Bibliographic record
Abstract
The value of “ultracapacitors” (also referred to as “supercapacitors” or “electric double layer capacitors” in some literature) as an augmentation device when placed in parallel with “electrochemical” energy storage (i.e. battery) is presented in this paper. Since ultracapacitors possess unique attributes due to their higher value of energy storage density (or Joules/WattHrs per mass) compared to conventional capacitors while maintaining the peak power providing capability (to some degree) typical of conventional capacitors they may provide a near term solution in applications demanding longer battery operating life when placed in parallel. Such demands may be pronounced by the onset of More-Electric-Aircraft peak loads and “cold-crank” Auxiliary Power Unit (APU) electric-starting in demanding cold temperature environments. The potential benefits of an ultracapacitor in parallel with a battery will be illustrated through a very simple lab demonstration as well as a more complex system consisting of a large motor inrush load (cold-cranking condition) through the use of a computer simulation tool (Simulink). In addition to the ultracapacitor discussions and demonstration, other forms of advanced energy storage will be reviewed for their potential near or long term application in meeting the evolving needs of More Electric Aircraft as presented at the 2011 International Double Layer Capacitor and Hybrid Energy Storage Seminar in Deerfield Beach Florida (Reference 3).
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 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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".