Collaborative Research: Bridging the Gap Between Particle-Scale Thermal - - Transport and Device-scale Predictions

合作研究：弥合粒子尺度热传输和设备尺度预测之间的差距

• 批准号: 1903564
• 负责人: Lian Shen
• 金额: $ 15.56万
• 依托单位: University of Minnesota-Twin Cities
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-06-01 至 2024-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1903564&HistoricalAwards=false
• 关键词: Collaborative; Research; Bridging; Gap; Between

Understanding how heat is transported in gas-particle mixtures is critical to improving the performance, efficiency and reliability of clean energy technologies and predicting environmental flows. The conversion of coal and biomass into useful fuel, thermal storage by particulate material, and particle-based solar receivers, all represent promising technologies that rely heavily on heat transfer in multiphase systems. Current tools used in industry and academia rely on simplistic models for average reaction rates as well as heat transfer coefficients that are known to vary by several orders of magnitude. This project introduces a new modeling approach that will enable researchers to address the huge range of challenges associated with heat transfer through gas-particle mixtures. For a broader educational impact, a toy version of a fluidized bed will be built with beads sprayed with thermochromic liquid crystals that change color with temperature. This will be presented at local schools to illustrate fundamental concepts of fluid-particle interactions.In this project, researchers from Michigan, Iowa State, and Minnesota collaborate to develop a new paradigm in multiphase heat transfer modeling. The aim is to bridge the gap between particle-scale thermal processes and device-scale predictions. A consistent modeling framework is formulated that scales from a well-accepted physics-based model to a larger scale of interest. Current approaches typically ensemble-average data obtained from the microscale physics directly, without taking into account local variations on scales resolvable by the simulation framework. The proposed effort will connect the spatially-averaged, large-scale representation to two-phase statistics obtained from particle-resolved numerical simulations. A previously overlooked ergodic consistency requirement will be enforced so that the numerical solution converges to the ensemble-averaged two-fluid equations in the limit of large filter width. Model validation will be based on simultaneous multi-camera imaging at different scales, and a novel application of Voronoi tessellation to quantify clustering in dense suspensions.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

了解热在气体-颗粒混合物中的传输方式对于提高清洁能源技术的性能、效率和可靠性以及预测环境流动至关重要。煤和生物质转化为有用的燃料，颗粒材料的储热，以及基于颗粒的太阳能接收器，都是很有前途的技术，这些技术严重依赖于多相系统中的热传递。目前工业和学术界使用的工具依赖于平均反应速率以及已知变化了几个数量级的换热系数的简化模型。该项目引入了一种新的建模方法，使研究人员能够解决与气体-颗粒混合物中的热传递相关的一系列巨大挑战。为了产生更广泛的教育影响，将用喷洒有热致变色液晶的珠子建造一个玩具版的流态化床，这些液晶会随着温度变化而变色。在这个项目中，来自密歇根州、爱荷华州和明尼苏达州的研究人员合作开发了一种多相传热模型的新范例。其目的是弥合粒子尺度的热过程和设备尺度的预测之间的差距。制定了一致的建模框架，从公认的基于物理的模型扩展到更大的感兴趣的范围。目前的方法通常直接从微尺度物理获得集合平均数据，而不考虑可由模拟框架解析的尺度上的局部变化。这项拟议的工作将把空间平均的大尺度表示与从粒子分辨数值模拟获得的两相统计联系起来。一个先前被忽视的遍历一致性要求将被强制执行，以便在大过滤器宽度的限制下，数值解收敛到集合平均的双流体方程。模型验证将基于不同比例的同时多摄像机成像，以及Voronoi镶嵌在密集悬浮物中量化聚集的新应用。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Experimental analysis of particle clustering in moderately dense gas–solid flow

• DOI: 10.1017/jfm.2021.1024
• 发表时间: 2021-12
• 期刊: Journal of Fluid Mechanics
• 影响因子: 3.7
• 作者: Kee Onn Fong;F. Coletti