Based on compressible and inviscid flow solutions computed by shock-capturing solvers, a new technique for detecting and recognizing two-dimensional shock wave interaction patterns is proposed. The implementation process of this algorithm is illustrated from three aspects. Firstly, using a traditional shock wave detection approach based on local flow parameters, a series of grid-cells near the shock waves are identified. Next, the shock cells are divided into various clusters by means of a classical K-means clustering algorithm, and the category of each cluster is defined according to its adjacent information. Finally, a criterion is introduced to merge related adjacent clusters and to further determine the locations of shock interaction points. The clusters contained in each shock wave are recorded, and then all the fitting shock lines can be obtained by the Bézier curve algorithm. Numerical experiments show that this newly developed technique can be used in different types of mesh, The new technique produces fitted shock lines with high quality and positional accuracy. Meanwhile, the multiple shock wave interaction patterns are clearly recognized and provide a good visualization method for analyzing the motion and evolution of shock waves in complex, unsteady flow.
CHANG Siyuan
,
BAI Xiaozheng
,
LIU Jun
. A two-dimensional shock wave pattern recognition algorithm based on cluster analysis[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2020
, 41(8)
: 123626
-123626
.
DOI: 10.7527/S1000-6893.2019.23626
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