Alin-Adrian Anton

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The size of the output originating from high performance scientific simulations is a major bottleneck in parallel computing. Submodelling allows for the local refinement of the results without the necessity to load and store the larger, global simulation data. This enables a user to focus in several regions of interest and disregard the rest of the analysis(More)
Orbiting satellites and other spatial vehicles have complex trajectories that can usually be precisely approximated with analytical or numerical trajectory estimation algorithms. However, some scenarios, such as LEOP (Launch and Early Orbit Phase) or during critical manoeuvres, present greater angular uncertainty. During these, large dish antennas used for(More)
The scope of this work is to reduce the storage requirements for scientific simulation data by reconstructing space-time windows defined inside the solution frame, with increased or lower mesh resolutions. The price for handling large datasets of scientific simulations is very restrictive for most computational science and engineering users. It is necessary(More)
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