Investigating surface textures to optimize flow dynamics using multi-fidelity surrogate modeling

About the Project

Supervisory team: Haris Moazam Sheikh

This project will develop a multi-scale surrogate modeling framework to optimize passive surface textures (like dimples) for maximum fluid drag reduction. By enabling efficient shape optimization and identifying critical flow parameters, this research seeks to resolve conflicting results and advance the theoretical understanding and practical application of cost-effective flow control in transportation.

To achieve zero-carbon targets and comply with emission reduction regulations in transportation, the development of effective fluid drag-reducing technologies has become crucial in recent years. Passive flow modulation using engineered surface textures, such as dimples, have gained significant attention for their manufacturing simplicity, and cost-effectiveness in both air and sea transportation. These textures can affect the turbulent boundary layer to reduce skin friction, without requiring any active input. However, the underlying physics governing the spatially evolving turbulent boundary layer is not well understood, and there are contrasting views on the flow mechanisms involved in drag reduction.

Although several studies have examined textured surfaces, there is significant disparity among the results. Many experimental works observe drag reduction for certain dimple geometries and flow conditions, whereas others only report drag increase and flow separation. Identifying and exploring the range of crimodeling approach that can perform unbiased sample-efficient shape optimization.

This project will identify key parameters influencing fluid drag over textured surfaces, and provide a comprehensive understanding of the underlying physical mechanisms that affect fluid drag. Ultimately, this investigation will enable engineers to develop textured surfaces that significantly reduce drag, advancing both theoretical understanding and practical applications.

You will be joining a collaborative group dedicated to addressing complex real-world engineering problems. The group is focused on conceptualizing cutting-edge data-driven topology and optimization methodologies. These techniques are specifically developed with the aim of solving real-world engineering challenges such as fluid structures, turbo-machinery, metamaterials, etc.

The University of Southampton boasts extensive high-performance computing (HPC) and experimental facilities making this a unique opportunity to conduct high fidelity, multi-disciplinary research and collaborate with world-class researchers.

Entry requirements:

You must have a UK 2:1 honours degree or its international equivalent in one of the following or a related field:

• mechanical engineering
• mathematics
• physics
• computer sciences

Fees and funding:

We offer a range of funding opportunities for both UK and international students. Horizon Europe fee waivers automatically cover the difference between overseas and UK fees for qualifying students. Competition-based Presidential Bursaries from the University cover the difference between overseas and UK fees for top-ranked applicants.

Competition-based studentships offered by our schools typically cover UK-level tuition fees and a stipend for living costs for top-ranked applicants.

Funding will be awarded on a rolling basis, so apply early for the best opportunity to be considered.

For more information, please visit our postgraduate research funding pages.

How to apply:

Apply now

You need to:

• choose programme type (Research), 2026/27, Faculty of Engineering and Physical Sciences
• select Full time or Part time
• search for programme PhD Engineering & the Environment (7175)
• add name of the supervisor in section 2 of the application

Applications should include:

• your CV (resumé)
• 2 academic references
• degree transcripts and certificates to date
• English language qualification (if applicable)

We are actively searching for a highly motivated candidate with the following qualifications:

• proficiency in at least one high level scientific computing language such as MATLAB, Python, etc
• capability to conduct research independently and collaboratively
• passion to explore new scientific idea, solving problems with scientific rigor and about and producing high-impact research

Candidates matching this description are highly encouraged to contact supervisor Dr Haris Moazam Sheikh.