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

该项目旨在发现沸腾液体在一个被称为核态沸腾(DNB)的区域内接近临界热通量(CHF)时具有特别好的传热能力的原因。学生将致力于发现流体经历DNB时振荡行为背后的物理推理，以了解如何将沸腾的流体保持在该区域相当长的时间。这项工作的成果将提供关于将流体从DNB区域转化为CHF和过渡沸腾以外的触发器的实用知识。这个项目的一个关键方面是确定如何最好地整合机器学习(ML)技术，以促进对物理行为的理解，并提高计算速度。如果该项目在这方面取得成功，最终将能够设计出比以前更接近瑞士法郎的安全运行系统。因此，该项目有可能在广泛的应用中通过沸腾来增加热传递。为了做到这一点，学生将系统地开发先进的计算工具，以了解项目每个阶段背后的物理行为。这些工具将结合计算流体动力学(CFD)和机器学习(ML)的尖端技术。

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).