Preliminary Design and Performance Analysis of Aircraft Propellers Using a 0-D Model
Bibliographic record
Abstract
Abstract This paper presents a comprehensive methodology for the preliminary design and performance analysis of aircraft propellers using a 0-D physics-based analytical model coupled with a parametric lift/drag polar. The model is calibrated using public domain data for 41 different propellers, showing prediction errors under 2% for power coefficient and 0.5 degrees for pitch angle, while maintaining physical representativeness across a wide range of Mach numbers and angles of attack. A detailed case study explores the influence of blade number, diameter, activity factor and airfoil type on propeller performance, highlighting the model’s capability to deliver rapid and precise results. Through this analysis, activity factor is shown to be reduced when number of blades or diameter increase, while for low advance ratios, the number of blades effect is negligible compared to that of diameter. Methods of polar scaling are proposed for cases of limited calibration data. These methods are highlighted with the presentation of an illustrative case in which an already tuned polar is scaled when limited performance data are available, and with general scaling guidelines for cases where no data are available. This research provides a reliable framework for propeller design, offering valuable insights and a robust tool for the preliminary phase of aircraft propeller development.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".