Minimization of classification samples for supercritical and subcritical patterns of supersonic inlet

In order to investigate sample minimization for classification of supercritical and subcritical patterns in supersonic inlet, three optimization methods, namely, opposite one towards nearest method, closest one towards the hyper-plane method and random selection method, are proposed for investigation on minimization of classification samples for supercritical and subcritical patterns of supersonic inlet. The study has been carried out to analyze wind tunnel test data and to compare the classification accuracy based on those three methods with or without priori knowledge. Those three methods are different from each other by different selecting methods for samples. The results show that one of the optimization methods needs the minimization samples to get the highest classification accuracy without priori knowledge. Meanwhile, the number of minimization samples needed to get highest classification accuracy can be further reduced by introducing priori knowledge. Furthermore, it demonstrates that the best optimization method has been found by comparing all cases studied with or without introducing priori knowledge. This method can be applied to reduce the number of wind tunnel tests to obtain the inlet performance and to identify the supercritical/subcritical modes for supersonic inlet.