Models and Algorithms of Locomotive Layout Optimization in Regional Heavy Haul Rail Network

2013 ◽  
Vol 13 (9) ◽  
pp. 1544-1550
Author(s):  
Junhua Chen ◽  
Qi Liu ◽  
Jian Yu ◽  
Yajing Zheng
2021 ◽  
pp. 52-58
Author(s):  
Maxim Viktorovich Basharkin ◽  
◽  
Alevtina Gennadyevna Isaycheva ◽  

The paper investigates the limits of change in resistance value of traction rail network elements due to dynamic loads arising during the movement of trains with increased weight and length. An augmented electric diagram of rail joint with a duplicating junction coupler taken into account has been presented. The ways of traction current flow during simultaneous passing of heavy-weight trains along the adjacent track connected by intertrack junctions have been determined. Conclusions have been made about the necessity of constant monitoring of traction rail network elements condition, which can be ensured only by implementing special automated systems.


2013 ◽  
Vol 32 (3) ◽  
pp. 852-854
Author(s):  
Hou-qing LU ◽  
Hui YUAN ◽  
Cheng LIU

Electronics ◽  
2020 ◽  
Vol 10 (1) ◽  
pp. 13
Author(s):  
Balaji M ◽  
Chandrasekaran M ◽  
Vaithiyanathan Dhandapani

A Novel Rail-Network Hardware with simulation facilities is presented in this paper. The hardware is designed to facilitate the learning of application-oriented, logical, real-time programming in an embedded system environment. The platform enables the creation of multiple unique programming scenarios with variability in complexity without any hardware changes. Prior experimental hardware comes with static programming facilities that focus the students’ learning on hardware features and programming basics, leaving them ill-equipped to take up practical applications with more real-time constraints. This hardware complements and completes their learning to help them program real-world embedded systems. The hardware uses LEDs to simulate the movement of trains in a network. The network has train stations, intersections and parking slots where the train movements can be controlled by using a 16-bit Renesas RL78/G13 microcontroller. Additionally, simulating facilities are provided to enable the students to navigate the trains by manual controls using switches and indicators. This helps them get an easy understanding of train navigation functions before taking up programming. The students start with simple tasks and gradually progress to more complicated ones with real-time constraints, on their own. During training, students’ learning outcomes are evaluated by obtaining their feedback and conducting a test at the end to measure their knowledge acquisition during the training. Students’ Knowledge Enhancement Index is originated to measure the knowledge acquired by the students. It is observed that 87% of students have successfully enhanced their knowledge undergoing training with this rail-network simulator.


Author(s):  
Eva Loukogeorgaki ◽  
Constantine Michailides ◽  
George Lavidas ◽  
Ioannis K. Chatjigeorgiou

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