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

了解热量如何在气体-颗粒混合物中传输，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。将煤和生物质转化为有用的燃料，颗粒材料的热储存以及基于颗粒的太阳能接收器，都是非常依赖多相系统中的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单模型，已知这些模型会有几个数量级的变化。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与珠喷热变色液晶，改变颜色随温度。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新的多相传热模型。其目的是弥合粒子尺度的热过程和设备规模的预测之间的差距差距。一个一致的建模框架，制定了从一个公认的基于物理的模型，以更大的规模感兴趣的规模。当前的方法通常直接集成从微尺度物理学获得的平均数据，而不考虑模拟框架可解决的尺度上的局部变化。拟议的努力将连接的空间平均，大规模的代表性，从粒子分辨数值模拟获得的两相统计。一个以前被忽视的遍历一致性要求将强制执行，使数值解收敛到整体平均的两流体方程的限制大的过滤器宽度。模型验证将基于不同尺度的同时多相机成像，以及Voronoi细分的新应用，以量化密集悬浮液中的聚类。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估来支持。

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.