Longitudinal Dynamics in Connected Trains Yurii Davydov A, Maxim Keyno A,*

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Longitudinal Dynamics in Connected Trains Yurii Davydov A, Maxim Keyno A,* Available online at www.sciencedirect.com ScienceDirect Procedia Engineering 165 ( 2016 ) 1490 – 1495 15th International scientific conference “Underground Urbanisation as a Prerequisite for Sustainable Development” Longitudinal Dynamics in Connected Trains Yurii Davydov a, Maxim Keyno a,* aFar Eastern State Transport University, Khabarovsk, Russia 680021 Abstract The present work aims to describe currents developments for the connected train technique for Russian Railways. Longitudinal dynamics are a major issue for long freight train integrity and safety. Using the developed wireless measurement equipment, detailed data was obtained and a thorough analysis of longitudinal dynamics made. This provides new methods of implementation for the haulage of connected trains for a thousand kilometers of the Transsib and Baikal-Amur mainlines. © 20162016 PublishedThe Authors. by Elsevier Published Ltd. Thisby Elsevier is an open Ltd access. article under the CC BY-NC-ND license Peer(http://creativecommons.org/licenses/by-nc-nd/4.0/-review under responsibility of the scientific). committee of the 15th International scientific conference “Underground UrbPeer-reviewanisation under as a responsibility Prerequisite offor the Sustainable scientific committee Development. of the 15th International scientific conference “Underground Urbanisation as a Prerequisite for Sustainable Development Keywords: freght train, dynamics, multi-body systems 1. Introduction Despite just a slight decrease in the total turnover of Russian Railways in 2014, the major Far East seaports all increased their turnover significantly: Port Vostochny was up to 57.8 mil. tons (+19.7%), Vanino to 26.2 mil. tons (+10.4%), Nakhodka to 20.7 mil. tons (+13%), Vladivostok to 15.3 mil. tons (+5.3%), and Pos’et to 6.7 mil. tons.[1] Most of the export cargo was delivered to ports by the Transsib and Baikal-Amur routes, which are now running at the upper limit of their throughput, with some of their sections practically overloaded. An increase in the capacity of Russian Railways in the Far East region requires the use of new technologies for heavy haul freight trains. One of these technologies is coupled trains driving, which is the first step to reaching a fully functional remote distributed power (RDP). Despite the wide use of RDP and the well-developed research on the longitudinal dynamics of long trains, several practical issues remain. During the test trips, an urgent need was identified for the automated identification of the location of the origin of the longitudinal impulse. * Corresponding author. Tel.: +7 914 541-7240 E-mail address: [email protected] 1877-7058 © 2016 Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of the scientific committee of the 15th International scientific conference “Underground Urbanisation as a Prerequisite for Sustainable Development doi: 10.1016/j.proeng.2016.11.884 Yurii Davydov and Maxim Keyno / Procedia Engineering 165 ( 2016 ) 1490 – 1495 1491 The longitudinal dynamics for long freight trains is a well-developed field. Wide spectrum models for classic train composition were developed by both Russian and foreign researchers. Two types of models, the continual and the discrete, allow calculation of the forces of trains for typical regimes, the main purposes of which are safety and reliability. The fundamental Russian works on railroad vehicle dynamics were published in 1919 by Nicolay Zhukovsky. Later, thorough research works were developed by academician Vsevolod Lazaryan (Dnipropetrovsk) and his school: Evgenii Blokhin, Lev Manashkin and others. These studies enabled increases in train length when new types of electric and diesel locomotives were widely commissioned in the USSR during 1950–65. The successful development of heavy freight trains in North America from the late 60s and the rapid development of heavy haul lines in Australia and South Africa in the 70s established a new margin for railway throughput. The “Dynamics of Railway Vehicle Systems” by Garg and Dukkipati [2] was translated into Russian in 1988 and allowed for the comparison of domestic and foreign approaches to the problem of train dynamics. The works of Duncan and Webb [3], van der Meulen and Lombard [4, 5], “Handbook of Railway Vehicle Dynamics” by Simon Iwnicki [8], Cole and Spiryagin [6, 9] the form the foundation for most modern research worldwide. Modern Russian researches in longitudinal dynamics, mostly conducted by the Railway Research Institute (VNIIZHT), Moscow, allow exploring the in-train forces and finding the root causes of couplers breakages [7] and provide the effective workshop for computer-aided modelling of train dynamics [10, 11]. Growing of the computational power in the last decades open the ways to build effective virtual numerical models for train dynamics [13, 14, 15, 16], but real-time computing for all car bodies, draft gears and couplers in the real train still very difficult task, even for supercomputers. An approach to the capacity limits of Russian railroads in the mid 80s prompted a new period of research into long trains and longitudinal dynamics. The longest coal train in the USSR at this time, of 439 cars, weighing 43,467 tons and 6,450 meters in length, was hauled in 1986, which increased the interest in long freight trains worldwide. 2. Context Now, three decades later after successful tests, the typical train length is 71 cars for a loaded train and 100 cars for an unloaded train for the East Polygon of Russian Railways. Connecting loaded trains allows for up to 142 cars on one train, but the use of these trains still has a seasonal and local character, often when one track side is closed for maintenance work. Looking for the ways to increase the capacity, the regular operation for the connected unloaded trains with fixed schedule was successfully implemented for the Far Eastern Railway last year. The typical total length of the connected train is close to 2 km (200 cars). Increasing coal and other cargo flows to the Far East ports of Russia requires Russian Railways to use new and powerful 3- and 4-unit electric locos, 3ES5K and 4ES5K, and wagons (gondola cars) with the axle load 25 tonnes per axle. The total weight of the connected train can reach 14,200 metric tons or more. The major issue is the use of this train for long runs or even for entire trips. Initiation of the regular operation of the connected trains is very important now as Russian Railways begins an extensive modernization program for the East Region. Massive track and bridge renovation works will leave only single track availability on double track lines in the summer season and will fully close single track lines for part of the day. Coal trains traveling from Kuzbass in Western Siberia to the Far East ports have to run more than 6,000 km. The trains pass through flat, mild, and mountainous sections with uphill and downhill gradients of up to 2.7%. Many other places have flat or mild terrain, but drivers need to regulate the speed of the train for various reasons. The sections connecting the Trans-Siberian and Baykal-Amur Railroads with the major sea ports have a complicated mountain profile. The locomotives are characterized by extremely high levels of traction draft. Heavy freight trains are usually hauled using multiple traction units at the front and the back of the train. Safe train handling in the present circumstances depends greatly on the skills of the locomotive crews. Inconsistent or incorrect operations lead to unintended coupler breakage. Any reckless manipulations or changes of tractive or braking forces pose a potential threat of train decoupling or car derailment due to in-train forces. For a long time, as the main procedures to reduce coupler breakage numbers were the focus of the technical training of locomotive engineers, an emphasis was placed on the explanation of the specific features of locomotive 1492 Yurii Davydov and Maxim Keyno / Procedia Engineering 165 ( 2016 ) 1490 – 1495 traction control and the breaking apparatus under different conditions of movement, and building of long-term skills in locomotive hauling techniques and algorithms. However, because of the increase in freight train length, even experienced engineers cannot objectively assess the longitudinal dynamics of the entire train to formulate the algorithms of safe hauling while driving the train. Therefore, effective instrumentation must be used to identify the places, values, and mechanism for high-level longitudinal dynamics in trains. 3. Research The classic solution for the measurement of in-train dynamics is to use specially prepared couplers equipped with strain gauge sensors, but this technique is very difficult for routine field testing and requires enough time for the installation of several couplers to cars as well as the yard work for placing the cars in their positions on the train. Because the main purpose is the measurement of train longitudinal dynamics, a measurement system was developed based on easy mounting sensors: accelerometers and ultrasonic range meters. In addition, several channels for braking system monitoring can be added: ten channels for the air pressure sensors (by five per coupled car) and one channel for the infrared pyrometer. The core of the mobile measurement system (Figure 1) is the hybrid FPGA-microprocessor board from National Instruments, USA. A 400 MHz ARM processor, Xilinx FPGA, precision onboard 16-bit analog-digital convertor, and solid-state storage card interface enable the solving of any routines for data acquisition, preprocessing, and saving and sending to the master workstation. The ambient operating temperature range is from minus 40°C to 85°C, and the wide power supply voltage between 9 and 30 VDC, to allow for the use of light and efficient LiFePo4 batteries for 12–14 working hours. Fig. 1. Mobile measurement system Wireless data transmission can be performed ZigBee modules.
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