Use of physical and numerical modelling data to create digital twins for improved floating offshore wind operations and fault response
DoS: Matthew Craven (ma************@pl******.uk)
2nd Supervisor: Deborah Greaves > (De*************@pl******.uk)
3rd Supervisor: Martyn Hann (ma*********@pl******.uk)
4rd Supervisor: Edward Ransley > (Ed************@pl******.uk)
5th Supervisor: Chong Ng (ch******@or*.uk)
Applications are invited for a three-year PhD studentship. The expected start date of this studentship is 1 January 2024 or 1 April 2024 start for the right candidate.
Floating offshore wind turbines (FOWT) are widely seen as an essential part of many countries’ drive to achieve ‘net-zero’. However, the Levelised Cost of Energy of FOWT is still high compared with fixed foundation offshore wind, and therefore additional innovation is needed.
A digital twin of a floating offshore wind turbine (FOWT) can provide a means to support this innovation, through everything from improved turbine and platform control, O&M strategy, fault detection and response etc. A digital twin is a model-based representation of a real assist trained or developed using real data, and for a FOWT can be designed and used with many different objectives. A validated digital twin can be used for conducting testing and research on new operations and maintenance procedures without the risk of experimenting on real wind turbines. However, an issue with such an approach is the availability of suitable data sets to both train and validate the digital twin. Waiting to get data from a deployed asset means that a digital twin will not be available in this initial stage of a project. The inclusion of low probability extreme events in the training data will also clearly be governed by the random occurrence of such events. As understanding and potentially improving the FOWT response to storms is one of the potential advantages of a digital twin, this is clearly a limitation. The same is true for fault response and detection, with the digital twin response to such faults not being trained or validated until such faults are actually measured in the deployed asset.
The use of scaled physical modelling as a source of training data for the digital twin offers a potential solution to these issues. This data set can be generated in advance of asset deployment. It can be collected in a systematic way to cover both low and high probability events and can theoretically be used to train the surrogate with failure response data.
This PhD will develop a FOWT digital twin approach that can be trained and validated initially with experimental and numerical data and is then able to self-update as full-scale data becomes available.
Supported by the ORE Catapult, the successful candidate will gain skills in both state of the art physical and numerical modelling of FOWT. It is envisioned that the student will work with industry partners to support their physical modelling within the COAST laboratory.
Eligibility, Funding and To Apply
For further information on Eligibility and Funding, please click on the links below:
To apply for this position please visit here .
Please clearly state the name of the DoS and the studentship that you are applying for at the top of your personal statement.
Please see here for a list of supporting documents to upload with your application.
The closing date for applications is 12 noon on 10 November 2023.