PIV Measurements and Computational Studyof Aeroacoustics of a Drone Propeller
Jianwei Sun1, Ryo Aoki1, Koichi Yonezawa2, 3, and Hao Liu1, 3(*)
1 Graduate School of Engineering, Chiba University, Japan
2 Central Research Institute of Electrical Power Industry, Japan
3 Center for Aerial Intelligent Vehicles, Chiba University, Japan

Outline• Background and Motivation• Objective and Methodology• Comparison• Flow field• Acoustic performance• Aerodynamic performance• Conclusion

2

Background and Motivation• Drone propellers rely on aerodynamic and aeroacoustic
performance for optimal flight.
• Predicting these performance characteristics accurately is asignificant challenge.
• Improving our ability to predict their performance will allow us
to design more efficient and effective drone propellers.

3Background and Motivation
Experiment

CFD simulation

Quantitative description of flow phenome Quantitative prediction of flow phenomen
na using measurementa using CFD software
For a quantity at a time

For all desired quantities

High frequency resolution

High time resolution

For a limited range of problems and oper Virtual problems
ating conditions
uncertainty

Not 100 % reliable

Expensive and slow

Cheap(er) and fast(er)4Objective and Methodology
Free-run and phase-locked PIV:

5

Objective and Methodology
Free-run and phase-locked PIV:

Particles on
Smoke

High-speed
cameraphotography

Illuminate oflaser

6

Imagesprocessing

Objective and Methodology
Free-run and phase-locked PIV:
RPM sensor

Laser

Cameraexposure

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Objective and Methodology
Free-run and phase-locked PIV :Phase-locked PIVFree-fun PIV

Phase-averaged

Time-averagedேே଴

଴௜ୀଵ

௜ୀଵ•

Phase-averaged algorithm: Enhances the periodic component of thesignal with a known period in RPS, and filters out the random component.

•Time-averaged algorithm: Filters both the periodic andcomponents of the signal, but retains the trend component.
8randomObjective and Methodology
Hover-stand experiment in anechoic chamber:
Key parameters:
Rotational speed

5400rpm

Diameter

200mm

Pitch angel

14deg

Fs(Hz) Sampling
rate

50,000Hz/20,000Hz

Distance

800mm

Polar angle

75deg (mic1) & 50deg (mic2)& 25deg(mic3) & 5deg (mic4)& -5deg (mic5) & -25deg (mic6) & -50deg (mic7) & -75deg(mic8)

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Objective and Methodology
rFlow3D & rNoise:

Tanabe, et. al.(2012)10Objective and Methodology
rFlow3D & rNoise:

Thickness noise

Far field loading noise

Near field loading noise
Farassat, et. al.(2007)11

Objective and Methodology

Rotational speed5400rpm

Diameter

200mm

Pitch angel

14deg

background195*195*147(X*Y*Z)

Blade grid(each)

121*143*61

M_TURB_IG

0

Output number(Msd)1024

Fs(Hz)

92,16012Comparison – FLOW field (phase-average)
CFDPIV•Our phase-averaged results present the velocity fluctuation in theslipstream of the drone propeller, which can inform future design
improvements.
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Comparison – FLOW field (time-average)
CFDPIV•

Time-averaged result shows reasonable agreement in the slipstream
between PIV and CFD methods, providing confidence in the accuracy ofour methods and results.
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H = 0.2r

Velocity (m/s)

Velocity (m/s)

Comparison – FLOW field (time-average)•H = 0.4r

H = 0.6r

H = 0.8r

H=r

Comparison of velocity distribution at different planes parallel to therotational plane between CFD and PIV shows good agreement, with slight
discrepancies at H = 0.2r and 0.4r.
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rFlow3D (m/s)

Comparison – FLOW field (time-average)y = 1.0476*x
R2 = 0.9539

PIV (m/s)•PIV measurement

The high R-squared value obtained between the time-averaged results of
PIV and CFD methods indicates a good agreement between the twomethods in terms of the velocity values.
16Comparison – acoustic
Overall SPL SIM. vs. EXP.

simulation

experiment< 3.2dB•Our comparison of predicted and experimental OASPL results shows
good agreement, with a difference of less than 3.2 dB
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Comparison – acoustic
Overall SPL SIM. vs. EXP.
simulation
experimentexperiment avg.

64.8dB (exp.) vs 66.2 dB (sim.)•A similar trend between the predicted and experimental
results in the time domain, especially at blade passing
frequency (BPF), although the simulation result has morehigh-frequency noise.

18Comparison – acoustic
Overall SPL SIM. vs. EXP.

simulation

experimentexperiment
simulation•

Our frequency analysis confirms that the CFD model predicts more highfrequency noise than the experimental result.
19Comparison – acoustic
Polar angle of Mic (deg)Propeller + motor noise (EXP)

Frequency (Hz)•The noise SPL at the BPF of the propeller reveals that the noiselevel is higher at lower polar angles, while the high-frequency noise
is higher at higher polar angles.
20Comparison – acoustic(sim. 2K-10KHz)(sim.)

Induced flow

Induced flow•

The CFD simulations predict the similar noise emission patternwith the experimental result.
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Comparison – acoustic•

Induced flow

Induced flow

Induced flow

Induced flow

The rFlow3D/rNoise simulations predict the similar noise emission
pattern with the theoretical result.

Made, et. al.(1970)22

Comparison – acoustic
simulation
experiment

1st BPF SPLOASPLband (2K-10K)

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Comparison – acousticMonopole•

DipoleAcoustic Radiator Models: Monopole model and Dipole model.

https://en.wikibooks.org/wiki/Aeroacoust
ics/Acoustic_Sources24Comparison – acoustic

Induced flow

Induced flow

Thickness noise-Monopole•

Loading noise (near)-Dipole

The rFlow3D/rNoise simulations predict the sound source emission
pattern in time scale.
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Comparison – acoustic

Induced flow

Thickness noise-Monopole•

Induced flow

Total noise (PTF + PNL+PFL)

The rFlow3D/rNoise simulations result explain that the tonal noise
is enhanced at lower polar angle, broadband noise is enhanced at
higher polar angle.
26Comparison – Aerodynamic performance< 6.5%< 8.0%• The evaluation of aerodynamic forces using rFlow3D shows anerror rate of less than 8%27

Conclusion• The results indicate that rFlow3D produces flow field velocity data that is
consistent with PIV measurement, as verified by goodness-of-fit analysis.
• However, it should be noted that rFlow3D/rNoise may introduce anoverestimation of noise levels in positions where the polar angle is greaterthan 60 degrees due to the influence of higher frequency components.
Nonetheless, the patterns of noise emission observed align with theoreticalpredictions and experimental evidence.
• Furthermore, the evaluation of aerodynamic forces using rFlow3D shows anerror rate of less than 8%, which is acceptable. These findings suggest thatrFlow3D is a reliable tool for analyzing aerodynamic performance.

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