Modelling the Powertrain and Aerodynamic Behaviour of a Small Electric Aircraft
Bibliographic record
Abstract
The environmental impact of fossil fuels has driven major industries towards electrification for a sustainable future. Aviation, though contributing only 3% of global GHGs, is expected to triple emissions by 2050 [1]. Electric aircraft rely on battery packs, which require enhanced performance, reliability, and lifespan to ensure safe operation. Hence, the development of comprehensive simulation models of aircrafts is essential for studying and analyzing the battery behavior under various flight conditions. This poster presents a simulation model of the powertrain and aerodynamic behaviour of a small electric aircraft. A mathematical model representing the aerodynamic behavior is developed, which determines the thrust generated by the propeller, and computes the load torque and rotational speed (RPM) for the powertrain model, based on a real-life flight dataset [2]. The motor load torque and RPM are provided as reference inputs to the powertrain control system. The electric powertrain is modeled in MATLAB Simulink and includes two battery packs, an inverter, a permanent magnet synchronous motor (PMSM), and a motor control unit. The PMSM is controlled using field-oriented control (FOC), with the RPM as the reference. Proportional-integral (PI) controllers are carefully tuned to ensure accurate tracking of speed and torque profiles.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".