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Details of Grant 

EPSRC Reference: EP/R006768/1
Title: Digital twins for improved dynamic design
Principal Investigator: Wagg, Professor DJ
Other Investigators:
Elliott, Professor S Neild, Professor SA Worden, Professor K
Au, Professor S Clarkson, Professor J Langley, Professor RS
Friswell, Professor MI
Researcher Co-Investigators:
Project Partners:
Airbus Group Limited EDF Energy Leonardo (UK)
LOC Group (London Offshore Consultants) Romax Technology Limited Schlumberger
Siemens Stirling Dynamics Ltd Ultra Electronics Ltd
Department: Mechanical Engineering
Organisation: University of Sheffield
Scheme: Programme Grants
Starts: 01 February 2018 Ends: 31 January 2023 Value (£): 5,112,624
EPSRC Research Topic Classifications:
Design Engineering Statistics & Appl. Probability
EPSRC Industrial Sector Classifications:
Aerospace, Defence and Marine Energy
Related Grants:
Panel History:
Panel DatePanel NameOutcome
01 Nov 2017 Programme Grant Interviews - 1 November 2017 (Engineering) Announced
Summary on Grant Application Form
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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Organisation Website: http://www.shef.ac.uk