Engineers are building virtual copies of wind farms, nuclear power stations, and aircraft—digital twins that mirror physical systems in real time—to predict how they will behave before problems arise. Current industrial design relies on large computer models that are often too slow or inaccurate for critical decisions. A digital twin fuses live sensor data with physics-based models of varying detail, using uncertainty analysis and model reduction to create a continuously updated virtual proxy. This project will develop the fundamental science needed to make digital twins work reliably for power generation, automotive, and aerospace sectors. If successful, the technology could radically improve design methodology, cutting costs by catching flaws early. It would also transform how industry manages uncertainty in key assets, reducing operation and maintenance expenses across the entire lifecycle—from design through decommissioning. The result would be safer, more efficient infrastructure that quietly underpins energy grids, transport networks, and manufacturing supply chains.
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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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