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Record W7048651913

Manufacturing of a Fiber-Reinforced Thermoplastic Composite Micro-Generating Wind Turbine Blade

2023· dissertation· en· W7048651913 on OpenAlexafffundabout

Bibliographic record

VenueUWSpace (University of Waterloo) · 2023
Typedissertation
Languageen
FieldPhysics and Astronomy
TopicMagnetic confinement fusion research
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of CanadaOntario Centre of Innovation
KeywordsThermosetting polymerRenewable energyWind powerTurbine bladeThermoplasticTurbineEpoxyRenewable resourceEngineered wood
DOInot available

Abstract

fetched live from OpenAlex

In response to climate legislation attempting to curb global greenhouse emissions, renewable energy technologies have seen rapidly increasing global demand over the past two decades, with significant attention paid to the wind energy sector. Large, MW-scale wind turbines routinely exceed 100-meters in blade length, enabling reliable clean energy to be accessed by urban communities. However, such structures are often not feasible in remote communities that are not connected to the electrical grid. In Northern Canada, these communities are often reliant on diesel generators for reliable energy despite their inefficiency and emission of harmful pollutants. Thus, the development of renewable energy solutions that can be applied to smaller communities – such as small-scale, micro-generating wind turbine blades that produce up-to 100 KW of power – are a timely engineering problem. \n \nGenerally, high-performance blade structures take advantage of fiber-reinforced plastic (FRP) composite materials that allow for high strength-to-weight ratios and excellent fatigue performance. However, legacy turbine blade material systems suffer from poor end-of-life recyclability due to issues with recovering conventional thermosetting epoxy polymer resin systems that cannot be melted and reformed. Thus, the composites industry is rapidly transitioning to alternate plastic formulations that maximize performance while considering holistic product lifecycles. In-situ polymerizable thermoplastic resins represent one such alternative. These novel resin systems comprise monomers that polymerize in-situ during part production. The low flow viscosity of these resins enables their use with cost-effective processes such as vacuum assisted resin-transfer molding (VARTM). Unlike thermosetting polymers (such as epoxy) that undergo irreversible curing during processing, thermoplastic materials can be melted and reformed at sufficiently high temperatures, allowing for full reclamation of the composite material system at the conclusion of its service life. \n \nThe goal of this thesis was to manufacture a full-scale micro-generating wind turbine blade comprising glass-fiber reinforced in-situ polymerizable thermoplastic resin shells using a VARTM process in a cost-effective manner. Three main objectives were identified to complete this project. The first objective was to optimize a baseline VARTM process to reliably produce flat panels with optimized fiber volume fraction (𝑉𝑓) and void volume fraction (𝑉𝑐). Second, a suitable adhesive joining system for the composite was to be identified, with Mode I double cantilever beam and Mode II end notched flexural tests performed at room and low temperatures to simulate the expected ambient conditions in remote Northern Canadian communities, with a consideration for bond-line thickness effects. Finally, the findings from the first two tasks were to be synthesized and adapted to enable the production of full-scale, high-quality micro-generating wind turbine blades by joining two separately produced shells. \n \nA process study was performed to minimize void formation during flat panel manufacturing stemming from transverse flow, dual-scale flow, and race-tracking, while maintaining sufficient compaction during polymerization. Microscopic analysis and visual inspection were used to measure fiber volume fraction and observe void formations. The optimized high-quality panels exhibited an average fiber volume fraction of 46.28 (±1.2)%. The removal of a polymeric veil from the unidirectional non-crimp fabric architecture contributed to an increase in fiber volume fraction of 8.2%. The observed fiber volume fractions with the optimized VARTM process are within 2.5% of literature results for E-glass UD-NCF composite panels produced with SCRIMP processes. Adhesive test results showed that the methyl methacrylate (MMA) adhesive experienced ductile cohesive failure at room temperature (RT; 23°C), and brittle failure within the adhesive and substrate at low temperature (LT; -40°C) depending on the load condition. RT DCB tests showed an increase of 45% in fracture energy in the adhesive joint with increasing bond-line thickness, while LT test specimens exhibited lower loads and an oscillatory crack tip propagation pattern implying accumulated residual stresses in the adhesive layer due to thermal expansion incompatibility with the composite substrate. ENF specimens showed a reduced load capability at LT conditions with a higher elastic stiffness and failure within the substrate. \n \nFabrication of a full-scale proof-of-concept blade showed that accommodating fabric drapability was critical for a high-quality infusion, and that the material system can be successfully adapted for production pending revisions to the adhesive surface geometries. While the trailing edge of the blade allowed for a robust adhesive joint, the blade shell concavities along the leading edge meant that the shells mated along the thickness of the composite, creating a weak bond with significant cracks and porosities in the adhesive layer. Future trials should incorporate a representative fabric stacking sequence and modified joint geometries to compare the static deflection of the updated thermoplastic material system with legacy carbon fibre-reinforced epoxy composite blades.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.009
GPT teacher head0.208
Teacher spread0.199 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2023
Admission routes3
Has abstractyes

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