Hierarchical Multi-scale Modelling of Thermal/Fluid Transport Processes in Energy-intensive Applications

能源密集型应用中热/流体传输过程的分层多尺度建模

• 批准号: RGPIN-2014-06128
• 负责人: Amon, Cristina
• 金额: $ 4.23万
• 依托单位: University of Toronto
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2016
• 资助国家: 加拿大
• 起止时间: 2016-01-01 至 2017-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=612049
• 关键词: Hierarchical; Multi; scale; Modelling; Thermal

Advances in nanoscale science and technology have enormous potential to improve the performance of engineered systems, by allowing us to design materials and structures at the smallest length and time scales while enhancing functionality. Increasing computational power has enabled simulation of the physical behavior of nanoscale systems, with increasing detail and accuracy. However, detailed component-level and system-level simulations of multi-scale systems whose performance is dictated by nanoscale phenomena cannot be carried out, even with current supercomputers. This is a major roadblock for simulation-based design and optimization of multi-scale complex engineered systems. Our previous research has focused on hierarchical modelling of semiconductor thermal transport across multiple scales. Building on this expertise, the proposed research will expand the hierarchical multi-scale methodology to multi-physics thermal/fluid transport in energy-intensive applications. The main goals are (a) to increase our understanding of fundamental phenomena occurring at the smallest relevant length and time scales, and (b) to develop a methodology for representing and transferring this knowledge to larger scales. This will enable formulation of novel strategies and design innovations at the smallest scales to improve or revolutionize component- and system-level performance. We envision applications in electronic devices and sustainable energy systems, as described below. Reducing transistor size while increasing transistor density has been the key approach for increasing electronics performance, with substantial gains achieved by reducing the transistor’s channel from 3200 to 22 nm over the last decades. As transistor density increases, thermal energy is generated at a higher rate than can be removed, creating localized hotspots and high temperature gradients, with detrimental effects on performance and reliability. In addition, the growth in the number of material interfaces has made energy transport across interfaces a significant contributor to thermal performance. Hence, understanding thermal transport across nanoscale interfaces is essential for designing next-generation electronics. We will build on our previous work in hierarchical modeling to enable multi-scale multi-physics engineering of next-generation electronic devices. Sustainable energy is another domain in which the proposed hierarchical modelling methodology can have tremendous impact, and we will focus on wind energy and fuel cell technologies. In wind farms, wake losses can be as large as 10%-20% of the total energy production. Previous work has identified the design of the wind turbine layout as the most significant factor affecting wake generation and propagation. The proposed hierarchical approach bridging models at multiple length scales will allow optimal design based on accurate first-principle modelling of wind wakes in complex terrains. Improving mass and thermal transport in gas diffusion layers (GDL) is essential to advance PEM fuel cell technology. Leveraging my group’s experience in both traditional and mesoscopic methods for simulation of porous media, we propose a hierarchical approach to model GDL transport from pore-level to system-level. Elucidating the main factors affecting GDL transport at the smallest length scales and their effect on system-level performance will generate novel designs for next-generation PEM fuel cells. Overall, the multi-scale hierarchical modelling methodology aims to develop physics-based, first-principle models of system behavior at the smallest relevant scales. These models will increase understanding of the underlying phenomena and their effect on macroscale performance by bridging nanoscale to macroscale predictions.

纳米科学技术的进步使我们能够以最小的长度和时间尺度设计材料和结构，同时增强功能，因此具有提高工程系统性能的巨大潜力。计算能力的提高使得能够模拟纳米级系统的物理行为，并且细节和准确性不断提高。然而，即使使用当前的超级计算机，也无法对性能由纳米级现象决定的多尺度系统进行详细的组件级和系统级模拟。这是多尺度复杂工程系统基于仿真的设计和优化的主要障碍。 我们之前的研究重点是跨多个尺度的半导体热传输的分层建模。基于这些专业知识，拟议的研究将把分层多尺度方法扩展到能源密集型应用中的多物理热/流体传输。主要目标是（a）增加我们对在最小相关长度和时间尺度上发生的基本现象的理解，以及（b）开发一种方法来表示并将这些知识转移到更大的尺度。这将使在最小规模上制定新颖的策略和设计创新成为可能，从而改善或彻底改变组件和系统级的性能。我们设想在电子设备和可持续能源系统中的应用，如下所述。 减小晶体管尺寸并增加晶体管密度一直是提高电子性能的关键方法，在过去几十年中，通过将晶体管沟道从 3200 纳米减小到 22 纳米，取得了显着的收益。随着晶体管密度的增加，产生热能的速度比消除热能的速度要快，从而产生局部热点和高温梯度，对性能和可靠性产生不利影响。此外，材料界面数量的增长使得跨界面的能量传输成为热性能的重要贡献者。因此，了解纳米级界面的热传输对于设计下一代电子产品至关重要。我们将在之前的分层建模工作的基础上，实现下一代电子设备的多尺度多物理工程。 可持续能源是所提出的分层建模方法可以产生巨大影响的另一个领域，我们将重点关注风能和燃料电池技术。在风电场中，尾流损失可能高达总发电量的 10%-20%。先前的工作已确定风力涡轮机布局的设计是影响尾流产生和传播的最重要因素。所提出的多长度尺度的分层方法桥接模型将允许基于复杂地形中风尾流的精确第一原理建模进行优化设计。 改善气体扩散层 (GDL) 中的质量和热传输对于推进 PEM 燃料电池技术至关重要。利用我的团队在多孔介质模拟的传统方法和介观方法方面的经验，我们提出了一种分层方法来模拟从孔隙级到系统级的 GDL 传输。阐明在最小长度尺度上影响 GDL 传输的主要因素及其对系统级性能的影响将为下一代 PEM 燃料电池产生新颖的设计。 总体而言，多尺度分层建模方法旨在开发最小相关尺度的基于物理的系统行为第一原理模型。这些模型将通过将纳米尺度的预测与宏观尺度的预测联系起来，增进对潜在现象及其对宏观尺度性能影响的理解。