Evidence map›Paper›PMID 39684006›Full record

ArticlePolymers2024

Study on Acoustic Emission Characteristics and Damage Mechanism of Wind Turbine Blade Main Spar with Different Defects.

Yanan Zhang, Shaojie Xue, Chuanyong Chen, Tianchang Ma, Bo Zhou

Abstract read
In one paragraph

Article in Polymers, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

1 citing paper in PubMed.

  1. Article
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

5 authors.

Yanan ZhangSchool of Mechanical Engineering, Shenyang Ligong University, Shenyang 110159, China.ORCID 0000-0002-4349-8764
Shaojie XueSchool of Mechanical Engineering, Shenyang Ligong University, Shenyang 110159, China.
Chuanyong ChenSchool of Energy Engineering, Zhejiang University, Hangzhou 310058, China.ORCID 0000-0001-9606-4575
Tianchang MaSchool of Mechanical Engineering, Shenyang University of Technology, Shenyang 110870, China.
Bo ZhouSchool of Mechanical Engineering, Shenyang University of Technology, Shenyang 110870, China.

Funding

National Natural Science Foundation of China 51575361
6 · The paper itself

Abstract

This paper aimed to understand the AE signal characteristics and damage mechanism of wind turbine blade main spar materials with different defects during the damage evolution process. According to the typical delamination and wrinkle defects in wind turbine blades, the GFRP composite with defects is artificially prefabricated. Through acoustic emission experiments, the mechanical properties and acoustic emission characteristic trends of wind turbine blade main spar composites with different defects under tensile loading conditions were analyzed, and the damage evolution mechanism of different defects was explained according to the microscopic results. The results show that the existence of artificial defects will not only affect the mechanical properties of composite materials but also affect the damage evolution process of the materials. The size and location of delamination defects and the different aspect ratio of the wrinkle defects have a certain influence on the damage mechanism of the material. K-means cluster analysis of AE parameters identified the damage models of GFRP composites. The types of damage modes of delamination defects and wrinkle defects are the same, and the range of characteristic frequency is roughly the same. This study has important reference significance for structural damage monitoring and damage evolution research of wind turbine blade composites.

Indexed as

acoustic emissiondamage mechanismdamage modesglass/epoxy compositesmain sparwind turbine blade

Identifiers

PMID39684006
PMCPMC11644212

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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.