The shadow position mixing model tested for two turbulent flows

Abstract

Predicting the behavior of turbulent flows and how thermochemical properties develop such as temperature, enthalpy or concentrations is an import aspect in various fields. However, numerical calculations have a high time complexity. Therefore, models were constructed to approximate these flows. This thesis focuses on using probability density functions (pdf) to predict scalar probabilities in these flows. Since the pdf does not contain spatial information, small scaled mixing has a closure problem and has to be modeled using micromixing. A micromixing model, called the Shadow Position Mixing Model (SPMM) was introduced previously to calculate this mixing. In this project, its performance is determined for the mixing in two different flows. The model shows promising results for mixing of two passive scalar, initially mainly in one of three states. Comparing the results of various mixing models to the provided DNS, the SPMM predicts the evolution of the pdf over time adequately, but still has visible differences compared to the DNS. In the second test, the model is tested using an imposed mean scalar gradient in order to check the convergence of the mixing model. The SPMM does show promising results, but can not reliably predict a correct statistically stationary state. This is due to the fact that varying number of scalars and particles lead to significant inconsistencies. Under the current conditions, the SPMM cannot be reliably applied to flows with such an additional scalar force. The SPMM uses a near-neighbor sorting algorithm which has a high algorithmic complexity. For practical purposes, a lower complexity algorithm has to be developed in order to use this model for high number of particles.