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排序方式: 共有915条查询结果,搜索用时 15 毫秒
911.
912.
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。 相似文献
913.
《Advances in Space Research (includes Cospar's Information Bulletin, Space Research Today)》2023,71(1):286-297
This paper investigates the ionospheric storm of December 19–21, 2015, which was initiated by two successive CME eruptions that caused a G3 space weather event. We used the in situ electron density (Ne) and electron temperature (Te) and the Total Electron Content (TEC) measurements from SWARM-A satellite, as well as the O/N2 observations from TIMED/GUVI to study the ionospheric impact. The observations reveal the longitudinal and hemispherical differences in the ionospheric response to the storm event. A positive ionospheric storm was observed over the American, African and Asian regions on 20 December, and the next day showed a negative storm. Both these exhibited hemispheric differences. A positive storm was observed over the East Pacific region on 21 December. It is seen that the net effect of both the disturbance dynamo electric field and composition differences become important in explaining the observed variability in topside ionospheric densities. In addition, we also discuss the Te variations that occurred as a consequence of the space weather event. 相似文献
914.
《Advances in Space Research (includes Cospar's Information Bulletin, Space Research Today)》2023,71(6):2566-2574
Due to the influence of various errors, the orbital uncertainty propagation of artificial celestial objects while orbit prediction is required, especially in some applications such as conjunction analysis. In the orbital error propagation of artificial celestial objects in low Earth orbits (LEOs), atmospheric density uncertainty is one of the important factors that require special attention. In this paper, on the basis of considering the uncertainties of position and velocity, the atmospheric density uncertainty is also taken into account to further investigate the orbital error propagation of artificial celestial objects in LEOs. Artificial intelligence algorithms are introduced, the MC Dropout neural network and the heteroscedastic loss function are used to realize the correction of the empirical atmospheric density model, as well as to provide the quantification of model uncertainty and input uncertainty for the corrected atmospheric densities. It is shown that the neural network we built achieves good results in atmospheric density correction, and the uncertainty quantization obtained from the neural network is also reasonable. Moreover, using the Gaussian mixture model - unscented transform (GMM-UT) method, the atmospheric density uncertainty is taken into account in the orbital uncertainty propagation, by adding a sampled random term to the corrected atmospheric density when calculating atmospheric density. The feasibility of the GMM-UT method considering atmospheric density uncertainty is proved by the further comparison of abundant sampling points and GMM-UT results (with and without considering atmospheric density uncertainty). 相似文献
915.