ABSTRACT
Trucks experience excitations during haulage due to road roughness, generating dynamic loads. Current mine haul road design techniques assume static tire loads, ignoring dynamic forces. This paper presents mathematical models for estimating tire dynamic forces on haul roads. Models were solved in Simulink® and RStudio® to generate random road profile (class D) according to ISO 8608, and compute dynamic forces for 59/80R63 tire. Results show that road roughness significantly affects impact forces on roads, with tire dynamic forces (1638.67 kN) ~ 1.6 times static forces (~1025 kN) at rated tire payloads. The method presented gives realistic estimates of tire impact forces, which serves as useful input for haul road design.
Nomenclature
The symbols and abbreviations used in the mathematical model are defined in this section.
DAF Dynamic amplification factor
IRI International roughness index
PSD Power spectral density
CBR California bearing ratio
G(n) Displacement power spectral density
G(n0) Reference displacement power spectral density
n Wave number (also referred to as road unevenness frequency)
n0 Reference wave number (n0 = 0.1 cycle/m)
Z Road surface elevations (tire vertical excitation)
N Number of frequencies between lower and upper bound wave numbers
nmin Lower bound wave number
nmax Upper bound wave number
Δn Wave number increment
ϕ Random phase angle of road surface roughness
Tire vertical velocity
Tire vertical acceleration
Δt Time interval between successive surface elevations
Zt Instantaneous road surface elevation (i.e. elevation at time t)
Zt+Δt Road elevation at time t+ Δt
Wt Total tire impact forces
g Acceleration due to gravity (9.81 m/s2)
h Dynamic tire sinkage
b Tire width
dw Tire diameter
s Soil deformation exponent
kc Cohesive modulus of soil deformation
kϕ Frictional modulus of soil deformation
Rl Tire-loaded rolling radius
Rf Tire-free rolling radius
θ Tire contact angles
f Tire deflection
Tire deflection angle
RDyn Dynamic tire force (taken as total tire force)
RStat Static tire force
Acknowledgements
The authors are grateful to the Heavy Mining Machinery Research group under which the research is being funded. The inputs of Dr. Wedam Nyaaba, Prosper Ayawah and Charlotte Atiiru to the paper are greatly acknowledged.
Disclosure statement
No potential conflict of interest was reported by the authors.