About the project
Applications are invited for a fully funded PhD position on applying computer vision (CV) and machine learning (ML) approaches to experimentally measure and model coalescence in turbulent dispersed multiphase flow.
Turbulent flows where one phase is dispersed in another, e.g. gas bubbles or solid particles in a liquid, are common. Examples include rain formation, wastewater treatment, oil and gas extraction and the synthesis of biofuels and pharmaceuticals. A long-standing challenge in predicting such flows is modelling coalescence of the dispersed phase, e.g. how smaller bubbles grow into larger bubbles. To proceed, experimental data are required to inform and test new models.
This PhD project will apply state-of-the-art particle tracking flow measurements, coupled with modern CV and ML techniques, to measure and model coalescence in multiphase turbulence. One possible topic is microbubble aeration, which is used industrially to manufacture pharmaceuticals, cell cultures, biofuels and treat wastewater. Here, an open question is how buoyancy and turbulence interact to influence the coalescence rate and, ultimately, the aeration efficiency. Another possible topic is the sequestration of carbon by marine snow in the biological carbon pump, which is formed by the aggregation of organic detritus. Here, a similar interplay of gravity and turbulence govern the large-scale sequestration of atmospheric carbon. There is scope for variation within this theme and the opportunity to define a specific problem to focus on for your PhD.
Applicants should have a strong background in fluid mechanics and scientific computing. A demonstrable aptitude for practical laboratory work is essential. Applications are invited from candidates who possess (or expect to gain) a first-class honours MEng, MSc or higher degree equivalent in Engineering, Physics or allied disciplines.
If successful, you will join a thriving research community of over 50 PhDs, postdoctoral researchers and academics in the Aerodynamics and Flight Mechanics group, with shared specialisms in optical measurements and machine learning approaches applied to turbulent and multiphase flows. In particular, this project benefits from access to a world-leading, experimental fluid mechanics laboratory space opened in 2018 equipped with state-of-the-art flow diagnostics equipment. You will develop advanced experimental data analysis and scientific computing skills, including machine learning and computer vision, which will enable you to pursue a career in academia or industry. You will be supported to submit your research for publication in leading academic journals, to travel and present your findings at major international conferences and develop collaborations with research groups across the world.
