PhD Studentship: Adaptive Mesh Refinement for More Efficient Predictions of Wall Boiling Bubble Dynamics

Adaptive Mesh Refinement for More Efficient Predictions of Wall Boiling Bubble Dynamics

Supervisor: Mirco Magnini

PhD Project Description

The aim of this PhD is to robustly validate and demonstrate the utility of an adaptive mesh refinement approach in interface resolving Computational Fluid Dynamics (CFD) simulations of flow boiling at conditions relevant to nuclear thermal hydraulics. Boiling is a technology central to both fusion and fission nuclear reactors, also including thermal management of several reactor components. The aim of these simulations is to generate data that can be leveraged to account for the detailed characteristics of a heat transfer surface on bubble dynamics during flow boiling, to provide an approach for generating more representative inputs for the wall boiling models used in component scale CFD assessments. In particular, this concerns quantifying the effects of the heat transfer surface’s detailed topography, porosity and wettability on near-wall bubble dynamics that govern flow boiling heat transfer and critical heat flux. The work ultimately contributes towards the development of improved methods for predicting critical heat flux in nuclear reactors, which can ultimately limit their justifiable performance, also advancing the design of both fusion and fission reactor components, and thereby contributing to increase their power density and decrease plant size.

The simulation approach will be applied to small sets of bubbles on representative patches of heat transfer surfaces. An adaptive mesh refinement approach will be used to enable the liquid-vapour interface of each bubble to be captured both accurately and computationally efficiently, by refining and coarsening the mesh each time step to reflect the prevailing flow field with minimal user effects. This approach will then be deployed to simulate the behaviour of bubbles over a range of flow conditions and heat transfer surfaces with different characteristics. This data set will finally be used to train surrogate models that can instantly predict quantities required by component scale CFD wall boiling models for different flow conditions and heat transfer surfaces. 

This is a fully-funded 3.5-years PhD studentship. The research will be conducted at the University of Nottingham within a wider research team comprising academics, post-graduate and post-doctoral researchers. The project will also involve close collaboration with Rolls-Royce and UKAEA as industrial partners. It is expected that the student will undertake a placement at Rolls-Royce during the project.

Candidate requirements: 

• Due to funding restrictions, the position is only available for UK candidates
• 1^st or 2:1 academic qualification in Engineering or Physical Sciences or a related discipline, with expertise in fluid mechanics and heat transfer
• Experience with OpenFOAM simulation software
• Programming skills with software such as Matlab and/or Python

How to apply

Please send an email with subject “PhD studentship: Adaptive Mesh Refinement for More Efficient Predictions of Wall Boiling Bubble Dynamics” to Dr Mirco Magnini, mirco.magnini@nottingham.ac.uk, attaching a cover letter, CV and academic transcripts. Incomplete applications will not be considered. Suitable applicants will be interviewed, and if successful, invited to make a formal application. Please note only shortlisted candidates will be contacted and notified.

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