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
Pavement Evaluation September 15-18 Blacksburg, VA
9/24/2014
• 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
Pavement Evaluation September 15-18 Blacksburg, VA
9/24/2014
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
Pavement Evaluation September 15-18 Blacksburg, VA
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
9/24/2014
Pavement Evaluation September 15-18 Blacksburg, VA
9/24/2014
• 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
Pavement Evaluation September 15-18 Blacksburg, VA
Rubber Road Contact
9/24/2014
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
Pavement Evaluation September 15-18 Blacksburg, VA
Estimated Friction
9/24/2014
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
Pavement Evaluation September 15-18 Blacksburg, VA
Nanovea JR25
9/24/2014
• 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
Pavement Evaluation September 15-18 Blacksburg, VA
Friction Tester
9/24/2014
• 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
Pavement Evaluation September 15-18 Blacksburg, VA
Road Profile Characterization
9/24/2014
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
Pavement Evaluation September 15-18 Blacksburg, VA
μ-Slip Curve for Tires
9/24/2014
Pavement Evaluation September 15-18 Blacksburg, VA
Circumfrential plane
Tread and belt diagram
Side wall diagram
Tire-Soil MBD Model
9/24/2014
Pavement Evaluation September 15-18 Blacksburg, VA
Terramechanics Rig
Test Tire: Michelin LTX A/T2 235/85/R16
9/24/2014
Pavement Evaluation September 15-18 Blacksburg, VA
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.
9/24/2014
Pavement Evaluation September 15-18 Blacksburg, VA
Ice Project • 2 inches thickness of ice • Ambient Temperature= 15-170C
9/24/2014
Video
Pavement Evaluation September 15-18 Blacksburg, VA
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
9/24/2014
9/24/2014 Pavement Evaluation September 15-18 Blacksburg, VA
Macro Road Profilometer Prototype System
Tire Society Conference 2014
9/24/2014 Pavement Evaluation September 15-18 Blacksburg, VA
• Identify scan matching algorithm’s ability to assemble road profiles – Image features are trackable – Depth measurements are preserved
Experimental Setup
Tire Society Conference 2014
9/24/2014 Pavement Evaluation September 15-18 Blacksburg, VA
Road Profiling Results
Tire Society Conference 2014
Was constructed while the system was driven at 2 km/h
Endpoints
Was constructed while the system was driven at 5 km/h
9/24/2014 Pavement Evaluation September 15-18 Blacksburg, VA
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
9/24/2014 Pavement Evaluation September 15-18 Blacksburg, VA
Results
21
9/24/2014 Pavement Evaluation September 15-18 Blacksburg, VA
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
Pavement Evaluation September 15-18 Blacksburg, VA
Tire State Measurement System-TSMS
9/24/2014
TSMS
Tire Health
Friction Potential
Tire Slip Conditions
Tire Wear Condition
Tire Temperature
Tire Pressue
Pavement Evaluation September 15-18 Blacksburg, VA
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
9/24/2014
Pavement Evaluation September 15-18 Blacksburg, VA
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
9/24/2014
Pavement Evaluation September 15-18 Blacksburg, VA
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
9/24/2014
Pavement Evaluation September 15-18 Blacksburg, VA
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
9/24/2014
Pavement Evaluation September 15-18 Blacksburg, VA
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
9/24/2014
Pavement Evaluation September 15-18 Blacksburg, VA
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
9/24/2014
Pavement Evaluation September 15-18 Blacksburg, VA
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
9/24/2014
Pavement Evaluation September 15-18 Blacksburg, VA
9/24/2014
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.
Pavement Evaluation September 15-18 Blacksburg, VA
Load Estimate
9/24/2014
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)
Pavement Evaluation September 15-18 Blacksburg, VA
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..
9/24/2014
Pavement Evaluation September 15-18 Blacksburg, VA
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
9/24/2014
Pavement Evaluation September 15-18 Blacksburg, VA
Thank You! Questions?
9/24/2014
[email protected] www.Centire.org
Top Related