PhD Studentship: Physics Informed Neural Surrogates for real time Digital Twins and CFD Visualisation of Offshore Wind Turbines

Director of Studies (DoS): Dr Yeaw Chu Lee (yeawchu.lee@plymouth.ac.uk)

2^nd Supervisor: Dr Ji-Jian Chin (ji-jian.chin@plymouth.ac.uk)
3^rd Supervisor: Dr Dena Bazazian (dena.bazazian@plymouth.ac.uk)

Applications are invited for a 3.5-year EPSRC funded UDLA PhD studentship. The studentship will start on 1^st October 2026.

Project Description

Offshore wind turbines operate in highly non‑stationary atmospheric conditions involving shear, veer, yaw misalignment, and wake interactions. High‑fidelity CFD methods (RANS/LES) can capture these effects but are too computationally expensive for operational decision‑making, interactive design, or control‑in‑the‑loop visualisation. Machine‑learning surrogates offer speed, yet purely data‑driven models often extrapolate poorly and may violate physical constraints. Physics‑Informed Neural Networks (PINNs) embed PDE residuals and boundary conditions directly into training, while operator‑learning approaches provide mesh‑agnostic mappings from inputs to flow fields. Combining these techniques enables fast inference with strong physical fidelity, creating a pathway toward real‑time digital twins that fuse live measurements with simulation.

This PhD project will develop physics‑informed neural surrogates to support real‑time digital‑twin CFD for offshore wind turbines. Key objectives include:

• Designing PINN and operator‑learning models that enforce incompressible Navier–Stokes physics and turbine boundary conditions.
• Achieving millisecond‑ to sub‑second‑scale inference for interactive analytics and control‑aware scenario exploration.
• Validating performance against high‑fidelity CFD across laminar, transitional, and turbulent regimes, including rotor and near‑wake benchmarks.
• Demonstrating scalability and generalisation across geometries, inflow conditions, and boundary treatments.
• Integrating the surrogate models into a digital‑twin pipeline for real‑time data ingestion, assimilation, and visualisation.

The project will deliver a real‑time digital‑twin demonstrator for an offshore turbine capable of streaming data and producing CFD‑quality flow and wake fields with ultra‑fast inference. All code, datasets, and reproducible workflows will be openly released to support engagement with the wider ORE research community.

Eligibility

Applicants should have a first or upper second class honours degree in an appropriate subject and preferably a relevant Masters qualification. Background knowledge and experience in engineering and computer science disciplines and in areas such as CFD post-processing (OpenFOAM), wind turbine renewable energy systems, big data management, ELT/ETL pipelines, AI/ML, Unity and Unreal Engine development, shading, real-time simulation, immersive scientific visualisation, digital twin, C/C++, Python are desirable. Applications from both UK and overseas students are welcome.

The studentship is supported for 3.5 years and includes full Home tuition fees, Bench fee plus a Stipend of £21,805 per annum 2026/27 rate.  The studentship will only fully fund those applicants who are eligible for Home fees with relevant qualifications.  Applicants normally required to cover International fees will have to cover the difference between the Home and the International tuition fee rates.  The international component of the fee may be waived for outstanding international applicants.

There is no additional funding available to cover NHS Immigration Health Surcharge (IHS) costs, visa costs, flights etc.

• The studentship is supported for 3.5 years of the four-year registration period. The subsequent 6 months of registration is a self-funded ‘writing-up’ period.

If you wish to discuss this project further informally, please contact Dr Yeaw Chu Lee (yeawchu.lee@plymouth.ac.uk).

To apply for this position please click on the Apply button above.

For more information on the admissions process generally, please contact research.degree.admissions@plymouth.ac.uk

The closing date for applications is 12 noon on 24 April 2026.

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