Friction Studies-From Passive to Intelligent Tires › PDFs › PE-2014 › Taheri.pdf · CB-filled...

Pavement Evaluation September 15-18 Blacksburg, VA

9/24/2014

Friction Studies-From Passive to Intelligent Tires

Saied Taheri, PhD

Associate Professor and Director Center for Tire Research (CenTiRe)

Mechanical Engineering Department Virginia Tech

Blacksburg, VA 24060


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• The Center for Tire Research will provide the forum for industry/university cooperative research for the further development, validation, and industrial implementation of the emerging technologies of tire materials, manufacturing, modeling, and testing.

CenTiRe Vision


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Relevant Projects – Multi-Scale Modeling of Tire-Road Contact and

Adhesion – Tire-Soil Interaction Model – Pneumatic Tire Performance on Ice – Macro Road Surface Profiling – A Portable Low-Cost System for High-Speed

High-Precision Surface Profiling – High-Precision Tire Modeling and Analysis for

Tire-Road Noise Prediction – Estimating Tire-Road Friction from Probe

Vehicles


Multiscale Modeling • To better understand tire and vehicle performance, a better

understanding of tire-road contact mechanics is needed • Accurate road profiles -> extract characteristic properties (e.g.,

friction, roughness) • Build a comprehensive, multi-scale database of Pavement Surface

Characteristics and friction • Tire-road contact modeling using Wavelet and Fractal based

approach

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• A major problem in highway safety and traffic engineering is to understand the mechanisms of friction between the tire and the road.

• Pavement surface texture significantly contributes to tire-pavement friction.

• Several researchers have claimed that road profiles are fractal, and that this fractality is related to the friction properties of the road.

• The objective here is to present texture properties and contact mechanics that can predict tire-pavement friction.

Background/Introduction


Rubber Road Contact

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In order to be able to estimate friction between rubber and a rough surface, we need to:

– Measure the surface profile (possible through using the Nanovea optical profilometer)

– Characterize the surface – Characterize the tread compound – Calculate the real area of contact – Estimate cold friction – Include the effect of flash temperature


Estimated Friction

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Power Spectral Density of profile height

Simplified version for self-affine surfaces

The stress probability distribution in the contact area on each length scale

Kinetic coefficient of friction

PSD vs. Wavevector

CB-filled SBR


Nanovea JR25

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• Designed with leading edge optical pens using superior white light axial chromatism. • Excellent vertical and spatial resolution. • Add-on features:

Contour measurement Fracture surface measurements Surface wear subtraction Adhesion surface topography


Friction Tester

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• Outdoor testing: Weather conditions. Time -- Money -- Human resources

• Indoor Testing: Reduced cost Controlled laboratory environment Improved data accuracy and reproducibility Ease of data accessability and processing


Road Profile Characterization

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Profile Mean Square Roughness Power Spectral Density

B0 0.48 0.0511 0.0502

B90 0.39 0.0405 0.0398

C180 0.35 0.0374 0.0366


μ-Slip Curve for Tires

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Circumfrential plane

Tread and belt diagram

Side wall diagram

Tire-Soil MBD Model

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Terramechanics Rig

Test Tire: Michelin LTX A/T2 235/85/R16

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DOE – Individual Parameter Change

0 20 40 60 80 1000

0.05

0.1

0.15

0.2

Slip (%)

Dra

wba

r Pul

l Coe

ffici

ent

All L1L2 FzL2 Press.L2 Comp.

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Ice Project • 2 inches thickness of ice • Ambient Temperature= 15-170C

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Video


Results

0 10 20 30 40 50 60 70 80-0.05

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

Slip(%)

Norm

alis

ed D

raw

Bar P

ull

Curve Fitting

60LIRun-160LIRun-260LIRun-3100LIRun-1100LIRun-2100LIRun-3

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9/24/2014 Pavement Evaluation September 15-18 Blacksburg, VA

Macro Road Profilometer Prototype System

Tire Society Conference 2014


• Identify scan matching algorithm’s ability to assemble road profiles – Image features are trackable – Depth measurements are preserved

Experimental Setup



Road Profiling Results


Was constructed while the system was driven at 2 km/h

Endpoints

Was constructed while the system was driven at 5 km/h


20

Core procedures: • Images taken with single LED on each

time • 3D surface reconstruction from

images via Shape from Shading (SfS) • Scan matching for large area

measurements

DSLR camera LED light

Cover

Proposed conceptual system [1]

…

Scan matching

Special features: • Fast data acquisition due to area

scan • Compact and mobile

Micro Road Profiling System


Results

21


Noise Modeling

22

Inputs Road surface

Tire with tread Contact/impact

ALE FEA

Transient response Frequency-domain response

Sensitivity analysis

Angular velocity Toe angle Camber angle

Mechanistic model

Slip noise Acoustic analysis

CFD

Slip Impact Impact noise

Air pumping

Outputs Deformation Road noise

Pattern noise


Tire State Measurement System-TSMS

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TSMS

Tire Health

Friction Potential

Tire Slip Conditions

Tire Wear Condition

Tire Temperature

Tire Pressue


Tire State Measurement System

Robust And Prompt Information About The Contact Dynamics, Health, Temp, Pressure, ..

Eliminating Uncertainty-adding Procedures

Direct Estimation Technique

Methodology

Attach Sensor Modules To The

Innerliner

Intelligent Tire System

Add “Intelligence” To The Modern Day

Passive Tire

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Low Grip

On-board Vehicle Controller

Driver Assist System

The Tire of The Future

(Improve the performance of current control systems like

ABS/VSC)

Vehicle Equipped With Intelligent Tires

Tire Force Feedback Based Advanced Chassis Control Systems for Vehicle Handling and Active Safety

“Tire- In -The Loop (TIL) System”

(Drivers can adjust their driving style) Feedback

From The Tire

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500 600 700 800 900 1000 1100 1200 1300 1400

0

500

1000

1500

2000

2500

3000

3500

4000

Time [S]

Acc

eler

atio

n [m

/sec

2 ]

Time Domain Signal

Single-point Sensing System – “Useful” Data Available Once Every Tire Revolution

Radial acceleration measurement on the tire innerliner

Sensor placed in the crown region

Sensor Location

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Tire Instrumentation and Testing

Extensive Outdoor Testing

High Speed Testing Wet Testing

Asphalt/Concrete Testing Gravel Testing

Tri-axial accelerometer Sensor placed in the crown region

Sensor Location

Mounting: Adhesive

Evaluate the system performance in real world conditions

Goal: Examine Sensor Performance

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0 50 100 150 200 250 300 350-50

0

50

100

150

Wheel Turn [°]

Acce

lera

tion

[g]

Lateral Acceleration

0 50 100 150 200 250 300 350-300

-200

-100

0

100

200

300

Wheel Turn [°]

Acce

lera

tion

[g]

Radial Acceleration

0 50 100 150 200 250 300 350-300

-200

-100

0

100

200

300

Wheel Turn [°]

Acce

lera

tion

[g]

Circumferential Acceleration

X

Y

Z

Sensor Signal for One Tire Rotation

Sensor Signal

D Y N A M I C P H E N M E N O N

Linked to 0 50 100 150 200 250 300 350

-300

-200

-100

0

100

200

300

Wheel Turn [°]

Acce

lera

tion

[g]

CircumferentialLateralRadial

Leading Edge Trailing Edge

0 1 2 3-800

-600

-400

-200

0

200

400

600

800Constant Speed Test

Acc

eler

atio

n[m

/s2 ]

Time [S]0 1 2 3 4 5

-300

-200

-100

0

100

200

300

400Acceleration (traction) Test

Acc

eler

atio

n[m

/s2 ]

Time [S]0 1 2 3 4

-600

-400

-200

0

200

400

600

Time [S]

Acc

eler

atio

n [m

/s2 ]

Decceleration (braking) Test

9 10 11 12 13 14-1000

-800

-600

-400

-2000

200

400

600

800

1000

Time [S]

Acc

eler

atio

n [m

/s2 ]

Steering TestTire

Engineering Dimensions & Characteristics

Algorithm Development Process

Feature Extraction Algorithm

R A W

S I G N A L

Test Data From

Extensive Outdoor Testing

Goal: Derive a correlation between the signal and physical phenomenon under investigation

Raw Signal

Valuable Information

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Signal Processing and Feature Extraction

0 50 100 150 200 250 300 350

-300

-200

-100

0

100

200

300

Wheel Turn [°]

Acce

lera

tion

[g]

CircumferentialLateralRadial

Raw Signal Peak Detection

Digital Integrator

Signal Amplitude

Slope Estimation

Power Spectral Density

Wavelet Transform

20 40 60 80 100 120 140 160 180 200 22026

28

30

32

34

36

38

40

42

44

46Total Signal Power per Revolution

Nor

mal

ized

pow

er [d

B]

Revolution Number

Contact Patch Length

Signal Slope

Signal Power (Domain Extracted)

Multiresolution Signal Decomposition (Signal

Energy Content)

Locus Of Deformation

Vibration Ratio Summary of Signal Feature Extraction Algorithms

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System Performance

2.2 2.3 2.4 2.5 2.6 2.7 2.8

x 104

0

20

40

60

80

100

Sample Number

Acc

eler

atio

n

Dry Asphalt Wet Asphalt

TIME DOMAIN SIGNAL

FUZZY LOGIC CLASSIFIER

0 5 10 15 20 25 30 35 40 450.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

Sample Number

Roa

d Su

rfac

e Ty

pe

Terrain Classification

Dry Asphalt

Wet Asphalt

The experimental results show that, the proposed method detects friction change from dry asphalt to wet asphalt while the tire travels at constant speed without braking, accelerating or cornering

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Low-slip conditions High-slip conditions

Classifier was successfully able to distinguish between the different road surface conditions

Classifier performance was unsatisfactory

Higher misclassification rates under high slip conditions were attributed to the increased vibration levels in the circumferential acceleration signal due to the stick/slip phenomenon linked to the tread block vibration modes.

Friction condition can be estimated when vehicle is not necessarily performing any dynamic or handling maneuver.


Load Estimate

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Artificial Neural Network (ANN) Based Parameter Estimation Algorithm

Fz

Features: Footprint length Radial Deformation

0 50 100 150 200800

900

1000

1100

1200

1300

1400

1500

1600

1700

1800

Tire

Loa

d [lb

s]

Time[S]

ActualEstimated

Tire Speed (mph)

System Accuracy (%)

30 90%

45 93%

65 89%

System Performance (Outdoor Tests)


On-Board Vehicle Sensors

Tire Embedded Sensor

≈ 5 Hz

≈ 200 Hz

Vehicle Control System

Data Flow Frequency Mismatch

Way Forward: To exploit the fullest potential of the intelligent tire technology, a data fusion approach with more complex data processing algorithms would be required….

Consequences of Adopting a Single-point Sensing System..

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Closing Remarks • A good example of closing the gap between pavement

characteristics and tire-vehicle system, is IRI

• Up to this point, a single entity that could close the gap in all aspects of pavement characteristics and tire and vehicle dynamics did not exist

• Center for Tire Research (CenTiRe), with the major OEMs and tire companies as members, can become the research partner with DOTs/FHWA/RPUG to further evaluate the existing pavement characterization methodologies and add new ones to close the gap mentioned above

• Also, partnership with private companies can help with the development and commercialization of the technologies being developed

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Thank You! Questions?

9/24/2014

[email protected] www.Centire.org

mailto:[email protected]

http://www.Centire.org

Friction Studies-From Passive to Intelligent Tires › PDFs › PE-2014 › Taheri.pdf · CB-filled...

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Transcript of Friction Studies-From Passive to Intelligent Tires › PDFs › PE-2014 › Taheri.pdf · CB-filled...