Research on Control Augmentation System for a Flying Wing Aircraft

Requirements of the longitudinal control augmentation system are presented through studying the 25thChina Civil Aviation Regulations and C* flight quality evaluation criterion; The C* control augmentation scheme commonly used in modern large airliners is adopted to design the control augmentation system due to the stability and handling problems for the flying wing aircraft; The particle swarm optimization algorithm is proposed due to the parameter-scheduling difficulties in the designed control augmentation system. Numerical simulation shows that the designed C* control augmentation scheme is feasible.

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Particle Swarm Optimization Algorithm in Numerical Simulation of Saturated Rock Slope Slip

Mathematical Problems in Engineering ◽

10.1155/2021/6682659 ◽

2021 ◽

Vol 2021 ◽

pp. 1-11

Author(s):

Bowen Liu ◽

Zhenwei Wang ◽

Xiaoyong Zhong

Keyword(s):

Numerical Simulation ◽

Particle Swarm Optimization ◽

Optimization Algorithm ◽

Rock Slope ◽

Particle Swarm Optimization Algorithm ◽

Particle Swarm ◽

Pso Algorithm ◽

Swarm Optimization ◽

Saturated Rock ◽

The Stability

With the continuous popularization and development of highway traffic in mountainous areas, the number of rock slopes is also increasing. In order to improve the stability of rock slope and reduce the harm caused by slope slip, this paper carries out numerical simulation of rock slope sliding based on particle swarm optimization algorithm. Firstly, this paper combines the differential evolution algorithm and simplex method to improve the global and local search ability of particle swarm optimization (PSO) algorithm and analyzes the performance of the algorithm. ABAQUS software is used to simulate rock slope sliding, the finite element method is used to analyze the stability of rock slope, and LS-DYNA program is used to simulate rockfall impact rock slope. During the numerical simulation, the improved algorithm is used to analyze all the data. Experimental data show that the improved PSO algorithm converges after nearly 100 iterations and the convergence speed and optimization accuracy are high. In the numerical simulation, the average failure probability of the left and right sides of the main section at the top, middle, and foot of the slope is 0.0820 and 0.0723, 0.0772 and 0.0492, and 0.0837 and 0.0677, respectively, indicating that the overall instability probability of the left side of the rock slope is higher than that of the right side. The rock slope with the same direction through joint is mainly affected by the joint at the toe of the slope, the rock slope with reverse through joint is mainly affected by the joint in the slope, and the sliding occurs from the middle to both ends. In addition, with the increase of the size and height of rockfall, the total energy of rock slope is also increasing, and the possibility and degree of rock slope sliding are higher. This shows that the improved particle swarm optimization algorithm can effectively analyze some factors affecting slope slip in numerical simulation of saturated rock slope slip.

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