LMS Flutter Testing - aerospacetesting.com · Gvt/Flutter/FEC LMS Flutter Analysis. ... 20...

Aerospace Testing 2011, Hamburg, Germany, April 6 2011Jan Debille – Solutions Manager Aerospace & Defense

Industrial solutions for in-flight & offline experimental flutter analysisA. Lepage, P. Naudin, J. Roubertier, A. Cordeau ONERAM.A. Oliver-Escandell, S. Leroy, AIRBUSJan Debille, LMS

2 copyright LMS International - 2010

Presentation outline

Flutter testing: What, When and How?

Validation

Required technology

Industrial implementation

1

5

2

3

Conclusions

4


What is Flutter?

Flutter is an aero-elastic phenomenonUnstable self-excited vibrationStructure extracts energy from the air stream

Flutter starts to occur at a certain speedNegative damping start to occur at flight points

where two modes are coupled in an unstable wayTypical coupling: wing bending/torsion, wing

torsion/control surface, wing/engine


Component CAE Component Physical Test

Subsystem CAE Subsystem Physical Test

Full Virtual Prototype Full Physical Test

Perform

ance

Explorat

ion

Perform

ance

Explorat

ion

Component

Concept Validation & Target

Concept Validation & Target

Cascading

Cascading

Certific

ation

Certific

ation

Upfront Engineering Detailed Engineering Refinement Engineering

Full Aircraft

Models &Loads

Subsystem

Feasibility Definition InServiceConcept Development

Market

StudyConce

pt Sele

cted

Agreemen

t With

Primary

Partners

Authority T

o Offe

rPro

gramLau

nch Major

Assem

blies

Entry In

to Serv

ice

Certific

ation

First F

light

Major B

ody

Sectio

ns

Component

Design

GVT/Flutter/FEC

Stages of Aircraft Development & Flutter: When?


Flight Envelope Clearance

Flutter in the design process flow

Pre-Test & De-Risking

Ground Vibration Test

Identify & ValidateModes

CorrelateModel

GO-NO/GO First Flight

Update / refine Models

Virtual PrototypeFE Model

Analytical Modal Model

Physical Prototype

Flutter Simulation &

Prediction

Define Flight Envelope

Flight Envelope Opening

Flight Envelope Expansion

Feasibility Definition InServiceConcept Development

Market

StudyConce

pt Sele

cted

Agreemen

t With

Primary

Partners

Authority T

o Offe

rPro

gramLau

nch Major

Assem

blies

Entry In

to Serv

ice

Certific

ation

First F

light

Major B

ody

Sectio

ns

Component

Design

Gvt/Flutter/FEC

LMS Flutter Analysis


Aero-elastic simulation and in-flight flutter testing

FE Model Test Model (GVT) Aerodyn. Panel Model Physical prototype

0)()()()( =−++ xFtKxtxCtxM a&&&

Traditional FEM, GVT-updated FEM, or direct GVT

Aerodynamic panel method

Due to presence of aero-dynamic term, modes of structural system are changing with airspeed and altitudeFlutter analysis = assessing evolution of modes (zero-crossing of damping value)


Flutter procedure: extract from NASA technical memo

Fly at several stabilized speedsIncreasing dynamic pressureIncreasing MACH number

Ref: NASA Technical Memorandum 4720, “A Historical Overview of Flight Flutter Testing,” October 1995


Flight flutter testing & in-flight modal analysis

Background Testing Analysis

s

Rea

l( m

/s2)

Dam

ping

Airspeed

Flutter

Ampl

itude

g2

Telemetry link

Hz-180.00

180.00

°


Flutter testing procedure

Find frequency and damping of critical modesFor increasing Speeds increases the dynamic loadAt different Altitudes the lower the altitude, the higher the dynamic loadAt different MACH values

True Air Speed(knots)

Altitude (feet)

40,000

30,000

20,000

10,000

100 200 300 400 500

MACH 0.95

MACH 0.90

MACH 0.85




Validation

Required technology


1

5

2

3

Conclusions

4


Flutter testing requirements

Get accurate damping estimate in an operational situationAccuracy

Waiting Time is Money aircraft is airborne during the analysis

Waiting Time is Dangerous during the analysis time, the aircraft may be exposed to near-flutter conditions!

Speed

Modal Analysis on operational (output-only) dataModal Analysis


EUREKA project FLITE2 – Structural testing and modal analysis for aeronautics and space applications

Airbus FranceDassault AviationLambert Aircraft EngineeringPZL Mielec

LMS

ILOTINRIAONERASOPEMEA

University of Brussels (VUB)University of Krakau (AGH)University of Leuven (KUL)University of Manchester (UMAN)

LMS Net Funding in FLITE2:378 kEUR


EUREKA project FLITE2 – Structural testing and modal analysis for aeronautics and space applications

Faster testing for GVTSmart combination of broadband / sweep / stepped

Assessment of non-linear behavior using multi-sinesModal parameter estimation: iterative methods using noise information and yielding uncertainty bounds on estimates (PolyMAX results as starting values)Flight flutter testing: OMAX identification framework, i.e. combination of known and unknown excitation (EMA + OMA)Use of GVT for flutter safety prediction

Aerodynamic panel model

Hz

dB( (

m/s

2)/N

)

0.00

1.00

Ampl

itude

/

F FRF0F Variance0B COH0


Stable, robust and reliable modal analysison operational data

Identification of modal parameters from response data (accelerations) measured in operating conditions

EigenfrequenciesDamping ratiosMode shapes

Operational modal analysis = identifying HBased on YWithout knowing U(BUT white noiseassumption) White noise

HU Y

White noise + harmonic


Output only: artificial vs. natural excitation

Operational Modal Analysis: Output-only analysis no FRFs but Crosspowers between responses and reference responsesReference responses: wing tips, tail tips, nose; in general: well excited pointsOperational PolyMAXRequires natural, operational excitation!

OMA with artificial excitationOnly operational responses are considered…but: all modes are well-excited due to force input!…and: additional operational excitation used

0 . 0 0

0 . 1 0

Log

( g/N

)

H z- 1 8 0 . 0 0

1 8 0 . 0 0

Phas

e

°

PolyMAXPolyMAX

© NASA


Modal analysis LMS PolyMAX - theory & implementation

Step 1:Denominator matrix

polynomial (in z-domain)Poles and participation factors

Step 2:Stabilisation diagram

Step 3:LSFD to estimate mode

shapes and upper/lower residues from selected poles

[ ] [ ][ ] [ ] [ ] [ ]"")()()(

00

111 zzz

ABHp

pp

p−

−− β++β+β=ωω=ω

K

00

11 zzz p

pp

p ⎥⎦⎤

⎢⎣⎡−

⎥⎦⎤

⎢⎣⎡

−⎥⎦⎤

⎢⎣⎡ α++α+α K

[ ]

[ ][ ]

[ ]0))(,( 1

0

=

⎟⎟⎟⎟⎟

⎠

⎞

⎜⎜⎜⎜⎜

⎝

⎛

α

αα

ωω

p

HML

[ ] { } { }URLRlvlv

Hn H

iiTii +−

><+

><=ω ∑

*

)(jji ii ωλ−ωλ−ω=

21

*


Modal analysis LMS PolyMAX vs. LSCE

Same 3-step procedure

Step 1 differsLSCE uses impulse responsesPolyMAX uses FRFs

Big difference in stabilization diagram

LMS PolyMAX excels in both high and low damping cases !

Tim

e M

DO

FP

olyM

AX


Modal analysis LMS PolyMAX vs. Other Frequency-Domain Methods

Frequency domain methods

Powers of the frequency axisNumerical conditioning problemsConsequences:

• Limited frequency range (ω)• Limited model order (p)

Not in PolyMAX!

[ ] [ ] [ ] [ ]( ) [ ] [ ] [ ]( ) 10

110

11 )()(.)()()( −−

−−

− α++ωα+ωαβ++ωβ+ωβ=ω KK pp

pp

pp

pp jjjjH

LMS PolyMAX excels in broadband, high model order analyses !

tjez ∆ω= )(21)(2

1

1

fftff

end −=∆−π=ω Re

Im

f1

f2

…

fend


Modal analysis LMS PolyMAX

Extremely clear stabilization Easy pole selectionFaster analysisUser-independent resultsMore modes found

“General purpose” methodSingle broadband analysisHigh & low dampingNoisy data

“Modal analysis, an area where no substantial advances were to be expected …?”

“LMS PolyMAX, A Revolution in Modal Parameter Estimation!”


LMS Test.Lab automatic modal parameter selection Speed up modal analysis

Rule-based methodNot affected by ability of human mind

to treat informationHigh accuracy on pole selectionReduce uncertainty

Improve productivity

Guidance tool for all

Extensible to automatic modal analysisAnalyze multi-patches measurementLow modal density cases (ex. Body-

in-white car)Flight qualification of aircraftStructural damage detection /

Structural health monitoring

One push instead of manual

selections

All physical poles selected at a glance in stabilization

diagram

© NASA




Validation

Required technology


1

5

2

3

Conclusions

4


Telemetry ground station

LMS Scadas Mobile data acquisition system

Flutter testing procedure: Data acquisition

In-flight data recorder & telemetry transmitter

On-board flight data recorder and

telemetry system

Ground station: receive data, split fast/slow channels

Data acquisition with Scadas F/E or

3rd part software

Dynamic data

Flight parameters

D/A

TCP/IP

Flight data3rd party software:

Data tape/card reader

ONLINE data preparationOFFLINE data preparation

(Altitude, CAS, MACH, etc.)


Flutter testing procedure: cyclic

Flight envelope definition: Determined by real-time flight parameters.After 1 cycle, the pilot is instructed to move on to the next flight pointComplete offline processing possible: allows in-depth analysis afterwards, accounts for telemetry-based data errors

Measure with Spectral Testing from telemetry

Average flight parameters fix the flight point

Automate OMA (minimal

interaction)

Evaluate evolution of f and ζ for each mode in display

The Flutter application consists of 2 dedicated GUIs:A Flutter Progress window that is always on top:The dedicated Flutter worksheet which


Flutter analysis: viewing results

Measurements are displayed in the flight envelope

Selected poles are added to the Pole Table, based on a match in frequency using “pole tolerance” parameter.

Poles are displayed in U/L display with amplitude in the upper display and damping in the lower display.

The Displays group allows to select a different x-axis, or to fix against a slow channel parameter (e.g. MACH value)

Support for military damping standard g=2*ζ


Flutter testing: Overview

Operational Modal Analysis with artificial excitation & PolyMAX parameter estimation method

Analysis

AMPS: automatic modal parameter selection in stabilization diagram

Automated

AMPS: deterministic method! Always yields the same results with the same parameters

Repeatability

Flutter condition can be determined in as fast as 10 seconds

Speed

Experts can interact with the automated procedure

Expert interaction




Validation

Required technology


1

5

2

3

Conclusions

4


ONERA flutter data set generator

Aim:Simulate data for testingClassical wind-bending and torsion coupling

Procedure:Input altitude and speedRun simulation to get time histories

Output:Time domain data of 4 simulated sensor responses

Model (state-space):Structural: GVT result: first 7 symmetrical wing vibration modesAerodynamic: Generalized aero-elastic forces - Doublet Lattice Method

Input for time-response calculation: impulsion on the command of a wing aileron

Altitude Speed

Time domain vibration response to wing aileron impulse

Transonic flutter

simulator


ONERA Flutter simulatordataset

Available data set: 8 flight points

constant MACH #decreasing altitudeincreasing airspeed

=> increasing dynamic pressure

Constant MACH

0

1000

2000

3000

4000

5000

6000

7000

8000

9000

10000

870 880 890 900 910 920 930 940

Speed (km/h)

Alti

tude

(m)

MeasuredMACH 0.8


ONERA Flutter Simulatorsample time data (1)

4 channels per flight point16 seconds per flight point32 seconds for near-flutter 4000 m set


ONERA Flutter Simulatorsample time data (2)

Evolution vs. altitudeDecrease in damping clearly visible in response


ONERA Flutter Simulatorevolution of modes vs. flight conditions

Flutter analysis

0

5

10

15

20

25

30

35

40

475.0 480.0 485.0 490.0 495.0 500.0 505.0 510.0

CAS (knots)

Freq

uenc

y (H

z)

Mode 1: 9.79HzMode 2: 10.05HzMode 3: 16.76HzMode 4: 19.03HzMode 5: 27.75HzMode 6: 34HzMode 7: 34.02Hz

Flutter analysis

0

2

4

6

8

10

12

14

475.0 480.0 485.0 490.0 495.0 500.0 505.0 510.0

CAS (knots)

Dam

ping

(%)

Mode 1: 9.79HzMode 2: 10.05HzMode 3: 16.76HzMode 4: 19.03HzMode 5: 27.75HzMode 6: 34HzMode 7: 34.02Hz

7 modes in modelModes 1 & 2 coupleStructural frequencies: 8.98 Hz ; 5.88 %10.47 Hz ; 1.81 %


LMS PolyMAX results – 8000 m altitude

First bending mode: 8.9 Hz ; 5.9 % First torsion mode: 10.5 Hz ; 1.8 %


LMS PolyMAX results – 4000 m alt. – near-flutter condition

Strong coupling between first bending and first torsion mode

First bending mode: 9.8 Hz ; 12 %VERY HIGH damping

First bending mode: 10.1 Hz ; 0.16 %VERY LOW damping


Quality check of PolyMAX modal parameter extractionSynthesis of Cross Powers

70.4e-6

0.09

Log

V2 (1/s

)s

CrossPow er Aile:Point11:+Z/Aile:Point15:+ZSynthesized Crosspow er Aile:Point11:+Z/Aile:Point15:+Z

0.00 40.00Hz-180.00

180.00°

Synthesized Crosspow er Aile:Point11:+Z/Aile:Point15:+Z

15.1e-6

2.22e-3

Log

V2 (1/s

)s

CrossPower Aile:Point11:+Z/Aile:Point15:+ZSynthesized Crosspower Aile:Point11:+Z/Aile:Point15:+Z

0.00 40.00Hz-180.00

180.00

°

Synthesized Crosspower Aile:Point11:+Z/Aile:Point15:+Z

Green = Synthesized – Red = measured

8000 m – safe flight point 4000 m – dangerous flight point


LMS PolyMAX results overview

Evolution of frequency & damping as a function of altitudeDecreasing altitude increase of dynamic pressure


Final check: comparison analytical modes vs PolyMAX resultFrequencies

Frequency vs Altitude

8.5

9

9.5

10

10.5

11

3000 4000 5000 6000 7000 8000 9000 10000 11000 12000

Altitude (m)

Freq

uenc

y (H

z)

Analytical Mode 1Analytical Mode 2Calc. Mode 1Calc. Mode 2


Final check: comparison analytical modes vs PolyMAX resultDamping

Damping vs Altitude

-2

0

2

4

6

8

10

12

14

3000 4000 5000 6000 7000 8000 9000 10000 11000 12000

Altitude (m)

Dam

ping

(%)

Analytical Mode 1Analytical Mode 2Calc. Mode 1Calc. Mode 2


Flutter testing at Airbus

Fly-by-wire: excitation via control surfacesSweep: detailed engineering / Pulse: crew/aircraft safetyONERA: MEFAS (Methodes et Exploitation des essais de Flottement de l’Avion Souple)Pierre Vacher, Alain Bucharles: “A Multi-Sensor Parametric Identification Procedure in the Frequency Domain for the Real-Time Surveillance of Flutter”, SYSID 2006.


Accelerometers

“General Group”Only primary control surface accelerometers have been removed

Otherwise control surfaces modes identified rather than structural ones

150 accelerometersDistributed over the main aircraft structure


Geometry representation (adding slave DOFs)


Accelerometer sub-set

“Reduit Symetrique Group”Used for in-flight real-time analysis (MEFAS)Fast and accurate identificationAccelerometers with best SNR

Wing tipsElevator tipsEnginesSome on fuselage


Symmetric sweep

Left wing tip and right wing tip

s

Ampl

itude

Time WINL:951:+ZTime WINR:951:+Z

s

Ampl

itude

Time WINL:951:+ZTime WINR:951:+Z


FRFs – fuselage

Hz

dB( g/N

)

0.00

1.00

Rea

l

/

F FRF 2:50700002:+Z/2:11111111:+ZB Coherence 2:50700002:+Z/2:11111111:+Z

Hz

dB( g/N

)

0.00

1.00

Rea

l

/


Hz

dB( g/N

)

0.00

1.00

Rea

l

/


Hz

dB( g/N

)

0.00

1.00

Rea

l

/


nose rear

Central – bottom front


FRFs – engines

Outer Z Outer Y

Inner Z Inner Y

Hz

dB( g/N

)

0.00

1.00

Rea

l

/


Hz

dB( g/N

)

0.00

1.00

Rea

l

/

F FRF 1:50710041:+Y/2:11111111:+ZB Coherence 1:50710041:+Y/2:11111111:+Z

Hz

dB( g/N

)

0.00

1.00

Rea

l

/


Hz

dB( g/N

)

0.00

1.00

Rea

l

/

F FRF 1:50720041:+Y/2:11111111:+ZB Coherence 1:50720041:+Y/2:11111111:+Z


FRFs – wings

Hz

dB( g/N

)

0.00

1.00

Rea

l

/

F FRF 0:50760950:+X/2:11111111:+ZB Coherence 0:50760950:+X/2:11111111:+Z

Hz

dB( g/N

)

0.00

1.00

Rea

l

/


Hz

dB( g/N

)

0.00

1.00

Rea

l

/

F FRF 0:50770950:+X/2:11111111:+ZB Coherence 0:50770950:+X/2:11111111:+Z

Hz

dB( g/N

)

0.00

1.00

Rea

l

/


Left X Left Z

Right X Right Z


FRFs – elevator

Hz

dB( g/N

)

0.00

1.00

Rea

l

/


Hz

dB( g/N

)

0.00

1.00

Rea

l

/


Left Right


Hz0.00

1.00

Ampl

itude

/

Coherence & geometry

128 coherence functions in excitation frequency band

Averaged over excitation frequency bandAveraged over DOFs / nodeAveraged coherence color scale from 0 - 1

Geometry mapping


FRF – PolyMAX

Accelerometer sub-set

Hz

dB( g/N

)

F Sum FRF SUMF Synthesized FRF SUM


FRF – PolyMAX

All sensors

-80.00

-30.00

dB( g/N

)

Sum FRF SUMSynthesized FRF SUM

Hz-180.00

180.00

Phas

e°


Influence of pre-processing

Varying block size N, N/2, N/4, N/8Other FRF estimation parameters constant (Hanningwindow, overlap)

PolyMAX resultsFrequency variations small (±2%)Dramatic damping ratio variations (+200%)

Trade-offSmaller block size: Hanning window bias largerLarger block size: noise variance larger (few averages)

Frequency variations

Damping variations


Influence of pre-processing

Workaround for trade-off1st step: large block size FRFs suffering from noise2nd step: IRFs truncated by rectangular window

Influence on modal parameter estimatesBiased participation factors (closed-form expression describing bias exists)

AlternativesExponential windowFrequency-averaging


Output-only modal parameter estimation procedureSpectrum estimation: leakage-free and Hanning window-free

Weighted correlogramHigh-speed estimation of correlations with positive time lagsExponential window

• Reduces the effect of leakage• Reduces the influence of the

higher time lags having a larger variance

• Compatible with the modal model ( ↔ Hanning window with biased damping)

DFT of windowed correlation sequence

Practical: selection of referencesLeft wing tip, right wing tip, tail plane


PolyMAX: EMA vs. OMAdB( g

/N)

Sum FRF SUMSynthesized FRF SUM

Hz-180.00

180.00

Phas

e°

dB

Sum Crosspow er SUMSynthesized Crosspow er SUM

Hz-180.00

180.00

Phas

e°


Pulse excitation and wing response

s

Rea

lN

Rea

l

g

F Time Force:ref:+ZB Time WINR:952:+Z

Hz

dBN dB g

F Spectrum Force:ref:+ZB Spectrum WINR:952:+Z

s

Rea

lN

Rea

l

g

F Time Force:ref:+ZB Time WINR:952:+Z

Hz

dBN dB g

F Spectrum Force:ref:+ZB Spectrum WINR:952:+Z

Raw

Tim

e do

mai

nFr

eq. d

omai

n

Filtered / decimated


Pulse excitation – OMA results

dBg2 (1

/s)s

AutoPow er FIN:091:+YSynthesized Crosspow er FIN:091:+YAutoPow er WINL:952:+ZSynthesized Crosspow er WINL:952:+Z

-180.00

180.00Ph

ase

°


In-flight OMA mode shape (1/4)


Conclusions

Extensive studyStandard EMA vs. Operational Modal AnalysisParameter estimation: MEFAS, PolyMAX, TimeMDOF, logarithmic decrementWindowing, SNR, sensor groups, frequency resolution, …

OMAOnly response signals used in analysis, but artificial excitation was usedUse of output cross-correlationsGood performance wrt. noiseEliminate windowing problems: exponential window leads to unbiased damping estimates




Validation

Required technology


1

5

2

3

Conclusions

4


Conclusions


Altitude (feet)

40,000

30,000

20,000

10,000

100 200 300 400 500

MACH 0.95

MACH 0.90

MACH 0.85


Altitude (feet)

40,000

30,000

20,000

10,000

100 200 300 400 500

MACH 0.95

MACH 0.90

MACH 0.85

OMA: some important flutter-critical modes not excitedEMA: some modes mainly excited by the turbulences may not be identifiedConclusion: beneficial to use artificial excitation, but data analysed with stochastic methods that also take into account the unknown excitation

Airbus flight test team evaluated LMS Test.Lab using large-aircraft data“We actually achieved better results using operational techniques than with classical EMA. We found more modes. The synthesis was better with higher correlation and fewer errors. And the in-flight mode shapes looked much nicer!”“We found that the exponential window, which allowed for cross-correlation calculations was a good de-noising tool for our in-flight data.”

Aerospace Testing 2011, Hamburg, Germany, April 6 2011Jan Debille – Solutions Manager Aerospace & Defense

Thank you

LMS Flutter Testing - aerospacetesting.com · Gvt/Flutter/FEC LMS Flutter Analysis. ... 20...

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Transcript of LMS Flutter Testing - aerospacetesting.com · Gvt/Flutter/FEC LMS Flutter Analysis. ... 20...