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Record W7134144667

Low-order methodology for the design of propellers with serrated trailing edges

2025· other· en· W7134144667 on OpenAlexaboutno aff
Jorge Hernán Santamaría Osorio

Bibliographic record

VenueEspace École de technologie supérieure (École de technologie supérieure) · 2025
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsAirfoilTrailing edgeSerrationNoise (video)Leading edgeSawtooth wavePropellerRakeRotor (electric)Computational fluid dynamics
DOInot available

Abstract

fetched live from OpenAlex

The exponential increase in applications where UAVs or drones are used raises concerns about potential noise pollution in large cities. Trailing edge noise is a significant broadband noise source of hovering UAV propellers, which can be reduced by employing bio-inspired trailing edge serrations. The high computational cost of high-fidelity CFD simulations put them at the end of the design cycle rather than at the beginning. Therefore, propeller designers need a fast method for predicting noise reductions. Analytical models for straight and serrated edges need as essential input the single-point wall-pressure fluctuations spectrum. This can be modeled using low-cost Reynolds-Averaged Navier-Stokes simulations. A low-order methodology is proposed to estimate the potential noise reductions resulting from trailing edge serrations using RANS simulations for a representative drone propeller based on a NACA0012 airfoil with constant pitch and constant chord. Ayton’s theoretical model provides predictions for serrated trailing edge noise generated by a fully turbulent flow over an infinitesimally thin plane. The extension of Ayton’s model, proposed by Li and Lee, provides a heuristic three-dimensional model for a finite span applicable to rotor blades. This model reveals the potential benefits of using a square wave serration compared to the traditional sawtooth serration. This thesis addresses the limitations of Li and Lee’s model by deriving a new model for the square wave using Ayton’s model and Curle’s analogy. Measurements of a NACA0012 airfoil at low-Reynolds numbers, typical of small drones, were performed in an anechoic chamber at the Université de Sherbrooke, for straight, sawtooth, and square wave edges. Noise reductions of up to 5 dB are measured, with the square wave outperforming the sawtooth serration. Theoretical predictions are in reasonable agreement with the experimental results. Li and Lee’s model is then extended to rotating blades using Schlinker and Amiet’s model. The model is verified in the limit of zero serration amplitude finding good agreement at high frequencies and high observer angles. Single-blade passage RANS simulations of the NACA0012 propeller are performed, and aerodynamic validation is made with experimental data. The results highlight the importance of adequately refining the mesh around the propeller tip vortices and using transitional turbulence modeling. The wall-pressure fluctuations spectrum was modeled based on the RANS results, and the propeller far-field acoustics were calculated using the in-house code PyFanNoise. The acoustic predictions agree fairly well with experimental measurements, especially at high rotational speeds, where secondary flows are weaker and the onset of turbulence matches more favorably with the fully turbulent k- SST model used in the RANS. Li and Lee’s model is then used to study the sensitivity of noise reductions to different shapes. The square wave serration is shown to outperform the sawtooth and sinusoidal shapes for all frequencies and observer angles, particularly for small propeller blades typically used in drones. However, for larger chord blades typically used for ducted fans, combinations of sawtooth and sinusoidal serrations provide better noise reductions. The methodology is validated by considering the effects of serration installation and manufac turing. Several propellers were 3D printed and tested in an anechoic chamber, where far-field noise and aerodynamic performance were measured. The baseline configuration exhibits clear evidence of laminar boundary-layer instability noise. Cut-in and add-on serrations alleviate this noise mechanism. Similarly, to overcome the influence of the laminar-to-turbulent transition over the blade surface, some propellers also include additional surface roughness to trigger turbulence. Cut-in serrations experience additional vortex-shedding noise characterized by a Strouhal number based on the serration root thickness. The results show that serrations are a viable method for controlling trailing edge noise at low RPM, where broadband noise dominates over tonal noise, and that add-on serrations with a trip are in better agreement with the theoretical results, thus highlighting the importance of the manufacturing method during the design phase.

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How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.053
GPT teacher head0.304
Teacher spread0.251 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreMethods

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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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