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The primary objective of this research project was to produce a research capability to perform detailed CFD simulations and Scientific Visualization analysis of unsteady, three-dimensional, compressible, viscous flow fields about flight vehicle configurations of interest to the Department of Defense (DoD). The target applications include, but are not limited to, separation of single or multiple stores from aircraft or missiles, launch vehicle or missile booster separation, separation of crew escape modules or ejection seats, separation of fairings, etc. from missile systems, and maneuvering aircraft or missiles. Target flow conditions include, compressible flow regimes with vehicles operating at Mach numbers from high subsonic to moderate hyper-sonic, laminar and turbulent viscous flow, and flow of gases in chemical and thermal equilibrium.

The definition of similarity is a key prerequisite when analyzing complex data types in data mining, information retrieval, or machine learning. However, the meaningful definition is often hampered by the complexity of data objects and particularly by different notions of subjective similarity latent in targeted user groups. Taking the example of soccer players, we present a visual-interactive system that learns users' mental models of similarity. In a visual-interactive interface, users are able to label pairs of soccer players with respect to their subjective notion of similarity. Our proposed similarity model automatically learns the respective concept of similarity using an active learning strategy. A visual-interactive retrieval technique is provided to validate the model and to execute downstream retrieval tasks for soccer player analysis. The applicability of the approach is demonstrated in different evaluation strategies, including usage scenarions and cross-validation tests.

How do problem solvers represent visual-spatial information in complex problem solving tasks? This paper explores the predictions of embodied problem solving and a neurocomputational theory for what factors influence internal representation choices. Data are collected from experts and novices in three different, complex visual-spatial problem-solving domains (weather forecasting, submarine target motion analysis, and fMRI data analysis). Internal spatial representations are coded from spontaneous gestures made during cued-recall summaries of problem solving activities. Analyses of domain differences, expertise differences, and changes over time with problem solving suggest that neurocomputational constraints play a larger role than the nature of the visual input or the nature of the underlying real world being examined through problem solving, especially for expert problem solvers. The particular neurocomptuational feature that was found to drive internal representation choice is the required spatial precision of the main goals of problem solving.

How do problem solvers represent visual-spatial information in complex problem solving tasks? This paper explores the predictions of symbolic computation, embodied problem solving and a neurocomputational theory for what factors influence internal representation choices. Across two studies, data are collected from experts and novices in three different, complex visual-spatial problem-solving domains (weather forecasting, submarine target motion analysis, and fMRI data analysis). Internal spatial representations are coded from spontaneous gestures made during cued-recall summaries of problem solving activities. Analyses of domain differences, expertise differences, and changes over time with problem solving suggest that neurocomputational constraints play a larger role than the nature of the visual input or the nature of the underlying real world being examined through problem solving, especially for expert problem solvers. The particular neurocomptuational feature that was found to drive internal representation choice is the required spatial precision of the main goals of problem solving.