Investigation of regenerative braking on the energy consumption of an electric taxiing system for a single aisle midsize aircraft
Bibliographic record
Abstract
This paper investigates the effect of regenerative braking on the overall energy consumption of an electric taxiing system which is integrated in the main landing gear of a single aisle midsize aircraft. In the evaluated system, electric motors are responsible for the aircrafts propulsion while taxiing on ground. First, the system is modeled and analyzed in order to design an electrified traction system for the taxiing of the aircraft. The followed method to design the electric taxiing powertrain system is presented. Based on the aircraft's mass and the interaction between the wheels and the tarmac, a simulation model is developed for the electric taxiing system. This model is simulated over a real taxiing takeoff drive cycle to evaluate and characterize the energy and power requirements of the traction system. Moreover, the power and energy rating of the traction system (electric motors, power electronics and battery capacity) are determined by the consideration of the specific taxing scenario and evaluated drive-cycle. The assumptions and model used to size the powertrain especially the electric motors are confirmed by the simulation results. Furthermore, the results of the considered drive cycle show that regenerative braking can potentially enable a reduction in the overall tractive energy up to more than 8%.
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 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".