IFDT – Intelligent In-Situ Feature Detection, Extraction, Tracking and Visualization for Turbulent Flow Simulations

نویسندگان

  • Daniel Hiepler
  • Christopher P. Stone
  • Steve M Legensky
  • Christopher Muelder
  • Jishang Wei
چکیده

Intelligent In-Situ Feature Detection, Tracking and Visualization for Turbulent Flow Simulations (IFDT) is a new prototype visualization and CFD data analysis software system for flow feature data tracking and extraction. 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 a flow solver via a Python Interface Framework to avoid the overhead of saving data to file. This prototype system enables the user to readily explore, detect, track and analyze flow features predicted by large scale unsteady CFD simulations.

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تاریخ انتشار 2012