Direct Numerical Simulation of Monodisperse and Polydisperse Particulate Flows

University Of Texas At San Antonio, San Antonio TX

Investigators

Abstract

This is a collaborative research program between the Center for Simulation, Visualization and Real Time Computing (SiViRT) at the University of Texas at San Antonio (UTSA) and the Department of Computer Science at the University of Texas Pan American (UTPA). The proposal aims to bring together faculty members and graduate students of the two institutions for the purpose of enhancing existing research and improving the education of minority and women graduate students. The objectives of this collaborative research proposal are: a) to develop a highly efficient and reliable Direct Numerical Simulation method (DNS) that can characterize particle-particle and particle-fluid interactions in both monodisperse and polydisperse particulate flows; b) to apply the DNS as a tool to develop constitutive equations and closure laws that can be used for the Two-Fluid Model (TFM); and c) to establish strong connections and ties between the two minority Institutions and integrate educational activities within the proposed research by attracting under-represented minority students to engineering research programs. Particulate flows are involved in many natural and engineering processes. Examples are: colloids in chemical engineering processes; dust and aerosols in the atmosphere; particulate and droplet transport associated with sediments in rivers; fluidized bed reactors; and drugs and supplements delivered through the human circulatory system. The size and properties of particles in a particulate system have wide ranges (polydisperse system). Numerical simulations have been a very effective tool for understanding the nature of the particle-particle and particle-fluid interactions. However, the primary challenge in modeling life-sized particle-fluid systems is the large separation of scales, ranging from macro-scale (device scale) to micro scale (particle scale). The DNS provides detailed information on particle-particle and particle-fluid interactions; it can be used to study many poorly understood phenomena in particulate flows, such as particle "bubbling", segregation, and agglomeration.