Digital twins for improved dynamic design

Completed

Nuclear Fission and Fusion(Nuclear Fission, Nuclear supporting technologies)
Renewable Energy Sources(Wind Energy)
Not Energy Related

Basic and strategic applied research

PHYSICAL SCIENCES AND MATHEMATICS (Applied Mathematics)
ENGINEERING AND TECHNOLOGY (Mechanical, Aeronautical and Manufacturing Engineering)

Not Cross-cutting

Professor DJ Wagg
Mechanical Engineering
University of Bristol

Standard

EPSRC

01 February 2018

30 September 2023

68 months

£5,112,624

Mathematical sciences

South West

NC : Engineering

Professor DJ Wagg, Mechanical Engineering, University of Bristol

Professor S Au, Engineering (Level 1), University of Liverpool
Professor J Clarkson, Engineering, University of Cambridge
Professor S Elliott, School of Engineering Sciences, University of Southampton
Professor MI Friswell, Engineering, Swansea University
Professor RS Langley, Engineering, University of Cambridge
Dr SA Neild, Mechanical Engineering, University of Bristol
Professor K Worden, Mechanical Engineering, University of Sheffield

Project Contact, Leonardo (UK)
Project Contact, Siemens AG, Germany
Project Contact, Ultra Electronics Limited
Project Contact, LOC Group (London Offshore Consultants)
Project Contact, Romax Technology
Project Contact, Schlumberger Cambridge Research Ltd
Project Contact, EDF Energy
Project Contact, Airbus UK Ltd
Project Contact, Stirling Dynamics Ltd

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

14/09/18