Electric Motor Chain Modeling Using Artificial Neural Networks and Semi-Empirical Methods
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it