Fast Multi-Objective Aeroacoustic Optimization of Propeller Blades
Bibliographic record
Abstract
In this paper, the authors present a novel framework where the OptiSound® software is used in conjunction with low-order aerodynamic design tools to allow for fast iterations during preliminary design while evaluating both the aerodynamic performance as well as the acoustic emissions of a propeller system. Using this environment, the authors generate a Pareto frontier in a multidisciplinary response pertaining acoustic and aerodynamic performance metrics. While it was shown that an analysis of the raw data from the Pareto frontier can enable the identification of generic trends, recourse to visualization methods such as Self-Organizing Maps and Principal Component Analysis are however more helpful in determining parameter/objective correlations for multi-parametric/multi-objective optimizations. A CFD simulation to verify the propeller performance predicted by the low-level optimization framework for the lowest tonal noise configuration from the Pareto front was also carried out. It allowed to validate that the tonal steady loading rotor noise and the trailing edge self-noise mechanisms were well represented by the low-order evaluation method. The evaluation of turbulence ingestion noise at the leading edge is however shown to be of critical importance for propeller configurations for the obtention of an accurate evaluation of the overall noise emitted.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".