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 流体动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-阿什顿纪念奖学金联合资助，没有任何公司或合作者参与。项目摘要：热交换器 (HX)供热、制冷和电力系统的关键部件，提高其传热性能对系统效率和全球能源可持续性有直接影响。同样，催化反应器在化学合成、制造和有害污染物去除方面发挥着不可替代的作用。该博士学位的目的是探索增材制造 (AM) 与机器学习 (ML) 相结合如何为新颖的几何形状让路，从而为传热和传质应用提供效率的阶跃变化。该项目属于 EPSRC 流体动力学和空气动力学研究领域。其目的是利用增材制造技术通过数值和实验方式探索新的几何形状，而这些几何形状是无法使用传统方法制造的。鉴于增材制造仍然是一个新兴领域，其在传热传质方面的应用刚刚实现，本研究旨在揭示基于增材制造的HX和催化表面的挑战和机遇。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），它只能通过增材制造技术制造，并且在应用于结构区域时已经显示出成功，并且具有有前景的热工水力性能在初步研究中。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。这些模拟将使用在项目期间设计的用于测试 3D 打印几何形状的实验装置中获得的结果进行验证。目标是了解几何形状的功能，包括压力范围、温度范围、合适的雷诺数和 3D 打印参数。可以使用机器学习来开发增材制造提供的设计多功能性，以开发新颖、性能最佳的几何形状。随着最近机器学习工具的可访问性的进步和计算能力的提高，人们越来越多地争论将机器学习应用于设计的各个方面。然而，关于结合 CFD 的面向 AM 的 ML 设计工作流程几乎没有提出任何工作，而这位博士希望在这个领域进行探索和贡献。这可能会采取开发工作流程的形式，该工作流程结合了基于密度的拓扑优化和遗传多目标优化算法。基于密度的拓扑优化已被成功证明可以提高扩压器和弯管设计的性能，有助于优化流动的包络线。遗传算法将用于优化定义设备内部结构的参数，并补充基于 TPMS 的结构。 CFD 将集成到工作流程中，以模拟几何形状并根据指定的目标函数评估相对性能。最终目标是结合 ML 和 AM 的研究来开发优化设计工具，以根据给定设计完全定义候选最佳几何形状包络线和边界条件。这将对许多行业的加热、冷却、化学和电力系统的效率和设计产生重大影响，特别是减少能源需求、制造成本，并为具有严格封装要求的组件中的其他组件提供更大的空间。