Large-scale structures in stratified turbulent Couette flow and optimal disturbances

Direct numerical simulation data of a stratified turbulent Couette flow contains two types of organized structures: rolls arising at neutral and close to neutral stratifications, and layered structures which manifest themselves as static stability increases. It is shown that both types of structures have spatial scales and forms that coincide with the scales and forms of the optimal disturbances of the simplified linear model of the Couette flow with the same Richardson numbers.

Visualization of Droplet Dynamics in Cloud

Data visualization uses charts, graphs and maps to illustrate some information. Visualizing data helps us understand information faster. The study of droplet dynamics is a critical part of cloud physics and includes studying droplet properties. The aim of this work is to visualize the droplet dynamics obtained from DNS (Direct Numerical Simulation) data due to evaporation and condensation of the droplets. This simulation contains coupled Eulerian and Lagrangian frames. Animation is created for both Eulerian grid data and Lagrangian droplet movement. Scientific visualization provides a way to analyze these turbulent properties in a part of a cloud and learn about the nature of droplets and mixing process in such highly turbulent areas.

Optimal disturbances of stably stratified turbulent Couette flow

Direct numerical simulation data of a stably stratified turbulent Couette flow contains two types of organized structures: the rolls that arise at neutral and close to neutral stratification, and the layered structures, which manifest themselves as the static stability increases. It is shown that both types of structures have spatial scales and forms that coincide with the scales and forms of the corresponding optimal disturbances of the simplified linear model of the Couette flow with the same Richardson numbers.

Reynolds Stress Perturbation for Epistemic Uncertainty Quantification of RANS Models Implemented in OpenFOAM

Reynolds-averaged Navier-Stokes (RANS) models are widely used for the simulation of engineering problems. The turbulent-viscosity hypothesis is a central assumption to achieve closures in this class of models. This assumption introduces structural or so-called epistemic uncertainty. Estimating that epistemic uncertainty is a promising approach towards improving the reliability of RANS simulations. In this study, we adopt a methodology to estimate the epistemic uncertainty by perturbing the Reynolds stress tensor. We focus on the perturbation of the turbulent kinetic energy and the eigenvalues separately. We first implement this methodology in the open source package OpenFOAM. Then, we apply this framework to the backward-facing step benchmark case and compare the results with the unperturbed RANS model, available direct numerical simulation data and available experimental data. It is shown that the perturbation of both parameters successfully estimate the region bounding the most accurate results.