Exploring Applications of Additive Manufacturing for Flow Control, Heat Transfer and Mass Transfer

探索增材制造在流量控制、传热和传质方面的应用

• 批准号: 2742549
• 金额: --
• 依托单位: University of Oxford
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2742549
• 关键词: Exploring; Applications; Additive; Manufacturing; Flow

This project falls within EPSRC Fluid dynamics and aerodynamics research area.This project is jointly funded by EPSRC via the Department of Engineering Science and the Oxford-Ashton Memorial scholarship, there are no companies or collaborators involved.Project Summary: Heat exchangers (HX) are a key component in heating, cooling, and power systems and improving their heat transfer performance has a direct impact on the system efficiencies and global energy sustainability. Likewise, catalytic reactors play an irreplaceable role in chemical synthesis, manufacturing and removal of harmful pollutants. The aim of this PhD is to explore how additive manufacturing (AM), combined with machine learning (ML) can make way for novel geometries that provide a step change in efficiency for both heat transfer and mass transfer applications. This project falls within the EPSRC fluid dynamics and aerodynamics research area.The intention is to numerically and experimentally explore novel geometries using AM, that could not otherwise be manufactured using conventional methods. Given that AM is still an emerging field and its application in heat and mass transfer is just being realised, the work aims to uncover the challenges and opportunities for AM based HX and catalytic surfaces. A primary area of interest are a set of mathematically defined surfaces, known as triply periodic minimal surfaces (TPMS), which can only be manufactured via AM techniques and have already shown success when applied to the area of structures and have promising thermal-hydraulic performance in initial studies. The numerical studies would be conducted using CFD, potentially utilising the in-house cluster to perform some high-fidelity simulations. These simulations would be validated using results, taken from an experimental rig designed during the project to test 3D printed geometries. The goal is to get an understanding of capability of the geometries, including pressure range, temperature range, suitable Reynolds numbers and 3D printing parameters.The design versatility afforded by AM can be exploited using machine learning to develop novel, best performing geometries. With recent advancements in the accessibility of ML tools and increases in computational power, there is a greater argument apply ML in all aspects of design. However, little work has been proposed on an AM oriented ML design workflow that incorporates CFD - an area in which this PhD hopes to explore and contribute. This would likely take the form of developing a workflow that combines density based topology optimisation and a genetic multi-objective optimisation algorithm. Density based topology optimisation has been been successfully demonstrated to improve performance in the design of diffuser and pipe bends, lending itself for use in optimising the envelope for the flow. The genetic algorithm would be used to optimise the parameters which define the internal structure of the device and would complement TPMS based structures. CFD would be integrated within the workflow to simulate the geometries and assess relative performance against specified objective functions.The ultimate aim is to combine research on ML and AM to develop an optimisation design tool, to fully define a candidate optimal geometry based on a given design envelope and boundary conditions. This would have a significant impact on the efficiency and design of heating, cooling, chemical and power systems throughout many industries, in particular reducing energy demand, cost of manufacture and allowing greater space for other components in assemblies with tight packaging requirements.

该项目属于EPSRC流体力学和空气动力学研究领域。该项目由EPSRC通过工程科学系和牛津-阿什顿纪念奖学金共同资助，没有公司或合作者参与。项目摘要：热交换器(HX)是供热、冷却和电力系统中的关键部件，其传热性能的提高直接影响到系统的效率和全球能源的可持续性。同样，催化反应器在化学合成、制造和去除有害污染物方面发挥着不可替代的作用。该博士学位的目的是探索加法制造(AM)与机器学习(ML)相结合如何为新的几何结构让路，这些几何结构为传热和传质应用提供了效率的阶梯变化。该项目属于EPSRC流体力学和空气动力学研究领域。其目的是利用AM通过数值和实验探索无法用传统方法制造的新几何形状。鉴于AM仍然是一个新兴领域，其在热质传递中的应用才刚刚实现，本工作旨在揭示AM基HX和催化表面所面临的挑战和机遇。一个主要感兴趣的领域是一组数学定义的表面，称为三周期最小表面(TPMS)，它只能通过AM技术制造，在应用于结构领域时已经显示出成功，并在初步研究中具有良好的热工水力性能。数值研究将使用CFD进行，可能会利用内部集群来执行一些高保真模拟。这些模拟将使用从项目期间设计的实验台获得的结果来验证，以测试3D打印几何图形。目标是了解几何图形的性能，包括压力范围、温度范围、合适的雷诺数和3D打印参数。AM提供的设计多功能性可以利用机器学习来开发新的、性能最佳的几何图形。随着最近ML工具的可访问性和计算能力的提高，有更多的理由将ML应用于设计的方方面面。然而，在面向AM的ML设计工作流程中融入CFD的工作建议很少，这是这位博士希望探索和贡献的领域。这可能采取的形式是开发一种结合了基于密度的拓扑优化和遗传多目标优化算法的工作流程。基于密度的拓扑优化已被成功证明可以提高扩散器和弯管设计的性能，可用于优化流动的包络。遗传算法将被用来优化定义设备内部结构的参数，并将补充基于TPMS的结构。CFD将被集成到工作流程中，以模拟几何形状，并根据指定的目标函数评估相对性能。最终目的是将ML和AM的研究结合起来，开发一个优化设计工具，基于给定的设计包络和边界条件，充分定义候选的最佳几何形状。这将对许多行业的供暖、制冷、化学和电力系统的效率和设计产生重大影响，特别是降低能源需求、制造成本，并为包装要求严格的组件中的其他组件留出更大的空间。