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Record W23145988 · doi:10.1111/jnc.12091

Analysis of translating hydrofoil power generation systems (hydrokites)

2012· article· en· W23145988 on OpenAlexfundno aff
Kelsey McConnaghy

Bibliographic record

VenuePhDT · 2012
Typearticle
Languageen
FieldEngineering
TopicAerospace Engineering and Energy Systems
Canadian institutionsnot available
FundersCanadian Institutes of Health ResearchFundação de Amparo à Pesquisa do Estado de São PauloLudwig Institute for Cancer ResearchAlzheimer's Association
KeywordsPower (physics)Computer sciencePhysics

Abstract

fetched live from OpenAlex

The hydrokite is a novel hydro-power system that is based on emerging kite wind-energy systems which are currently being designed for use at high altitudes. The hydrokite system is comprised of a hydrofoil and a support system, and is designed to capture kinetic energy from the flow of a river while reducing negative impacts on the river ecology by minimally interfering with the rivers natural flow (i.e. no dams or river diversions are needed). This work presents some initial results which demonstrate the power performance capabilities of the hydrokite. Two different steady-state models for this system were studied to determine the effects of model parameters on power generation. A dynamic model was also developed and preliminary results are presented. These simplified initial models provide an upper bound for the power performance of an actual system as well as providing an understanding of the effects that parameter changes have on the system performance. This initial work shows that such a system could be a feasible, low impact method for generating renewable energy from low-head hydro sources.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.008

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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.013
GPT teacher head0.199
Teacher spread0.185 · 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 designSimulation or modeling
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

Citations2
Published2012
Admission routes1
Has abstractyes

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