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System Identification: a Cornerstone of System Identification: a Cornerstone of Structural Design in the Aerospace and Structural Design in the Aerospace and
Automotive IndustriesAutomotive Industries
Herman Van der AuweraerHerman Van der AuweraerSCORES WorkshopSCORES WorkshopLeuven, 12-10-2004Leuven, 12-10-2004
SCORES 2004Leuven 12/10/04 2
Overview
Objective: Objective: To discuss the vital importance of System To discuss the vital importance of System Identification in the Mechanical Identification in the Mechanical Design EngineeringDesign Engineering Process Process
To identify the specific challenges for this kind of problems and To identify the specific challenges for this kind of problems and to illustrate the research needsto illustrate the research needs
Illustrate with typical products: Illustrate with typical products: cars,cars, aircraft, satellites, ….aircraft, satellites, …. where where adequate mechanical product behaviour is vitaladequate mechanical product behaviour is vital
SCORES 2004Leuven 12/10/04 3
Overview
Introduction: the role of Structural Dynamics in Introduction: the role of Structural Dynamics in
Mechanical Design EngineeringMechanical Design Engineering
Approach and methodology for Structural Dynamics Approach and methodology for Structural Dynamics
Analysis: Analysis: Experimental Modal AnalysisExperimental Modal Analysis
Modal Parameter Identification methodsModal Parameter Identification methods
Applications of modal analysisApplications of modal analysis
Recent evolutions and challenges for the futureRecent evolutions and challenges for the future
ConclusionsConclusions
SCORES 2004Leuven 12/10/04 4
IntroductionMechanical Design Engineering
Market Demand: Delivering products with the required Market Demand: Delivering products with the required mechanical characteristics: mechanical characteristics: Excel in Excel in
Operational quality (performance specifications…)Operational quality (performance specifications…)
Reliability (load tolerance, fatigue, life-time…)Reliability (load tolerance, fatigue, life-time…)
Safety (vehicle crash, aircraft flutter….)Safety (vehicle crash, aircraft flutter….)
Comfort (noise, vibration, harshness)Comfort (noise, vibration, harshness)
Environmental impact (emissions, waste, noise, Environmental impact (emissions, waste, noise, recycling…) recycling…)
Process process challenges: Process process challenges: Excel in Excel in
Time-to-Market: reduce design cycleTime-to-Market: reduce design cycle
Reduce design costs Reduce design costs
Product customizationProduct customization
SCORES 2004Leuven 12/10/04 5
Introduction Economic Impact: Some Figures
• Typical vehicle development programs require investment Typical vehicle development programs require investment budgets of 1 .. 4 B$ budgets of 1 .. 4 B$
• New Mercedes C-class New Mercedes C-class (Automotive Engineering Intl., Aug. 2000)(Automotive Engineering Intl., Aug. 2000): : • 600 M$ development + 700M$ production facilities600 M$ development + 700M$ production facilities
• Developed in less than 4 years Developed in less than 4 years
• New Mini: 200M£ development costsNew Mini: 200M£ development costs (+ as much in marketing...)(+ as much in marketing...)
• Chrysler minivan Chrysler minivan (“The Critical Path” by Brock Yates)(“The Critical Path” by Brock Yates)::• 2 B$ development budget, of which 250 M$ R&D2 B$ development budget, of which 250 M$ R&D
• 36 different body styles, 2 wheelbases, 4 engines36 different body styles, 2 wheelbases, 4 engines
SCORES 2004Leuven 12/10/04 6
IntroductionTime Pressure Increases Recall Risks
Warranty costs may explode the overall budgetWarranty costs may explode the overall budget
• 2000 warranty cost (Mercedes-Benz) : 1.5 b$2000 warranty cost (Mercedes-Benz) : 1.5 b$• Warranty cost exceeds R&D costWarranty cost exceeds R&D cost• Warranty cost x 3 in 2 years ...Warranty cost x 3 in 2 years ...
Warranty costs may explode the overall budgetWarranty costs may explode the overall budget
• 2000 warranty cost (Mercedes-Benz) : 1.5 b$2000 warranty cost (Mercedes-Benz) : 1.5 b$• Warranty cost exceeds R&D costWarranty cost exceeds R&D cost• Warranty cost x 3 in 2 years ...Warranty cost x 3 in 2 years ...
SCORES 2004Leuven 12/10/04 7
IntroductionMechanical Design Engineering
Early Design Optimization is EssentialEarly Design Optimization is Essential
Product design has to go beyond the “Form and Fit”Product design has to go beyond the “Form and Fit”
Focus on “Functional Performance Engineering”Focus on “Functional Performance Engineering”
For mechanical performances: For mechanical performances: structural analysisstructural analysis Static: strength, load analysisStatic: strength, load analysis Kinematic: mechanisms, motionKinematic: mechanisms, motion Dynamic: vibrations, fatigue, noiseDynamic: vibrations, fatigue, noise
Basic approach: is Basic approach: is through the use of structural modelsthrough the use of structural models A priori (Finite Element) and A priori (Finite Element) and experimental (Modal)experimental (Modal) Analyze the effect of dynamic loadsAnalyze the effect of dynamic loads Understand the intrinsic structural dynamics behaviourUnderstand the intrinsic structural dynamics behaviour Derive optimal design modificationsDerive optimal design modifications
SCORES 2004Leuven 12/10/04 8
System TransferSource Receiver
AccessoriesAccessories
Environmental Environmental SourcesSources
Total Vehicle Total Vehicle SystemSystem
Road InputRoad Input
Wheel & Tire Wheel & Tire UnbalanceUnbalance
Steering Wheel Steering Wheel ShakeShake
Seat VibrationSeat Vibration
Rearview mirror Rearview mirror vibrationvibration
Noise at Driver’s Noise at Driver’s & Passenger’s & Passenger’s
EarsEars
TA
CT
ILE
TA
CT
ILE
VIS
UA
LV
ISU
AL
AC
OU
ST
ICA
CO
US
TIC
EngineEngine
Introduction: A Systems ApproachA Source-Transmitter-Receiver Model
X =
SCORES 2004Leuven 12/10/04 9
Overview
Introduction: the role of Structural Dynamics in Introduction: the role of Structural Dynamics in
Mechanical Design EngineeringMechanical Design Engineering
Approach and methodology for Structural Dynamics Approach and methodology for Structural Dynamics
Analysis: Analysis: Experimental Modal AnalysisExperimental Modal Analysis
Modal Parameter Identification methodsModal Parameter Identification methods
Applications of modal analysisApplications of modal analysis
Recent evolutions and challenges for the futureRecent evolutions and challenges for the future
ConclusionsConclusions
SCORES 2004Leuven 12/10/04 10
Experimental Modal AnalysisPrinciples
Structural dynamics modelling: relating force inputs to Structural dynamics modelling: relating force inputs to displacement/acceleration outputs displacement/acceleration outputs
Multiple D.o.F. System:Multiple D.o.F. System:
Continuous structures approximated by discrete number of Continuous structures approximated by discrete number of degrees of freedom -> Finite Element Matrix Formulationdegrees of freedom -> Finite Element Matrix Formulation
Majority of methods and applications: Majority of methods and applications: Linear Linear and and Time-Time-InvariantInvariant models assumed models assumed
( ) ( ) ( ) ( )M x t C x t K x t f t g
rou
nd
m 1
c 1
k 1
f1
(t)
m 2 m n
gro
un
d
k n+1k 2
c 2c n+1
f2
(t) f n (t)
x1
(t) x2
(t) xn
(t)
SCORES 2004Leuven 12/10/04 11
Experimental Modal AnalysisPrinciples
Modal Analysis: Related to Eigenvalue AnalysisModal Analysis: Related to Eigenvalue Analysis
Time domain equationTime domain equation
Laplace domain equationLaplace domain equation
Eigenvalue analysis -> system poles and EigenvectorsEigenvalue analysis -> system poles and Eigenvectors System pole -> Resonance frequency and damping valueSystem pole -> Resonance frequency and damping value
Eigenvector -> Mode shapeEigenvector -> Mode shape
Transformation vectors to “Modal Space”Transformation vectors to “Modal Space”
( ) ( ) ( ) ( )M x t C x t K x t f t
2( ) ( ) ( )s M sC K X s F s
* 2, 1k k k k k kj
SCORES 2004Leuven 12/10/04 12
Experimental Modal AnalysisPrinciples
Modal Shape: Eigenvector in the physical space: physical Modal Shape: Eigenvector in the physical space: physical interpretation (Example “Skytruck”)interpretation (Example “Skytruck”)
SCORES 2004Leuven 12/10/04 13
Modal Analysis Principle;Decomposition in Eigenmodes
Modal Analysis: The modal superpositionModal Analysis: The modal superposition
==
++
++
++
aa11 aa22
aa33 aa44xx xx
xx xx
++
++ ……
……
SCORES 2004Leuven 12/10/04 14
Experimental Modal AnalysisPrinciples
Modal Analysis: An input/output relationModal Analysis: An input/output relation
Transfer Function Formulation:Transfer Function Formulation:
Model reduction (Finite number of modes):Model reduction (Finite number of modes):
2 1
( ) ( ) ( )
( ) [ ]
X s H s F s
H s s M sC K
*
*1
( )n
k k
k k k
A AH s
s s
{ } Tk k k kA Q
* 2, 1k k k k k kj
SCORES 2004Leuven 12/10/04 15
Experimental Modal AnalysisPrinciples
Experimental Analysis: using input/output measurementsExperimental Analysis: using input/output measurements
Non-parametric estimates (FRF, IR) -> Data reductionNon-parametric estimates (FRF, IR) -> Data reduction
Black box models (ARX, state-space)Black box models (ARX, state-space)
Modal modelsModal models
Standard experimental modal analysis approach: Standard experimental modal analysis approach: Fitting the Fitting the Transfer Function model by Frequency Response Function Transfer Function model by Frequency Response Function measurementsmeasurements
HHuu((tt))
UU(ω)(ω)
yy((tt))
YY(ω)(ω)
InputInput SystemSystem OutputOutput
SCORES 2004Leuven 12/10/04 16
Experimental Modal AnalysisTest Procedure
• ExcitationExcitation• Shakers (Random, Sine) Shakers (Random, Sine)
or Hammer (Impulsive)or Hammer (Impulsive)• Load cell for force meas.Load cell for force meas.
• ResponseResponse• AccelerometersAccelerometers• Laser (LDV)Laser (LDV)
• Cross-spectra averaging Cross-spectra averaging to estimate FRFsto estimate FRFs
• Measurement systemMeasurement system• FFT analyzer (2-4 channel)FFT analyzer (2-4 channel)• PC & data-acquisition PC & data-acquisition
front-end (2-1000 front-end (2-1000 channels)channels)
• ““patching” -> non-patching” -> non-simultaneous datasimultaneous data
SCORES 2004Leuven 12/10/04 17
Experimental Modal Analysis:Aircraft Test Setup Example
F4
F3
F2
F1
Force Inputs
Responses
Ground Vibration Test (GVT) System
44434241
34333231
24232221
14131211
HHHH
HHHH
HHHH
HHHH
InputsInputs
Res
po
nse
sR
esp
on
ses
0.00 80.00Hz
0.00
0.10
Log
( (m/s
2 )/N)
0.00 80.00LinearHz
0.00 80.00Hz
-180.00
180.00
Phase
°
qppq HH
• 1 row or column 1 row or column suffices to determine suffices to determine modal parametersmodal parameters
• Reciprocity Reciprocity
SCORES 2004Leuven 12/10/04 18
Experimental Modal AnalysisA Typical Experiment
Vehicle Body TestVehicle Body Test
• F F : 2 inputs: 2 inputs• Indicated by arrowsIndicated by arrows
• X X : 240 outputs: 240 outputs• All nodes in pictureAll nodes in picture
HH has 480 elements has 480 elements
HHFF XX
InputInput SystemSystem OutputOutput
Vertical force
Horizontal force
XX = = HH * * FF
SCORES 2004Leuven 12/10/04 19
Experimental Modal AnalysisTypical FRFs
IndustrialIndustrial
Gear boxGear box
Vehicle Vehicle SubframeSubframe
SCORES 2004Leuven 12/10/04 20
Experimental Modal AnalysisTypical FRFs
Engine block Engine block driving point FRFdriving point FRF
Engine block Engine block FRFFRF
SCORES 2004Leuven 12/10/04 21
Experimental Modal AnalysisAmbient Excitation Tests
Many applications do not allow input/output testsMany applications do not allow input/output tests No possibility to apply inputNo possibility to apply input Typical product loading difficult to realise (non-linear effects)Typical product loading difficult to realise (non-linear effects) Large ambient excitation levels presentLarge ambient excitation levels present
Specific approach:Specific approach: Use output-only data (responses)Use output-only data (responses) Assume white noise excitationAssume white noise excitation Reduce output data to covariances or cross-powersReduce output data to covariances or cross-powers
SCORES 2004Leuven 12/10/04 22
Experimental Modal AnalysisThe Analysis Process
Modal Analysis: identification of modal model parameters Modal Analysis: identification of modal model parameters from the FRF (or Covariances)from the FRF (or Covariances)
Specific problems:Specific problems: Large number of inputs/outputs, long records (noisy data) Large number of inputs/outputs, long records (noisy data)
Non-simultaneous I/O measurementsNon-simultaneous I/O measurements
High system orders, order truncation, modal overlap High system orders, order truncation, modal overlap
Low system damping (0.1 .. 10%), Large dynamic rangeLow system damping (0.1 .. 10%), Large dynamic range
Specific approach:Specific approach: Simultaneous (“global”) analysis of all reduced (FRF) data Simultaneous (“global”) analysis of all reduced (FRF) data
Order problem: Repeated analysis for increasing orders Order problem: Repeated analysis for increasing orders
-> The stabilisation diagram-> The stabilisation diagram
SCORES 2004Leuven 12/10/04 23
Experimental Modal AnalysisPrinciples
Experimental Modal Analysis: using FRF measurements in Experimental Modal Analysis: using FRF measurements in a reduced set of structural locationsa reduced set of structural locations
SCORES 2004Leuven 12/10/04 24
Overview
Introduction: the role of structural dynamics in Mechanical Introduction: the role of structural dynamics in Mechanical
Design EngineeringDesign Engineering
Approach and methodology for structural dynamics analysis: Approach and methodology for structural dynamics analysis:
experimental modal analysisexperimental modal analysis
Modal Parameter Identification methodsModal Parameter Identification methods Usually taking into account the physical modelUsually taking into account the physical model
Use of raw time data exceptional -> reduced FRF modelsUse of raw time data exceptional -> reduced FRF models
Time and frequency domain approachesTime and frequency domain approaches
Industrial and societal applications of modal analysisIndustrial and societal applications of modal analysis
Recent evolutions and challenges for the futureRecent evolutions and challenges for the future
ConclusionsConclusions
SCORES 2004Leuven 12/10/04 25
Modal Model Parameter Identification Main Methods
Frequency domain methods: rational polynomial FRF modelFrequency domain methods: rational polynomial FRF model
Nonlinear in the unknownsNonlinear in the unknowns Common denominator methodsCommon denominator methods Partial fraction expansion methodsPartial fraction expansion methods Linearized methodsLinearized methods State space formulations (“Eigensystem Realization”)State space formulations (“Eigensystem Realization”)
M
jjj
N
jjj
A).(
B).(
)(H
0
01
00
]).(][).([)(
M
jjj
N
jjj ABH
*
*1
( )n
k k
k k k
A AH
j j
SCORES 2004Leuven 12/10/04 26
Modal Model Parameter Identification Main Methods
• Linear frequency domain methodLinear frequency domain method
• Weighted or notWeighted or not
• LS, TLSLS, TLS
• Maximum Likelihood: takes data variance into account -> Non-Maximum Likelihood: takes data variance into account -> Non-linear error formulation -> iterative; Error bounds!!linear error formulation -> iterative; Error bounds!!
• Continuous or discrete frequency domainContinuous or discrete frequency domain• Preferred approach: “PolyMAX”, Least Squares Discrete Preferred approach: “PolyMAX”, Least Squares Discrete
Frequency Domain LS/TLS, originating from VUB.Frequency Domain LS/TLS, originating from VUB.
N
j
M
jjjjj A)()(HB)(
0 0
0
SCORES 2004Leuven 12/10/04 27
Modal Model Parameter Identification Main Methods
• Time domain: Complex damped exponential approach (UC)Time domain: Complex damped exponential approach (UC)
• Impulse responses or correlations are solutions of the Impulse responses or correlations are solutions of the “characteristic equation”“characteristic equation”
• Poles: found as eigenvalues of [WPoles: found as eigenvalues of [Wii] companion matrix] companion matrix
• Modeshapes: Least-squares fit of FRF matrixModeshapes: Least-squares fit of FRF matrix
m
rr
N
r
Tr
tkr
Tr
tkrk LeLeR
1
** }{}{][*
0...11 ttkkk WRWRIR
SCORES 2004Leuven 12/10/04 28
• Time domain: Discrete time state space model -> Subspace method • In particular used with output-only data: stochastic subspace
• Estimate [A] and [C] from
• output-only data (KUL…)
• covariances (INRIA):
x A x w
y C x v
k k k
k k k
1
1]][][[][ A
rrt
r ie r r rr C ][
Modal Model Parameter Identification Main Methods
SCORES 2004Leuven 12/10/04 29
Modal Model Parameter Identification Main Methods
Stabilisation diagram: discrimination of physical poles Stabilisation diagram: discrimination of physical poles versus mathematical/spurious poles -> heuristic approachversus mathematical/spurious poles -> heuristic approach
SCORES 2004Leuven 12/10/04 30
Overview
Introduction: the role of structural dynamics in Introduction: the role of structural dynamics in
Mechanical Design EngineeringMechanical Design Engineering
Approach and methodology for structural dynamics Approach and methodology for structural dynamics
analysis: analysis: experimental modal analysisexperimental modal analysis
Modal Parameter Identification methodsModal Parameter Identification methods
Applications of modal analysisApplications of modal analysis
Recent evolutions and challenges for the futureRecent evolutions and challenges for the future
ConclusionsConclusions
SCORES 2004Leuven 12/10/04 31
EMA Example: Aircraft Modal Analysis
• Component DevelopmentComponent Development• Engine, landing gear, ….Engine, landing gear, ….
• Aircraft Ground Vibration TestsAircraft Ground Vibration Tests• Low frequency: 0 … 20… 40 HzLow frequency: 0 … 20… 40 Hz
• > 50 orders, > 250 DOF> 50 orders, > 250 DOF
• Model Validation & updatingModel Validation & updating
• Flutter predictionFlutter prediction
SCORES 2004Leuven 12/10/04 32
EMA Example: Aircraft Modal Analysis (Dash 8)
SCORES 2004Leuven 12/10/04 33
EMA Example: Aircraft Modal Analysis for Aeroelasticity (Flutter)
Fre
quen
cy (
Hz)
Dam
ping
(%
)
Airspeed (kts)
SCORES 2004Leuven 12/10/04 34
EMA Example: Aircraft FE Model Correlation and Updating
FEMFEM
GVTGVT
0
1
2
3
4
5
6
0 1 2 3 4 5
Measured Frequencies [Hz]
An
aly
tic
al F
req
ue
nci
es
[Hz]
FEM
GVT
GVT
FEM EigenfrequencyEigenfrequencycorrelationcorrelation
Mode shapeMode shapeCorrelation (MAC)Correlation (MAC)
Courtesy H. Schaak, Airbus France
+ 5%
- 5%
SCORES 2004Leuven 12/10/04 35
EMA Example:Business Jet, Wing-Vane In-Flight Excitation
• In-flight excitation, 2 wing-tip vanesIn-flight excitation, 2 wing-tip vanes• 9 responses9 responses• 2 min sine sweep2 min sine sweep• Higher order harmonicsHigher order harmonics• Very noisy dataVery noisy data
4.00 20.00Linear
Hz
0.00
0.10
Lo
g
( g/N)
4.00 20.00LinearHz
Hz
-180.00
180.00
Ph
as
e
°
PolyMAXPolyMAX
SCORES 2004Leuven 12/10/04 36
In-Operation Modal Analysis Example: PZL-Sokol Helicopter Testing
• Flight tests in different conditions (speed, climbing, hover…)Flight tests in different conditions (speed, climbing, hover…)• 3 flights needed, 90 points3 flights needed, 90 points• Correlation lab. / flight resultsCorrelation lab. / flight results• No problem with rotor frequenciesNo problem with rotor frequencies
SNR GROUND TESTMODE 6.40 Hz
CLIMBING FLIGHT TESTMODE 6.37 Hz
MR-I ODSMR-I ODS 6.4 Hz mode6.4 Hz mode
SCORES 2004Leuven 12/10/04 37
EMA Example: Car Body and Suspension Tests
• Suspension EMA for a Suspension EMA for a rolling-noise problem : rolling-noise problem : Booming noise at 80HzBooming noise at 80Hz
• Main contribution from Main contribution from rear suspension mountsrear suspension mounts
Body EMA for basic Body EMA for basic bending and torsion bending and torsion analysis (vehicle analysis (vehicle stiffness)stiffness)
25.00 75.00Linear
Hz
0.00
0.13
Lo
g
( (m
/s2
)/N)
25.00 75.00LinearHz
25.00 75.00Hz
-179.96
179.98
Ph
as
e
°
SCORES 2004Leuven 12/10/04 38
EMA Example:Civil Structures Dynamics
Øresund BridgeØresund Bridge
Input-output Input-output testingtesting
Output-only Output-only testingtesting
SCORES 2004Leuven 12/10/04 39
Example:Civil Structures - The Vasco da Gama Bridge
In-operation Modal AnalysisIn-operation Modal AnalysisCovariance DrivenCovariance DrivenStochastic SubspaceStochastic Subspace
SCORES 2004Leuven 12/10/04 40
Overview
Introduction: the role of structural dynamics in Introduction: the role of structural dynamics in
Mechanical Design EngineeringMechanical Design Engineering
Approach and methodology for structural dynamics Approach and methodology for structural dynamics
analysis: analysis: experimental modal analysisexperimental modal analysis
Modal Parameter Identification methodsModal Parameter Identification methods
Applications of modal analysisApplications of modal analysis
Recent evolutions and challenges for the futureRecent evolutions and challenges for the future
ConclusionsConclusions
SCORES 2004Leuven 12/10/04 41
Industrial Model Analysis: What are the issues and challenges?
• Optimizing the Optimizing the Test processTest process• Large structures (> 1000 points, in operating vehicles…)Large structures (> 1000 points, in operating vehicles…)
– Novel transducers (MEMS, TEDS…)Novel transducers (MEMS, TEDS…)
– Optical measurementsOptical measurements
• Complex structures, novel materials, high and distributed damping Complex structures, novel materials, high and distributed damping (uneven energy distribution)(uneven energy distribution)
– Multiple excitation (MIMO Tests)Multiple excitation (MIMO Tests)
– Use of a priori information for experiment designUse of a priori information for experiment design
– Nonlinearity checks, non-linear model detection and Nonlinearity checks, non-linear model detection and identificationidentification
– Excitation Design: Get maximal information in minimal timeExcitation Design: Get maximal information in minimal time
SCORES 2004Leuven 12/10/04 42
Industrial Model Analysis: What are the issues and challenges?
• Optimizing the Optimizing the Analysis processAnalysis process• High model orders, numerical stabilityHigh model orders, numerical stability
• Discrimination between physical and “mathematical” poles Discrimination between physical and “mathematical” poles
• Automated modal analysisAutomated modal analysis
• Test and analysis duration and complexityTest and analysis duration and complexity
• Test-right-first-time Test-right-first-time
• Support user interaction with “smart results”Support user interaction with “smart results”
• Automating as much as possible the whole processAutomating as much as possible the whole process
• Quantifying data and result uncertaintyQuantifying data and result uncertainty
-> -> bring intelligence in the test and analysis processbring intelligence in the test and analysis process
SCORES 2004Leuven 12/10/04 43
1
x2
x1
hid1 hid2
2
x2
Automatic Assessment and Classification of FRF Quality and PlausibilityAutomatic Assessment and Classification of FRF Quality and Plausibility
Innovation and Challenges:Data Quality Assessment
2.00 30.00Hz
0.00
1.00
Am
plitu
de/
F Coherence lfw :38:-Z/MultipleF Coherence rgw :38:-Z/Multiple
Coherence analysis (225 spectral lines X 540 DOFs)Coherence analysis (225 spectral lines X 540 DOFs)
SCORES 2004Leuven 12/10/04 44
Uncertainty and Reliability: A Research Context
• Methods to assess uncertainty and variability of CAE models:Methods to assess uncertainty and variability of CAE models:• Input distribution -> response distributionInput distribution -> response distribution
• Fuzzy-FE, transformation method, Monte-Carlo…Fuzzy-FE, transformation method, Monte-Carlo…
• Robust design and reliability considerationsRobust design and reliability considerations
• What about test data confidence limits?What about test data confidence limits?
ININ
OUTOUT
Uncertainty in front craddleUncertainty in front craddle• Young’s modulus (190-210 GPa)Young’s modulus (190-210 GPa)• mass density (7600-8000 kg/mmass density (7600-8000 kg/m33))• shell thickness (1.6-2.4 mm)shell thickness (1.6-2.4 mm)
SCORES 2004Leuven 12/10/04 45
• Mimic the human operator (rules, implicit -> NN)?Mimic the human operator (rules, implicit -> NN)?• Iterative methods (MLE)Iterative methods (MLE)• Fundamental issue: Fundamental issue: discriminate mathematical and physical polesdiscriminate mathematical and physical poles
• Indicators (damping value, p-z cancellation or correlation…)Indicators (damping value, p-z cancellation or correlation…)• Fast stabilizing estimation methodsFast stabilizing estimation methods• Clustering techniquesClustering techniques
Innovation and Challenges:Automating Modal Parameter Estimation
PolyMAXPolyMAX
SCORES 2004Leuven 12/10/04 46
Industrial Model Analysis: What are the issues and challenges?
• Novel applicationsNovel applications
• Combined Ambient – I/O testing Combined Ambient – I/O testing
• Nonlinear system detection and identificationNonlinear system detection and identification
• Build system-level models combining EMA and FE models Build system-level models combining EMA and FE models
• Vibro-acoustic modal analysis: include cavity modelsVibro-acoustic modal analysis: include cavity models
• Mechatronic and control Mechatronic and control
• End-of-line controlEnd-of-line control
• Model-based monitoringModel-based monitoring
• ……....
Healthy structureHealthy structure Damaged structureDamaged structure
22ndnd mode shape mode shape
SCORES 2004Leuven 12/10/04 47
HSS
Engine MountsEngine Mounts BushingsBushings
Subframe Subframe & & CrossmemberCrossmember
Body Body Vibro-acousticsVibro-acoustics
Engine Engine & & BracketsBrackets
HybridSystemSynthesis
Innovative Applications: Building Hybrid System Models
SCORES 2004Leuven 12/10/04 48
• Acoustic resonances, coupled structural-acoustical Acoustic resonances, coupled structural-acoustical behaviour can be modelled by vibro-acoustic modal modelsbehaviour can be modelled by vibro-acoustic modal models
K K
K
x
pj
C
C
x
p
M
M M
x
p
f
pq
S C
f
S
f
S
cf
0
0
0
02
Innovative Applications: Vibro-Acoustic Modal Analysis
• Excitation by shakers and Excitation by shakers and loudspeakers -> Balancing of test loudspeakers -> Balancing of test data needed (p/f, x/f, p/Q, x/Q)data needed (p/f, x/f, p/Q, x/Q)
• Non-symmetrical modal modelNon-symmetrical modal model• Through structural acoustic Through structural acoustic
couplingcoupling
• Different right and left Different right and left eigenvectorseigenvectors
SCORES 2004Leuven 12/10/04 49
f = 32.9f = 32.9 Hz Hz = 8.5= 8.5%%
Vibro-Acoustic Modal AnalysisExample: Aircraft Interior Noise
f = 78.3f = 78.3 Hz Hz = 7.0= 7.0%%
ATR42ATR42
F100F100
SCORES 2004Leuven 12/10/04 50
Summary and Outlook
• Early product optimization is essential to meet market demandsEarly product optimization is essential to meet market demands
• Mechanical Design Analysis and Optimization heavily rely on Mechanical Design Analysis and Optimization heavily rely on Structural ModelsStructural Models
• Experimental Modal Analysis is the key approach, it is a de-facto Experimental Modal Analysis is the key approach, it is a de-facto standard in many industriesstandard in many industries
• While EMA is in essence a system identification problem, While EMA is in essence a system identification problem, particular test and analysis issues arise due to model size and particular test and analysis issues arise due to model size and complexitycomplexity
• Important challenges are related to supporting the industrial Important challenges are related to supporting the industrial demands (test time and accuracy) and novel applicationsdemands (test time and accuracy) and novel applications
• Research efforts should Research efforts should alsoalso pay attention to “state-of-the-use” pay attention to “state-of-the-use” breakthroughsbreakthroughs