Gaidai-Xing reliability method validation for 10-MW floating wind turbines – Scientific Reports
Gaidai-Xing reliability method validation for 10-MW floating wind ... Nature.com
sdgtalks
Introduction
The development of more efficient wind turbines is crucial in achieving the Sustainable Development Goals (SDGs) and reducing greenhouse gas emissions. Wind turbines must be designed to withstand extreme wind and wave loads for at least 20 years, especially in offshore environments. However, the complexity of these systems and the cross-correlation between multiple dimensions pose challenges for traditional reliability methods. This article presents a novel reliability approach that can effectively analyze the dependability of multi-dimensional structural responses.
Background
Offshore wind turbines are subjected to severe wind and wave loads, which can cause significant stresses on the system components. The failure of these components can result in costly downtime and maintenance. In the past, wind turbines were built with large safety margins, but as turbines have become larger and more expensive, it has become necessary to accurately estimate the loads and optimize the design to minimize costs. Various modeling and probabilistic approaches have been developed to estimate the extreme loads on wind turbines, but there is still a need for more accurate and efficient methods.
Methodology
The proposed reliability approach utilizes the Gaidai-Xing method, which is based on deconvolution and statistical analysis techniques. The method allows for the estimation of extreme response levels for multi-dimensional dynamic systems using limited data sets. The method has been validated using a numerical simulation model of a 10-MW floating wind turbine. The simulation model incorporates the aerodynamics, hydrodynamics, structural dynamics, and control system dynamics of the wind turbine.
System Description
The 10-MW floating wind turbine system consists of a reference wind turbine (RWT) and a semi-submersible floater with a mooring system. The RWT is a conventional three-bladed turbine with variable speed and collective pitch control. The floater is a post-tensioned concrete structure with three outer columns mounted on a star-shaped pontoon. The mooring system consists of three catenary lines with clumped masses. The FAST simulation program is used to generate the empirical bending moments for the investigation.
Results
The Gaidai-Xing method is applied to the bending moment data from the wind turbine simulation. The method accurately predicts the extreme response levels for the blade root flapwise bending moment and the tower bottom fore-aft bending moment. The results are compared to the bivariate modified Weibull method, and it is found that the Gaidai-Xing method provides a more robust prediction within the 95% confidence interval.
Conclusion
The novel reliability approach based on the Gaidai-Xing method is demonstrated to be effective in estimating the extreme loads on a 10-MW floating wind turbine. The method can handle multi-dimensional dynamic systems and cross-correlation between system responses. It provides a more accurate and efficient alternative to traditional reliability methods. The proposed approach can be applied to various engineering disciplines and has the potential to optimize the design and operation of wind turbines, contributing to the achievement of the SDGs.
SDGs, Targets, and Indicators
1. Which SDGs are addressed or connected to the issues highlighted in the article?
- SDG 7: Affordable and Clean Energy
- SDG 9: Industry, Innovation, and Infrastructure
- SDG 13: Climate Action
- SDG 14: Life Below Water
2. What specific targets under those SDGs can be identified based on the article’s content?
- SDG 7.2: Increase the share of renewable energy in the global energy mix
- SDG 9.4: Upgrade infrastructure and retrofit industries to make them sustainable
- SDG 13.2: Integrate climate change measures into national policies, strategies, and planning
- SDG 14.7: Increase the economic benefits to small island developing states and least developed countries from the sustainable use of marine resources
3. Are there any indicators mentioned or implied in the article that can be used to measure progress towards the identified targets?
Yes, the article mentions several indicators that can be used to measure progress towards the identified targets. These include:
- Efficiency of wind turbines in achieving net-zero emissions target
- Reduction in construction, maintenance, and operational costs of wind turbines
- Accuracy of wind turbine load estimation
- Reduction in downtime and associated costs due to wind turbine failure
Table: SDGs, Targets, and Indicators
| SDGs | Targets | Indicators |
|---|---|---|
| SDG 7: Affordable and Clean Energy | 7.2: Increase the share of renewable energy in the global energy mix | Efficiency of wind turbines in achieving net-zero emissions target |
| SDG 9: Industry, Innovation, and Infrastructure | 9.4: Upgrade infrastructure and retrofit industries to make them sustainable | Reduction in construction, maintenance, and operational costs of wind turbines |
| SDG 13: Climate Action | 13.2: Integrate climate change measures into national policies, strategies, and planning | Accuracy of wind turbine load estimation |
| SDG 14: Life Below Water | 14.7: Increase the economic benefits to small island developing states and least developed countries from the sustainable use of marine resources | Reduction in downtime and associated costs due to wind turbine failure |
Behold! This splendid article springs forth from the wellspring of knowledge, shaped by a wondrous proprietary AI technology that delved into a vast ocean of data, illuminating the path towards the Sustainable Development Goals. Remember that all rights are reserved by SDG Investors LLC, empowering us to champion progress together.
Source: nature.com
Join us, as fellow seekers of change, on a transformative journey at https://sdgtalks.ai/welcome, where you can become a member and actively contribute to shaping a brighter future.
