Unsteady Flow and Surface Loading Around a Wall-Mounted Porous Cylinder in a Turbulent Boundary Layer

About the Project

Supervisory Team: Dr Elias Arcondoulis, Professor Phillip Joseph and Professor Philip Nelson

This project investigates the fundamental unsteady flow physics and surface-loading mechanisms acting on wall-mounted porous cylinders subject to turbulent boundary layers. The goal is to determine how boundary-layer structure, turbulence intensity, and porous geometry influence near-wall flow behaviour, vortex shedding suppression, and fluctuating force pathways.

Porous coatings applied to canonical bodies such as a slender cylinder are highly effective at attenuating vortex shedding in uniform flows, yet their performance in boundary-layer and junction-flow environments is relatively unknown. In addition, the mechanisms by which turbulence, shear layers, and horseshoe vortices generate fluctuating surface stresses on the body remain only partially explored. Understanding how boundary-layer turbulence interacts with rough, porous-coated, or cylindrical geometries is essential for predicting erosion processes such as scour, structural fatigue and environmental loading in offshore and coastal systems.

In this PhD, you will conduct experiments in the wind tunnel facilities at the University of Southampton, by combining advanced fluid mechanics diagnostic tools and developing multi-sensor measurement and reconstruction techniques to map these loading pathways in space and time.

You will explore how porous geometry, turbulence intensity, and boundary-layer structure influence and control:

• near-wall flow development
• vortex formation and suppression
• unsteady pressure and shear loading
• pathways leading to flow separation, scour, and fatigue

The project will combine:

• time-resolved flow diagnostics including particle image velocimetry and hot-wire anemometry
• multi-sensor signal processing using surface-pressure microphones

Applicant background:

Applicants with backgrounds in experimental fluid mechanics, aerospace/mechanical engineering, civil/environmental engineering, or applied physics/maths are encouraged to apply. Prior experience with any of the experimental methods, signal-processing methods and coding using MATLAB/Python is desirable.