On the 6th August Dr Andrea Novoa presented her research work ‘a real-time digital twin of a hydrogen-fuelled annular combustor’ as part of the AI for Net Zero webinar series.
Dr Novoa’s webinar gave us an insightful view into what is meant by a real-time digital twin by starting off her presentation by giving us a succinct definition:
“a real-time digital twin is a set of virtual information constructs that mimic the structure context and behaviour of a real system. It is dynamically updated with data from the physical twin. It is the bidirectional interaction between the virtual and physical that is central to the digital twin.”
Continuing with the webinar, Dr Novoa explains to us about the complexities and challenges of building such systems.
Dr Novoa paints the picture of the current situation that focuses on low fidelity models at low computational costs, that put data and physical models together but use many assumptions and thus add bias and/or error to the output.
This is where Dr Novoa is stepping in with her bias-aware real-time data assimilation tool that puts everything together in a real example of a real-time digital twin of a hydrogen-fuelled annular combustor.
This is a very complex system consisting of multi physics, and a combination between acoustics flame dynamics, aerodynamics, and the turbulent environment of the combustion chamber.
In this webinar Dr Novoa explains how she collected the raw data she needed to create the latest digital twin of the annular combustor contributing to the overall success of the AI for NetZero Energy & Transport project.
This webinar, along with the entire series, can be seen on the AI for NetZero YouTube channel.