The problems of dealing with extremely large solution data can be overcome with the application of data compression. In this thesis two methods for the compression of result data from computational fluid dynamics (CFD) are presented. They are suitable to compress numerical data on irregular volumetric grids with non-unique cell types, as these grids are often used in CFD. Both methods are based on the same general lossy compression scheme and differ from each other in the decorrelation techniques that are applied. One of the two follows a prediction scheme, while the other one is based on a transformation method. Both methods are evaluated on the basis of CFD data and compared in regard to compression and performance measures. It is shown that both methods yield good compression results, while the prediction-based method achieves a significantly higher data throughput.
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