Predictive models for heat and mass transfer in multiphase flows are needed for implementing carbon-neutral energy-generation technologies such as chemical looping combustion with coal or biomass, and CO2 capture from the flue gas of power plants. Recent direct numerical simulations (DNS) of flow past fixed particle assemblies suggest that heat and mass transfer models currently in use do not accurately capture relevant phenomena. To address this shortcoming, this research will investigate heat and mass transfer in fluid-particle suspensions using a combination of DNS and laser measurements of velocity and scalar fields, resulting in improved models for fluid-particle suspension flows. The improved and validated models for heat transfer can then be used by industry to evaluate and optimize fluidized bed design for CO2 capture.
Intellectual Merit: Current models for heat and mass transfer in two-fluid CFD models have not been fully validated. Specifically, unknown terms in the transport equations have not been quantified. This research addresses this need by quantifying these unknown terms using laser measurements and DNS. This new understanding will lead to improved models and simulation methodologies for interphase heat and mass transfer in fluid-particle suspensions. The validity of these models will be verified in a riser flow configuration.
Broader Impacts: Improvements to heat and mass transfer models will result in more accurate simulation of fluidized beds for chemical looping combustion and CO2 capture using dry sorbents. This will enable engineers to rapidly evaluate the viability of proposed designs. The project will leverage various institutional outreach programs to increase participation of students including those from underrepresented groups.
