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Record W2096011555 · doi:10.1109/iemdc.2009.5075337

Test rig for high speed electromechanical flywheels in Sub Saharan Africa

2009· article· en· W2096011555 on OpenAlexaff
Richard Okou, Azeem Khan, Paul Barendse, Pragasen Pillay

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicElectric Motor Design and Analysis
Canadian institutionsConcordia University
Fundersnot available
KeywordsFlywheelAutomotive engineeringVibrationFlywheel energy storageModal analysisEngineeringModalEnergy storageStructural engineeringMechanical engineeringMaterials scienceFinite element methodAcousticsPower (physics)

Abstract

fetched live from OpenAlex

This paper presents a test rig designed at the University of Cape Town to evaluate the performance of a high speed electromechanical flywheel for energy storage. The electromechanical flywheel is specifically designed to enhance rural electrification through improving the energy storage component in solar home systems. A safe, flexible and low vibration test rig has been designed. Modal analysis using an FE package ANSYS was used to validate the low vibrations at high speed rotation. Special attention was given to the alignment issues and this was done to avoid imbalance in the rig. The rig has the ability to test up to 40,000 rpm flywheel with maximum diameter of 0.55 m as is. With changes in the bearings, much faster flywheels can be tested. In addition, the rig was built to ensure testing under various operating environments. A thermal model was developed for the rig and simulated with analytical and FE packages. Sensors are installed in the containment to monitor performance of flywheel system.

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)0.001
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 score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.010
GPT teacher head0.201
Teacher spread0.192 · 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
Published2009
Admission routes1
Has abstractyes

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