Digital twins for improved dynamic design

Lead Research Organisation: University of Sheffield
Department Name: Mechanical Engineering


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.

Planned 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.


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Bull L (2021) Probabilistic Inference for Structural Health Monitoring: New Modes of Learning from Data in ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering

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Bull L (2021) Foundations of population-based SHM, Part I: Homogeneous populations and forms in Mechanical Systems and Signal Processing

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Chakraborty S (2021) The role of surrogate models in the development of digital twins of dynamic systems in Applied Mathematical Modelling

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Chatterjee T (2021) Gaussian process assisted stochastic dynamic analysis with applications to near-periodic structures in Mechanical Systems and Signal Processing

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Chatterjee T (2020) Uncertainty propagation in dynamic sub-structuring by model reduction integrated domain decomposition in Computer Methods in Applied Mechanics and Engineering

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Chatterjee T (2021) Multilevel Decomposition Framework for Reliability Assessment of Assembled Stochastic Linear Structural Systems in ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering

Description The project is in the early stages of development, but has already made fundamental contributions to the new area of digital twin research. Full details will be given in the mid-term report, but as an example, a 3-storey structure has been developed as a small-scale digital twin prototype in order to test all relevant algorithms underlying the twin functionality. Furthermore, fundamental research work has been carried out in the key areas relating to verification & validation, uncertainty analysis, control, jointing, design and hybrid testing.
Exploitation Route The outcomes of this award are being used by the project industry partners, and we anticipate this will extend more widely in the future.
Sectors Aerospace, Defence and Marine

Description Digital Twin Workshop 
Form Of Engagement Activity Participation in an activity, workshop or similar
Part Of Official Scheme? No
Geographic Reach National
Primary Audience Study participants or study members
Results and Impact This workshop was hosted jointly between the University of Sheffield Advanced Manufacturing Research Centre and Faculty of Engineering, and was held in the state-of-the-art AMRC Factory 2050 on the Sheffield Advanced Manufacturing Park. The purpose of the workshop was to bring together leading UK based researchers currently working on topics related to digital twin. The contributions included both application specific, and basic research presentations. The event helped to promote the 'Gemini Principles', the 'National digital twin', and the EPSRC funded `DigiTwin' project.
Year(s) Of Engagement Activity 2019