# A Combined High and Low Cycle Fatigue Life Prediction Model for Wind Turbine Blades
## 5 Wind Turbine Blade Life Prediction
This type of relationship is crucial in fatigue life prediction models, as it helps estimate the material's endurance limit and predict failure under HCF fatigue loading. The combination of \(N_{L}\), \(N_{H}\), and Equation (21) is shown in Table 3. \(N=2.4794\times 10^{8}\) cycles. The empirical formula for blade fatigue life calculation is shown in Equation (29):  
\[L=\frac{N}{H\times\omega\times 60} \tag{29}\]  
where \(H\) denotes the effective wind speed distribution time in a year, and \(\omega\) denotes the wind turbine speed. Figure 10: HCF \(S\)-\(N\) curve. Figure 9: Weibull wind speed distribution curve. Substituting the values of each parameter into the life expectancy Equation (29) yields \(L=24\) years. The number of combined failure load blocks under CCF loading and the cumulative fatigue damage can be expressed as Equation (30). The 3D damage evolution of the blade under different high and low peripheral stress frequency ratios and failure cycle load blocks was established, as shown in Figure 11.  
Figure 8:0  
The estimated life of the model in this proposal is comparable to the theoretical life of the 5 MW wind turbine blade, which was determined to be 23 years by applying the stress analysis function of a finite element to calculate the stress at the blade's root and combining it with the Weibull wind speed distribution data [42]. When the traditional Miner's rule was used to predict the blade fatigue life, the prediction error from the theoretical life was 13%. However, the prediction error was reduced to 4.3% by the method proposed. The results show that fatigue life can be accurately predicted using the model in this proposal, leading to better design of wind turbine blades to ensure safety and lightweighting. While the proposed model significantly improves fatigue life predictions by considering load interaction effects and nonlinear damage accumulation, it does come with an increase in computational complexity compared to traditional models, such as Miner's rule. The complexity arises primarily from the need to solve nonlinear damage evolution equations and the integration of load interaction effects in each cycle of the fatigue life prediction. This results in an increased number of calculations per cycle, especially in scenarios involving CCF. However, the computational burden can be managed by utilizing optimization techniques and parallel computing methods. The model's implementation can be parallelized to efficiently handle large-scale simulations, where multiple fatigue life predictions for different loading conditions can be computed simultaneously. In practice, this approach allows for scalable predictions, especially when applying the model to real-world engineering systems such as wind turbine blades, where multiple components or conditions must be evaluated simultaneously. Furthermore, simplifications and approximations can be applied to reduce computational costs when needed. In cases where load history is predictable or simplified, certain parts of the model can be approximated without significantly affecting the accuracy of the prediction. In such scenarios, the model can achieve a balance between computational efficiency and predictive accuracy, making it feasible for large-scale applications.