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Active Noise Control for Rocket Engine Noise

Using ANC technology to reduce the noise produced from the SpaceX Rocket Engine Test Facility towards the town of McGregor by 6dB.

Sergio Rodriguez

on 4 May 2014

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Transcript of Active Noise Control for Rocket Engine Noise

Simulating random white noise as an output in Mod2/AO0

Recording the random white noise through the error microphone, Mod1/AI0

Generating transfer coefficients of the system

Transfer coefficients are stored and will be implemented in the ANC Filtered-X LMS adaptive filter

Sound Pressure (Pa) measured from all 16 microphones with respect to time (ms)
Data will determine what frequencies the rocket engine produces and will help produce non-linear shock wave model of noise
Peak sound pressure was 1000 Pa = 153.98 dB

Active Noise Control for Rocket Engine Noise

Future Work
Rocket Engine produces sound pressure levels in excess of 153dB.
McGregor, TX is 3 miles NE of the test facility.
Goal: Reduce sound pressure by at least 6dB.
Use the ANC system to invert the incoming noise signal
Play the inverted signal through high intensity speakers
Single tone frequencies were reduced successfully.
ANC system generates good filter coefficients and models a control waveform that mirrors the rocket noise.

Long Term Goals
Implementation in enviromental settings, Bryan RC Airfield
Implementation of ANC System at SpaceX in McGregor, TX.
Short Term Goals
Programming MIMO system and evaluating results in lab
Dirk de Haan,
Jonathan Jolly,
Ryan Marietta &
Sergio A. Rodriguez
Undergraduate Seniors of Department of Electrical and Computer Engineering

Yong-Joe Kim, Ph.D.
Assistant Professor of Department of Mechanical Engineering
Director of Acoustics and Signal Processing Laboratory

Date: 03/27/2014
Task 1: On-site measurements & data analysis
Measurement setup at Merlin Test site.
Placement of the microphones was to understand noise propagation characteristics in the space.
16 total microphones were used for measurements, 4 high pressure microphones and 12 low pressure microphones.
The high-pressure microphones are appropriate for this high-level noise measurement. However, they are expensive and only four high-level microphones are available. Thus, they are place where the highest noise levels are measured.

Generating an MP3 file from the measured rocket noise data for testing
Experimental setup & Hardware
ANC software development using LabVIEW
LMS algorithm
Filtered-X LMS adaptive filter
Lab performance
Task 2: Development of SISO ANC software and in-lab performance evaluation
Phase I
Task 3: Development of MIMO ANC Software and In-lab Performance Evaluation
Enhance SISO ANC system
SISO ANC testing
Method behind the MIMO ANC software
Task 4: On-site implementation of the MIMO ANC system and performance evaluation
Continue to develop the MIMO system.
To test in the Bryan RC airfield to simulate environmental conditions
Evaluate the results from the field and lab results and make adjustments
Implementation at SpaceX
Phase II
Research Design and Method
Overview of PHASE I
TASK 3: Development of MIMO ANC Software and In-lab Performance Evaluation
Future Work: TASK 4: Environmental implementation and performance evaluation.
Questions or Comments
Research Design and Method
Phase 1:
Task 1: On-site noise measurements and data analysis.
Task 2: Development of SISO ANC software and in-lab performance evaluation.
Phase II:
Task 3: Development of MIMO ANC Software and In-lab Performance Evaluation
Task 4: Environmental implementation of the MIMO ANC System and performance evaluation
This graph shows the overall dB levels of the Rocket
The overall average level was 136 dB
Sound pressure measurements collected on-site at SpaceX-McGregor
Onsite Noise Measurement Procedure shown below
3 Microphones (b&K 2671)
1 Reference mic
1 Error mic
1 Performance evaluation mic
2 Speakers (BX5a deluxe)
Source & control speakers
NI-DAQ (NI-PXIe-1082)
CompactRIO (NI cRIO-9022)
Module NI 9234 (input)
Module NI 9269 (output)
Graphical spectrum of sound pressure with a range frequencies over time
Lower frequencies have greater sound pressure levels compared to higher frequencies
Lower frequencies travel further than higher frequencies over a distance due to absorption.

MIMO ANC setup
LMS Algorithm
ANC Filtered-X LMS Adaptive Filter
LMS Algorithm Coefficients
1 reference mic
2 Control Speakers
2 error mics
Graphical representation of transfer coefficients
The transfer coefficients converge about the x-axis
The ANC Filtered-X LMS Adaptive Filter is ready to be used.
Mod1/AI1 is the reference microphone: x(n)
Simulated rocket noise is recorded through this microphone
Mod1/AI0 is the same error microphone from the LMS algorithm: e(n)
Recording the diminished waveform from the source and anti-noise speakers
Mod2/AO0 is the anti-noise speaker: y(n)
Produces the inverted waveform to cancel the source noise
100Hz Single Tone Test
300Hz Single Tone Test
200Hz Single Tone Test
400Hz Single Tone Test
Average dB before: 135dB
Average dB After: 121dB
Average reduction: 14dB
Percentage drop: 10.37%
Rocket Noise Test
Enhancement of SISO ANC system and setup
Average dB before: 152.5dB
Average dB after: 130dB
Average reduction: 22.5dB
Percentage drop: 17.31%
Average dB before: 147.5dB
Average dB After: 136.5dB
Average reduction: 11dB
Percentage drop: 7.45%

ANC Real Time
LMS Algorithm Continued
Rotated control speaker to face the same direction as the rocket noise simulator.
The combination of the sound waves will sum to the diminished noise.
Integrate an external pulse generator to the system.
LMS Algorithm Equations
π‘₯(𝑛)=π‘œπ‘Ÿπ‘”π‘–π‘›π‘Žπ‘™ π‘€β„Žπ‘–π‘‘π‘’ π‘›π‘œπ‘–π‘ π‘’ π‘ π‘–π‘”π‘›π‘Žπ‘™


𝑑(𝑛)= White noise input from microphone

ΞΌ=convergence coefficient

𝑒(𝑛)=π‘’π‘Ÿπ‘Ÿπ‘œπ‘Ÿ π‘ π‘–π‘”π‘›π‘Žπ‘™ (recorded white noise)

h(n+1)= h(n)+ΞΌ* e(n)*d(n)
h(n+1) = updated transfer coefficient vector
Average dB before: 157dB
Average dB After: 144dB
Average reduction: 13dB
Percentage drop: 8.28%

Graphical representation of the control output y(n), and the reference input noise x(n).

The control output is to inversely match the delayed reference input noise as it is generated from the filtered-X LMS adaptive filter.

Since x(n) is the input into the system, y(n) is the output to generate delayed inverted wave through the control speaker.

The noise level plot is the error microphone.

The secondary path is the transfer function coefficients.

Experimental Conclusion
Rocket engine noise was measured at the SpaceX Merlin Testing Stand with 16 spatially-distributed microphones.

The maximum noise level was 154 dB at 34 meters away from the stand.
It is found that the noise components below 500 Hz contribute significantly to the overall noise level. Thus, these low frequency noise components will be aimed to be reduced with the proposed ANC system.

The ANC system has been built and evaluated in Dr. Kim’s Acoustics lab.

The current ANC system with one error microphone and one control speaker was proven to reduce the simulated rocket engine noise levels by 1-2 dB.
The ANC performance is expected to be further improved by having two error microphones and two control speakers (MIMO) and by focusing the control in the low frequency range below 500 Hz. The current target frequency band is below 2 kHz.

ANC Equations
Active Noise Control
Average dB before: 141.2dB
Average dB after: 140.6dB
Average reduction: 0.6dB
Percentage drop: 0.42%
Block Diagram of ANC System
Questions or Comments?
Pressure vs. Time
Data Collection Setup
Color Spectrum
Overall dB
Microphone Setup
Full transcript