Effect of pavement surface and structural conditions on rolling resistance and fuel consumption:
Evaluation of empirical and mechanistic models
Karim Chatti, Ph.D.Department of Civil and Environmental Engineering
Michigan State University
ISAP Technical Committee: Pavement Field Evaluation (TC-PFE)ISAP Day at TRB (January 7 2018)
AcknowledgmentsDGMENT OF SPONSORSHIP
SPONSORSHIP1. NCHRP1-45 Project2. University of California Pavement Research Center (John Harvey)
California Department of Transportation 3. USDOT UTC Center for Highway Pavement Preservation
RESEARCH COLLABORATIONS1. Tom Papagiannakis, Jorje Prozzi and Jolanda Prozzi, Stefan Romanoschi
MSU postdoctoral, graduate, and undergraduate students2. John Harvey and group (UCPRC)
Erdem Coleri (Oregon SU)Argavan Louhghalam (U. Mass)
3. Roozbeh Dargazany, Nizar Lajnef (MSU), Imad Al-Qadi and Hasan Ozer (UIUC)
INSTITUTIONAL COLLABORATIONS1. Texas DOT, Michigan DOT, NCAT2. UC Davis, Oregon State U., U Mass.3. UIUCCLA
Factors Affecting Fuel Consumption
• Vehicle
• Pavement (Rolling Resistance)
3
Thermodynamic efficiency of the engineAerodynamicsWeightTechnological characteristics of the tire:
GeometrySurface characteristics**Structural behavior of the pavement*
• Inflation Pressure• Temperature• Design, materials, dimensions
4Chatti & Zaabar (2012)
5
Field tests experimental matrix
6
Loading conditions
Light truck Articulated truck
6,000 lbs of concrete blocks 45,000 lbs of steel sheets
7
Effect of Surface Characteristics (ANCOVA)
Vehicle class
Speed (km/h)
Summer Winter
Significance (p-value)* Number of Data Points
Significance (p-value)* Number of Data Points IRI MPD Grade lack of fit IRI MPD Grade lack of fit
Medium car
56 0.00 0.54 0.00 0.77 136 0.00 0.81 0.00 0.61 136 72 0.00 0.22 0.00 0.78 146 0.00 0.13 0.00 0.56 146 88 0.00 0.90 0.00 0.75 136 0.00 0.84 0.00 0.78 136
Van 56 0.00 0.35 0.00 0.96 136 0.00 0.83 0.00 0.79 136 72 0.00 0.38 0.00 0.68 146 0.00 0.21 0.00 0.97 146 88 0.00 0.29 0.00 0.77 136 0.00 0.22 0.00 0.78 136
SUV 56 0.00 0.20 0.00 0.75 136 0.00 0.61 0.00 0.86 136 72 0.00 0.40 0.00 0.86 146 0.00 0.83 0.00 0.91 146 88 0.00 0.70 0.00 0.71 136 0.00 0.35 0.00 0.88 136
Light truck 56 0.00 0.50 0.00 0.79 136 0.00 0.96 0.00 0.75 136 72 0.00 0.60 0.00 0.75 146 0.00 0.40 0.00 0.86 146 88 0.00 0.10 0.00 0.77 136 0.00 0.72 0.00 0.71 136
Articulated truck
56 0.00 0.03 0.00 0.79 137 No tests were conducted in winter 72 0.00 0.06 0.00 0.97 146
88 0.00 0.07 0.00 0.78 137
8
Effect of pavement type on fuel consumptionSummer Winter
Sig. Not Sig. Sig. Not Sig.Passenger Car √ √VAN √ √SUV √ √Light Truck √* √† √
Articulated Truck √* √† N/A
* Trucks driven over AC at 56 km/h (35 mph) consume more than trucks driven over PCC in summer conditions
† Not significant at 72 and 88 km/h (45 and 55 mph)
9
Articulated Truck - Summer Condition (30oC)
10
Light Truck – Summer Condition (30oC)
11
NCHRP 720 (HDM 4) Model
Aerodynamic forces
Rolling resistance
Gradient forces
Curvature forces
Inertial forces
( )1000
ircgatr
FFFFFP
++++=ν
2*****5.0 υρ AFCDCDmultFa =
gGRMFg **=
−
= −3
22
10**
***
,0maxCsNw
egMR
M
Fc
υ
( )( )2*13*12*1*11**2 υbMbCRNwbFCLIMCRFr ++=
aaaaMFi *2arctan*10* 3
+=υ
Tractive power
[ ]DEFaIRIaTdspaaKcrCR *3*2*1022 +++= Surface factor
12
13
Step 1: All the PCC site data were used to calibrate the model for PCC pavements (a3*DEF=0). Step 2: The DEF values were backcalculated for all the AC site data points. Step 3: The new DEF values were used to estimate a relationship between deflection, speed and
temperature for trucks. Step 4: All backcalculated deflection parameter (DEF) data were regressed versus speed. Step 5: A linear function was assumed for the temperature adjustment factor. This assumption is
based on the AASHTO1993 overlay design method.
Calibration of the HDM 4 model
00.050.1
0.150.2
0.250.3
0.350.4
0.45
0 20 40 60 80
Def
lect
ion
(mm
)
Speed (km/h)
All dataAT positive dataAll data medianAT medianAT all data
00.20.40.60.8
11.21.4
-10 0 10 20 30 40
Adj
ustm
ent f
acto
r
Temperature ( ͦC)
0
0.1
0.2
0.3
0.4
0.5
0.6
0 20 40 60 80 100 120
Def
lect
ion
(mm
)
Speed (km/h)
-1001020304050
0
2
4
6
8
10
12
14
16
0 20 40 60 80 100
Diff
eren
ce in
fuel
con
sum
ptio
n (%
)
speed (km/h)
15 ͦ C30 ͦ C
Effect of roughness on fuel consumptionHDM 4 versus regression data
Before calibration After calibration14
Effect of texture on fuel consumptionHDM 4 versus regression data
15
EFFECT OF PAVEMENT SURFACE ROUGHNESS ON ROLLING RESISTANCE AND FUEL ECONOMY:
Evaluation of different rolling resistance models
16With Imen Zaabar, Ph.D.
Initial attempts to infuse mechanistic analyses in NCHRP 720 HDM4 model (Zaabar & Chatti 2014)
17
Original NCHRP 720 HDM4
Modified NCHRP 720 HDM4 using DLC
Modified NCHRP 720 HDM4 using DLI
Recent work (Louhghalam et al. 2015)
18
Figure 1. Calibrated Louhghalam et al. model: Roughness-induced change in fuel consumption as function of IRI at V = 70 and 100 km/h for a medium car (Louhghalam et al. 2015)
Vehicle parameters per axle (Louhghalam et al. 2015)
Recent work (Kim et al. 2017)
19
Mechanistic ApproachMichigan State Univ.
20
10-3
10-2
10-1
100
101
102
Wavenumber (cycle/m)
10-14
10-12
10-10
10-8
10-6
10-4
10-2
Elev
atio
n sp
ectra
l den
sity
(m2
. m
/cyc
le)
IRI = 1 m/km
IRI = 3.5 m/km
Example of simulated surface profiles
21
IRI=3.5 m/kmIRI=1 m/km
0 20 40 60 80 100 120 140 160
Distance (m)
-20
-15
-10
-5
0
5
10
15
20
Elev
atio
n (m
m)
0 20 40 60 80 100 120 140 160
Distance (m)
-20
-15
-10
-5
0
5
10
15
20
Elev
atio
n (m
m)
10-3
10-2
10-1
100
101
102
Wavenumber (cycle/m)
10-14
10-12
10-10
10-8
10-6
10-4
10-2
Elev
atio
n sp
ectra
l den
sity
(m2
. m
/cyc
le)
IRI = 1 m/km
IRI = 3.5 m/km
Vehicle Simulation: ¼ car model
Passenger car: - vehicle parameters for ½ of an axle- 4 wheels
Articulated truck: - vehicle parameters for ½ of an axle with dual wheels
(Maxle=8.8t)- 18 wheels
22
Vehicle Simulation: ½ car model
23
Passenger car Heavy truck
Vehicle Simulation: full car model
Calculation of Fuel ConsumptionEnergy dissipated:
- ξb = Calorific value of fuel X engine efficiency= 10.5 MJ/L for passenger car= 16 MJ/L for articulated truck
24
Total fuel consumption:
Passenger car: ( ) ( )( )
0 0* * IRI iIRI i IRI i
b
DFuel Fuel Nw Fuel Fuel Nw
ξ= + ∆ = +
Articulated truck: ( ) ( ) ( )( )
0 0/ 4.4 * * IRI iIRI i IRI i
b
DFuel Fuel M Fuel Fuel N
ξ= + ∆ = +
- Fuel0 = total fuel consumption assuming no rolling resistance from the NCHRP 720 model
Results for Passenger Car
25
1. The mechanistic approach is able to predict (without calibration) the field-observed effect of roughness up until 3.5 m/km.
2. Difference could be caused by the full contact at all times hypothesis between the tires and the surface, which could be violated at higher levels of roughness.
Results for Passenger Car
26
1.00
1.02
1.04
1.06
1.08
1.10
1.12
1.14
1.16
1.18
1.20
1.22
0 5 10 15 20 25 30 35 40No
rmal
ized
fuel
cons
umpt
ion
Speed m/s
1.1
2.3
3.4
4.6
5.7
IRI (m/km)
3. Effect of roughness increases then decreases as the vehicle speed increases, with the maximum being at about 20 m/s
20 9.8 0.647
Vf HzDπ π
= = =×
Stimulus frequency
Unbalanced wheel state
10.7 NWHHz f≈ =
Figure. Effect of speed on fuel consumption at different roughness levels
Results for Articulated Truck
27
1. Reasonable agreement (without any calibration)
2. There are other sources of energy dissipation in the suspension such as:• Friction between various
complex components of a truck suspension.
• Non-linearity in the suspension, etc.
EFFECT OF PAVEMENT SURFACE TEXTURE ON ROLLING RESISTANCE AND FUEL ECONOMY:
Evaluation of different rolling resistance models
28
NCHRP 720 report Texture Model
29
Relationships between pavement surface MPD and a) Adjustment factor for fuel consumption, b) Adjustment factor for rolling resistance, and c) Rolling resistance force at v=80km/h
The rolling resistance adjustment factor is defined as AMPD = x / AMPD = 0.5 , at the speed of 80km/h, temperature of 30°C, IRI of 1 m/km and grade of 0%
MIRIAM Project (2012)Fr= Cr × m × 9.81
Cr = CR0 + IRI × v × Cr1 + MPD × Cr2, with Cr0 = Cr00 + CrTemp (5-T)m = vehicle mass (kg) and Cr0, Cr1, and Cr2 = rolling resistance parameters.
The effect of texture as reported in the MIRIAM study is significantly higher than that reported in the NCHRP 720 report.
For example, at a speed of 90 km/h, alignment standard of 1, increasing a unit value of MPD (1 mm) will cause: • 2.8% increase in the total fuel consumption for a car, • 3.4% for a truck, and • 5.3% for a truck with a trailer. • These values are much higher for the rolling resistance force. 30
31
Boere Study (2009)
Boere Study (2009)
• The tire-pavement surface texture interaction is accounted for by applying a nonlinear contact stiffness to the road.
• Numerical results obtained from the model were compared to measurements using a trailer on test tracks in the Netherlands.
• Measurements are reported in terms of a rolling resistance coefficient (ratio between rolling resistance and axle forces).
• Root mean square (RMStex) of the surface profile is used as the surface texture measure.
• Regression equations for: Numerical model: CRR = 0.001 RMS + 0.0135Empirical model: CRR = 0.001 RMS + 0.0087
32
Experimental and numerical relationships between rolling resistance and texture RMS at v=80km/h on 30 test tracks (Boere, 2009)
Comparison of NCHRP 720 Model with Numerical & Experimental Results by Boere• Rolling resistance Adjustment factor for a light truck (4.1t) at v=80 km/h:
33
• NCHRP 720 results and the experimental results reported by Boere are very close. • Boere’s numerical model predicts the effect of texture on rolling resistance very well.• The difference in the slope shown here is due to the effect of the intercept, since the
model was not calibrated
MPD = 1.729 RMS + 0.019 (Avaik et al. 2013)
Comparison of NCHRP 720 and MIRIAM Models
• Rolling resistance adjustment factors for:a) car (1.5t):
b) heavy truck with trailer (30t):
• The effect of texture is significantly higherin the MIRIAM model. 34
Summary Comparison of RR Texture Models• The increase in rolling resistance with a 1mm increase in MPD for the different
models are summarized below:
• NCHRP 720 predictions agree with the independent rolling resistance measurements reported by Boere, suggesting that the predictions from the MIRIAM model are unreasonably high.
35
Car Light truck Articulated truck
NCHRP-720 3.23% 6.08% 6.08%
MIRIAM 17% - 25%
Boere-experiment - 6% -
Boere-numerical - 4% -
The objective of this work is to investigate the effect of pavement surface texture and roughness on tire rolling resistance using mechanistic approach.
Different components of tire rolling resistance: • Tire bending• Tread slippage• Tread deformation
Scope of the study:• Modeling of surface texture• Characterizing the rubber material• Influence of pavement micro-texture on
rubber block deformation and tread slip• Influence of pavement macro- and mega- texture
on tire deformation (ongoing work)• Influence of pavement roughness on tire and
vehicle suspension system36
Objective and scope
Pavement texture
Rubber material characterization
Micro-scale modeling
Ongoing and future work
On-going Work: Mechanistic Modeling of the Effect of pavement texture on tire rolling resistance
Bending Hysteresis
Tread Slip Hysteresis
Tread Deformation
Grip
AdhesionHysteresis
Shear
Slip Speed
Rolling Resistance 45
IMPACT OF PAVEMENT STRUCTURAL RESPONSE ON ROLLING RESISTANCE AND FUEL ECONOMY
37Danilo Balzarini (Ph.D. candidate)
38
SRR can be defined as the energy required for a rollingwheel to move uphill.The positive slope is caused by the delayed deformation of the pavement.
Structural Rolling Resistance (SRR)
Chupin et al. (2013)
39
Flexible Pavement Response: ViscoWave II-M
-4 -2 0 2 4 6Space[m]
0
50
100
150
200
250
Z D
ispl
acem
ent[m
icro
nmet
ers]
Vehicle Energy Loss – Flexible pavement
Assumptions: Non-dissipative tires Constant vehicle speed
1
ni
diss i ii i
wW p Sx=
∆=
∆∑
40
Traffic direction
dissRR
b
WFuelξ
=
100 100dissRRexcess
C b C
WFuelFuelFuel Fuelξ
= ⋅ = ⋅⋅
ξb is the calorific value of the fuel.
HMA SectionsCaltrans-UCPRC Study
41
Dynamic back-calculation of Relaxation modulus Et, complex modulus E*, and shift factor from FWD time histories (VISCOWAVE-II GP)
Mechanistic PredictionsEffect of Structural Capacity and Season
42
Mechanistic PredictionsEffect of Speed, Temperature and Vehicle Weight
43
HMA vs. PCC: Mechanistic Predictions
44
Rigid Pavement Response - DYNASLAB (Chatti, 1992)
Any Questions?45
Thank you!
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