PhD Studentship: Lattice-Boltzmann Simulations of Boundary-Layer Flows over Low-Drag Engineered Surfaces

Rates below are for full-time (FT) mode.

Year 1: £24,780 (£20,780 UKRI rate + London weighting = £2,000 + Enhanced bursary = £2,000)

Year 2: In line with UKRI rate + LW = £2,000 + EB = £2,000

Year 3: In line with UKRI rate + LW = £2,000 + EB = £2,000

Year 4*: In line with UKRI rate + LW = £2,000 + EB = £2,000

In addition, the successful candidate will receive a contribution to tuition fees, equivalent to the University Home Rate, currently £5,006 (FT), for the duration of their scholarship. International applicants may need to pay the remainder tuition fee for the duration of their scholarship **.

* The bursary is for 3 years with a potential extension of up to a maximum of 12 months. Funding extensions may be granted if the student demonstrates, to the satisfaction of the M34Impact Principal Investigators and PhD supervisors, that the thesis can be completed during the granted extension period.

** For exceptional international applicants the tuition fees may be covered by the M³4Impact.

This fee is subject to an annual increase.

The Challenge:

The aviation, marine and turbomachinery sectors face a sustainability challenge: drag on aircraft wings, ship hulls and turbine blades causes billions of pounds in excess fuel use and CO₂ emissions every year. To reach Net Zero, we must reduce surface drag through engineered fluid–surface interactions. Bio-inspired surfaces, such as shark-skin riblets and lotus-leaf-inspired superhydrophobic textures, have demonstrated drag-reduction potential in laboratory settings. However, translating these concepts into engineered surfaces for real-world transport applications remains an open scientific problem.

The Project:

This PhD studentship is the computational component of a wider research project to generalise boundary-layer theory to include engineered surfaces. While classical boundary-layer theory assumes the non-slip condition at the surface, this project asks: how is drag modified when a slip velocity is introduced at the surface?

You will use High-Performance Computing (HPC) and the Lattice-Boltzmann Method (LBM), a mesoscopic computational method, to model flows over a variety of engineered surfaces. You will investigate how engineered surfaces can delay flow separation and influence laminar–turbulent transition, with implications for drag reduction in aircraft, marine and turbomachinery applications.

Methodology:

Using the open-source software, you will:

1. Simulate: Model boundary-layer flows over aircraft wings & turbine blades with engineered surfaces and compare numerical results with theoretical predictions from classical boundary-layer theory extended to slip-modified boundary conditions.
2. Resolve: Move beyond effective slip-length approximations to fully resolve the micro-scale geometry of engineered surface textures within the LBM framework.
3. Integration with ML/AI: Apply data-driven methods to construct reduced-order models that bridge analytical theory & high-fidelity simulation data, enabling rapid drag prediction across surface parameter spaces.

The Environment:

This studentship is fully and securely funded by the University's £9M Research England-funded M³4Impact expansion programme. This project is part of the wider Computational Science & Engineering Group’s goals and will be a foundational project for the Climate & Sustainability Modelling group:

1. Supervision: You will be supervised by Dr. Samuel Tomlinson, a Lecturer in Materials Science & Engineering with a track record of research in fluid mechanics, boundary-layer theory & engineered surfaces.
2. Collaboration: You will be co-supervised by Tim Reis, an expert in LBM with links to the lattice Boltzmann community. The project also features collaborative links with Imperial College London, providing access to the UK's modelling community.

Career Growth:

As the founding PhD student of the Climate & Sustainability Modelling group, you will be fully embedded within the wider M³4Impact doctoral cohort, benefiting from training in HPC, software development & academic leadership.

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