Experimental Characterization of Low-Speed Passive Discharge Losses of a Flywheel Energy Storage System
Bibliographic record
Abstract
Flywheel energy storage has a wide range of applications in energy grids and transportation. The adoption of high-performance components has made this technology a viable alternative for substituting or complementing other storage devices. Flywheel energy storage systems are subject to passive discharge attributed primarily to electrical machine losses, bearing rolling friction, and aerodynamic drag of the flywheel rotor. In the present study, measurements are presented for complete discharge experiments using a flywheel system featuring a vacuum enclosure. Best-fit equations were applied to the test data and compared to analytical models. Analysis of the best-fit equations indicates that they may serve as empirical models for approximating passive discharge under given conditions. Bearing losses, which varied linearly with velocity but were otherwise unaffected throughout the experiments, were larger than aerodynamic drag at low air pressures and low velocities. Aerodynamic drag became significant as velocity exceeded approximately 3400 rpm. The electrical machine was found to be the most significant source of passive discharge at all velocities and pressures. Based on these findings, it is recommended to maintain a low-pressure environment in the flywheel enclosure and to decouple the electrical machine from the rotor whenever possible to eliminate associated losses.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".