# Bionic Intelligent Algorithms Used in Helicopter Individual Blade Control Optimization
## 3 Results
### Validation of Aero-Elasticity Dynamic Model  
This section is the validation of the aero-elasticity dynamic model; this article validated it from a different perspective. For example, the response of the tip flapping and lift coefficient represents the moment and force on the rotor, and the stability of the two parameters reflect the reliability of the aero-elasticity model. In the hovering state, the aerodynamic environment of the rotor paddle was unsteady, so the response of the blade tip flapping and the response of the lift coefficient remained unstable. In this paper, the isolated rotor model was adopted, and the collective pitch in the hovering state was \(\theta=6^{\circ}\). The dimensionless response of the blade tip flapping and lift coefficient in the hovering state were obtained by bringing in the aeroelasticity dynamic model, as shown in Figures 14 and 15:  
Figure 13: Control model of swarm algorithm. The calculation process of the model was an iterative flow. The response of tip flapping in Figure 14 reflects the bending moment of the blade, and the response of lift coefficient in Figure 15 reflects the overall lift of the whole rotor. If the two parameters convergence after several iterations, it means that the aeroelasticity dynamic model is feasible. This model has been modified several times and the sample data selected has been proven to be reliable; thus, both the response of tip flapping and lift coefficient converged quickly. Figures 14 and 15 show that when the number of iterations is reached about 150 times, the blade tip flapping response converges, while the blade lift coefficient converges when the number of iterations is reached about 120 times. The validation results proved that the aeroelasticity dynamic model had good stability. Because of the uneven inflow and induced velocity distribution, the hub load was in a periodic change. Before the calculation of forward flight state, the collective pitch and periodic pitch in the steady state needed to be given. In this paper, the obtained state parameters are shown as follows:  
The trim parameters calculated in Table 1, above, were substituted into the aeroelasticity dynamic model to obtain the hub vibration load. Among the three forces and three moments on the hub, the vertical force of the hub had the greatest influence on the vibration of the fuselage. The vertical force load of the hub under the forward flight state was obtained through the calculation in Figure 16: \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Advanced ratio \(\mu\) & 0.3 \\ \hline Lift coefficient \(C_{T}\) & 0.0072 \\ \hline Collective pitch \(\theta_{0}/(^{\circ})\) & 6 \\ \hline Horizontal periodic pitch \(\theta_{1c}/(^{\circ})\) & 2.75 \\ \hline Longitudinal periodic pitch \(\theta_{1s}/(^{\circ})\) & \(-4.76\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Parameters in forward flight. Figure 14: Response of blade tip flapping. Figure 15: Response of rotor lift coefficient. Because the model used in this article consisted of three blades, the period of the force in Figure 16 was nearly 120\({}^{\circ}\); if there were N blades, the period would be 360\({}^{\circ}\)/N. This means that the rotor underwent three periods in 1/rev. However, due to the complex air flow environment and the difference between each blade, the force was not a strict harmonic wave. Figure 16 showed that there was some slight difference between each period. By extracting the response of the hub vertical force within a period by FFT, the harmonic component of the hub vertical force could be obtained:  
The 0-order force in Figure 17 represented the average force on the rotor (the lift), as the higher order force represented the vibration load.