Speaker
Description
Turbulent mixing is an ubiquitous phenomena and plays an important role in our everyday life and numerous industrial applications. The numerical study of turbulent mixing processes poses a standing challenge since it requires a capture of all relevant length and time scales while still being computationally feasible. To eliminate this dilemma, an efficient and full-scale resolving modeling approach for turbulent mixing, termed Hierarchical Parcel-Swapping (HiPS), was introduced by A.R. Kerstein [J. Stat. Phys. 153, 142-161 (2013)]. HiPS mimics the effects of turbulence on time-evolving, diffusive scalar fields by a stochastic, hierarchical swapping mechanism. The diffusive scalar fields are interpreted as a binary tree structure. Length scales decrease geometrically with increasing tree level, and corresponding time scales follow Kolmogorov's inertial range scaling. The state variables reside only at the base of the tree and are understood as fluid parcels. To model the effects of turbulent advection, sub-trees are swapped stochastically at rates determined by turbulent time scales. Mixing only take place between adjacent fluid parcels at rates consistent with the prevailing diffusion time scales. The HiPS formulation is extended to consider multiple scalars with arbitrary diffusivities. In the talk, we will detail HiPS as a mixing model and put it into context of the most common mixing models. Results for the scalar power spectra and scalar dissipation rate will be shown for varying Schmidt numbers. Additionally, particle dispersion is compared to experimental measurements. To demonstrate the efficiency and capabilities of HiPS, results of a full engine cycle simulation using HiPS as mixing model will be presented. Considering the reduced order and associated computational efficiency, HiPS is an attractive mixing model, which can be used as a subgrid model for coarse-grained flow simulations.
