The influence of vehicle system dynamics on rail foot heat transfer.
Modern railways are always looking for new ways to increase efficiency and drive down costs. Track maintenance is an important issue for both above and below rail operators. Below rail operators need to identify and monitor faults as early as possible so intervention can take place to repair rail flaws before they escalate to an unsafe and costly level. Above rail operators need guarantees on track availability to move their product in an efficient manner. Any fault in the track may damage rollingstock and, in the worst case, derail and potentially destroy parts of a train and that becomes extremely costly for all parties.
A recent trend in track maintenance has seen a move towards detection technologies that are integrated into revenue raising rollingstock. Some examples of this include the Centre for Railway Engineering's Infrastructure Wagon (McClanachan et al. 2001) and LBFosters' IntelliTrack Technology (2016). There are a number of benefits to this approach with the end result being an efficiency improvement for both above and below rail operators. The first benefit is the reduction in need for specialised rollingstock equipment for track monitoring. This in turn leads to a throughput improvement as track time is not taken up specifically for maintenance activities. The second benefit is that a solution integrated into general rollingstock is generally cheaper, and may be rolled out across a wider variety of rollingstock types to further increase the rate at which the track is monitored.
The majority of research conducted into track maintenance technologies is focused on the rail head and Rolling Contact Fatigue (RCF) flaws. While there is significant justification to this research, another part of the rail, the foot, continues to also experience flaws that lead to broken rails and potentially to derailment. From 1992 to 2002 rail foot flaws were the 4th highest occurring flaw type accounting for 4.6% of the total defects identified by the FRA with a damage bill exceeding $US49 million. Although these statistics are over 15 years old the lack of development in detection technologies during this time would indicate that the occurrence of rail foot flaws has likely not reduced. In Australia, heavy haul rail operators still struggle with rail foot flaw detection and are the primary funding source for research into the problem presented here in this paper.
Rail foot flaws occur for a number of reasons that include manufacturing defects, incorrect installation and use, and the rail's inability to withstand any further fatigue (Cannon et al. 2003). There is limited research or products available for foot flaw detection, and even less for integration of detection systems onto general rollingstock.
A common technique in flaw detection for Non Destructive Testing (NDT) is Thermography. A specific type, Infrared Thermography (IRT), is the process of analysing an infrared response from an object and detecting unexpected temperature differentials that may be indicative of a fault. There are two approaches to Thermography, passive and active. Passive Thermography relies on natural temperature differentials between the object being measured and its surrounds. Passive Thermography is commonly used in medical sciences and surveillance (Bagavathiappan et al. 2013).
Active Thermography on the other hand uses excitation to generate a heat flux in the object being measured. There are three common types of excitation used for Active Thermography, namely optical, mechanical and electromagnetic (Maldague 2002). Active Thermography is most commonly used for flaw detection where surface or sub-surface cracks in their passive state aren't distinguishable from the surrounds (Yang and He 2016).
IRT has been chosen as a potential methodology for rail foot flaw detection as it presents a number of potential advantages over other non-contact measurement techniques such as laser guided wave ultrasonic and optical. It is more immune to crack direction, such as longitudinal web-foot separation. In this scenario, ultrasonic requires multiple sensors at different orientations for detection of transverse and longitudinal cracks. Initial crack growth is typically very fine and almost invisible, however, thermal resistance may still be high enough to generate temperature variation around the crack. Like optical, IRT captures imagery and this allows for location, size and orientation of the crack to be detected through post-processing analysis, providing sufficient heat flux is present at the site of the flaw.
It is proposed that, to use IRT as a detection technique, an active approach is required; for this paper a Modulated Thermography (MT) approach will be explored. In MT, a periodic heat source is applied to the surface of an object and the temperature rising process is captured by an infrared sensor (Badghaish and Fleming 2008). Where the heat flow encounters variations in thermal diffusivity caused by flaws, it is interrupted and either an increase or decrease in temperature is experienced in this region. The temperature differential is image captured by the infrared sensor and identified as a flaw.
The next section will detail different locations of heat generation in the rail and set the stage for investigating how heat transfer will influence an IRT detection technique.
1.2. Sources of heat
There are four sources of heat generation in the rail attributed to bending, wheel/rail contact, sleeper/rail contact and the natural environment. A diagram of these interactions is shown in Figure 1. Bending and reverse bending of the rail occurs due to the normal and shear stresses exerted by the wheel/rail contact and sleeper attachment, causing strain in the rail and resulting in heat generation. A side effect of these shear stresses are stability problems in the vehicle that may affect the system dynamics with the result being a change to the temperature generated in the head of the rail due to fluctuations in wheel/rail contact. The influence of this may be captured through advanced multi-body modelling and simulation. Direct quantisation of this effect is not demonstrated by this paper.
Wheel/rail contact generates friction at the contact surface and conduction between the wheel and rail. The contact friction results in an increased temperature at the rail surface (Ertz and Knothe 2002) and permits heat transfer vertically through the rail. The wheel/rail conduction is generally known as rail chill (Crowe and Raj 1998) as the rail is typically at a lower operating temperature than the wheel.
The sleeper/rail interaction results in longitudinal rubbing due to the strain in the rail as it bends under load with heat generated by the rubbing surfaces. There is also a conductive effect between sleeper, wear plate and rail. However, depending on the material composition of the sleeper, this effect is quite small and often ignored in this type of model (Kesler and Zhang 2007).
The final heating effect is due to environmental factors such as solar radiation, convection and body radiation. Zhang presents an energy equilibrium model incorporating these factors for a rail weather prediction system that considers the rail as a beam floating in space (Zhang and Lee 2008).
Of the four sources of heat presented, this paper will focus only on heat transfer resulting from wheel/rail contact friction. In terms of a MT approach, the heat excitation is periodically generated at a point on the surface of the rail by the wheel/rail contact friction of each passing wheelset. The key challenge in this approach is the effect that VSD has on the periodic surface temperature excitation and if sufficient heat is generated to flow to the foot of the rail.
This paper shall present a train simulation and rail foot heat transfer methodology for the Australian Heavy Haul Industry where axle loads up to 40 tonnes are in service with 200 cars or more in a train consist. It shall determine the role VSD contributes in affecting the temperatures experienced in the rail foot under different operating conditions and discuss whether wheel/rail contact friction excitation may be used in a MT detection technique. The paper will not consider variations in thermal diffusivity in a foot flaw itself or the heat self-generated due to its internal frictional rubbing. This will be covered in future work.
The development of a MT foot flaw detection approach using wheel/rail contact friction excitation first requires an understanding of the heat flux process from the running surface to the foot. To achieve this, a theoretical approach for the modelling and simulation of the heat flux vertically through the rail foot is developed. Initial field tests conducted internally by the Centre for Railway Engineering, CQUniversity have measured variations of foot temperature in the range of 0-2 [degrees]C after a heavy haul train of approximately 100 vehicles has passed. Due to these low temperature increases, it is concluded that a full train simulation methodology is required for modelling the heat transfer through the rail.
The simulation approach combines full train system dynamics using advanced VSD simulation techniques, calculation of wheel/rail contact temperature excitation and a vertical rail heat transfer model. A block diagram of the overall methodology is shown in Figure 2. For a MT approach to be valid, the wheel/rail contact friction excitation must generate a temperature increase greater then 15milli-Kelvin in the rail foot. This is equivalent to the minimum sensitivity for an uncooled micro-bolometer-type infrared camera (FLIR Ex-Series Infrared Cameras 2016).
2.1. Rail heat simulation
The rail vertical heat transfer problem is presented as a one-dimensional boundary value problem with the 'top' boundary condition representing the running surface, or excitation surface of the rail, and the 'bottom' boundary condition as the foot of the rail. The temperature of the excitation surface is calculated using the surface contact temperature algorithm presented by Spiryagin (Spiryagin et al. 2016) and the basic flow chart for the algorithm is shown in Figure 3. The bottom boundary condition is assumed to be insulated. It is also assumed that the non-boundary elements are insulated, in other words, only the top boundary condition considers convection and radiation. As the demonstration of a simulation methodology this is sufficient, however, future enhancements of the model should consider non-boundary convection and radiation. The heat transfer is currently solved using the Finite Difference Method (FDM) in Equation (1).
[u.sub.j.sup.m+1] = [u.sub.j.sup.m] + s([u.sub.j+1.sup.m] - 2[u.sub.j.sup.m] + [u.sub.j-1.sup.m] and s = k[[DELTA]t/[([DELTA]x).sup.2] (1)
The rail is divided into arbitrary elements, with each representing a one dimensional heat transfer in the rail. The inertial frame, or absolute position, of every wheel is maintained by the simulator and the excitation surface temperature boundary condition for an element updated when the wheel is determined to be within the region of that particular element. It is a limitation of the surface temperature model that it assumes the initial temperature of the rail surface is at equilibrium, i.e. it has not been heated by any previous rolling contact. Therefore, before updating the element boundary condition, if the existing temperature of the boundary condition is higher than the calculated temperature, the new lower temperature is ignored. Further model development will focus on correcting this behaviour and considerations for wheel/rail conduction.
The surface temperature algorithm requires as input the longitudinal creepage and velocity for each wheel in the train. Therefore, at the end of a train simulation time step, the current vehicle velocity and longitudinal creepages are passed to the rail heat model. Each inertial frame wheel position can be determined by the inertial frame mid-body vehicle position and knowledge of the relative vehicle dimensions.
Once the excitation boundary condition for all wheel contacted rail elements has been updated, convection and radiation can be applied to the entire set of element excitation boundaries as a cooling process. The Finite Difference Method is then applied to update the heat transfer through all elements in the rail model.
2.2. Train simulation
The train simulation captures the key elements of a heavy haul train, including length and number of wheelsets. The rationale for an approach simulating the entire train is to ensure that all kinematic features that occur in long trains are captured and evaluated through the rail heat model. This is important for a MT approach as understanding the minimum number of periodic heating cycles required is essential for determining practical limitations of the detection technique.
The outputs required from the train simulation for every vehicle include its position, longitudinal creepage of each wheel and velocity. These parameters are used as input to the rail heat model for calculating the running surface excitation. To capture the kinematics of a full train, a hybrid simulation approach is used comprising the Centre for Railway Engineering's Longitudinal Train Simulator (CRELTS) (Cole 2006) and the Gensys Multibody simulator (Gensys.1607 n.d.).
CRELTS provides modelling and simulation of train dynamics for the longitudinal degree-of-freedom as shown in Figure 4, with a major focus on non-linear draft gear modelling. Each vehicle in the train is modelled as a rigid body connected by non-linear draft gear connections with bespoke force application depending on vehicle characteristics. The force applicators include propulsion resistance, curve resistance, tractive and dynamic braking effort, train braking and gravity. The draft gear connection models accurately capture the function of different draft gear configurations such as friction clutch and polymer gears. Various control strategies are provided for driving the train such as Fuzzy, feed-forward PID and locomotive logger input. CRELTS is highly optimised and can execute at speeds up to 200x real-time depending on consist configuration. Cole (2006) describes further details on the CRELTS system.
GENSYS is designed as a general multipurpose software package for modelling mechanical, electrical and/or multibody systems. Modelling of rail vehicles using computers was begun by ASEA (Allmanna Svenska Elektriska Aktiebolaget or General Swedish Electrical Company) in Sweden in 1971 in the lead up to the development by that company of the X2000 high-speed tilt train. After initially producing a linear programme in the frequency domain to model a bogie frame with two wheel-sets called LSTAB, a non-linear time-domain simulation programme called SIMFO quickly evolved to model a whole railway vehicle. In 1992, a three-dimensional general multibody dynamics analysis programme called GENSYS was developed. At that time, the responsibility for the software package moved to a new company, AB DEsolver, which now has the sole task of developing and supporting the package (Spiryagin et al. 2014).
An approach described by Spiryagin et al. (2012) for the hybridisation of Gensys with an external simulator is through co-simulation. Co-simulation is the process where two simulator entities share information between each other in either a half or full duplex manner. In a half-duplex model, one simulator acts as a provider. An example of this might be a simulator running in real-time that provides a timing tick to another non real time simulator. In a full duplex system, there is a bi-directional flow of information where both simulators are reliant on each other for data inter-dependence. In the methodology described by this paper and for the remainder of this section, co-simulation is merged with a parallel computing architecture, redistributing the computational complexity and reducing the run-time of a large multi-body simulation problem.
To use Gensys in a co-simulation environment, it is started as a TCP server listening on a port. An external process or simulator can then connect in a peer-to-peer arrangement and share variable information with the Gensys simulator. An example of this is a Gensys vehicle model co-simulating with an external simulator that calculates influences on the vehicle model such as connections, tractive efforts or resistive forces and transfers these to Gensys for application on the vehicle body per time step.
The simulation approach defined by this paper extends the single vehicle co-simulation approach to model an entire train through a single instantiation of CRELTS and multiple Gensys servers representing each vehicle in the train. The challenge presented by a full train simulation approach using MBS is computation time. If each Gensys vehicle instance executes in sequence, the computation time for the train simulator is represented by Equation (2).
[t.sub.comp] = (n[C.sub.CRELTS] + n[C.sub.Genys]) x [t.sub.sim] (2)
Where n is the number of vehicles in the train and [C.sub.CRELTS] and [C.sub.Genys] are the times taken to run one numerical integration step of CRELTS and Gensys, respectively, for one vehicle. Time profiling of the two simulators, although heavily model dependent, shows Gensys is at least three orders of magnitude longer in execution time compared to CRELTS. In the profile comparison conducted for the unit case of 1 vehicle simulating for 1s, CRELTS takes 0.0076s whilst Gensys takes approximately 10s. Using the time Equation in (2) for a 250 vehicle heavy haul train executing a 500s simulation would take around 2 weeks to complete. In this case, it is clearly evident that the Gensys simulation component heavily dominates the total computation time. In this form a full train simulation is unachievable due to the excessive computation time and an algorithm reduction in complexity is required for the Gensys component of simulation. A solution to this problem is to execute the Gensys vehicle instances in parallel with each other. The computational time function then reduces to Equation (3), where the Gensys component now executes in constant time.
[t.sub.comp] = (n[C.sub.CRELTS] + [C.sub.Genys] x [t.sub.Sim] (3)
In comparison, the computation time reduces from 2 weeks to 1.65 h with a reduction in Gensys simulation complexity from linear to constant time. The remainder of this section shall detail the parallel simulation approach.
This type of parallel simulation activity is becoming very practical with access to high-performance computing facilities and multi-core CPU workstations now prevalent throughout university and industry. The proposed Gensys vehicle model, simulation step rate and execution time result in non-volatile memory consumption into the many gigabytes for a single-vehicle model. When multiplied out over a full heavy haul train, this obviously accounts for hundreds of gigabytes of RAM usage for a full train simulation. These types of RAM configuration are still not commonplace in typical engineering work stations. The block diagram in Figure 5 shows the flow of operation and synchronisation for the simulation approach used within this paper.
2.2.2. Train co-simulation methodology
This section describes the general train co-simulation process shown in Figure 5. Before the simulation can commence, Gensys servers are started for each vehicle in the train. As can be appreciated, each vehicle may require a different model type depending on the configuration. In a typical unit train environment seen in heavy haul, there will be at least two models, one for the locomotives and one for the wagons. To ensure the correct data is transferred to the right server, the port listening address for each Gensys server must be known and configured in advance. This is achieved by configuring each server to a known address by using the convention of base port plus vehicle position offset. As an example, in a 250 vehicle train consist there would be 250 individual servers started with each listening on a unique port address.
The main thread, or system process, manages the CRELTS instance and controls the flow of execution between CRELTS and the Gensys vehicle servers. When it starts, it loads and initialises CRELTS and instantiates a thread per vehicle in the train up to the number of vehicles defined by the CRELTS consist. The purpose of each vehicle thread is to manage its co-simulation connection interface. Each thread opens a connection with its respective Gensys server according to the port convention defined for the Gensys servers and establishes the parameters to be transferred. Details on parameters transferred for the specific methodology defined by this paper are discussed in the Simulation Parameters section. Once the per vehicle thread initialisation is complete, each thread signals the main thread that it has completed initialisation and then enters a wait state for further instruction.
Once the main thread has received notification from all vehicle threads that initialisation is complete it can then start the system simulation. The first step is to execute a time step of CRELTS to simulate the current longitudinal state of the train. Upon completion of the CRELTS step the main thread broadcasts to all waiting vehicle threads to wake-up and start their respective co-simulation step. The main thread then enters its own wait state where it listens for completion notifications from its vehicle worker threads.
When each co-simulation vehicle thread wakes up due to the main thread signal, it must collate the required simulator state information for its respective vehicle position from the CRELTS interface and transfer it by TCP to the Gensys server. Each thread then commands its Gensys server to execute its simulation time-step. To maintain synchronisation between the two simulators the Gensys server does not respond to the command until it has completed simulation of its time step. Once the response from the Gensys server is received, the vehicle thread can initiate the results transfer process from Gensys. How these results are then used by the co-simulation or main thread is application dependent. When the co-simulation vehicle thread completes the results transfer, it signals the main thread with a completion event and then re-enters a wait state for the next simulation instruction.
When the main thread receives completion events from all co-simulation vehicle threads it can then execute any other user defined functions. In the case of this paper, it executes the rail heat transfer model using result data transferred from Gensys. Further detail in provided in the Rail Heat Transfer Simulation section.
Although the two systems, CRELTS and Gensys, share as much data as they can to ensure equivalence, there are still minor numerical differences that result in cumulative displacement and velocity error. There are three main sources of error that exist between the two systems, numerical integration, track curvature modelling and wheel/rail contact mechanics. To mitigate these errors a synchronisation controller is used to apply a small compensating force to the Gensys body centre of mass as originally proposed by Spiryagin et al. (2017) in Equation (4).
CV = P([[??].sub.MBS] - [[??].sub.LTS])
where P is the proportional gain, [[??].sub.MBS] the Gensys vehicle velocity and [??].sub.LTS] the CRELTS vehicle velocity.
This paper proposes a new approach that implicitly ties in Spiryagin's controller with an additional component to control displacement error. To ensure both systems are evaluated at the same track locale throughout the life of the simulation, the objective of the proposed controller is to minimise the displacement error between the two simulators. The standard form of a PID controller is shown in Equation (5) with the error function to control expressed in Equation (6).
CV = [K.sub.P]e(t) + [K.sub.I] [integral] e(t) + [K.sub.D][de(t)/dt] (5)
e(t) = [x.sub.MBS] - [x.sub.LTS] (6)
The proposed controller shall only use the proportional (P) and differential (D) components of (4) as the hysteresis provided by the Integration (I) component is unlikely to be useful for this system. Further consideration for integration may be considered in the future. The resultant controller is shown in Equation (7) with Equation (5) substituted into Equation (4) for full clarity.
CV = [K.sub.P]([x.sub.MBS] - [x.sub.LTS]) + [K.sub.D][d([x.sub.MBS] - [x.sub.LTS])/dt] (7)
The proportional velocity control of (4) when compared to the new controller (7) can be seen to be equivalent to the differential only component. Differential only control provides good control on transient changes, however, it is unable to manage any steady state error in the system. The effect on this type of system is that, over time, the displacement difference shall drift away in the direction of the steady state error. An example of this is shown in the first series of Figure 6 where, under a constant traction case, an increasing displacement error occurs. In contrast, the second series of Figure 6 shows that the new PD controller has control over the displacement error, allowing for indefinitely long simulations to be conducted. In this example the proposed controller uses the differential gain of 10kN/m/s and a proportional gain of 2kN/m.
3. Simulation parameters
This section describes the simulation configuration used for the CRELTS and Gensys simulators, the co-simulation data transfer and rail heat transfer simulation.
3.1. Vehicle parameters
For simplicity, the wagon model used in the simulation shares the same physical model parameters as the locomotives, with the exception of traction. This allows for a single model type in Gensys with tractive and dynamic braking effort always idle for wagon instantiations.
3.2. CRELTS configuration
Parameters describing the train consist and used by the CRELTS instantiation are shown in Table 2.
3.3. Gensys configuration
The vehicle model created in and used by Gensys is shown in Figure 7. Refer to the parameters in Tables 1 and 2 for more information about the vehicle.
3.4. Co-simulation parameters
The CRELTS-Gensys co-simulation system executes on a 1 ms time step. The data shared between the two simulators are shown in Table 3 below.
3.5. Rail heat transfer simulation parameters
Rail heat transfer parameters and external input stimuli such as Wheel/Rail contact friction and solar factors are shown in Table 4.
3.6. Test cases
The test cases to be demonstrated by this paper are shown in Table 5. There are four different test case with the same initial running speed of 16.67 m/s (60 km/hr) and total simulation time of 200s. There are two track types, the first being straight track and the second being a track with a single curve commencing 300 m after the lead locomotive start position with a radius of 800 m, transition lengths of 50 m and arc length of 600 m. Both track instances are level (without any gradients).
The results provided demonstrate the functionality of all components of the simulation methodology. Figure 8 shows the coupler forces calculated by CRELTS and are a subset of the data transferred on a per simulation tick to each Gensys vehicle model. Figure 9 shows the creep forces calculated from Gensys and these are transferred back to the CRELTS process as input to the heat model. Figure 10 indicates the synchronisation force calculated using the new PD controller presented to maintain displacement and velocity control for the co-simulation environment. Figures 11 and 12 are heat maps for the four test cases at two instances during the simulation, 60 and 160s, respectively.
5.1. Rail temperature modelling
There is a variation in surface excitation temperature experienced for the four case studies. It is shown in tangent track that negligible temperature excitation is generated, with a surface increase in less than 0.5 K for both the constant speed and accelerating trains. This results in no heat flux vertically through the track anywhere under the train. For the two curve case studies there is a surface excitation generated primarily by the locomotives due to their higher creep forces and this is shown in Figure 11(c and d). The wagons have a small increase in creep forces on the curve transitions as shown in Figure 9, and these are clearly identifiable as surface heat generated in Figure 12(c and d). However, the excitation is sporadic and only generates localised heat flow through the rail. The curve track - increasing speed case shows a heat rise greater than 1 K at a depth of 60 mm, but again this is localised to the curve. No further increases in temperature were measured at depths greater than this.
5.2. Modulated Thermography
From the MT detection technique perspective, the excitations generated have not been sufficient to generate heat flow through to the rail foot area, resulting in no possibility for thermal radiation around a crack and resultant temperature differentials. However, the rail heat model development is in its infancy, simplified and presented as a demonstration for further refinement as part of the greater modelling and simulation methodology.
Key areas for further development include the surface excitation modelling as its limitation of using the initial rail temperature does not capture small gradual increases that may occur in the head of the rail. As can be appreciated, MT relies on the surface excitation temperature so this facet is crucial. The beam models vertical elements are currently one dimensional and the next step of the model should include at least two-dimensional heat flow and possibly include cooling effects on their vertical surfaces. Future development for the self-heating of the flaw area from mechanical excitation due to normal and shear forces in the rail may present an additional form of excitation not yet considered.
Useful information that can be gleaned at this early stage of development is the effect that VSD has on surface excitation temperatures. Running surface temperature modelling dependent on longitudinal creepage and linear vehicle velocity will have large changes in temperature generated at the surface of the rail. While it may be possible under more extreme traction and braking scenarios that heat flow will transfer to the foot of the rail, it is likely that another source of excitation is required for a general purpose IRT foot flaw detection technique.
In the simulation scenarios presented, the curve radii is representative of modern heavy haul rail corridors where curves are 800 m or greater. In the case of older networks, or those with significant topographical constraints, corners with radii down to only 300 m are found in practise. In this environment, at low speeds and high tractive effort, the temperatures generated in the wheel/rail contact patch have been known to exceed 100 K above ambient track temperature. This poses the most likely situation where the excitation is sufficient for MT to be a suitable detection technique.
Figure 10 shows the forces required for locomotive and wagon synchronisation, respectively. The locomotive synchronisation control exhibits greater hysteresis at the start of the run and this is surmised to be a result of the differences in tractive effort application by CRELTS and Gensys. In the case of straight track and constant speed, the control results in negligible synchronisation requirements. However, in the increasing cases there continues to be a small amount of synchronisation force required and this is assumed to be caused by slip in the contact patch. The situation is similar in the wagon control; however, as there is no tractive effort, the control is able to maintain synchronisation without significant force application.
The curving cases demonstrate the source of error described earlier in the synchronisation methodology section. It can be seen in both the locomotive and wagon control cases that the synchronisation force required changes at approximately the 15 and 30s time sequences, respectively, for the vehicles. This aligns with when each vehicle enters the transition to the curve.
This paper presented a full train simulation methodology for calculating the heat transfer to the foot of the rail with consideration for variations in vehicle system dynamics and wheel/rail contact mechanics. A parallelised co-simulation approach was used that integrated the CRE Longitudinal Train Simulator and Gensys. It was shown for a MT detection technique that there are potential limitations for consideration in using surface only excitation for the detection of rail foot flaws. There are significant further developments of the rail heat model planned and required.
The authors would like to acknowledge the Australasian Centre for Rail Innovation (ACRI) for their support of this project.
No potential conflict of interest was reported by the authors.
This work was supported by the Australasian Centre for Rail Innovation [grant number HH#1].
Notes on contributors
Chris Bosomworth is a PhD student with the Centre for Railway Engineering at Central Queensland University. His thesis topic is Moving Vehicle Rail Foot Flaw Detection using Infrared Thermography. He graduated in 2001 with a Bachelor of Mathematical Science and Computing with Distinctions from Central Queensland University and has worked in both industry and academia in research and commercialisation specialising in software engineering for railway applications.
Maksym Spiryagin is the deputy director of the Centre for Railway Engineering at Central Queensland University. His research interests are locomotive traction, rail vehicle dynamics, contact mechanics, mechatronics, acoustics and real-time and software-enabled control systems. He received his PhD in the field of Railway Transport in 2004 at the East Ukrainian National University. He has published four books and has more than 100 other scientific publications and twenty patents as one of the inventors.
Sanath Alahakoon received his BSc Eng (Honours) degree in Electrical and Electronics Engineering from the University of Peradeniya, Sri Lanka in 1994. He received his PhD in Digital Motion Control from the Royal Institute of Technology (KTH), Sweden in 2000. From 2000 till the middle of 2009, he worked as a senior lecturer in the Department of Electrical and Electronic Engineering in the University of Peradeniya, Sri Lanka. Currently he is a lecturer in Electrical Engineering in the School of Engineering and Technology in Central Queensland University, Gladstone campus. His research interests are digital control, estimation and identification, non-linear control, electrical machines and drives, instrumentation, automation and hybrid electric systems.
Colin Cole is the director of the Centre for Railway Engineering at CQU. He has worked in the Australian rail industry since 1984, starting with six years in mechanised track maintenance for Queensland Railways. Since then he has focused on a research and consulting career involving work on track maintenance, train and wagon dynamics, train control technologies and the development of on-board devices. He has been extensively engaged with industry via the past nationally funded Rail CRC programs, and has continuing involvement via the Australian Centre for Rail Innovation and the new Rail Manufacturing CRC. His PhD was in Longitudinal Train Dynamics Modelling. He has authored and/or co-authored over 90 technical papers, one book chapter, two books, numerous commercial research and consulting reports and has developed two patents relating to in-cabin locomotive technologies.
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Chris Bosomworth (a,b), Maksym Spiryagin (a,b), Sanath Alahakoon (a,b) and Colin Cole (a,b)
(a) Centre for Railway Engineering, Central Queensland University, Rockhampton, Australia; (b) Australasian Centre for Rail Innovation, Canberra, Australia
CONTACT Chris Bosomworth [??] email@example.com
Table 1. Locomotive model parameters in both Gensys and CRELTS. Name Value Unit Locomotive model N/A Mass 134 t Length 20 m Number if axles 6 N/A Power Table 2. Key parameters for configuring CRELTS. Name Value Unit Number of vehicles 130 Locomotive positions 1 and 2 Wagon positions 3 to 130 Total wagon mass 17152000 kg Total train mass 17420000 kg Train length 2600 m Simulation step rate 1000 Steps/second Draft gear connection SL76 N/A Train braking system Standalone ECP N/A Table 3. Bi-directional data exchange between CRELTS and Gensys. Name Unit Description CRELTS to Gensys Front draft gear force (FDGF) N The resultant wagon body Rear draft gear force (RDGF) N application force is -FDGF + RDGF Rolling resistance force N Based on Davis equation Tractive/DB effort N Locomotive models only Synchronisation force N Force applied to Gensys vehicle body to maintain synchronisation with CRELTS Gensys to CRELTS (and heat model) Vehicle position M Used by CRELTS in Vehicle velocity m/s synchronisation controller Wheelset 1 left longitudinal m creepage Wheelset 2 left longitudinal m creepage Wheelset 3 left longitudinal m creepage Wheelset 4 left longitudinal m creepage Wheelset 5 left longitudinal m creepage Wheelset 6 left longitudinal m creepage Table 4. Rail heat transfer and boundary condition cooling parameters. Name Value Unit General parameters Thermal conductivity 40 W/milli-Kelvin W/R contact friction 0.47 Specific heat 450 J/kgK Rail density 7850 kg/[m.sup.3] Top rail surface area 0.0075 [m.sup.2] Radiation Emissivity 0.75 Convection Coefficient 6 Solar Atmospheric filtering factor 0.5 Solar absorptivity factor 0.75 Solar constant 1366 W/[m.sup.2] Table 5. Four simulation test cases. Name Power Description Straight--constant speed Notch 5 Maintains almost constant speed for the given notch Straight--increasing speed Notch 8 Increases in speed by almost 2 m/s over the simulation time frame Curve--constant speed Notch 5 See Straight--constant speed Curve--increasing speed Notch 7 See Straight--increasing speed
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|Author:||Bosomworth, Chris; Spiryagin, Maksym; Alahakoon, Sanath; Cole, Colin|
|Publication:||Australian Journal of Mechanical Engineering|
|Date:||Jun 1, 2018|
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