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

了解热量如何在气体-颗粒混合物中传递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，都代表了在多相系统中严重依赖传热的有前途的技术。目前工业和学术界使用的工具依赖于平均反应速率和传热系数的简化模型，这些模型已知会变化几个数量级。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-颗粒混合物传热相关的巨大挑战。为了更广泛的教育影响，一个玩具版的流化床将由喷有热致变色液晶的珠子组成，这些液晶会随着温度的变化而改变颜色。这将呈现在当地学校，以说明流体-粒子相互作用的基本概念。在这个项目中，来自密歇根州、爱荷华州立大学和明尼苏达州的研究人员合作开发了多相传热模型的新范例。其目的是弥合粒子尺度热过程和设备尺度预测之间的差距。制定了一个一致的建模框架，从一个被广泛接受的基于物理的模型扩展到更大的兴趣范围。目前的方法通常是直接从微尺度物理中获得的总体平均数据，而不考虑由模拟框架可分辨的尺度上的局部变化。提出的努力将空间平均，大规模的表示与从粒子解析数值模拟中获得的两相统计数据联系起来。一个先前被忽略的遍历一致性要求将被强制执行，以便在大滤波器宽度的限制下，数值解收敛于系综平均双流体方程。模型验证将基于不同尺度的同时多相机成像，以及Voronoi镶嵌的新应用，以量化密集悬浮中的聚类。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.