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Helicopter with the advantage of light, flexible and hovering in the air, is widely used. In both military and civilian domain, it gets more and more attention. It becomes the main subject of research in the world. In the power transmission system of helicopter, the tail rotor shaft is an important part. It is one of the key technologies to predict the dynamic characteristics of the shaft accurately for improving the overall performance of the helicopter. Based on the theory of elastic mechanics, rotor dynamics and the finite element analysis, the former main drive shaft is taken as an example. The dynamic characteristics of the transmission shaft is calculated by the multi/few degrees of freedom finite element method. The validity of the finite element method with few degrees of freedom is determined. Then the structure characteristics of the helicopter tail drive shaft are analyzed, and the dynamic characteristics of the helicopter tail rotor shaft are calculated by using the finite element method with few degrees of freedom. The research results provide the basis and guidance for the design of the helicopter tail drive system.

Multi objective optimization problems in aviation with micro and small speed reducer are often quite challenging because these problems often involve multiple and conflicting objectives. To solve the multi-objective optimization problems (MDOP), in this paper, we propose a new multi-objective differential evolution (MODE) variant, named improved double population differential cultural particle swarm optimization algorithm, inspired by improved strategy based on algorithm fusion. From the characteristics of cultural algorithm and particle swarm optimization (pso) algorithm, considering the advantages and disadvantages of both algorithms have strong complementarily. In the process of the evolution of the belief space, the strategy of "multi - layer space, the best choice" is adopted. In the process of the evolution of the group space, using the improved double group evolution difference method. To avoid a large number of high fitness infeasible solutions are discarded and the results of the algorithm are not ideal, and the convergence speed of the algorithm is improved. Multi objective optimization design of micro small speed reduction device for Aviation Based on CPSA.By comparison with the original design, the characteristics of the new design scheme are greatly improved.It is suggested that this paper proposed the method of multi objective optimization design based on the improved double population differential and multi layer culture particle swarm optimization is feasible.

Based on the lumped parameter theory and the gear transmission system’s engagement theory,a nonlinear dynamics model of the face gear transmission system is established, which includes time-varying mesh stiffness, comprehensive transmission error, mesh damping and tooth flash temperature;To solve and analyze the model,it can be found that the tooth flank’s maximum instantaneous temperature will be changed significantly when the face gear system is into engagement and out of engagement,and the tooth flank’s relative velocity direction will change near the high and middle range of the tooth.With the change of the gear parameters,the temperature also changes. It will provide a theoretical basis for the study of the tooth surface gluing problem, gears meshing thermal stress and other issues.

Monitoring the aircraft engine fuel flow is critical to the flight safety and the aircraft maintenance economy. Aim at predicting the aircraft engine fuel flow accurately and quickly, an aircraft engine fuel flow prediction method based on the process neural network is proposed in this paper. The learning speed of the existing learning algorithms (e.g. BP learning algorithm) for process neural network is too slow for the practical application. A Levenberg-Marquardt learning algorithm based on the expansion of the orthogonal basis functions is developed to raise the adaptability of the process neural network to the real problems. Finally, the proposed prediction method with the corresponding learning algorithm is utilized to predict the fuel flow of some aircraft engine, the results indicate that the proposed prediction method seems to perform well and appears suitable for using as an aircraft engine health condition monitoring tool, and the comparative results also indicate that the Levenberg-Marquardt learning algorithm has a faster learning convergence speed and a higher prediction accuracy than the BP learning algorithm.