Direct simulation of thermally and mechanically coupled particle-laden flow. (May 2022)

• Record Type: Journal Article
• Authors: Florio, Laurie A
• Is Part Of: Simulation. Volume 98:Number 5(2022)
• Page Start: 363
• Page End: 387
• Publication Date: 2022-05
• Subjects: Particle flow -- thermal interaction -- mechanical interaction -- computational fluid dynamics -- heat transfer; Computer simulation -- Periodicals; 003.3
• DOI: 10.1177/00375497211055104
• Languages: English
• ISSNs: 0037-5497

Abstract

This work describes a unique technique to simulate continuously and directly coupled fluid flow and moving particles including both mechanical and thermal interactions between the flow, particles, and flow paths. The particles/flow paths are discretized within a computational fluid dynamics flow domain so that the local flow and temperature field conditions surrounding each particle or other solid body are known along with the local temperature distribution within the particle and other solids. Contact conduction between solid bodies including contact resistance, conjugate heat transfer at the fluid–solid interfaces, and even radiation exchanges between solid surfaces and between solid surfaces and the fluid are incorporated in the thermal interactions and a soft collision model simulates the solid body mechanical contact. The ability to capture these local flow and thermal effects removes reliance on correlations for fluid forces and for heat transfer coefficients/exchange and removes restrictions on the flow regime and particle size and volume fraction considered. Larger particle sizes and higher particle concentration conditions can be studied with local effects captured. The method was tested for a range of particle thermal and mechanical properties, driving pressures, and for limited radiation parameters. The results reveal important information about the basic thermal and flow phenomena that cannot be obtained in standard modeling methods and demonstrate the utility of the modeling method. The technique can be applied to examine phenomena dependent on local thermal conditions such as chemical reactions, material property variation, agglomerate formation, and phase change. The methods can also be used as a basis for machine learning algorithm development for flows with large particle counts so that more detailed phenomena can be considered compared to those provided by standard techniques with reduced computational costs compared to those with fully resolved particles in the flow.