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

Fully-funded PhD Studentship: 

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

This exciting opportunity is based within the Fluids and Thermal Engineering Research Group at the Faculty of Engineering which conducts cutting edge research into experimental and computational heat and mass transfer, multiphase flows, thermal management, refrigeration, energy, combustion, and process optimisation. The project is focussed on the development of novel interface capturing Computational Fluid Dynamics methods for simulating boiling in Nuclear Thermal Hydraulics applications, and is intended to be a collaboration with Rolls-Royce and the UK Atomic Energy Authority.

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. 

Key milestones for the project will include:

1. A thorough review of (1a) interface capturing approaches for flow boiling simulations including adaptive mesh refinement, (1b) available models for predicting the density of nucleation sites over a boiling surface, and (1c) the conditions over which the microlayer is of importance to bubble dynamics during flow boiling, and subgrid near‑wall models to account for the effects of any unresolved evaporation microlayer.
2. Development and implementation of a robust adaptive mesh refinement approach in a three-dimensional bubble resolving CFD modelling framework based on the opensource toolbox OpenFOAM, that is able to account for the true characteristics of a heat transfer surface.
3. Development and implementation of a suitable subgrid near-wall model to account for the transition between contact line and evaporation microlayer regimes during bubble growth at different pressure levels, subject to the findings of 1c. 
4. Demonstration and initial validation of the approach using data from the open literature and experimental data generated by the Agile Flow Boiling Facility at the Nuclear Skills Academy in Derby, if available. 

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 potentially UKAEA as industrial partners. It is expected that the student will undertake a placement at Rolls-Royce during the project. This exciting research is industrially highly relevant and of great scientific interest; it will therefore offer the candidate the possibility to establish successful industrial and academic collaborations and disseminate research at prestigious national and international conferences.

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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