Evidence map›Paper›PMID 41390589›Full record

ArticleScientific reports2025

Operational characteristics and blade fatigue life analysis of a novel variable-pitch wind turbine under natural wind conditions.

Daorina Bao, Jun Zhang, Aoxiang Jiang, Zhongyu Shi, Yongshui Luo, Xiaohu Ao, Jingsen Chen

Abstract read
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
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0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Daorina BaoSchool of Energy and Power Engineering, Inner Mongolia University of Technology, Hohhot, China. bdrn@imut.edu.cn.
Jun ZhangSchool of Energy and Power Engineering, Inner Mongolia University of Technology, Hohhot, China.
Aoxiang JiangJiangsu Xinneng Haili Offshore Wind Power Generation Co.,Ltd, Nantong, Jiangsu, China.
Zhongyu ShiChina Petroleum Engineering & Construction Corporation Beijing Company, Beijing, China.
Yongshui LuoYunda Energy Technology Group Co., Ltd., Hangzhou, China.
Xiaohu AoInner Mongolia Yufeng Mugang Energy Co., Ltd., Ordos, China.
Jingsen ChenInner Mongolia Yufeng Mugang Energy Co., Ltd., Ordos, China.

Funding

Inner Mongolia Autonomous Region "Listed and Commanded" Project 2024JBGS0025National Natural Science Foundation of China 52266013
6 · The paper itself

Abstract

This study was designed to investigate the nonlinear load behavior and fatigue life of distributed small wind turbines in natural turbulent wind fields through an in-situ field experiment. A test platform was deployed in a representative wind resource area in Hohhot, Inner Mongolia, using a self-developed 5 kW variable-pitch wind turbine prototype to perform synchronous high-frequency monitoring of wind speed, direction, and blade root loads. The aerodynamic loads were estimated via blade element theory, and rain-flow counting was used to identify the stress cycle history at the blade root, followed by fatigue life prediction using S-N curves and Miner's linear damage model. A comparative study was conducted on two cases with comparable mean wind speeds but contrasting turbulence intensities. The analysis revealed that increased turbulence intensity significantly amplifies stress fluctuations, raising fatigue damage by 45.7% and reducing blade life by about 31.7%. The results further indicate that adjusting the pitch angle can effectively reduce excessive power output and peak structural loads, confirming the practical control performance of the newly developed pitch actuator. The research offers both measured data and theoretical insights for improving structural reliability and predicting fatigue life of small-scale wind turbines.

Indexed as

Fatigue lifeNatural wind field testingTurbulence intensityVariable-pitch wind turbine

Identifiers

PMID41390589
PMCPMC12824399

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.