# A Spectrum Correction Method Based on Optimizing Turbulence Intensity
## 1 Introduction  
The random turbulent wind is one of the critical factors causing the fatigue load of wind turbine blades. Aerodynamic analysis of wind turbines under the influence of turbulence is a vital basis for designing the ultimate load and fatigue load [1]. Meanwhile, with the trend of large-scale and flexible blades, the coupling of nonlinear aerodynamic and structural problems with the environment becomes more complex. Therefore, it is of great significance to establish a pulsating wind field model with high adaptability to multiple design conditions and complicated incoming flow environments in line with engineering applications.  
In engineering practice, considering the speed of the solution and data processing is important. While using the computational fluid dynamics (CFD) method to solve Navier-Stokes equations results in a high accuracy, this method is complex and heavily dependent on computer performance. Accordingly, the current research on turbulent wind field simulation tends to be based on classical stochastic process theory. The PSD (power spectral density) function is employed to simulate the time history of pulsating wind speed. Regularly, Harris spectrum, Von Karman spectrum, Simiu spectrum, and Kaimal spectrum are applied to power spectral density models [2; 3; 4; 5; 6]. [PERSON] proposed the harmonic synthesis method to settle the matter of the stationary Gaussian random process and non-stationary random process of wind speed time history simulation, introducing the double index frequency combined with FFT technology to achieve the ergodic properties of each state of the simulation curve [7; 8]. Lagrange interpolation, Hermite interpolation,PoD-based interpolation, and other interpolation methods [9; 10; 11] have significantly reduced the number of analog signal decompositions and have further optimized the operation efficiency. In essence, the power spectrum simulation method is a Monte Carlo numerical statistical method. To date, most studies have mainly concentrated on how to optimize the algorithm to improve the computational efficiency in order to solve more dense simulation points, or to verify the fitting degree between the power spectrum simulating fluctuating wind speed and the original spectrum [12; 13; 14]. However, few scholars have paid attention to the turbulence intensity of the simulated fluctuating wind field and the validity of the time history of the simulating point fluctuating wind speed.  
Based on applying the spectral representative method to simulate wind speed, the significant standard deviation's bias, generated due to valuing a truncated simulated frequency interval and the simulation processing itself, respectively, is proposed to shorten the time required to complete the method by creatively introducing a compensation coefficient. The corrective method modifies the original power spectral density function to produce more effective simulated points in the uniform simulated wind field that can generate the simulated turbulence intensity, meeting the set requirements. Moreover, this paper also discussed the correction method's further development by expanding its usage to the two-dimensional level. The modified method would be verified by simulating the uniform fluctuating wind field of a 15 MW referenced wind turbine, which may lay the foundation for simulating a turbulent field that is applied to a larger scale wind turbine.