A new data extract toolkit known as EPIC-Extract Plug-In Components Toolkit has been designed and implemented to create data surface extracts in situ via the TURBO CFD code. New FieldView extracts (eXDB) with a Proper Orthogonal Decomposition (POD) reduced order model were implemented, visualized and analyzed using a new prototype version of the CFD post-processor FieldView. Benchmarks show that the EPIC with POD surface extracts have some memory and computational cost overhead that may be mitigated by the reduced overall user workflow and the overall reduction in data files saved to disk. In addition, the resulting extracts provide new visualization and feature detection capabilities that may be obtained through analysis of the POD modes. This paper presents benchmarks at scale for unsteady Gas Turbine simulations (S-37 and BRI) and explores the feature detection capabilities of POD.
The flowfield 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.
One of the key problems facing users of large scale unsteady CFD is dealing with the huge amounts of results data. Writing, storing, moving and post-processing vast unsteady datasets can take far more time than running the calculations themselves and interferes with useful interpretation and reporting of results. A powerful solution that is emerging to resolve this issue is to perform data extraction for post-processing in a manner that is integrated within the calculations themselves. Termed 'in situ' post-processing, this method can ensure that the new simulations being contemplated can really be useful to the customer.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.