Job Description

The project combines advanced mathematical tools with state-of-the-art numerical simulations and modern data-driven techniques to model, control and optimise turbulent pipe flows.

Working under the supervision of the successful candidate will join a vibrant team of researchers within the at Sheffield as well as benefiting from national and international collaborations. The project aims to provide PhD students with top-quality, challenging training at the forefront of research into fundamental and applied fluid mechanics.

About the project: Pipes and ducts are the most common means to distribute fluids throughout society, with applications ranging from the oil & gas industry to domestic settings. Most often these flows are turbulent and a lot of energy is wasted due to the large associated frictional losses. It is estimated that around 20% of the global electric power consumption is spent by pumping systems to overcome frictional drag. This figure could be drastically reduced if flows in these systems were smooth and laminar rather than turbulent, with consequent huge cuts in pumping costs and carbon emissions.

This project thus aims to investigate efficient control strategies to completely suppress turbulence in pipe flow. The problem will be tackled using advanced mathematical tools combined with state-of-the-art numerical simulations and modern data-driven/machine-learning techniques. Different relaminarisation (i.e. the process of transition from turbulent to laminar flow) scenarios will be analysed in order to gain a unified fundamental understanding of the physical mechanisms underlying this process. Such knowledge will then be exploited to develop new control techniques that are applicable in practice.

During this project, you will gain significant experience in:

• Computational fluid mechanics using in-house high-fidelity numerical software and HPC
• Analysing large data-sets and developing codes for data processing
• Turbulence theory (modelling and control), flow instabilities and transition to turbulence
• Optimisation and data-driven/machine learning techniques for fluid mechanics.
• Working in a multidisciplinary/international environment

For informal inquires please contact Dr Elena Marensi at .

About the Department: PhD studentships at Sheffield provide students with the opportunity to study within one of the UK\'s leading Mechanical Engineering departments, internationally renowned for long-standing success in research which looks at some of today\'s most challenging issues and develops new innovations to solve them. The expertise of our staff covers a wide range of specialist areas and our mission is to carry out research in fundamental science through to practical industrial applications.

Requirements:

Education A very good 4-year/master degree in Mechanical, Aeronautical, Marine, Civil, Chemical Engineering, Applied Mathematics or Physics (at least a UK 2:1 honours degree, or its international equivalent).

Knowledge, skills Strong background in fluid mechanics, good programming skills (e.g. Fortran, Python/Matlab); desirable: wall-bounded turbulence, dynamical system theory, numerical analysis. Any previous experience in machine-learning is a plus. Desirable: final-year project on a fluid mechanics problem.

Other requirements We are looking for a dedicated, dynamics and self-motivated individual with a strong passion for fluid mechanics research. Excellent communication and scientific writing skills are also required as well as a strong ability to work both individually and in a team.

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