Energy and rotation-dependent stereodynamics of H(~2S) + NH(a^1?) → H_2(X^1Σ_g^+) + N(~2D) reaction

Quasi-classical trajectory calculations are performed to study the stereodynamics of the H（~2S） ＋ NH（a^1？） →H_2（X^1Σ_g~＋） ＋ N（~2D） reaction based on the first excited state NH_2（1~2A＇） potential energy surface reported by Li et al.[Li Y Q and Varandas A J C 2010 J. Phys. Chem. A 114 9644] for the first time. We observe the changes of differential cross-sections at different collision energies and different initial reagent rotational excitations. The influence of collision energy on the k-k＇ distribution can be attributed to a purely impulsive effect. Initial reagent rotational excitation transforms the reaction mechanism from insertion to abstraction. The effect of initial reagent rotational excitations on k-k＇ distribution can be explained by the rotational excitation enlarging the rotational rate of reagent NH in the entrance channel to reduce the probability of collision between incidence H atom and H atom of target molecular. We also investigate the changes of vector correlations and find that the rotational angular momentum vector j＇ of the product H_2 is not only aligned, but also oriented along the y axis. The alignment parameter, the disposal of total angular momentum and the reaction mechanism are all analyzed carefully to explain the polarization behavior of the product rotational angular moment.

Aero-engine fault diagnosis applying new fast support vector algorithm

A new fast learning algorithm was presented to solve the large-scale support vector machine ( SVM ) training problem of aero-engine fault diagnosis.The relative boundary vectors ( RBVs ) instead of all the original training samples were used for the training of the binary SVM fault classifiers.This pruning strategy decreased the number of final training sample significantly and can keep classification accuracy almost invariable.Accordingly , the training time was shortened to 1 / 20compared with basic SVM classifier.Meanwhile , owing to the reduction of support vector number , the classification time was also reduced.When sample aliasing existed , the aliasing sample points which were not of the same class were eliminated before the relative boundary vectors were computed.Besides , the samples near the relative boundary vectors were selected for SVM training in order to prevent the loss of some key sample points resulted from aliasing.This can improve classification accuracy effectively.A simulation example to classify 5classes of combination fault of aero-engine gas path components was finished and the total fault classification accuracy reached 96.1%.Simulation results show that this fast learning algorithm is effective , reliable and easy to be implemented for engineering application.