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371.
In this paper we analyze the possibilities of using machine learning algorithms for analysis of optical spectra of electric discharge spark in atmosphere. Breakdown in air can be initiated by intense laser pulse, making plasma which has a significant electrical conductivity. The formed plasma can be further maintained by electric current obtained from capacitor discharge. In such a case the capacitor voltage can be much lower than the striking voltage (the voltage needed to initiate the electric breakdown in air). Present setup has timing precision and low jitter of fast laser and arbitrary high energies corresponding to capacitance and voltage to which the capacitor is charged. We have used a streak camera equipped with a spectrograph to analyze optical emission of plasma obtained in this way. Q-switched Nd:Yag laser was used to achieve the initial breakdown in air. Machine learning methods were used in order to classify optical spectra of plasmas with different electron temperatures obtained with different excitation energies. We have shown that, instead of using the usual way of identifying the spectral peaks and calculating their intensity ratio, it is possible to train the computer software to recognize the spectra corresponding to different electron temperatures. Principal component analysis was used to reduce the dimensionality of problem. We present possibilities of plasma electron temperature estimation based on several clustering algorithms.  相似文献   
372.
Flow control using surface Dielectric Barrier Discharge(DBD) plasma actuators driven by a sinusoidal alternating-current power supply has gained significant attention from the aeronautic industry. The induced flow field of the plasma actuator, with the starting vortex in the wall jet,plays an important role in flow control. However, the energy consumed for producing the induced flow field is only a small fraction of the total energy utilized by the plasma actuator, and most of the total energy i...  相似文献   
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