Interactive Focus+Context Analysis of Large, Time-Dependent Flow Simulation Data

The visualization of time-dependent simulation data, such as data sets from computational fluid dynamics (CFD) simulation, is still a very challenging task. In this paper, we present a new approach for the interactive visual analysis of flow simulation data, which is especially targeted at the analysis of time-dependent data. This supports the flexible specification and visualization of flow features in an interactive setup of multiple linked views. Special emphasis is put on new mechanisms to capture time-dependent features (i.e. flow features that are inherently dependent on time). We propose the integration of attribute derivation into the process of interactive visual analysis to enable the subsequent user access to otherwise implicit properties of the unsteady data in our interactive feature specification framework. All views of this flow analysis setup are linked, in the sense that the features in focus are consistently emphasized in the visualization (more colorful, less transparent) whereas the rest of the data are only shown as context in reduced style. In addition to introducing our new approach, we also demonstrate its use in the context of several application examples.

Helmut Doleisch, Helwig Hauser, Martin Gasser, Robert Kosara, Interactive Focus+Context Analysis of Large, Time-Dependent Flow Simulation Data, Simulation, vol. 82, no. 12, pp. 861–865, 2006.

@article{Doleisch:Simulation:2006,
	year = 2006,
	title = {Interactive Focus+Context Analysis of Large, Time-Dependent Flow Simulation Data}, 
	author = {Helmut Doleisch and Helwig Hauser and Martin Gasser and Robert Kosara}, 
	journal = {Simulation}, 
	volume = {82}, 
	number = {12}, 
	pages = {861–865}, 
	abstract = {The visualization of time-dependent simulation data, such as data sets from computational fluid dynamics (CFD) simulation, is still a very challenging task. In this paper, we present a new approach for the interactive visual analysis of flow simulation data, which is especially targeted at the analysis of time-dependent data. This supports the flexible specification and visualization of flow features in an interactive setup of multiple linked views. Special emphasis is put on new mechanisms to capture time-dependent features (i.e. flow features that are inherently dependent on time). We propose the integration of attribute derivation into the process of interactive visual analysis to enable the subsequent user access to otherwise implicit properties of the unsteady data in our interactive feature specification framework. All views of this flow analysis setup are linked, in the sense that the features in focus are consistently emphasized in the visualization (more colorful, less transparent) whereas the rest of the data are only shown as context in reduced style. In addition to introducing our new approach, we also demonstrate its use in the context of several application examples.}, 
}