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This thesis presents new visualization techniques for engineering simulations in two different disciplines: Turbulent combustion and solid mechanics. Direct numerical simulations of turbulent combustion are used as a basis to develop and validate higher-level combustion models. A focus of interest in the analysis of such simulations is the flame surface, where most of the chemical reactions take place. The computational power of supercomputers is increasing much faster than the performance of<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.25673/33913">doi:10.25673/33913</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/6nt2zzlw6nb5hh7jzs4atylcm4">fatcat:6nt2zzlw6nb5hh7jzs4atylcm4</a> </span>
more »... orage infrastructures. This has caused the output and storage of simulation data to become the bottleneck in large-scale simulation runs. We introduce two new techniques for the visualization and analysis of the flame surface in large-scale simulations of turbulent combustion before the background of this storage bottleneck. The first is a space-saving sparse representation for certain types of flames. It allows for the analysis of a larger number of simulation time steps and is the basis for a new flame visualization technique. The second is an algorithm for tracking the flame surface in-situ during the simulation. The storage bottleneck is circumvented by only writing to disk the much smaller results. Both contribute to the continued ability of combustion researchers to analyze the data produced by their increasingly large simulations. Due to their many degrees of freedom, tensor fields are some of the most challenging types of data to visualize. One possibility to break down their complexity is feature-based visualization, which reduces the data to a set of geometric primitives that represent the occurrence of some kind of interesting behavior. The parallel vectors operator, which yields locations where two vector fields are parallel, is the basis of a number of line-type features in scalar and vector fields. We translate this operator to tensor fields by introducing the parallel eigenvectors operator, which yields locations where two tensor fields have parallel real eigenvectors. We then use this idea to introduce tens [...]
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