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Record W4399852913 · doi:10.3390/en17123042

Optimization of Flywheel Rotor Energy and Stability Using Finite Element Modelling

2024· article· en· W4399852913 on OpenAlexaboutno aff
Daniel Coppedé, Fábio da Silva Bortoli, João Manoel Losada Moreira, Nadja S. Magalhães, Carlos Frajuca

Bibliographic record

VenueEnergies · 2024
Typearticle
Languageen
FieldEngineering
TopicMagnetic Bearings and Levitation Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsFlywheelFinite element methodRotor (electric)Stability (learning theory)Structural engineeringMechanical engineeringEngineeringComputer scienceControl theory (sociology)

Abstract

fetched live from OpenAlex

An investigation on a flywheel is presented based on finite element modelling simulations for different geometries. The goal was to optimise the energy density (rotational energy-to-mass ratio) and, at the same time, the rotational energy of a flywheel rotor. The stress behaviour of flywheel rotors under the rotational speed at the maximum stress achievable by the flywheel was analysed. Under this condition, the energy density was obtained for the different geometries, as well as the rotational energy. The best energy density performance due to geometry was achieved with a flywheel rotor presenting a new Gaussian section, which is different from the known Laval disk shape. The best results using a single disk involved a rotational speed of nearly 279,000 rpm and a rotational energy density around 1584 kJ/kg (440 Wh/kg). These values still yielded low total energy; to increase its value, two or three rotors were added to the flywheel, which were analysed in regard to stability. In particular, the triple rotor energy density was ≈ 1550 kJ/kg (431 Wh/kg). As some instability was found in these rotors, a solution using reinforcement was developed to avoid such instabilities. The energy density of such a reinforced double rotor neared 1451 kJ/kg (403 Wh/kg), and the system achieved higher total energy. The material assumed for the devices was carbon fibre Hexcel UHM 12,000, a material kept constant throughout the simulations to allow comparison among the different geometries.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.718
Threshold uncertainty score0.265

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 head0.017
GPT teacher head0.206
Teacher spread0.189 · 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 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

Citations5
Published2024
Admission routes1
Has abstractyes

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