Interactive visual analysis in automotive engineering design

Abstract

Computational simulation has become instrumental in the design process in automotive engineering. Simulations can be repeated with varied parameter settings, representing many possible design choices.<br />The engineers' goal is to generate useful knowledge from the simulations' results. Computational analysis is common and necessary, but not always sufficient. This thesis presents techniques and methods for the interactive visual analysis (IVA) of simulation data sets.<br />Compared to computational methods, IVA offers new and different analysis opportunities.<br />We introduce a data model that represents the results of repeated simulations as families of function graphs. Well-known InfoVis plots and visualization techniques for families of function graphs are integrated into a coordinated multiple views framework. Focus+context visualization and iteratively defined compositions of brushes promote information drill-down. We propose glyph-based spatio-temporal visualizations for rigid and elastic multibody systems. We integrate the on-demand computation of derived data attributes of families of function graphs into the analysis workflow to facilitate the selection of deeply hidden data features. The system supports interactive knowledge discovery. The analysts can explore data features and relations; and generate, verify or reject hypotheses with visual tools; thereby gaining more insight into the data. They can solve complex tasks such as parameter sensitivity analysis and optimization. We discuss common tasks in the analysis of data containing families of function graphs. Two case studies demonstrate that the proposed approach is indeed useful in the analysis of simulation data sets in automotive engineering. The data model and the analysis procedures are also applicable to other problem domains.<br />