Abstract
Flow regime identification is very practical and academic significance for cavity research in cooling pump of engine but it is very complicated. A novel classification model that combined the particle swarm optimization (PSO) with support vector Machine (SVM) was put forward for flow regime identification in this study. This hybrid model seeks for SVM's optimal parameters in whole field and isn't prone to get in local minimization. It is easy to realize and tune SVM's parameters and has stronger ability to resolve nonlinear, non-differential and multimode problem. This identification model was validated by the test based on empirical mode decomposition (EMD), which extracted flow regime feature from differential pressure fluctuation. The result showed that this method has superiority of rapider training, better generality and higher accuracy of flow regime identification.
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