Development and optimization of a hybrid passive/active liner for flow duct applications

1

Development and optimization

Development and optimization

of a hybrid passive/active liner

of a hybrid passive/active liner

for flow duct applications

for flow duct applications

Design of an acoustic liner effective throughout the entire frequency range inherent in aeronautic applications, that is the fan noise propagating in the engine inlet: BPF and first harmonics or Buzz Saw Noise

State of art

• Passive treatments: e.g. SDOF, 2DOF

• Purely active absorbers: Acoustic liner

INTRODUCTION

• Middle and high frequencies

• Narrowband attenuation (resonance)

• Low frequency components attenuation p = 0

3 Hybrid active/passive technology combining passive properties of absorbent materials and active control

• To realize a control of the wall impedance in such a way to ensure an optimal noise reduction throughout a large frequency domain

THE HYBRID CELL CONCEPT

• Behaves as a classical passive absorber, mainly depending on d • Broadband equivalent of a

λ

• Versus passive solutions: increase frequency bandwidth to low frequencies • Versus purely active solutions: active system separated from the flow

Advantages Resistive layer Actuator Low frequencies Active field High frequencies Passive field Rigid wall p = 0 v = 0 d

Instrumentation ducts Acoustic primary source

Anechoic outlet

Test region absorbent treatment Silent flow generation system Pressure measurements 4 1 2 3 0.066 m 3.2 m

THE MATISSE EXPERIMENTAL TEST BENCH

Simple geometry: hybrid cell optimization process applied to Matisse set-up

• Plane wave analysis domain: 700 - 2500 Hz • Flow velocities up to 50 m/s

5 1. Determination of the optimal impedance

2. PASSIVE PART OPTIMIZATION † _{3. ACTIVE PART OPTIMIZATION} ‡

• Significant noise reduction • Achievable hybrid absorber Selection of the most suited porous layer according to the compromise:

Geometry of the cell

Actuator characteristics and control microphone selection Controller design

Surface impedance

measurements of different

porous configurations Pressure cancellation

4. Experimental validation on the Matisse facility (flow duct under grazing acoustic incidence): performance

assessment THEORETICAL STUDY NORMAL INCIDENCE MEASUREMENTS †_{Sellen et al., 9th AIAA/CEAS}

Aeroacoustic conference, Hilton Head, 2003,

AIAA-2003-3186

‡_{Hilbrunner et al., 9th} AIAA/CEAS Aeroacoustic

conference, Hilton Head, 2003, AIAA-2003-3187

DESIGN AND OPTIMIZATION PROCESS

‡_{Mazeaud et al., 10th} AIAA/CEAS Aeroacoustic

conference, Manchester, 2004, AIAA-2004-2852

Results

1. DETERMINATION OF THE OPTIMAL IMPEDANCE

Optimal impedance for different flow velocities

• Frequency dependence of real and imaginary parts • Negative decreasing reactance

7

1. DETERMINATION OF THE OPTIMAL IMPEDANCE

Results

Sensibility study

800 Hz 2500 Hz 4000 Hz

Insertion loss parameter

• Frequency dependence of optimal impedance

• Optimal attenuation zone narrow, large noise reduction loss outside optimal region (especially at low frequencies)

• Sometimes two optimal areas appear: with or without flow

2. PASSIVE PART OPTIMIZATION

Wire mesh WM2 :

20 mm air cavity Pressure cancellation

Remarks

• Reactance: strongly negative

• Resistance: quite good, slightly low when frequency increases

• Reactance: almost zero

• Resistance: quite good, slightly low when frequency increases

σ e = 0.3 Z0

Different materials were tested (wire meshes, rockwool, …) The best compromise

9 Hybrid functioning • Active mode • Pressure cancellation • Passive mode • 10 mm air cavity • 15 mm air cavity • 20 mm air cavity

Optimization of the hybrid functioning

• Depending on the authorized size of the complete system, from specifications • Determination of a commutation frequency (1800 Hz) between active and passive modes

WM2 : 0.3 Z₀

2. PASSIVE PART OPTIMIZATION

ACTIVE

Increasing attenuation levels

Treatment length Number of walls covered Mixed resistive layer

Remark

• Increasing treatment length

• increase attenuation especially in low frequency range

WM1

WM2

WM2-WM1

• increase attenuation over almost the whole frequency bandwidth

• Two symmetrical walls covered

Insertion loss simulation: MATISSE duct wall

WM1

WM2

WM2 WM1

10 dB 10 dB

10 dB

11 Back cavity

Wire mesh

PZT actuator Error sensor

3. ACTIVE PART OPTIMIZATION

Collaboration with Metravib

55 mm

Bets position for the sensor : at the center Homogeneity of the pressure

• Feedforward structures

Selection of the most suited type of controller

Turbojet inlets covering ⇒ extension of the liner surface ⇒ MIMO system

• Upstream reference insufficiently correlated with the sound to cancel

• Excessive memory and calculations requirements for real-time applications with huge number of cells

Adaptive feedback cell by cell (IMC-MDFXLMS algorithm)

3. ACTIVE PART OPTIMIZATION

13 IMC-FXLMS block diagram

Remarks

• Perfect secondary-path model ⇒ the feedback contribution is removed

⇒ the system acts as a feedforward controller Adaptive digital controller

with the

Filtered-x LMS algorithm applied to the

IMC architecture

(Internal Model Control, Elliott 95)

• Performance necessarily connected to the predictability of the perturbation d(n)

Frequ ency (H_z) Time ( s) Magni tu de ( dB) Simulation results • Strong attenuations

• Fast convergence (< tenth a second)

Performance of a two-tone in noise control

0.8 and 1.8 kHz tones (Sampling frequency 10 kHz) S/N = 15 dB 20 taps Control ON at 0.2 s • Permanent stability

15 ⇒ Memory costs and computation loads become limiting factors for

real-time applications

Objective: development of a multi-channel algorithm based on a parallel functioning cell by cell

3.2. MULTI-CHANNEL STRUCTURE

Object of the IMC: Estimation of the primary noise at the error sensor

For MIMO systems, all the secondary contributions have to be taken into account

• Only the self and main feedback produced by the cell is reduced • Cross-contributions then seen as part of the signal to minimize

3.2 MULTI-CHANNEL STRUCTURE

Cells independent from the algorithm point of view

• Drawback Acoustic coupling remains due to a biased estimation of the primary noise Stability problem • Advantage Magni tu de ( dB) Frequency (Hz) Tim e (s ) Simulation results for 4 hybrid cells

Instabilities

1.2 kHz tone

2 taps, control ON at 0.2 s

• Stability assured by means of a parallel bandpass filtering around each fixed and known tone of the primary noise

17 Multi-tone ANC based on self adaptive

band pass filtering of the reference

Bidirectional swept sine 1.5 kHz & 2 kHz tones

SNR = 10 dB

3.2. OPTIMIZATION OF THE MULTI-CHANNEL STRUCTURE

Freq uenc y (H z) Freq uenc y (H z) Time (s) Time (s) Magni tu de ( dB) Magni tu de ( dB)

• Hybrid behavior: tone over 1.8 kHz are not concerned by the ANC

• Control of evolving signals: fast convergence with few taps

20 taps

Control ON at 0 s

Without control With control

Simulation results for 4 hybrid cells

17 ACTIV E mod e PASS IVE mod e

18 Frequency (H Tim e (s ) Ma gn itud e ( d B ) Frequency (Hz) Ma gn itud e ( d B ) Tim e (s ) 1 kHz & 1.5 kHz tones 20 m.s-1_flow 8 taps, control ON at 4 s 10 Hz .s-1 _{unidirectional} linear sweep 40 m.s-1_flow 8 taps, control ON at 20 s

4. EXPERIMENTAL VALIDATION

• ANC of evolving signals

18 Low number of control

filters’ coefficients

Experimental results for 4 hybrid cells

• Algorithm implementation system: Simulink®

• Compilation to the floating-point DSP: Matlab/Real-Time Workshop®

• Monitoring & acquisition systems: dSPACE ControlDesk® _{& I-deas}®

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4. EXPERIMENTAL VALIDATION

Flow velocity dependence 2 active cells / 4 active cells

mean flow 20 m/s

Remarks

• Attenuation decreases as flow velocity increases

• Flow dependence essentially at low frequencies • High attenuation with 2 active cells • Importance of the treatment length

WM 2

Comparison between predictions and measurements for v = 50 m.s-1 Frequency (Hz) TL (dB) ACTIVE PASSIVE — Passive prediction + Passive measurements — Active prediction * Active measurements

Hybrid cell functioning

4. EXPERIMENTAL VALIDATION

• experimental behaviour as predicted • commutation frequency : 1800 Hz • high attenuation

21 Conclusion

Current investigation

Development of a self-contained hybrid cells thanks to

Experimental validation of the theoretical predicted results

• Fast convergence and excellent stability • Low number of taps for the control filters

• Up to 20 dB at low frequencies and 15 dB at higher frequencies

Test the hybrid liners on a more realistic test bench

• More hybrid cells (~ 50 cells)

• Higher flow velocities (~ M=0.3) • The IMC-MDFXLMS algorithm

• An adaptive bandpass filtering based on a multi-tone detection system

Broaden the frequency range of control to narrowband noise