A Machine Learning Approach to Unsteady Fluid Flow Characteristics in Boiling Approaching Critical Heat Flux

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The project looks to discover the reasoning behind particularly good heat transfer abilities of boiling fluids approaching Critical Heat Flux (CHF) in a region known as Departure from Nucleate Boiling (DNB). The student will aim to discover the physical reasoning behind oscillatory behaviour as fluid undergoes DNB to understand how a boiling fluid can be kept in this region for substantial time. Outcomes of this work will provide a working knowledge of the triggers which transform a fluid from the DNB region to CHF and transition boiling beyond. A key facet of this project is determining how best to integrate Machine Learning (ML) technology to advance understanding of physical behaviour and improve speed of computations. Provided the project is successful in this regard, this will ultimately deliver the ability to design systems which can safely operate much closer to CHF than previously. Therefore, this project presents the potential to increase heat transfer through boiling for a wide range of applications. To do so, the student will be systematically developing advanced computational tools to understand the physical behaviour behind each stage of the project. These tools will combine the cutting edge of Computational Fluid Dynamics (CFD) and Machine Learning (ML).