Evidence map›Paper›PMID 38998411›Full record

ArticleMaterials (Basel, Switzerland)2024

Effects of Graphene Reinforcement on Static Bending, Free Vibration, and Torsion of Wind Turbine Blades.

Hyeong Jin Kim, Jin-Rae Cho

Abstract read
In one paragraph

Article in Materials (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. 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

2 authors.

Hyeong Jin KimDepartment of Mechanical Engineering, University College London, London WC1E 7JE, UK.ORCID 0000-0001-5344-3209
Jin-Rae ChoDepartment of Naval Architecture and Ocean Engineering, Hongik University, Sejong 30016, Republic of Korea.ORCID 0000-0002-5367-4012

Funding

National Research Foundation of Korea RS-2023-00240618
6 · The paper itself

Abstract

Renewable energy markets, particularly wind energy, have experienced remarkable growth, predominantly driven by the urgent need for decarbonization in the face of accelerating global warming. As the wind energy sector expands and turbines increase in size, there is a growing demand for advanced composite materials that offer both high strength and low density. Among these materials, graphene stands out for its excellent mechanical properties and low density. Incorporating graphene reinforcement into wind turbine blades has the potential to enhance generation efficiency and reduce the construction costs of foundation structures. As a pilot study of graphene reinforcement on wind turbine blades, this study aims to investigate the variations of mechanical characteristics and weights between traditional fiberglass-based blades and those reinforced with graphene platelets (GPLs). A finite element model of the SNL 61.5 m horizontal wind turbine blade is used and validated by comparing the analysis results with those presented in the existing literature. Case studies are conducted to explore the effects of graphene reinforcement on wind turbine blades in terms of mechanical characteristics, such as free vibration, bending, and torsional deformation. Furthermore, the masses and fabrication costs are compared among fiberglass, CNTRC, and GPLRC-based wind turbine blades. Finally, the results obtained from this study demonstrate the effectiveness of graphene reinforcement on wind turbine blades in terms of both their mechanical performance and weight reduction.

Indexed as

finite element structural analysisgraphene platelet-reinforced composites (GPLRC)graphene reinforcementmechanical characteristicswind turbine blades

Identifiers

PMID38998411
PMCPMC11243585

What OpenQuestion holds

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Registered trials

None linked

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.