Digital twins for improved dynamic design
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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.
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Potential Impact:
This project will have economic impact in the offshore wind, nuclear power, aerospace and automotive industries. The development of new digital twin technology will enable companies working in these sectors to design and operate their products and assets with lower design and operational costs. There may also be benefits in terms of extending operational life. In terms of societal impact, this will contribute to lower energy costs, reduced CO2 emissions, and employment security in the UK. The development of new knowledge, both in the academic domain and translated to industry will happen in parallel with the training and development of a cohort of expert early career researchers. These expert researchers are a key resource for the UK skills base, and they will contribute to the ongoing competiveness of the industrial sectors mentioned above.
University of Sheffield | LEAD_ORG |
Siemens AG | PP_ORG |
Romax Technology | PP_ORG |
Leonardo | PP_ORG |
Schlumberger Cambridge Research Ltd | PP_ORG |
Stirling Dynamics Ltd | PP_ORG |
EDF Energy Plc | PP_ORG |
EADS Airbus | PP_ORG |
Ultra Electronics Limited | PP_ORG |
LOC Group (London Offshore Consultants) | PP_ORG |
David Wagg | PI_PER |
Michael Ian Friswell | COI_PER |
Robin Langley | COI_PER |
Keith Worden | COI_PER |
Scott Ferson | COI_PER |
Siu-Kui Au | COI_PER |
Hamed Haddad Khodaparast | COI_PER |
John Clarkson | COI_PER |
Steve Elliott | COI_PER |
Simon Neild | COI_PER |
Subjects by relevance
- Digital technology
- Industry
- Emissions
- Technological development
- Costs
- Digital television
- Forecasts
- Technology
- Wind energy
- Information technology
- Technology companies
- Career development
Extracted key phrases
- Digital twin
- New digital twin technology
- Dynamic design
- Engineering design
- Low design
- Design methodology
- Critical design
- Key industrial asset
- UK industry
- UK skill base
- Virtual prediction tool
- Operational cost
- Low energy cost
- Model reduction
- Nuclear power station