推进剂管理装置的可靠性增长及试验验证

采用原始的焊接方式焊接筛网式推进剂管理装置的收集器合格率低、可靠性差。为此,采用纯钛箔作为过渡层进行了不锈钢筛网与钛合金支压板的电阻缝焊试验以及随后这两者与钛合金骨架的电子束焊试验,然后进行了地面力学环境下的振动试验,以验证焊接方式改进后收集器的可靠性。焊接试验结果表明,纯钛箔过渡层一方面可以大幅降低筛网在电阻缝焊时的焊接热量,有效地减小筛网的热变形;另一方面可以在电阻缝焊后形成牢固、封闭的纯钛/不锈钢筛网焊缝过渡区,有效地增加电子束焊时筛网的变形抗力,从而使得焊接后试样的合格率和性能大幅提高。振动试验结果表明,焊接方式改进后收集器的可靠度置信下限从0.90增加到了0.96。     

Time resolved study of laser triggered electric discharge spark in atmosphere: Machine learning approach

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.