Author’s Accepted Manuscript

Modelling of track wear damage due to changes infriction conditions: a comparison between AC andDC electric drive locomotives

Sheng Liu, Ye Tian, W.J.T. (Bill) Daniel, Paul A.Meehan

PII: S0043-1648(16)30108-9DOI: http://dx.doi.org/10.1016/j.wear.2016.05.023Reference: WEA101701

To appear in: Wear

Received date: 30 November 2015Revised date: 25 May 2016Accepted date: 27 May 2016

Cite this article as: Sheng Liu, Ye Tian, W.J.T. (Bill) Daniel and Paul A.Meehan, Modelling of track wear damage due to changes in friction conditions: acomparison between AC and DC electric drive locomotives, Wear,http://dx.doi.org/10.1016/j.wear.2016.05.023

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Modelling of track wear damage due to changes in friction conditions: a

comparison between AC and DC electric drive locomotives

Sheng Liu

1*, Ye Tian

1, 2, W.J.T. (Bill) Daniel

1, 2, Paul A. Meehan

1, 2

1School of Mechanical and Mining Engineering, the University of Queensland, Queensland, Australia 4072

2Cooperative Research Centre for Railway Engineering and Technology (CRC Rail), Queensland, Australia

*E-mail: s.liu3@uq.edu.au

ABSTRACT

The locomotive traction motor electric dynamics and its impact on the rail and vehicle have not been

investigated deeply in transient wheel-rail contact conditions. Such transient traction behaviour could be more

significant to dynamic traction performance and track degradation (i.e. corrugation formation, fatigue, wear,

etc.) than steady state behaviour. In order to study this, detailed numerical simulations are performed to

investigate the locomotive multi-body dynamic response to a change in contact conditions with an AC and DC

electrical traction motor respectively. In particular, creep response, locomotive vibration, and dynamic normal

and tractional forces are determined using a developed full scale locomotive dynamics model. The model

includes the detailed AC and DC motor dynamics. Further investigation has been undertaken to understand the

wear caused during and after the change of friction conditions from dry to wet and wet to dry. The result shows

the GT46Ace locomotive AC drive can achieve higher traction efficiency compared to the equivalent DC drive

running under notch 8, especially in the wet condition. It is also demonstrated that the AC drive produces

slightly lower wear on average than the DC drive after the transient. However, the transient dynamic oscillation

of wear caused by the AC drive is significantly higher than the DC drive.

Keywords: Locomotive traction, multibody dynamics, AC/DC motor dynamics, track wear, contact condition

change

1 Introduction

Track wear is one of the most significant sources of running costs in the railway industry. Track wear damage can

also cause increased energy consumption, excessive vibration/noise, vehicle damage, over/under traction/braking,

and/or accidents in extreme conditions [1]. Predictive modelling methods of wear growth have been investigated for

decades, and many effective models have been proposed[1-5]. However the existing have not typically been applied

to the real complex environment of locomotives, such as under natural perturbations in friction/lubrication,

imperfect wheel/rail profiles, track curvature etc. In particular, according to recent publications [6], a change in the

contact conditions can cause considerable dynamic response in vertical and longitudinal forces. Such transient

traction behaviour could be more significant to dynamic traction performance and track damage than steady state

behaviour. Scott et al. have found that AC drive may cause various of rail defects, these defects are experimentally

investigated using ultrasound [7]. For instance, a sudden change of friction conditions also leads to a change in the

creep behaviour. As a result, the dynamic force and change in the creep behaviour will cause a complex wear growth

phenomenon. This process has not been comprehensively investigated for locomotives.

On the other hand, the progressive development of AC traction drives has changed the railway industry dramatically

due to its high power capacity, reliability and low cost. In general, locomotives with new AC traction motors are

operated with much higher continuous traction forces and adhesion levels compared to locomotives with DC motors.

Recently, it was shown that the rail/wheel contact mechanics and the dynamic response caused by a change in

contact conditions of the new AC driven locomotives is significantly different to the DC models [8]. Thus it is

necessary to investigate and compare the effect of the AC and DC drives dynamics on the wear growth

independently by establishing detailed electric dynamic models with AC and DC drive models. The comparison of

both models can also be valuable to the locomotive design, maintenance and engineering in future locomotive

designs.

Newton/Lagrangian full locomotive models for locomotive dynamic analysis have been built by means of basic

Newton principle or Lagrangian method [9, 10]. Combining with contact mechanics model [11], and complex

electric dynamics model [8] to investigate the effect of wheel-rail contact surface [6], and electric dynamics, the

model is capable to simulate many features such as wear behaviour of a locomotive in real operation conditions. It

has been concluded that the transient dynamics can induce excessive traction and normal force oscillation, therefore

cause extra wear for a short period of time after a change in friction conditions [12]. However the quantitative

analysis of such wear is missing.

The aim of this research is firstly to develop a locomotive dynamic model with AC and DC drives, which is capable

to simulate the dynamic response of a locomotive at the change of contact friction conditions that may cause wear

damage. Then, based on the result, the complex wear growth phenomenon on and after the change in contact

conditions will be investigated. Finally a comparison between AC and DC models is presented and discussed.

2 Simulation modelling

In order to investigate the dynamics of a full size locomotive and the interactions between its different components,

a locomotive dynamic models with AC and DC drives has been developed using MATLAB and Simulink. In the

proposed model, three major subsystems are taken into consideration namely, locomotive dynamic model, AC/DC

electric drive model with creep control, and a Polach traction model. The overall structure of the model is shown in

Fig. 1a. The locomotive dynamics model is based on the Newton-Euler method[13]. The wheel-rail contact in this

model employs a Polach’s contact mechanics model[14]. Comprehensive modular AC/DC electric drive models are

developed to be used in the same environment therefore the results are comparable. These modules are introduced

separately in the following sections.

2.1 Locomotive dynamic modelling

A 2-dimensional locomotive diagram is as shown in Fig.1b, including longitudinal, vertical and pitch modes of

locomotive operation, and is detailed in[8]. This model has 21 degrees of freedom (DOF), including 9 DOF on the

longitudinal, vertical and pitch motions of locomotive body and its two bogies, and 12 DOF on vertical and rotating

motions of six wheelsets. The normal motions between the track and the wheels are simplified and modeled as

wheel contact stiffness. The wheel-track longitudinal motions are modeled using Polach contact mechanics model,

which is detailed in the following section. A simplification has been made that the motors are modeled as fixed on

the bogie evenly with no relative displacement to improve the efficiency of the model. Additionally, lateral

displacement between wheels and bogies is also neglected, however the longitudinal movement of the wheels and

the rail is represented in the contact mechanics model.

(a) (b)

Fig. 1 (a) Overall model structure of the dynamic model of a locomotive, (b) Diagram of locomotive multibody structure.

The locomotive model is built to simulate a full size GT46Ace locomotive. The detailed parameters for the

GT46Ace locomotive dynamics model are listed in Table 1. The contact between the carriages and the tract is

simplified as a longitudinal load and a load mass to reduce the complexity of the model. The longitudinal load is

chosen to be 90% of its maximum traction effort, and the load mass is simulated as 50×90t per carriage. The AC and

DC electric drive dynamics, the multibody dynamics and contact mechanics module are detailed in the previous

section.

Table 1: Detailed parameters of the locomotive model [8]:

Parameter Value

Mass of each bogie frame (kg) 12121 Total mass of locomotive (t) 134

load mass (kg/carriage × no. of carriages) 90000 × 50

Load force (N) 4.8 × 105 Gear Ratio 17/90

Primary suspension springs (Zb1-2) (N/m) 89 × 106

Yaw viscous dampers stiffness (N/m) 45 × 106 Vertical viscous dampers stiffness (N/m) 44 × 106

Secondary suspension springs (Zw1-6) (N/m) 5.2× 106

Longitudinal and lateral shear stiffness (Xb1-2) (N/m) 0.188 × 106 Traction rods stiffness (N/m) 5 × 106

Wheel contact stiffness (N/m) 2.4 × 109

Effective moment of inertia of axle and motor at the axle (kg·m2) 1.351 × 103 Primary suspension vertical damping (kg/s) 10 × 103

Secondary suspension vertical damping (kg/s) 2 × 104

Rail damping (kg/s) 1 × 106 Locomotive body length (L1) (m) 22

Locomotive body hight – without bogie (Lch) (m) 1.93

Bogie length (L2) (m) 3.7 Bogie height (Lbh) (m) 0.733

Horizontal distance between bogies mass centre (m) 13.7

Horizontal distance between axles (m) 1.3 Vertical distance between body bottom and bogie top (m) 0.3605

Vertical distance between bogie bottom and wheel top (m) 0.127 Wheel diameter (m) 1.016

Simulation time step (s) 5×10-6

Creep threshold 4%

An eigenmode analysis was performed in Matlab to identify all the dynamic modes of vibration and to determine the

stability of the system. This analysis is also used to validate the simulation result. The key modes of vibration and

corresponding frequencies are provided in Table 2.

Table 2: Modal frequencies of the locomotive dynamic system vibrations (Hz) and corresponding eigenvalues

Modes Frequency

(Hz)

Car body vertical 0.45

Car body pitching 3.71

Bogie 1 horizontal 1.77

Bogie 2 horizontal 1.77

Bogie 1 vertical 3.72

Bogie 2 vertical 3.79

Bogie 1 pitching 3.55

Bogie 2 pitching 4.55

Wheelsets vertical 137.53

2.2 Contact mechanics

The Polach contact mechanics model is widely used to determine the longitudinal tractive force due to the

interaction between the wheelsets and rail tracks as it is efficient and compares well with field measurements [14]

and more complex models [15]. Therefore the Polach model is employed in this research for efficiency. The

implementation of the Polach model uses the inputs of locomotive velocity, normal contact force, wheel speed and a

set of switchable parameters characterising different contact conditions (such as dry, wet or oil wheel-rail) including

kA, kS, µ0, A and B. The output is the adhesion coefficient, defined as the ratio between the longitudinal force and

normal contact force. In the model, the longitudinal tractive force can be written as:

2

2arctan

1 ( )

As

A

kQF k

k

where Q is the wheel vertical load, ε is the gradient of the tangential stress, kA, kS are adjustable parameters, and µ is

the effective friction coefficient that determined by:

0 1 BA e A where µ0 is the maximum friction coefficient, ω is the slip velocity and A, B are adjustable parameters.

The model is implemented in Matlab Simulink based on the code provided in [16]. The parameters for dry and wet

contact conditions are listed in Table 3, according to Polach’s work, where A is the ratio of friction coefficient as

defined as µ∞/µ0, B is the coefficient of exponential friction decrease (s/m), kA is the reduction factor in the area of

adhesion and kS is the reduction factor in the area of slip. The Polach model parameters kA and kS are tuned for

different contact conditions as shown subsequently.

Table 3: parameters for different contact conditions [8]:

Conditions

Parameters

Dry Wet

kA 1 0.3

kS 0.3 0.75

µ0 0.55 0.3

A 0.4 0.4

B 0.25 0.09

Fig 2: a) Creep, speed and adhesion coefficient relation under dry contact condition; b) Creep, speed and adhesion coefficient relation under wet

contact condition [8]

2.3 AC/DC electric drives

The detailed AC drive co-co locomotive model includes 2 bogies, each with 3 wheelsets/AC drives. This component

is used for the investigation of transient dynamic response of the locomotive connected to the AC drive dynamics.

To achieve the required accuracy, a detailed AC drive model was developed, which includes the AC drives high

frequency electric dynamics caused by either the inverters or the thyristor [17]. An induction motor drive is a

complicated nonlinear system, that has been the subject of an large body of research and the control schemes

developed are complex [18]. The modelling allows the nonlinear motor drive dynamics to be controlled using well

understood linear control techniques [19].

The DC drive co-co locomotive model is much simpler than the AC detailed model, due to the natural of DC motors.

The electro-magnetic torque of a DC motor can be controlled directly by setting armature current value, given that

the value of field flux is fixed. The electromagnetic torque generated by the drive can be written as,

050

100 0 0.1 0.2 0.3 0.4 0.5

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

creep

Polach dry

loco velocity km/h

adhesio

n c

oeffic

ient

0

0.1

0.2

0.3

0.4

0.5

050

0 0.1 0.2 0.3 0.4 0.5

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

creep

Polach wet

loco velocity km/h

adhesio

n c

oeffic

ient

0.05

0.1

0.15

0.2

0.25

(a) (b)

e aT k I

The coefficient kΦ is field flux and in this way the relation between the electromagnetic torque generated by the DC

motor and the armature current is linear.

2.4 Wear model

In general wear between rail and wheel is categorized as Type I (Mild), Type II (Severe) and Type III (Catastrophic)

regimes [4]. According to Clayton [20], Type I wear combines both oxidation and rolling-sliding modes of wear

resulting in debris containing oxide and metal particles. And Type II is characterised by completely metallic wear

debris, the occurrence of microscope ripples on the wear surfaces and some metal transfer. This is a deformation and

fracture process with no evidence of fatigue-like cracks at the surface. While Type III wear involves an initial break-

in period that leads to the production of large pieces of wear debris. This causes self-inflicted wear of both contact

surfaces. To be able to simulate wear rate in the model, the wear rate is represented using ‘wear index’,

(where in which is the tangential force, is the creep rate and is the nominal contact area) [21]. Fig. 3 shows

the monotone relationship between the ‘wear index’ and the actual wear rate measured in an experimental test.

Fig. 3: Wear types identified during tests of BS11 Rail vs. Class D Tyre [4].

2.5 Simulation process

The model is set up to simulate a train running at a constant speed on a dry track for a short time to ensure all

dynamic responses are stabilized. Then the contact condition is suddenly changed to wet and maintained until the

whole system reaches a steady state. During the entire simulation, all the dynamic responses, including slip rate,

normal and longitudinal forces on each axle, vertical and pitching motions of the body and bogies, and torque output

of each motor are recorded. The results showed that substantial transient oscillations in creep and wear will occur

due to the change in frictional conditions.

The model is built in Matlab™ with the Simulink™ module. The efficiency of the model is optimized for real time

use, and takes approximately 5 ~ 10 mins for compiling and 10 ~ 20 mins for simulating 10 s of real-time when run

on a desktop computer, for DC and AC models respectively. Given that this model is in completely monitored

configuration, all dynamic response and electric drive conditions are continuously recorded for research purposes,

which engages considerable amount of memory. It is expected that the simulation time could be significantly

reduced when deployed in real time circumstances by removing those memory usages.

3 Results and discussions

To be comparable, both DC and AC models are run as the same initial speed (45 km/h). The entire simulation

covered 50s operation time. In the first 10s, the locomotive runs in dry wheel-rail contact condition until it reaches a

steady state. After 10s, the contact condition is changed instantly from dry to wet condition. The locomotive

continues running in wet contact condition for 40 seconds to allow the locomotive settle into a new steady state

again. Fig. 4 shows the velocity of the car body and bogies throughout the simulation with the AC drive. As shown

in the figure, during the 50s acceleration, the locomotive speed is increased from 45km/h to 90km/h and achieved a

relatively constant speed. As comparison, the velocity of the car body using the DC drive is also plotted in Fig. 4. It

is shown that the DC drive produced much less oscillation compared to the AC drive. The final speed of the DC

drive is also slightly lower than the AC drive. The history of tractive forces, creep, and wear index are recorded,

plotted and discussed in this section.

Fig. 4: Velocity of the body and bogies on and after the transient of friction condition

3.1 Creep response

Fig. 5 shows the creep response simulated using the DC model. Since the simulation started at a non-zero speed, the

initial creep and tractional force results shows much unrealistic oscillations and high creepage as shown in the figure.

It is demonstrated that it takes approximately 5s for the initial unstable state to settle. Since this time period is for the

numerical and control system to stabilize, therefore it does not reflect a real phenomenon. After that, the creep

become relatively steady at a relatively low value ~0.2% until the change of contact condition. Starting from 10s, the

locomotive runs into wet contact condition, where the creep value of all axles increase dramatically. Almost

instantly after the transient, creep of non-leading axles drop back to a lower value at approximately 1.8% and 1.5%

respectively, while the leading axles continue running at the maximum creep value of 4% for 10s. This is mostly due

to the effect of the creep controller that reduces torque/current supplied to the drive. There are not many oscillations

revealed in the DC model as the torque control of the DC drives are linear and direct compared to the detailed AC

drives that has non-linear electric dynamics. After 30s, all axles enter another steady state until the end of the

simulation.

Fig. 5. Creep response of the DC model.

Fig. 6 shows the creep response simulated using the AC model. The overall trend is similar to those from the DC

model, however substantial difference has been demonstrated. Firstly, after the change of friction conditions, the

creep of non-leading axles decreases gradually instead of instantly as in the DC case. This is due to the AC electric

drive and controller takes several cycles to reduce the torque to a desired level, while for the DC controller, the

torque can be changed instantly by changing the supplied current. It also show significant random like high

frequency oscillations through the entire simulation, while DC model acts smoothly. These high frequency

oscillations are most likely associated with high frequency electric dynamics of the detailed AC model. Detailed

discussion of the AC drive creep response is available in [12]. Furthermore, it takes a longer time (30s) after the

transient for the AC model to stabilize than that with the DC model (10s). Finally the steady creep for wet contact

condition is ~1.8% significantly higher compared to DC model which is ~0.7%. The higher creep leads to higher

Dry

Wet

traction ratio according to the Polach contact mechanics model. This is further confirmed in the traction force curve

results as shown in Fig. 8.

Fig. 6. Creep response of the AC model.

3.2 Traction effort

Fig. 7 shows traction force response of the DC model. It is demonstrated that in most of the running time, the

traction forces are smooth, except the initial and the after-transient unstable periods as discussed. Similar to the

creep response, there is not much dynamic response shown in this case after the first 5s initialization stage. The

steady traction force is approximately 48.5 kN.

Fig. 7. Traction force response of the DC model

Fig. 8(a) shows the traction force response of the AC model, and the highlighted transient response is zoomed in and

shown in Fig. 8(b). It is shown that through the entire simulation, AC has higher force output compared with the DC

model by 10% - 20%. Especially in the wet condition (57.4kN in wet-steady state, compared with 48.5kN for the

DC), as discussed, the creep value is kept relatively high in the AC model, and therefore, according Polach contact

mechanics model, higher traction ratio is achieved, according to Fig. 2. Fig 8(b) shows the dynamic response of the

traction force during the first 5 sec after the change from dry to wet conditions. It is shown that the frequency of the

oscillation is about 3.55 Hz. According to the eigenvalue analysis shown in Table 2, the oscillation can be identified

as the pitching motion of the two bogies.

Dry

Wet

Dry

Wet

Fig. 8. (a) Traction force response of the AC model, (b) zoom in of the transient response.

3.3 Wear

Fig. 9 shows wear index ( ) of the DC model. As discussed, the first 5s does not reflect the real situation, where

it shows an extraordinarily high wear (in the catastrophic regime). After that, the wear index drops back into the

mild wear range. Given that the wear coefficient k0 for dry condition is 0.55 g·s/mm2, and wet condition is 0.16

g·s/mm2. The average wear rate among all the 6 axles in steady dry condition is approximately 0.61 g/s, while the

average wear rate in transient wet condition (10s – 18s) is 2.08 g/s, and for steady wet condition, the wear rate drops

below 0.05 g/s.

Fig. 9. Wear index ( ) response of the DC model

Fig. 10 shows the wear index ( ) of the AC model. Similar to the creep response, frequency oscillations is

shown in the figure. It should be noted that after the change of the contact conditions, the wear rate oscillates in a

much higher amplitude (~40%) compared to the DC response. It is also shown that the AC model has slightly higher

wear rate in steady state of wet contact condition compared with the DC model, however significantly higher

traction effort is achieved.

Dry

Wet

Dry

Wet

(a) (b)

Fig. 10. Wear index ( ) response of the AC model

The wear index and corresponding wear rate for dry/wet conditions and with the AC/DC models are summarized in

Table 4. It is shown that for dry-steady contact, the DC drive produces slightly higher wear compared to the AC

drive. For wet-transient condition, both DC and AC drive produces 3-4 times higher wear compared to the dry

steady state, and over 10 times over the wet steady state. For the wet-steady condition, the AC drive still has slightly

more wear than the DC drive, however AC drive shows much more variation in wear during the transient as well as

a longer transient period of time, which might cause the track corrugation or localized damage [7]. Additionally, the

AC drive has longer (30s) high wear time after the transient, while the DC drive has only 10s. For the wet-steady

state, both DC and AC drives produce very little wear, due to the low wear coefficient for the wet contact condition.

Given that the DC drive produces less traction effort in this stage than the AC drive, in return wear is greatly

reduced from 0.14 to 0.032 g/s. This is a cost-effect problem, and more advanced control logic could further

optimize the wear while keep relatively high traction effort.

Dry

Wet

Table 3: Summary of wear growth rate for dry/wet conditions and with AC/DC models:

Condition Drive

model

Wear index

[N/mm2]

Wear rate

[g/s]

Dry-

steady

DC 1.1 ± 0.3 0.61 ± 0.17

AC 1.0 ± 0.5 0.55 ± 0.28

Wet- transient

DC 13.0 ± 0.1 2.08 ± 0.16 AC 8.8 ± 3.1 1.41 ± 0.49

Wet-

steady

DC 0.2 ± 0.02 0.032 ± 0.003

AC 0.9 ± 0.3 0.14 ± 0.048

3.4 Dry-wet-dry simulation

Further simulation has been performed to investigate the transient response when the contact condition is changed

from wet to dry condition. Similar to the previous case, both DC and AC models used the same initial speed (45

km/h). The entire simulation covered 50s operation time. Initially the locomotive runs in dry contact condition until

the contact condition is changed instantly from dry to wet at 10s. After that, the contact condition is changed again

at 20s from wet back to dry. The locomotive continues running in wet contact condition for 30 seconds to allow

settle into a new steady state again.

Fig. 11 shows wear index ( ) of the DC model for the dry-wet-dry simulation. The first 20 second is identical to

Fig. 9 before the contact condition is changed back from wet to dry condition. It is shown clearly there is a peak

showing instantly after the change of contact condition from wet to dry. The maximum wear index is approximately

2 times of the steady state wet condition, or 40 times of the steady dry condition. This peak indicated that when the

contact condition instantly changed from wet to dry, severe wear could be caused on the track for DC locomotives.

Fig. 11. (a) Wear index ( ) response of the DC model for dry-wet-dry condition; (b) zoom-in of the transient response

Fig. 12 shows wear index ( ) of the AC model for the dry-wet-dry simulation. The peak is also shown in the

figure when the contact condition is changed from wet to dry compared to the DC model. Similarly, the maximum

wear index is approximately 1.8 times of the steady state wet condition, or ~30 times of the steady dry condition.

The absolute value of the maximum wear index for the AC model (22.8 N/mm2) is also much higher than the DC

model (7.8 N/mm2). Therefore severe wear could also be caused on the track for AC locomotives.

Dry

Wet Dry Wet Dry

(a) (b)

Dry

Wet Dry Wet Dry

(a) (b)

Fig. 12. (a) Wear index ( ) response of the AC model for dry-wet-dry condition; (b) zoom-in of the transient response

Fig. 13 shows the traction ratio, traction force (F)/normal force (Q), as a function of creep from the AC drive.

According to the Polach model, two separate creep curves, shown as blue and red in the figure, are representing dry

and wet contact conditions respectively. Variations in the high creep range for the dry curve is caused by the

different speed when the locomotive travels into the high creep range. Arrows in the figure shows approximate

status of the locomotive on the creep curve during the simulation. After the initialization unstable stage, the creep

value has dropped down to the steady state at approximately 1%. After that the creep value kept dropping slowly as

the locomotive is accelerating during the first 10s, as shown in the solid arrow (1) in the figure. After 10s, the

contact condition is changed from dry to wet, where the creep suddenly increased to up to 4% as shown in the figure

as the dashed arrow (2). From 10s to 20s, the contact condition is wet, and creep of all axles started to reduce and

stabilize into a new steady state, as the solid arrow (3) in the figure. After 20s, the contact condition is changed

again from wet to dry, shown as dashed arrow (4) in the figure. There has been an instantaneous high creep point,

after that, the locomotive returned to the initial stable accelerating stage where creep response kept reduce slowly,

shown as solid arrow (5).

Fig. 13. Creep curve of dry-wet-dry simulation

4 CONCLUSIONS

The proposed locomotive dynamics model could be used to predict the wear rate under dynamic conditions through

calculating the wear coefficient. The transient stage from dry to wet causes the maximum damage to the track,

which is nearly 5 times than the steady dry contact for both AC and DC drive models. The AC drive produces 10%-

20% higher traction effort and approximately 33% lower transient wear but for a longer period of time (~10s

compared with ~25s). Therefore the overall volume of wear is close to 60% higher than the DC drive. On the other

hand, the AC drive model produces greater high frequency oscillations in creep and contact forces compared to the

DC model due to the different electric dynamics. Such oscillations causes the AC model has higher dynamic wear

after the transient that may result in uneven wear and lead to corrugation or local fatigue etc.

Future study can be focused on the effect of different velocity and operation status, e.g. accelerating, decelerating, or

constant speed running. Additionally, further investigation could also include extending the model to a wider range

of contact conditions such as oil, mud, and sand, developing advanced control logic to minimize the wear growth,

and adding lateral force response into the model for simulating curved tracks. Experimental work can also add

comprehensive experimental validation of the model using lab tests and field tests.

5 Acknowledgement

The authors are grateful to the CRC for Rail Innovation (established and supported under the Australian

Government's Cooperative Research Centres program) for the funding of this research Project No. R3.119

“Locomotive Adhesion”. The authors acknowledge the support of the Centre for Railway Engineering, Central

Queensland University and the many industry partners that have contributed to this project.

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Highlights

A full size locomotive traction model with DC/AC drive has been developed.

Wear on the track during the change from dry to wet is simulated.

The wear produced by the DC and AC motors has been analysed and compared.

AC drive produce ~20% higher traction and slightly higher wear than the DC drive.