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.
