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Record W7055851306

Development and numerical modeling of composite structures

2016· dissertation· en· W7055851306 on OpenAlexaboutno aff

Bibliographic record

VenueMspace (University of Manitoba) · 2016
Typedissertation
Languageen
FieldEngineering
TopicSuperconducting Materials and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsFibre-reinforced plasticTurbineTowerComposite numberGlass fiberFiberNumerical modeling
DOInot available

Abstract

fetched live from OpenAlex

This thesis deals with the development and numerical modeling of Fiber Reinforced Polymer (FRP) wind turbine towers and luminaires. More specifically, this project is designed to capitalize on the technologies developed at the University of Manitoba to design FRP composite structures for use in remote communities where the costs of transportation and erection make the use of steel towers prohibitive. The work presented includes the analysis of a 50 m tall 750 kW wind turbine tower according to International Electrotechnical Commission (IEC) and Canadian Standard Association (CSA) standards using Glass Fiber Reinforced Polymer (GFRP), Carbon Fiber Reinforced Polymer (CFRP) and conventional steel. Standard luminaires, 6 m and 12 m, were also designed according to American Association of State Highway and Transportation Officials (AASHTO) standards for highway luminaires. The results showed that FRP can be effectively used as an alternative material for wind turbine towers and luminaires. Fiber Reinforced Polymer (FRP) composite wind turbine towers and luminaires studied in this project are lighter than similar structures fabricated using steel. Furthermore, these structures also meet the structural performance requirements set by AASHTO, IEC and CSA standards.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.402
Threshold uncertainty score0.454

Codex and Gemma teacher scores by category

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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.016
GPT teacher head0.198
Teacher spread0.182 · 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 teacher head, 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

Citations1
Published2016
Admission routes1
Has abstractyes

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