## 5 Analysis of Results
### Numerical Calculation Analysis before and after Optimization
For the upstream neighbouring lobe channel, the angle of entry will increase, and the flow separation in this lobe channel will increase; conversely, the angle of entry impulse in the downstream neighbouring lobe channel will decrease, and the separation on the suction side of the lobe channel will be somewhat suppressed so that the stalled group achieves a reverse propagation on the blade row. Turbulent energy can characterize the intensity of fluid turbulence and the degree of turbulent pulsation. Its magnitude and spatial inhomogeneity also reflect, to some extent, the magnitude and extent of pulsation dispersion and viscous dissipation losses [20]. In order to investigate the effect of the structural design of the rear guide vane of the inclined flow fan on the internal flow characteristics of the fan, the turbulent energy distribution at 0.5 lobe height before and after optimization at the same flow condition is shown in Figure 23. The internal turbulent energy distribution in the original rear guide vane area is more significant than that in the moving vane section. However, the internal turbulent energy distribution in the rear guide vane area is sharply reduced after the change. Figure 23: The turbulent kinetic energy distribution at 0.5 blade height before and after optimization. Figure 22: The formation of flow instability. The wavelet transform derives significant fluctuating patterns in the time series by decomposing the time series into the time-frequency domain, i.e., the periodically varying dynamics and the temporal pattern of the periodically varying dynamics. After wavelet variation processing, weak local features are brought to the before [21]. As shown above, wavelet analysis decomposes a signal into a series of superimposed functions derived from a mother wavelet function through translation and scale scaling. This irregular wavelet function can be used to approximate the sharply varying parts of a non-stationary signal. It can also be used to approximate discrete, discontinuous signals with local characteristics, thus reflecting more realistically the changes in the original signal on a given time scale. This local nature of wavelet analysis makes it an effective tool for quantifying non-stationary, discontinuous time series. The definition of the continuous wavelet transform can be expressed as [22; 23]:  
\[W_{f}(a,b)=\frac{1}{\sqrt{|a|}}\int_{R}f(t)\overline{\varphi}\left(\frac{t-b}{ a}\right)dt \tag{3}\]  
where \(\varphi\) represents the mother function of the wavelet function, \(a\) represents the scale factor, and \(b\) represents the translation factor. The more wavelet function is used as the basis function for the transformation, and the order of the wavelet function is 5, cmor-5 wavelet, and the wavelet processing of the pressure pulsation signal of the modified guide vane is developed, as shown in Figure 17:0 The wavelet signal consists of a significant surge at the fundamental and octave frequencies, while there are also more apparent fluctuation values around 120 Hz. Fan noise is directional and needs to be based on the experimental results before and after the fan outlet and circumferential directional aerodynamic noise direction to characterize the overall fan aerodynamic noise as a way to judge the effect of the entire fan modification [24]. As shown in Figure 25, the fan inlet and outlet direction noise are the largest, the overall \"8\" type, the fan circumferential plane of the aerodynamic noise has dipole characteristics, and the overall noise in the circumferential plane has been reduced. Figure 17:1 A wavelet spectrum analysis of a modified guide vane.