Collaborative Research: Bridging the gap between particle-scale thermal transport and device-scale predictions

合作研究：弥合颗粒尺度热传输和设备尺度预测之间的差距

• 批准号: 1905017
• 负责人: Shankar Subramaniam
• 金额: $ 19.67万
• 依托单位: Iowa State University
• 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 2023-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1905017&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.

了解如何在燃气粒子混合物中运输热量对于提高清洁能源技术的性能，效率和可靠性和预测环境流的可靠性至关重要。煤炭和生物量转化为有用的燃料，颗粒物材料的热量存储以及基于粒子的太阳接收机，所有这些都代表着有希望的技术，这些技术严重依赖于多相系统中的传热。工业和学术界使用的当前工具依赖于平均反应率的简单模型以及已知会因多个数量级而变化的传热系数。该项目介绍了一种新的建模方法，该方法将使研究人员能够解决与通过燃气粒子混合物进行传热相关的巨大挑战。为了产生更广泛的教育影响，将使用带有热色液液晶的珠子建造一个玩具版本的流化床，随着温度改变颜色。这将在当地学校介绍，以说明流体粒子相互作用的基本概念。在这个项目中，密歇根州，爱荷华州和明尼苏达州的研究人员合作开发了多相传热建模的新范式。目的是弥合粒子尺度的热过程和设备尺度预测之间的间隙。制定了一致的建模框架，该框架从基于物理的良好模型到更大的兴趣范围。当前方法通常是直接从微观物理学获得的集合平均数据，而无需考虑模拟框架可分解的量表的局部变化。提出的努力将将空间平均的大规模表示形式与从粒子分辨的数值模拟获得的两相统计数据联系起来。将执行先前被忽视的沿贡族一致性要求，以使数值解决方案在大滤波器宽度极限的情况下收敛到集合平均两流体方程。模型验证将基于不同尺度上同时进行多摄像机成像，以及在密集悬架中量化聚类的新颖应用。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的智力和更广泛影响的评估来通过评估来获得支持的。

项目成果

Stochastic models for capturing dispersion in particle-laden flows

• DOI: 10.1017/jfm.2020.625
• 发表时间: 2020-09
• 期刊: Journal of Fluid Mechanics
• 影响因子: 3.7
• 作者: A. Lattanzi;Vahid Tavanashad;S. Subramaniam;J. Capecelatro

Stochastic model for the hydrodynamic force in Euler–Lagrange simulations of particle-laden flows

• DOI: 10.1103/physrevfluids.7.014301
• 发表时间: 2021-03
• 期刊: Physical Review Fluids
• 影响因子: 2.7
• 作者: A. Lattanzi;Vahid Tavanashad;S. Subramaniam;J. Capecelatro

Fluid-mediated sources of granular temperature at finite Reynolds numbers

• DOI: 10.1017/jfm.2022.351
• 发表时间: 2021-08
• 期刊: Journal of Fluid Mechanics
• 影响因子: 3.7
• 作者: A. Lattanzi;Vahid Tavanashad;S. Subramaniam;J. Capecelatro