# Enhancing Wind Turbine Blade Preventive Maintenance Procedure through Computational Fluid Dynamics-Based Prediction of Wall Shear Stress

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## Abstract

**:**

## 1. Introduction

## 2. Wind Turbine Geometry

## 3. Mathematical Formulation

#### Transport Equations for Transition SST Model

## 4. Solution Method

#### 4.1. Boundary Conditions and Initial Conditions

#### 4.2. Wind Turbine Grid Independence Analysis

#### 4.3. Validation Study

## 5. Results

#### 5.1. Incoming Wind Speed Distribution and Statistical Analysis

#### 5.2. Effect of Transition Turbulence Model

#### 5.3. Wall Shear Stress Distribution Compared with Current Damage

#### 5.4. Preventive Maintenance Schedule Planning for Delamination and Corrosion

## 6. Conclusions

- (1)
- Based on the Anderson–Darling test for goodness of fit, the statistical analysis indicated that the incoming airflow distribution at the Lamthakhong wind farm, as depicted in Table 2 and Figure 9, is best represented by the Weibull distribution. This finding aligns with other research, suggesting that the global trend of the Weibull function applies to the wind distribution for this wind farm. Understanding the characteristics of this distribution is needed for precise wind forecasting, estimation, and conducting future investigations and scenario analyses specific to the Lamthakhong Wind Farm. By considering the Weibull distribution, more accurate predictions and assessments can be made, aiding in optimizing operations and decision-making processes related to the Wind Farm.
- (2)
- The comparison between the SST k-ω and transition SST turbulence models in Figure 10 suggests no significant differences for the low pitch angle (PA) case, but a slight difference was observed at a high pitch angle. This finding is important for wind farms operating under high-pitch angle conditions and in designing airfoil shapes that need to account for this effect.
- (3)
- By comparing the computational results with the existing damage in Figure 12d, we were able to observe a consistent pattern, with the tip of the blade exhibiting both high wall shear stress and damage. Consequently, we can conclude that wall shear stress serves as a reliable predictor of blade surface delamination and associated damage.
- (4)
- Our prediction, that wall shear stress varies with inlet velocity as shown in Equation (5) and Figure 11, suggests that incoming air speed can be used to determine wall shear stress levels. Therefore, precise data from the WindSCADA system can be used for the future assessment of wall shear stress levels.
- (5)
- Based on the wind data collected throughout the entire year, as depicted in Figure 13, it is advisable to conduct monitoring for flow-induced delamination damage prior to June as a reference point, rather than comparing it with observations made after October when the wind conditions are less severe. Unique protocols are established for internal yearly maintenance improvement.
- (6)
- The analysis of surface wind speed in Southern Thailand in Figure 15 reveals heightened fluctuations influenced by both the monsoon season and coastal geography. As a result, an effective maintenance protocol capable of accommodating these conditions is required.
- (7)
- It is crucial to prioritize future enhancements in inspection methods to ensure ongoing efficiency and effectiveness. These improvements are essential for both the ongoing research projects in the Lamthakhong Wind Farm and Southern Thailand.

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

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**Figure 1.**Three-dimensional high-precision geometry of finished surface. (

**a**) FARO Laser Scanner Focus3D with 15 locations for scanning; (

**b**) full model of raw point cloud data; (

**c**) 3D-CAD model.

**Figure 12.**Wind turbine damage compared with wall shear stress. (

**a**) PA 90°, incoming air velocity 2.50 m/s, 4 rpm; (

**b**) PA 90°, incoming air velocity 5.50 m/s, 9 rpm; (

**c**) PA 90°, incoming air velocity 10.00 m/s, 15 rpm; (

**d**) current damage on wind turbine blade (2023).

Solution Controls | Methods |
---|---|

Pressure–Velocity Coupling | Coupled |

Flux Type | Rhie-Chow |

Pressure | 2nd Order |

Momentum | 2nd Order Upwind |

Turbulent Kinetic Energy | 2nd Order Upwind |

Specific Dissipation Rate | 2nd Order Upwind |

Intermittency | 2nd Order Upwind |

Momentum Thickness Re | 2nd Order Upwind |

Residual Monitors | Absolute Criteria 10^{−4} |

Flow Multigrid | F-Cycle |

Turbulent Kinetic Energy Multigrid | F-Cycle |

Specific Dissipation Rate Multigrid | F-Cycle |

Intermittency Multigrid | F-Cycle |

Momentum Thickness Re Multigrid | F-Cycle |

Distribution | TW1 | TW2 | TW3 | TW4 | TW5 | TW6 | TW7 | TW8 | TW9 | TW10 | TW11 | TW12 |
---|---|---|---|---|---|---|---|---|---|---|---|---|

Normal | 46.33 | 55.65 | 27.29 | 55.47 | 53.47 | 50.85 | 59.50 | 53.58 | 57.52 | 68.42 | 47.73 | 76.36 |

Lognormal | 221.86 | 162.30 | 128.91 | 168.41 | 185.66 | 154.75 | 228.87 | 214.28 | 193.67 | 163.35 | 207.80 | 151.74 |

Weibull | 36.58 | 38.36 | 11.07 | 20.52 | 20.86 | 12.95 | 36.55 | 33.52 | 30.24 | 18.20 | 20.59 | 26.20 |

Gamma | 106.78 | 79.59 | 50.70 | 67.46 | 76.21 | 56.12 | 103.70 | 101.50 | 83.47 | 59.96 | 85.24 | 61.62 |

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**MDPI and ACS Style**

Palasai, W.; Plengsa-Ard, C.; Kaewbumrung, M.
Enhancing Wind Turbine Blade Preventive Maintenance Procedure through Computational Fluid Dynamics-Based Prediction of Wall Shear Stress. *Sustainability* **2024**, *16*, 2873.
https://doi.org/10.3390/su16072873

**AMA Style**

Palasai W, Plengsa-Ard C, Kaewbumrung M.
Enhancing Wind Turbine Blade Preventive Maintenance Procedure through Computational Fluid Dynamics-Based Prediction of Wall Shear Stress. *Sustainability*. 2024; 16(7):2873.
https://doi.org/10.3390/su16072873

**Chicago/Turabian Style**

Palasai, Wasan, Chalermpol Plengsa-Ard, and Mongkol Kaewbumrung.
2024. "Enhancing Wind Turbine Blade Preventive Maintenance Procedure through Computational Fluid Dynamics-Based Prediction of Wall Shear Stress" *Sustainability* 16, no. 7: 2873.
https://doi.org/10.3390/su16072873