SPARSE–R: A point-cloud tracer with random forcing

Abstract

A predictive, point-cloud tracer is presented that determines with a quantified uncertainty the Lagrangian motion of a group of point-particles within a finite region. The tracer assumes a random forcing within confidence intervals to account for the empiricism of data-driven force models and stochasticity related to the chaotic nature of the subcloud scale dynamics. It builds on the closed Subgrid Particle-Averaged Reynolds Stress-Equivalent (SPARSE) formulation presented in Domínguez-Vázquez et al. (2023) that assumes a deterministic forcing. SPARSE–R describes the first two moments of particle clouds with moment equations in closed-form, with a theoretical third-order convergence rate with respect to the standard deviations of the cloud variables. The cloud model alleviates computational cost and enhances the convergence rate as compared to Monte Carlo (MC) based point-particle methods. The randomness in the forcing model leads to virtual stresses that correlate random forcing and field fluctuations. These stresses strain and rotate the random cloud as compared to a deterministically forced cloud and thus determine to what extent the random forcing propagates into the confidence intervals of the dispersed solution. In symmetric flows the magnitude of the virtual stress is zero. Tests in analytical carrier fields and in a decaying homogeneous isotropic turbulence flow computed with a discontinuous Galerkin (DG) compressible DNS solver are performed to verify and validate the SPARSE–R method for randomly forced particles.