Rapid propeller Performance estimation for eVTOL-Class Aircraft Using a Surface-Vorticity Flow solver
Bibliographic record
Abstract
eVTOLs use propellers in different orientations and configurations, and an ‘optimal’ configuration has yet to be defined. At the conceptual phase for new designs, it is necessary to obtain estimates of the performance rapidly. From a computational modelling and simulation perspective, this involves mesh generation, flow simulation and post-processing. The most challenging geometry for simulations is the propeller. The objective of this work is to evaluate a medium-fidelity flow solver for numerical predictions of the propeller performance for various operating configurations on a high-end laptop with engineering accuracy. The obtained solutions were found to be adequate while requiring less computational time than if high-order methods were used. This confirms that medium-fidelity solvers can be used to perform efficiently, with reasonable accuracy, parametric studies in the conceptual design phase for eVTOL configurations.
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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 | 0.000 |
| 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".