Aeroacoustics: Introduction, Measurement, Computation and Control Xun Huang Mechanics and Aerospace Engineering Peking University Lecture for China Aerodynamics Research Institute of Aeronautics, June 2010 [email protected] (Peking University) Aeroacoustics June 2010 1 / 46
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Aeroacoustics: Introduction, Measurement,Computation and Control
Xun Huang
Mechanics and Aerospace EngineeringPeking University
Lecture for China Aerodynamics Research Institute of Aeronautics,June 2010
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Personal Track of Record
95-99 NWPU 99-02 THU 02-03 GE GRC 03-09 UoS 09-Present PKUBackground: Aeroacoustics, Signal Processing, and Control.
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Some ANTC members
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My Research Group at PKU
(a) Long Bai (b) Qingkai Wei (c) Jianchao Ji
(d) Chi Xu (e) Igor Vinogradov
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Outline for the Lecture of Aeroacoustics
1 Motivation
2 Beamforming
3 Observer
4 Validations
5 Summary
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Motivation
The Research Objectives?
(h) Cruise noise? (i) Cabin noise reduction?
Carbin noise
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Motivation
Magic of Human Technology
”I had the privilege to fly once in an Air France Concord. The mostnoteworthy thing was that it was not really any different from any otheraircraft. The cabin noise was quite normal.”Where the noise mainly from?
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Motivation
Landing and Take-off Noise Problems
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Motivation
CARDC 2020 Objectives
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Motivation
Innovative Designs Emerging
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Motivation
Strategy Problem for Our Country
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Motivation
Aircraft Noise at Various Flight Stages
Take off noise Landing noise
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Motivation
Aircraft Noise Certification
To solve the problem
Set overall noise reduction objective;
Identify noise sources;
Modify/improve design accordingly;
Check aerodynamics and noise performance.
Applicable methodologies
Theory (aeroacoustics), experiments (array) and computation (CAA).
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Motivation
Certification
But provide little information for aeroacoustic design.
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Motivation
Noise Measurements
Flow-induced noise study hence focuses on noise measurements.
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Beamforming
Array Measurement Fundamentals
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Beamforming
Array Measurement Directivity
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Beamforming
Classical Beamforming Formulations
Noise source
Given x(t) ∈ R1 or X (jω) ∈ C
1
Array outputs
Time domain: y(t) = 14πr x(t − τ), τ = r
C .
Frequency domain: Y(jω) = 14πr X (jω)e−jωτ = G(r, jω)X (jω).
Classical beamforming
X (jω) = (G∗G)−1G∗Y(jω), following Moore-Penrose pseudoinverse.
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Beamforming
Problems in Classical Beamforming
Background noise can at least be partially resolved by algorithmdevelopment.
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Open problem: cases with coherent sources, i.e. < XBX ∗S > 6= 0.
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Beamforming
Problems in Classical Beamforming (Cont’)
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Beamforming
Problems in Classical Beamforming (Cont’)
−0.4
−0.2
0
0.2
0.4
−0.4 −0.3 −0.2 −0.1 0 0.1 0.2 0.3 0.4 0.5 0.6
−0.5
0
0.5
1
1.5
Background noise
Mic array
Signal of interest
Z(m)
X(m)
Y(m)
Beam pattern
Plane of interest
Low resolution and solutionsAdvanced beamforming method: CLEAN, DAMAS, LORI,adaptive beamforming, robust beamforming, etc.
Increase array diameter (better resolution) with more microphones(less spatial aliasing), but its beamforming cost increases as well.
Open problem: real-time computation burden.
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Beamforming
Problems in Classical Beamforming (Cont’)
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Beamforming
Problems in Classical Beamforming (Cont’)
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Beamforming
Anechoic Chamber Experiments
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Beamforming
Wind Tunnel Experiments
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Beamforming
Wind Tunnel Experiments
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Observer
Observer IntroductionOpen problems remain : real-time computation burden.Can we develop a real-time algorithm that can identify coherent noisesources? Borrow idea from classical control theory.
Signal model
x(t) = Ax(t) + Bu(t), state equation, a new equation.y(t) = Gx(t), measurement equation.
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Observer
Observer IntroductionOpen problems remain : real-time computation burden.Can we develop a real-time algorithm that can identify coherent noisesources? Borrow idea from classical control theory.
Signal model
x(t) = Ax(t) + Bu(t), state equation, a new equation.y(t) = Gx(t), measurement equation.
Observer˙x(t) = Ax(t) + Bu(t) + L(y − y).y(t) = Gx(t), where x is the estimation of x .
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Observer
Observer IntroductionOpen problems remain : real-time computation burden.Can we develop a real-time algorithm that can identify coherent noisesources? Borrow idea from classical control theory.
Signal model
x(t) = Ax(t) + Bu(t), state equation, a new equation.y(t) = Gx(t), measurement equation.
Observer˙x(t) = Ax(t) + Bu(t) + L(y − y).y(t) = Gx(t), where x is the estimation of x .
Estimation error
e = x − ˙x = (A− LG)e, e approaches 0 as long as the eigenvalue(s) of(A − LG) is(are) less than 0, which is assured by accordingly setting L.
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Observer
Observer in Frequency Domain
Time domain to frequency domain
y(t) =∑∞
m=−∞ Ymejmt , x(t) =∑∞
m=−∞ Xmejmt .
Signal model in frequency domain
Xm = AXm, Ym = GXm = 14πr Xme−jmτ .
Observer in frequency domain˙X = AX + L(Y − Y), Y = GX , whose discrete from is recursive oversampling blocks, in other words, the algorithm holds real-timecapability.
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Observer
Observer In Wind Tunnel
(
YB
YBS
)
=
(
GGejφ G
)(
XB
XS
)
, (1)
where φ is the phase shift due to the time difference between the twomeasurements of YB and YBS .
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Observer
Observer-Based Method
The linear state model of the sound propagation is:(
XB|k+1XS|k+1
)
=
(
A 00 A
)(
XB|kXS|k
)
, (2)
(
YB|kYBS |k
)
=
(
G 0Geiφ G
)(
XB|kXS|k
)
. (3)
The corresponding state observer are:(
XB|k+1
XS |k+1
)
=
(
A 00 A
)
(
XB|kXS|k
)
+ L
[
(
YB |kYBS |k
)
−
(
YB|kYBS |k
)]
,
(4)(
YB|kYBS |k
)
=
(
G 0Geiφ G
)
(
XB|kXS|k
)
. (5)
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Observer
Remarks of Observer Algorithm
A =?Given a stationary and ergodic signal process (the assumptionadopted in beamforming), A is an identity matrix for scanned point(s).
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Validations
Numerical ValidationTwo monopoles of 3 kHz are closely placed. Both are coherent with120 deg difference in φ. The bottom right one is regarded as abackground noise. An array consists of 56 microphones. Typicalbeamforming and observer-based method are tested to remove thecoherent background noise below.
(o) Classical beamforming, aver-aged over 100 blocks.
x-axis (m)
y-ax
is(m
)0.4 0.2 0 0.2 0.4
0.4
0.2
0
0.2
0.4
SPL12345678910
(p) Observer result of the 500thblock.
Animation
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Validations
Computational Costs
Beamforming cost
AB = YBY∗B, ABS = YBSY∗
BS , < AS >≈< ABS > − < AB >,|X (jω)| =
√
(G∗G)−1G∗ < AS > G(G∗G)−1.Cost O(N × N × b), N is array sensor number, and b sample blocks.
Observer cost
(
XB|k+1
XS|k+1
)
=
(
A 00 A
)
(
XB|kXS|k
)
+ L
[
(
YB|kYBS |k
)
−
(
YB |kYBS |k
)]
,
φ|k+1 = φ|k + mH∗(Y|k − Y|k ).
Cost O(N × N) for each sample block.
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Validations
Convergence Rate
The convergence of the estimation error (φ − φ) of each sampling datablock, where φ is approximated for each scanned points.
Blocks
Est
imat
ion
erro
r(de
g)
0 500 1000 1500 2000 2500 30000
5
10
15
20
25
30
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Validations
Convergence Rate (Cont’)Approximate φ for a representative scanned point only. Adjust φ in theoriginal data (black line), the estimation φ can approach φ quickly in10 blocks, which last less than 1 s. However, an error of 5 deg betweenφ and φ appears.
Blocks
φ
0 100 200 300 400 500
50
100
150
200
Phase shiftPhase estimation
(deg
)
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Validations
Experimental Setup of PKUArray
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Validations
Experimental Setup (Cont’)
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