Title :
In-Situ Feature Tracking and Visualization of a Temporal Mixing Layer
Author :
Duque, E.P.N. ; Hiepler, D.E. ; Legensky, S.M. ; Stone, C.P.
Author_Institution :
Intell. Light, Rutherford, NJ, USA
Abstract :
The flow field for a temporal mixing layer was analyzed by solving the Navier-Stokes equations via a Large Eddy Simulation method, LESLIE3D, and then visualizing and post-processing the resulting flow features by utilizing the prototype visualization and CFD data analysis software system Intelligent In-Situ Feature Detection, Tracking and Visualization for Turbulent Flow Simulations (IFDT). The system utilizes volume rendering with an Intelligent Adaptive Transfer Function that allows the user to train the visualization system to highlight flow features such as turbulent vortices. A feature extractor based upon a Prediction-Correction method then tracks and extracts the flow features and determines the statistics of features over time. The method executes In-Situ with the flow solver via a Python Interface Framework to avoid the overhead of saving data to file. The movie submitted for this visualization showcase highlights the visualization of the flow such as the formation of vortex features, vortex breakdown, the onset of turbulence and then fully mixed conditions.
Keywords :
Navier-Stokes equations; computational fluid dynamics; data analysis; feature extraction; flow simulation; flow visualisation; mixing; predictor-corrector methods; statistical analysis; turbulence; vortices; CFD data analysis software system; IFDT; LESLIE3D; Navier-Stokes equations; Python interface framework; feature extractor; feature statistics; flow features; flow field; flow solver; flow visualization; in-situ feature tracking; in-situ feature visualization; intelligent adaptive transfer function; intelligent in-situ feature detection; large Eddy simulation method; post-processing; prediction-correction method; prototype visualization; temporal mixing layer; turbulent flow simulations; turbulent vortices; visualization system; vortex breakdown formation; vortex feature formation; Computational Fluid Dynamics; Feature Detection; Feature Tracking.; In-Situ; Turbulence; Visualization;
Conference_Titel :
High Performance Computing, Networking, Storage and Analysis (SCC), 2012 SC Companion:
Conference_Location :
Salt Lake City, UT
Print_ISBN :
978-1-4673-6218-4
DOI :
10.1109/SC.Companion.2012.335