Malaysia. Kuala Lumpur, Malaysia. Keywords: Braking Distance, Gross Vehicle Weight (GVW), Vehicle Classification, Heavy Vehicle, Road Safety

Transcription

1 Applied Mechanics and Materials Online: ISSN: , Vol. 564, pp doi: / Trans Tech Publications, Switzerland Simulation and Analysis on the Effect of Gross Vehicle Weight on Braking Distance of Heavy Vehicle Airul Sharizli a,1, Rahizar Ramli a,2, Mohamed Rehan Karim c,3, Ahmad Saifizul a,4 a Department of Mechanical, Faculty of Engineering, University of Malaya, 50603, Kuala Lumpur, Malaysia c Center for Transportation Research (CTR), Faculty of Engineering, University of Malaya, 50603, Kuala Lumpur, Malaysia 1 airul7716@yahoo.com, 2 rahizar@um.edu.my, 3 rehan@um.edu.my, 4 saifizul@um.edu.my Keywords: Braking Distance, Gross Vehicle Weight (GVW), Vehicle Classification, Heavy Vehicle, Road Safety Abstract: Increasing number of fatalities caused by road accidents involving heavy vehicles every year has raised the level of concern and awareness on road safety situation in developing countries like Malaysia. This study attempts to explore the influences of vehicle dynamics characteristics such as vehicle weight and travel speed on its safety braking distance. This study uses a kind of complex virtual prototyping software to simulate vehicle dynamics and its braking performance characteristics. The software was used to generate braking distance data for various vehicle types under various loads and speed condition. The generated data was grouped according to GVW and then analyzed by two-way ANOVA to evaluate its relationship to braking distance. The finding of this study implies that the speed and GVW of various vehicle classifications has a significant effect to the heavy vehicle braking distance. Introduction Road accidents are complex events, often resulting from multiple contributing factors. Human behaviour, the roadway environment and vehicle failure are factors found to contribute approximately 94%, 34% and 12% to vehicle crashes, respectively [1]. An analysis of traffic accidents indicates that human factor is a contributory factor to road traffic accidents [2]. Human factor involved in large-truck crashes can be subdivided into various forms. The most common critical error made by drivers, whether they are truck drivers or other involved drivers, appears to be misjudgement of the save distance gap, which is due to drivers following too closely to the leading vehicle and are over confident in their ability to stop the truck before it crashes [3]. Most drivers consider themselves above average in terms of driving skill. A number of studies conducted in various countries around the world demonstrate that up to 90% of drivers think they are an above average, low- risk driver [4]. For that reason, drivers believe they can travel above the speed limit and not place themselves at high risk. The consciousness of the safe distance gap is very crucial for heavy vehicle drivers to prevent collision with the vehicle in front. Therefore, some countries have imposed rules concerning the minimum time gap or distance gap between two vehicles on the road to prevent front-end and rearend collision. For instance, in Netherlands, fines can be imposed if the distance between the two vehicles is less than 1 second. In Norway, for vehicles weighing more than 3.5 tonnes, a distance of between 0.5 and 1 second can lead to a suspension of the driver s license for 3 to 6 months. In South Australia, the Driver s Handbook describes 2 seconds as a reasonably safe distance [5]. Braking distance (BD) is the distance taken for a vehicle to stop from a specific speed without considering the driver s reaction time. The ability of a vehicle to achieve short braking distance under variables of speed and load is an essential aspect of heavy vehicle (HV) safety. One observation made regarding the parameter considered in most theoretical formula for braking distance that have been proposed is that, all the models only consider the speed of the vehicle. The other important independent parameters for HV such as vehicle classification and GVW, which may have a direct impact on vehicle braking performance, have not been explicitly considered. The characteristic of this important HV parameter is assumed to be the same for all types of vehicle. All rights reserved. No part of contents of this paper may be reproduced or transmitted in any form or by any means without the written permission of Trans Tech Publications, (ID: , Pennsylvania State University, University Park, USA-05/03/16,06:38:16)

2 78 Advances in Mechanical and Manufacturing Engineering Vehicle weight is one of the essential parameters in vehicle design study that can affect vehicle driving, braking and handling performance characteristics [6] and most of the time, vehicle dynamics influence the drivers behaviour when controlling their vehicles [7]. The study by Saifizul et. al. [8, 9] has shown that the GVW for heavy vehicles has a direct influence on speed whether the vehicle travels in a vehicle following situation or in free flow condition. The impetus for these studies arises from the intrinsic interest in understanding the factors which influence the BD of HV, from the fact that there was no detail investigation that relates the BD as a function of GVW. There are twofold objective in this studies. The first objective is to develop a HV simulation model using MSC ADAMs/Truck to generate a set of data for BD with difference GVW. The second objective is to investigate and analyses the effect of different GVW to the BD. Methodology The brake performance of vehicles can be analysed in several ways. This can be done through an actual experimental method or through computer simulation. Obviously, the process of building and instrumenting the prototype for actual experimental testing involves significant engineering time and expenses. With the evolution of computer science, computer simulation is more frequently used to understand physical problems. These techniques are often used as an alternative to very costly experimental methods. In this study, MSC ADAMS/Truck software was used to generate BD data for two to four axle single unit truck (SUT) under various GVW and speed conditions. There are three main steps involved to obtain the BD data from ADAMs Car which are (a) Virtual Vehicle Modelling, (b) Simulation and (c) Data generation and interpretation, as is detailed as Fig. 1. Since the aim of the study is to develop a model that can reflect an actual two to four axle BD situation, it is important to develop a more realistically simulated SUT models. Thus, in this study, the vehicle model and its specification for SUT 2-axle, 3-axle and 4-axle have been developed in accordance to the vehicle type available on the road as state in Table 1. All of these SUTs will be reconfigured according to the existing SUTs parameters. Simulation was carried out under the assumption that the vehicle has reached a steady state condition and stayed on the road at a constant speed before the brakes are applied at 285N. The braking analysis and tire properties to run this simulation are shown in Table 2 and Table 3. Furthermore, air drum brake and parabolic leaf spring suspension are used for the truck category. For this study, the road profile is straight and flat road whereby differences in road materials and stiffness are not significant. As stated in the objectives for this study, GVW is the crucial element for this simulation. The lump mass added in storage compartment will be assigned with different mass (5000Kg interval) for each simulation done. After HV is loaded, its GVW is calculated. The whole event is conducted under constant velocity starting from 40km/h with 10km/h intervals until 100km/h. After each speed interval has been tested out, the next GVW with 5000kg interval will be tested. Once the respective HV has gone through all the simulation steps, the procedures are repeated with the rest of the HVs that have been constructed.

3 Applied Mechanics and Materials Vol Braking system Front axle Drive axle 1 Drive axle 2 Suspension type Table 1: General Specification for 2-, 3- and 4-axles SUT General vehicle specifications for the Single Unit Truck Four axles Three axles Two axles Air Drum brakes Drive axle Front axle Drive axle 1 Drive axle 2 Parabolic leaf spring, shock absorber with anti roll bar Multilink suspension, shock absorber and air bag Drive axle Wheel configuration Four axles truck with rear driven axle Three axles truck with rear driven axle - - Two axles truck with rear driven axle Tyre size 315/80 R /80 R /80 R22.5 Wheelbase [m] [1] Axles space 1 [m] Axles space 2 [m] Axles space 3 [m] Maximum Vehicle Weight From 10,000 [kg] to 50,000 [kg] with 5,000 [kg] interval From 10,000 [kg] to 45,000 [kg] with 5,000 [kg] interval From 5,000 [kg] to 40,000 [kg] with 5,000 [kg] interval Items Analysis mode Road condition Starting velocity Table 2: The Braking Analysis Properties Time start to brake Brake force 285 [N ] Reaction time 0.2 [second] Details Braking Dry, Straight road 30 [km/h] to 100 [km/h] with 10 [km/h] interval At 5 [second] Table 3: The Tire Properties Items Value Unloaded Radius 507 [mm] Width [mm] Aspect Ratio 0.45 Vertical Stiffness 873 [N/mm] Vertical Damping 10 [Ns/mm] Rolling Resistance 3.0

4 80 Advances in Mechanical and Manufacturing Engineering Determination of braking distance of HVs under various speed, GVW and vehicle classification Stage 1 Virtual Vehicle Modelling Stage 2 Simulation Stage 3 Data Generation and Interpretation Acquire the general specification of respective design of HVs Lump mass assigned with different mass (5000Kg interval) Acquire data from ADAMS/PostProcess Model out HVs with different number of axles Identify and offset the hard-points of related components Calculate the curb weight of respective HVs Insert the lumped mass subsystem Calculate GVW Simulate with constant speed Specify output step size duration of maneuver Simulate the model with different constant speed started from 30km/hr to 100km/hr (10km/hr interval) Selecting user define filter, input requests and components Export data to spreadsheet Repeat the step above for different GVWs and number of axles Fig. 1: Summary of Simulation Workflow Result and Discussion In this research, data was generated through simulation as detailed in previous section. A total of 232 data were generating throughout the simulation. The data were then grouped according to GVW and speed for analysis. To investigate whether the effect is statistically significant, two-way ANOVA analysis was carried out. Table 4 shows that there was a significant interaction between the effect of speed and GVW on the HV BD, F (7, 88) =642.04, p<0.01 and F (6, 88) =51.015, p<0.01.

5 Applied Mechanics and Materials Vol Source Table 4: Two-way ANOVA test Type III Sum of Squares df Mean Square F Sig. Corrected Model a Intercept Speed GVW Speed * GVW Error Total Corrected Total a. R Squared =.983 (Adjusted R Squared =.973) Fig. 2: Effect of GVW on Heavy Vehicle Braking Distance Based on line graph plot in Fig. 2, it can be imply that HVs travelling at range 0 km/h to 30 km/h, the GVW has not much considerable effect on the total BD. However, the increase of BD is seen when HVs travel at 40km/h to 100km/h. From this result, it is important to note that the higher the HV s GVW, the longer the BD of the HV. Thus, in an emergency situation, the overloaded HV will not be able to stop in the same distance as a non-overloaded truck, no matter how hard the HV driver tries. It should be mentioned that the simulation of the braking distance is based on the ideal condition of the truck and road surface. If the truck condition is less than ideal (for e.g. poor brake condition, bad tires etc) and the road surface is wet/slippery, the outcome of an emergency situation may be fatal.

6 82 Advances in Mechanical and Manufacturing Engineering Summary This study aims to simulate and make a preliminary analysis of the influence of GVW on BD for various types of HV. Simulation has done using virtual prototyping simulation, ADAMs software. With advancement of computer technology and simulation techniques, a more accurate simulation data can be easily obtained at a much lower cost. The study suggests that the HV GVW were significant effect on BD especially during high speeding travelling. Major outcomes of this study indicate that: 1. Considering speed alone is not enough to explain the actual situation on road safety particularly those involving heavy vehicles. GVW is also important factor that need to be considered in BD formula or model. 2. Drivers of heavy vehicle should really be aware of their vehicle braking performance capability. Heavy vehicles have considerably lesser braking performance capability rates than passenger cars and take a minimum 60% more distance for emergency stops on dry roads, contributing to their higher rate of involvement in fatal accidents than any other road vehicles. Acknowledgement The authors would like to acknowledge the assistance from the flagship grant FL awarded by the University of Malaya. References [1] Evans L., Traffic Safety and the Driver, New York, Van Nostrand Reinhold, p [2] A Bener, T Lajunen, T Özkan & D Haigney: Int. J. Crashworthines, Vol. 11:5 (2006), pp [3] Transportation Research Circular E-C117, The Domain of Truck and Bus Safety Research, Transportation Research Board, May 2007 [4] SARTRE report, European drivers and road risk: Report on principal results, France, Institut de Recherche sur les Transport er leur Securite INRETS, [5] Hutchinson, T.P., Tailgating, Centre for Automotive Safety Research, CASR Report CASR046, [6] Bixel R.A., Heydinger G.J., Durisek N.J., Guenther D.A., Effect of loading on vehicle handling, SAE Paper , SAE International Congress and Exposition, [7] Wong J.Y, Theory of Ground Vehicle, John Wiley & Sons Inc., 1993 [8] Saifizul, A.A, Yamanaka, H., Karim, M.R: Accident Analysis and Prevention Vol. 43 (2011), pp [9] Saifizul, A.A, Yamanaka, H., Karim, M.R.: Proc. of the Eastern Asia Society for Transportation Studies Vol. 8 (2011) p

7 Advances in Mechanical and Manufacturing Engineering / Simulation and Analysis on the Effect of Gross Vehicle Weight on Braking Distance of Heavy Vehicle / DOI References [8] Saifizul, A. A, Yamanaka, H., Karim, M. R: Accident Analysis and Prevention Vol. 43 (2011), pp /j.aap