Closed SPARSE—A predictive particle cloud tracer. (April 2023) - Domínguez-Vázquez, Daniel; Klose, Bjoern F.; Jacobs, Gustaaf B.
Abstract: A closed and predictive particle cloud tracer method is presented that models the mean motion and deformation of a cloud of inertial particles at a singular point in space and along its Lagrangian trajectory in time. The tracer builds upon the Subgrid Particle-Averaged Reynolds Stress Equivalent (SPARSE) formulation first introduced in Davis et al. (2017) for the tracing of particle clouds. It was later extended to a Cloud-In-Cell (CIC) formulation in Taverniers et al. (2019) using a Gaussian distribution of a cloud's influence over a mesh-based, velocity field solution. SPARSE corrects the cloud's trace to second order by combining a Taylor series expansion of the drag coefficient and Nusselt number correction factors around the mean relative velocity of a cloud of particles with a Reynolds decomposition of the particle equations to obtain a governing system for the first two statistical moments of the cloud's position, velocity and temperature. Here, we close the thus far unclosed SPARSE formulation by determining the velocity field in the vicinity of the mean cloud location using a truncated Taylor series velocity representation and by combining that with averaging. The resulting tracer is predictive. It enables the tracing of a cloud of particles through a single point and so reduces the required degrees of freedom in the accurate tracing of groups of particles. We demonstrate the accuracy and convergence of the method in several one-, two- and three-dimensional test cases. Highlights: A cloud model is developed that traces the mean motion and deformation of an almagation of particles. A combination of a Taylor expansion and averaging and truncation closes the model. The accuracy is verified and validated for analytical and computed carrier-velocity fields.