Reference Number: SCEBE/21SF/015/ON
Aim and Scope
Digital twin (DT) models are continuously evolving digital profiles comprising of the historical data superimposed on the current behavior of the physical asset. The digital twin is based on cumulative, real world measurements across a wide range of operational parameters. Whilst in theory the DT concept is sound and it can indeed enable real value, in practice, deploying such models at full scale is still very challenging, for several reasons:
- Realization of digital profiles is heavily dependent on existence of large amounts of historical data.
- Access and processing of raw data relevant for asset’s performance is not always possible.
- Unclear requirements on how domain knowledge should be channeled in the DT design process.
- Data visualization and human factors are topics that present significant challenges while superimposing the real time operational and digital twin values in the most visually impactful way.
In this research study, the candidate is expected to:
- Conduct a literature review on the components of a DT - data, compute, algorithms
- Investigate domain knowledge, data ontologies, modelling dimensions, architectures, and ML/AI development platforms for instantiation of DT
- Develop and validate a design methodology for DTs targeting assessment of components’ health and overall asset performance.
The University has links with Howden Compressors Ltd to enable access to data to be used for the development and validation of DT solutions.
The candidate is expected to write a detailed proposal not more than 2000 words clearly stating how any of the points above can be executed in the context of DT.
The candidate needs to have an MSc degree in Engineering Subject or MSc in Computer Science with skills on development of physics-based models and integration of data analytics solutions.
A bench fee of £4000 is required for attendance of relevant conferences and trainings.
How to Apply
This project is available as a 3 years full-time or 6 years part-time PhD study programme.
Candidates are encouraged to contact the research supervisors for the project before applying.
Please note that emails to the supervisory team or enquires submitted via this project advert do not constitute formal applications; applicants should apply using our Application Process page, choosing Applied Science and their preferred intake date.
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