Electric Motor Chain Modeling Using Artificial Neural Networks and Semi-Empirical Methods
Bibliographic record
Abstract
The methodology presented in this paper is dedicated to a user who wants to design a power chain of an electric unmanned aerial vehicle. The power chain includes a brushless direct current (BLDC) motor, a propeller, and a battery. Three models are presented to predict the thrust, electrical consumption, mechanical torque, and rotational speed provided by each component of an electrical power chain. Only public data were used to design these models. The research was developed in order to be used by all people who want to buy electrical power chain components and therefore to model their properties. Neural networks set with MATLAB parameters were used to design BLDC motor output (rotational speed and mechanical torque) models. Empirical methods such as blade element theory (BET) and disk area were used to model propeller thrust. A battery discharge model was designed based on public datasheets. Very good prediction results were obtained for the rotational speed (0.20% of error) and torque (0.45% of error). Small thrust modeling errors of 1.06% were found with the BET method. Models presented in this research could be designed using a wide type of data, such as public data (provided on manufacturer websites) or real test bench measurements.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".