reviewed by Power Systems Consultants (PSC).
https://www.westernpower.co.uk/innovation/projects/harmonic-mitigation
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Funder(s): Royal Academy of Engineering
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Start Date: June 2020
End Date: May 2022
Funder(s): EPSRC
PI: Dr Grazia Todeschini, Swansea University
Project Partner(s): Measurable Ltd.; National Grid ESO; University of Texas at Austin
Research Area(s): Energy
The UK is committed to reducing its greenhouse gas emissions by at least 80% by 2050, relative to 1990 levels. Meeting this target will require a significant shift in the way energy is used (for example, through electrification of heat and transport) and generated. To reduce our dependency on fossil fuels, significant levels of Renewable Energy Sources (RESs) will need to be integrated into the power grid. RESs include energy sources that do not rely on fossil fuels and nuclear energy, such as solar, wind, tidal and wave. Some sources of renewable energy have been successfully incorporated (for example, pumped-hydro), and they are generally not classified as 'RESs' as this term refers to devices that are connected to the grid by means of power converters.
While the benefits of RESs are undisputable and their installation should be facilitated, the integration of large amounts of these devices requires the development of new paradigms and methods to maintain a reliable and safe power system. RESs have certain characteristics that differentiate them from traditional sources: they are less controllable than traditional energy sources, they cause unintended power flow patterns, and they impact voltage and current waveforms and the overall power quality of electricity.
This proposal will focus on the study of harmonic propagation in the UK power grid due to RESs. Harmonics are current and voltage components at frequencies multiple of the fundamental, and they are generated by the operation of the power converters. While low harmonic levels are tolerated and do not impact grid operation, increasing harmonic levels have detrimental effects including: increased losses, reduced efficiency, misoperation and reduced lifetime of equipment, and nuisance protection tripping. While the costs of harmonics are not easy to determine because of their long-term effect, it has become accepted by the power grid operators and users that increasing harmonic levels are detrimental for efficient grid operation. More importantly, they may compromise the future integration of RESs.
This proposal aims at assessing the harmonic impact of RESs in the future power grid. Carrying out this assessment requires the development of accurate models of both RESs and of the power system. At the same time, these models requires some form of simplification because of the number of components involved. Previous research has focused on either detailed power converter models, or the use of a large power system model with simplified converter representation. This NIA aims at combining both aspects in a model which is able to represent correctly harmonic generation from RESs, the transfer of harmonics between voltage levels, and the representation of statistical variations of harmonic levels in the system, for varying levels of penetration of RESs.
This research will involve close collaboration with two industrial partners, National Grid and Measurable Ltd, and with the University of Texas at Austin.
The impact of this research will be widespread: the models developed will help the academic community in understanding the best approaches to study harmonic behaviour of large energy systems. This work will assist the system operator in assessing the impact of new RESs on the power system. Furthermore, this research will inform the future developments of power quality standards and of harmonic mitigating solutions.
The ultimate goal of this project is to minimise the occurrence of harmonic problems in the future power grid, allowing for a smooth and clean integration of increasing amounts of RESs, not only in the UK, but on a global scale. Furthermore, it will benefit the UK as a whole in terms of maintaining the operations and efficiency of this ubiquitous infrastructure, whilst helping the government to meet targets for reducing CO2 emissions.
https://gow.epsrc.ukri.org/NGBOViewGrant.aspx?GrantRef=EP/T013206/1
Read more about this project on the IMPACT blog.
Start Date: 01 September 2020
End Date: 31 August 2022
Funder(s): Royal Academy of Engineering
PI: Dr Michael Jennings
Project Partner(s): Newport Wafer Fab Limited
Research Area(s): Electric Vehicles; Power Semiconductor Devices
Automobile pistons (contained within the internal combustion engine cylinder) of the future will be based on power semiconductors that are ubiquitous within electric vehicle power electronic (PE) systems. These PE systems are responsible for converting DC voltage (~500V) to AC for delivering power (50 – 200 kW) to the vehicle’s motor. In order to reach peak efficiency (~99%) and reduce carbon emissions, wide bandgap (WBG) semiconductors such as silicon carbide (SiC) and gallium nitride (GaN) will replace silicon technologies presently used.
This project builds upon an existing 1-year industrial fellowship between a global leader in power electronics chip manufacture and an academic with 15 years of power device experience. Together we aim to commercialise this WBG chip technology and qualify it for utilisation within the automotive sector. This is achieved through applying a progressive approach to the knowledge and corresponding procedures already acquired during the existing 1-year relationship.
Start Date: February 2018
End Date: January 2023
Funder(s): EPSRC
PI: Dr Llion Evans, Swansea University
Project Partner(s): Airbus Group Limited, Nikon (International), Synopsys Inc. (International), TWI Ltd, UK Atomic Energy Authority, University of Manchester
Research Area(s): Aerospace, Defence and Marine; Manufacturing
This fellowship programme will apply state-of-the-art 3D image processing and machine learning methods, developing them further where necessary, to deliver a new software tool that performs industrial production line 'virtual qualification' using part-specific simulations from 3D X-ray imaging in high-value manufacturing (HVM).
Qualification is when manufactured parts are verified fit for purpose, often achieved by performing experimental tests representative of in-service conditions. Virtual qualification will verify by modelling micro-accurate digital replicas of the final part (flaws included) replacing costly and time-consuming experimental methods. Additionally, this will assess defects for performance impact (rather than expensive but unspecific pass/fail testing). The challenge is that image-based modelling currently requires significant human interaction over a timescale of weeks. Applying this to many parts takes significant time to complete unless methodology can be changed. The novelty of this proposal is to use machine learning with foreknowledge, due to production line parts being similar, to automate conversion of microresolution 3D images into part-specific models that simulate in-service conditions. This automation is required for the technique to scale for deployment in industrial manufacturing. Additionally, because much of the decision making entailed is subjective, and therefore prone to human error, a consequential benefit of automation is consistent outputs by removing this variability.
This proposal focuses on image-based finite element methods (IBFEM), which merge real and virtual worlds to account for deviations caused by manufacturing processes not considered by design-based finite element methods (FEM), e.g. due to tolerancing or micro-defects. This implementation of part-specific modelling has applications in advanced manufacturing wherever there is variability from one component to another e.g. additive manufacturing or composites. A case study will be undertaken with the UK Atomic Energy Authority (UKAEA) for a heat exchange component. This will showcase the capabilities of the technique to automatically produce a report that estimates the impact of deviations from design on performance.
Unlike FEM, which have undergone extensive certification and are industry-wide trusted methods, there has not been a systematic approach which can be used to benchmark image-based modelling workflows against verified experimental data. This work will produce benchmarks based on standards for experimental measurements of thermomechanical material properties to give confidence in the technique for industrial adoption. The database of benchmarks will be useful for those wishing to use image-based modelling to validate workflows and could contribute towards establishing new standards in the field.
Central to this proposal is the use of FEM, the de-facto tool for predicting thermomechanical performance in engineering. Prof Zienkiewicz's research at Swansea University established it as a birthplace for FEM, and is now recognised as a leading research centre in the field. The team undertaking this fellowship, led by Dr Llion Evans, will be based at the Zienkiewicz Centre for Computational Engineering, Swansea University and will work in collaboration with the centre's head, Prof Nithiarasu, an expert in image-based modelling for biomechanics. Access to the equipment required for all aspects of this highly multidisciplinary work i.e. thermomechanical characterisation, 3D imaging and computing is available through complementary centres at the College of Engineering, Swansea University. To support this extremely multidisciplinary work, key industrial organisations will be collaborating on this project. Nikon Metrology Ltd. (X-ray imaging systems), Synopsys Inc. (image processing software), TWI (non-destructive testing and industrial standards), UKAEA (energy generation end-user) and Airbus (aerospace end-user).
https://gow.epsrc.ukri.org/NGBOViewGrant.aspx?GrantRef=EP/R012091/1
Start Date: October 2018
End Date: March 2022
Funder(s): Innovate UK
PI: Professor Johann Sienz, Swansea University
Project Partner(s): Hofer Powertrain UK
Research Area(s): Automotive supply chain; electrification, hybridization and internal combustion power
This project is focused on the development of unique technically advanced high performance e-motors and inverters that exceed currently available best in class standards. It will be undertaken by hofer powertrain UK with UK collaborators, supporting application in future high performance vehicle programmes for world leading UK based vehicle manufacturers and export customers.
From a base in the UK, the project supports the research, development and industrialisation of a completely new generation of technically advanced e-motor and inverter modules. As well a developing unique new knowledge and expertise in Britain, in part achieved through research collaborations with leading UK universities, the project will also benefit from the significant transfer of skills and research experience from hofer AG (Group) to the UK. This will will make a major contribution to upskilling the knowledge base in the UK by blending British and German cutting edge knowledge in a new way. This approach will improve UK productivity and help the global competitiveness of Britain's low carbon automotive supply chain, anchoring new EV/HEV capabilities here.
It will enable the project collaborators to develop unique expertise and capabilities in the UK in highly advanced e-motors and inverters that currently do not exist in Britain or elsewhere . This will support significant UK import substitution, a range of other economic benefits, such as inward investment, skills development and job creation in areas of high unemployment. The project will deliver significant environmental benefits while delivering new levels of performance, reliability and usability that will enable UK based manufacturers to secure and expand their position in high growth markets, such as China and the USA, where stringent environmental legislation could rule out, or limit the growth or the sale of vehicles with traditional ICE powertrains. hofer and its UK supply chain partners will also be well placed to exploit export markets for the advanced modular e-motors and inverters that this project will deliver.
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Funder(s): Royal Society
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Start Date: April 2019
End Date: March 2021
Funder(s): Innovate UK
PI: Dr Cinzia Giannetti, Swansea University
Project Partner(s): Vortex Iot Limited
Research Area(s): IoT
PARSER is an industrial research project developing a scalable solution to the combined problems of urban parking and air and noise pollution. Cars travelling at low speeds while searching for parking spaces contribute greatly to congestion and pollution. According to the British Parking Association drivers take an average 5.9 minutes searching for a parking space adding up to an estimated 4 days per year.
On average 40% of UK Councils' revenue is generated through parking and each council is also being pushed heavily to demonstrate environmental policy monitoring tools as our cities and urban areas will witness major change and population shift over the coming 20 years. PARSER is a product we feel hits a genuine urban need that can also enable new and sustainable urban business models.
There is an inherent and pressing need to build new energy, transport and urban systems to meet societal challenges such as a growing and ageing population, urbanisation and the need to reduce carbon emissions. _With_ _more than 70% of people estimated to be living in urban areas by 2040, city infrastructure and design is going to need to drastically change to better support its citizens._
Emerging internet-of-things (IoT) technologies can offer the ability to sense parking occupancy, traffic flows, congestion and air quality on a hyper-local scale. However, there are significant challenges to combine the technologies and develop the machine learning classifiers to interpret the data. The project aims to develop a LiDAR based parking monitoring solution coupled with air and noise pollution sensors. Together the consortium partners, Vortex IoT, BT, Swansea University and Swansea Council, will develop and deploy prototype sensors to test sites in the Swansea City region. Vortex IoT will take the lead on developing the sensors and wireless networks, Swansea University will focus on machine learning while Swansea Council will provide installation services and access to BT's on-street WIFI across areas of the city centre as designated for pilot.
This project will deliver significant export led growth, a substantial ROI, increased employment and further opportunity for R&D investment for all consortium partners.
Start Date: October 2020
End Date: March 2021
Funder(s): Welsh Government Office for Science (Sêr Cymru – Tackling COVID-19)
PI: Dr Hari Arora
Project Partner(s): 3DLifePrints, NHS
Reseaerch Area(s): Computational modelling, Healthcare
This project will deliver optimised patient-specific management strategies for the COVID- 19 pandemic via computational modelling of the lung. The models will predict how the lung will perform in response to different management strategies (supplemental oxygen, mechanical ventilation, fluid balance) and potential future treatment strategies outlined in the RECOVERY and REMAP-CAP trials (e.g. steroids, anti-inflammatories, antibiotics and plasma from recovered patients); innovatively factoring specific parameters of weight, height, age, general fitness and ethnicity – which unquestionably have acute relevance for recovery.
Start Date: March 2018
End Date: February 2020
Funder(s): EPSRC
PI: Dr Sanjay Pant
Project Partner(s): Freeman Group of Hospitals NHS Trust; Hospital Necker for Sick Children; INRIA Paris - Rocquencourt
Research Area(s): Biomechanics & Rehabilitation; Fluid Dynamics; Healthcare
The human cardiovascular system consists of two large arteries: the aorta (AO), which supplies oxygenated blood to the body, and the pulmonary artery (PA), which supplies deoxygenated blood to the lungs for oxygenation. In healthy individuals, the pressure in the AO is significantly higher than the pressure in the PA. Pulmonary artery hypertension (PAH) is a disease in which the PA pressure is abnormally elevated and is classified as one of the most devastating disorders by the pulmonary artery association UK. This is evidenced by a mean survival time after diagnosis of less than 30 months in adults and less than 12 months in children.
A recently proposed treatment for severe PAH, i.e. the case when PA pressure is higher than the pressure in AO, is to create a connection, known as the Potts shunt, between the PA and the AO. Just as a connection between two pipes carrying fluids with high and low pressures will lead to a reduction of pressure in the high-pressure pipe and an increase of pressure in the low-pressure pipe, the idea is that a Potts shunt can lead to a reduction of PA pressure in severe PAH patients. This reduction in PA pressure is desirable but will also result in mixing of oxygenated and deoxygenated blood, an undesirable effect. Clinical experience has shown favourable results of this treatment in some patients and unfavourable in others, which is attributed largely to a reduction in cardiac output, the total volume of blood ejected by the heart in one cardiac cycle. This project aims to develop computational models to assess three measures of Potts shunt treatment:
1) reduction of pulmonary artery pressure,
2) mixing of oxygenated and deoxygenated blood, and
3) reduction in cardiac output.
Through the computational models, this project will assess the mechanisms behind the success/failure of Potts shunt in relation to the above measures. The end-product will be a computer model which, given a new patient, can determine if a Potts shunt is likely to succeed in the patient. Furthermore, technology to optimise the design of Potts shunt for each patient individually, such that maximal clinical benefit is achieved, will be developed.
https://gow.epsrc.ukri.org/NGBOViewGrant.aspx?GrantRef=EP/R010811/1
Start Date: November 2018
End Date: October 2023
Funder(s): EPSRC
Project Partner(s): Tata Steel UK Limited
Research Area(s): Manufacturing Machine & Plant; Materials Processing; Manufacturing
Steel is a vitally important structural material, critical for infrastructure, transport, communications networks, water, energy and waste utilities. Future advances in key manufacturing sectors rely on new, innovative steel products. A successful steel industry must be able to deliver such innovation quickly to (i) satisfy customer requirements and (ii) grow its business. The conventional steelmaking innovation cycle is slow and iterative, requiring expensive trials using hundreds of tonnes of material, representing significant financial risk and limiting opportunities to investigate radically different alloys with disruptive solutions. Imagination and creativity are therefore inhibited.
The proposed solution:
This Prosperity Partnership will implement a Rapid Alloy Prototyping (RAP) process, analogous to the high-throughput methodologies used in the pharmaceutical industry. Instead of using tens of industrial scale ingots (tonnes) per alloy, large numbers (hundreds) of small laboratory scale samples (grams) of different steel alloy combinations will be tested for properties, processability and characterised using state-of-the-art imaging. In addition computer modelling will be used to design new compositions and predict through process behaviour. The efficacy of the method will be demonstrated by comparison to existing production data for established benchmark steel grades.
The RAP process will provide a rapid screening and ranking methodology for promising new alloys leading to quicker promotion from lab-scale tests (grams) to progressive upscaling tests (tens of kilograms). Alloys performing in the upscaling tests can then be promoted to full scale manufacturing (tonnes) resulting in an order of magnitude speed-up in the innovation cycle for new steel products.
Industry involvement from Tata Steel in the Partnership is essential at all decision points to ensure (i) the processability of new alloys on existing plant equipment, (ii) new alloys satisfy commercial requirements and (iii) to maintain research focus on commercially relevant alloys which will boost UK Prosperity. However, while this industrial 'reality check' is vital it does not preclude the investigation of more adventurous unorthodox alloys normally not considered. Indeed, the proposed RAP approach will widen the scope for discovery of new alloys at little or no risk to Tata Steel.
Currently 20% of primary steel comes from scrap steel which introduces impurity elements into the final product (e.g. Sn and Cu). The global trend of increasing scrap-use means there is an urgent need to prepare the UK and identify future opportunities. It is impossible for Tata Steel to carry out conventional plant trials to investigate the influence of increasing impurity elements from scrap-use, as thousands of tonnes of unsellable material would need to be cast at a high cost and possibly damage valuable assets. The RAP process will allow a comprehensive assessment of increased impurity effects on processing and properties of current and new steel products by simulating increased scrap use, as well as developing the fundamental understanding on these effects.
Benefit to the UK Economy, The ability to manufacture steel remains essential for a modern industrial economy. Tata Steel directly employs 8000 people in the UK and makes around 15,000 different steel products. In 2017 Tata sold directly to 200 customers in the UK and indirectly to over 1000 through stockists. The UK manufacturing sector employs 2.6M people and will benefit from provision of new steel grades with enhanced and tailored properties. This Prosperity Partnership is aligned to the clean growth agenda of the Industrial Strategy and the EPSRC Prosperity Outcomes, contributing to a Productive Nation. The increased ability of Tata Steel to react to global socio-economic conditions with new and innovative steel products arising from the proposed RAP process is aligned with the Resilient Nation outcome.
https://gow.epsrc.ukri.org/NGBOViewGrant.aspx?GrantRef=EP/S005218/1
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Funder(s): Cancer Research UK
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Start Date: October 2020
End Date: March 2021
Funder(s): Welsh Government Office for Science (Sêr Cymru – Tackling COVID-19)
PI: Dr Sarper Sarp
Project Partner(s): Welsh Water
Research Area(s): Sensor technology,
This project aims to develop a continuous sampling unit and a multi-analytic sensor probe to monitor COVID-19 levels in sewer wastewater. Upon proof of concept, the sampling unit and probe will be integrated within a complete system bearing continuous real-time monitoring capabilities (intelligent sensors) to detect the viral signature. The sensors will be based on the Swansea University’s graphene bio-sensing technology, already used for the successful detection of the hepatitis B virus, but recalibrated to detect COVID-19. On completion, the full system will be combined with artificial intelligence data analytics to monitor and record the captured data, then tested in simulated conditions to confirm its validity and readiness for commercial roll-out.
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Funder(s): Royal Society
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Start Date: October 2020
End Date: March 2021
Funder(s): Welsh Government Office for Science (Sêr Cymru – Tackling COVID-19 Call)
PI: Dr Sanjiv Sharma
Project Partner(s): SPTS Technologies Ltd
Research Area(s): Microneedle (MN) arrays for transdermal therapeutic drug delivery, Healthcare
The project will develop the world’s first smart vaccine device that will both deliver the COVID-19 vaccine and measures its efficacy through monitoring the body’s associated response. Measuring vaccine efficacy is extremely important as it indicates the protective effects of vaccination on an individual via the level of reduction of infection risk in a vaccinated person relative to that of a susceptible, unvaccinated individual. The project investigators have previously demonstrated the potential of microneedle (MN) arrays for transdermal therapeutic drug delivery and diagnostic applications. The team will build on these discrete technologies by developing the first dual functionality microneedle based COVID-19 smart patch, capable of delivering a vaccine and measuring the immune response in the form of protein biomarkers thus establishing the efficacy of vaccination. This measure of vaccination efficacy will address an unmet clinical need and would provide a step-change in vaccine development – changing the way in which vaccine efficacy trials are performed from a statistical assessment to a scientific measurement of patient inflammatory response to vaccination. Combining the diagnostic with an easy to apply, minimally invasive application using the MN delivery patch, would enable a personalised vaccination approach.
Start Date: January 2021
End Date: September 2021
Funder(s): UKRI Innovate UK
PI: Dr Michael Jennings
Project Partner(s): SPTS Technologies Limited, Newport; Cs Connected Limited; Compound Semiconductor Centre Limited, Cardiff; Newport Wafer Fab ltd
Research Area(s): Automotive Engineering; Supply Chains
SOCRATES will introduce silicon carbide (SiC) and GaN trench processing technologies to the UK, establishing a critical capability into the PEMD supply chain for power transistors. This 9-month project will define the critical semiconductor manufacturing processing steps required for introducing a disruptive SiC power MOSFET supply chain for automotive power electronics to the UK, aligned with the goals of the Driving the Electric Revolution (DER) initiative. We will establish a new UK SiC manufacturing capability - developing Trench MOSFET technology within the Materials and Components DER Centre and critically, pilot SiC trench etch processing, whilst also developing a backside SiC etch process module for future VGaN-on-SiC devices.
Current SiC diodes and transistors are still based on planar devices commercialised in 2001 and 2011 respectively -- which are limited in terms of efficiency and reliability. The proposed trench technology will revolutionise the performance of SiC transistors, with lower on-state resistances, and enhanced energy efficiencies -- to be employed in automotive systems. VGaN-on-SiC devices will further drive performance and costs advantages. This project intervention will accelerate their development at little additional cost.
This project addresses clear gaps in the PEMD UK supply chain; The lack of (1) a trench SiC power MOSFET process and (2) a high-volume supplier of SiC transistors for UK EV industry, with no current UK-based, high-volume 6"-8" SiC wafer fabs. In contrast, our international competitors are establishing key strategic PEMD links, in order to supply SiC devices to the future EV market; Infineon with Hyaundai, STMicroelectronics (already producing 4000 wafers per month) with Tesla and XFab with General Motors and Ford. Thus, the UK is in danger of losing its security of supply of this crucial technology to the UK automotive sector.
Start Date: April 2017
End Date: March 2019
Funder(s): EPSRC
PI: Dr Hamed Haddad Khodaparast
Project Partner(s): Airbus Group Limited
Research Area(s): Aerodynamics Design & Testing Technology; Eng. Dynamics & Tribology; Aerospace, Defence and Marine
The accurate and efficient estimation of aircraft loads is an important problem in the aerospace industry. The extreme values of these loads are typically the key parameters used for sizing the aircraft's structure and, therefore, have a significant influence on the performance of an aircraft. Inaccurate estimations of these loads will lead to poor design choices, most typically a conservative structure design that is overweight, increasing energy consumption, toxic emissions, and noise pollution.
This research will develop tools for the efficient identification of uncertainties and nonlinearities in aircraft structural components and investigate how they affect aircraft loads. Only nonlinearities and uncertainties in the joints will be considered in this project, but the developed framework can be used for other sources of nonlinearities and uncertainties, such as aerodynamics and flight control systems. Developing such a framework is crucial in order to overcome the predominant practice whereby aircraft designs are restricted to a deterministic and linear regime, despite rapidly increasing demands to consider unavoidable uncertainties and nonlinearities from technological, economic and safety considerations.
In this project, a detailed and sophisticated numerical model of an aircraft structure will be used to explicitly identify the possible range of variations in the dynamic response due to geometric and material uncertainties and nonlinearities of a wing-store joint. These data will then be used to identify the parameters of reduced-order models (ROMs). ROMs are extremely useful for computational efficiency, and are used to efficiently predict extreme unsteady loads. The validated reduced-order structural model will be incorporated into the aeroelastic model and will allow the designer to include the effects of nonlinearities and uncertainties in the process of load calculations. The goal is to move away from the conventional safety-factor-based approach and explore the wide horizon of stochastic nonlinear aeroelastic analysis.
This project will be carried out in partnership with Airbus, which will provide the data necessary to undertake the research. Since it has been observed that multiple sources of nonlinearities and uncertainties affect aircraft performance, the study aims to investigate the following questions: 1- How can detailed numerical models be used in the identification of sources of nonlinearities and uncertainties? 2- How can the structural model updating tools (the tools that will be used to validate the numerical model based on experimental data or detailed finite element predictions) be used to obtain ROMs that are capable of including the nonlinear and uncertain effects estimated by detailed numerical models? 3- How can these validated ROMs be used to efficiently investigate the effects of uncertainties and nonlinearities in aircraft load predictions (important parameters that have significant impact on aircraft performance)?
The aforementioned questions will be answered by considering an important source of nonlinearities and uncertainties in the aircraft structure, i.e. structural joints.
https://gow.epsrc.ukri.org/NGBOViewGrant.aspx?GrantRef=EP/P01271X/1
Start Date: June 2017
End Date: March 2019
Funder(s): EPSRC Fellowship
Research Area(s): Materials Processing; Materials Synthesis & Growth
Size-selected clusters are collections of atoms, from 2 to ~20,000, with diameters between a few Angstroms and ~20nm. The remarkable physical and chemical properties of clusters depend on the number of atoms they contain and differ dramatically from the corresponding individual atoms and bulk solids. They have numerous potential applications, as catalytic particles, as components of novel electronic and photonic materials and sensors, as binding sites for protein molecules in biochips, as environmental reference materials, etc etc.
The best current cluster beam sources are limited to the production of ~1microgram of clusters per day. I propose a method to increase that rate by 10 orders of magnitude, transforming the field.
The enormous increase in the availability of size-selected clusters produced by the "Matrix-Assembly Cluster Source" (MACS) is expected to open up completely new horizons across a wide range of disciplines - from physics and chemistry to biology and engineering - and applications, creating significant opportunities for radical advances in both basic science and advanced technology, notably in the nanoscale design of functional materials.
https://gow.epsrc.ukri.org/NGBOViewGrant.aspx?GrantRef=EP/K006061/2
Start Date: June 2018
End Date: June 2021
Funder(s): EPSRC
PI: Dr Cinzia Giannetti, Swansea University
Project Partner(s): Crown Packaging, TATA Steel UK Limited, Vortex IoT
Research Area(s): Artificial Intelligence, Manufacturing
Digital Manufacturing relies on pervasive and ubiquitous use of Information and Communication Technology (ICT), sensors, intelligent robots to deliver the next generation of intelligent, co-operating and interconnected manufacturing systems. The research is aimed to improve techniques that can be used to develop digitalised manufacturing systems to reduce existing inefficiencies in production processes that impact on production costs, unplanned downtime, quality and yields. This is not only detrimental to manufacturing businesses but has a negative impact on the UK Economy. The current productivity levels of UK manufacturers and suppliers is lagging behind global competitors and prevents the UK from successfully competing with other countries in the manufacturing domain - which is vital to keep businesses and jobs in the UK rather than relocate production abroad.
The UK Government wants to increase the strength of the UK Manufacturing Sector. A key means of doing this is the widespread adoption of industrial digital technologies (IDT). Cyber Manufacturing Systems (CMS) are the building blocks of digitalised manufacturing and generate vast amount of data that can be used for real time decision making to achieve optimised performance through predictive and prescriptive analytics. The latter are techniques that use, combine and analyse available data to develop computational models that can predict future outcomes and determine the best course of action.The research, under the fellowship, solves some of the existing problems in this area (CMS), developing new techniques and resources for predictive and prescriptive analytics with the potential to increase efficiency, accuracy and productivity of manufacturing processes. Businesses are therefore more likely to adopt IDTs and improve profitability and sustainability and provide high-quality jobs in a thriving part of the economy.
This project will study novel and robust data analytics methods that will enable to build predictive models that take into account uncertainty, complexity and dynamic behaviour of productions systems. The project will involve:
Objective 1 - develop algorithms that can reuse previously acquired data/knowledge to build more accurate predictive models that work well in the presence of noise (i.e. 'robust'), are able to adapt to changes over time (i.e. 'resilient') and can be scaled up across multiple factories (i.e. 'transferable').
Objective 2 - develop and test novel non-parametric methods for estimation of uncertainty and risks associated to a decision to enable real time mission and safety critical decision making (both automated and human driven) based on predictions.
Objective 3 - iteratively develop, deploy and test predictive and prescriptive models in real and simulated industrial scenarios to obtain acceptable level of performance, usability and robustness.
There will be significant involvement from industrial collaborators who will provide labelled and aggregated datasets for testing the proposed methods through computer simulations and enable feasibility studies to be conducted in factory environments.
The outcomes of the research, as mentioned above, are ultimately to improve the quality of products, achieving less wastage and unnecessary costs. Through increased adoption of IDTs, the production of goods will, importantly, be more efficient, reliable and profitable. This will support the regeneration of the Manufacturing Sector and boost the global competitiveness of the UK.
https://gow.epsrc.ukri.org/NGBOViewGrant.aspx?GrantRef=EP/S001387/1
