Details of Grant

EPSRC Reference:

EP/R006768/1

Title:

Digital twins for improved dynamic design

Principal Investigator:

Other Investigators:

Au, Professor S	Elliott, Professor S	Worden, Professor K
Haddad Khodaparast, Professor HH	Friswell, Professor MI	Clarkson, Professor J
Langley, Professor RS	Neild, Professor SA	Ferson, Professor S

Researcher Co-Investigators:

Project Partners:

Airbus Operations Limited	EDF	Leonardo UK ltd
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:

30 September 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 Date	Panel Name	Outcome
01 Nov 2017	Programme Grant Interviews - 1 November 2017 (Engineering)	Announced
26 Mar 2021	Programme Grant Mid Term Review	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.

Key Findings

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Potential use in non-academic contexts

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Impacts

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Summary
Date Materialised

Sectors submitted by the Researcher

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Project URL:

Further Information:

Organisation Website:

http://www.shef.ac.uk