Aerodynamic Modelling of a Small Electric Aircraft
Bibliographic record
Abstract
Aviation industry is witnessing a major shift towards electrification, with a lot of research and development efforts focused on improving battery performance. Developing comprehensive models of airplanes can play an essential role in facilitating the performance evaluation of electric plane battery packs under various flight scenarios and investigating approaches to improve their utilization towards maintaining a longer and healthier life. In this paper, a mathematical model representing the aerodynamics of a small electric plane is developed. The objective is to determine the required thrust based on an actual flight dataset and determine the profiles of the required torque exerted on the shaft of the electric motor and rotational speed of the motor. These profiles can then be used as references for the load torque and motor speed control loops in the model of aircraft electric powertrain, allowing studies on the performance of the battery pack and research on how to improve battery performance. The propeller, as the component responsible for generating thrust is analyzed and its coefficients are obtained. Thrust is calculated for various flight phases and conditions based on the dynamic equations of motion considering pitch, roll and yaw orientations. The flight data of Pipistrel Velis Electro, which is a small electric aircraft, is used for validation of the model through comparison of the motor speed estimated by the model to the actual motor speed provided by the dataset.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".