Multistage Impact Energy Distribution for Whole Vehicles in High-Speed Train Collisions: Modeling and Solution Methodology

With the increasing speed of railway vehicles, deciding how to reasonably distribute impact energy to each vehicle has been a widespread concern in safety protection systems. This article formulates a three-dimensional train–track coupling dynamics model using MAthematical DYnamic MOdels (MADYMO) multibody dynamics software. A train-to-train collision is then simulated using this model. A hybrid solution methodology that combines the non-dominated sorting genetic algorithm II (NSGA-II), modified best and worst method with cloud model theory and grey relational analysis is proposed. The optimization parameters and objectives are determined based on the EN15227 crashworthiness requirements for railway vehicles. An empirical case of an existing train with eight vehicles that have been in operation in China is applied to verify this dynamics model derived from a high-speed train and solution methodology. Analysis and discussion are conducted to monitor the robustness of the results and the practical implications for rail transportation are summarized. The results prove that the obtained optimal solution by this research has better crashworthiness than an existing solution.

[1]  Y. Ravalard,et al.  One-dimensional modelling of contact impact problem in guided transport vehicle crash , 1995 .

[2]  David C. Tyrell,et al.  US rail equipment crashworthiness standards , 2002 .

[3]  David C. Tyrell Passenger rail train-to-train impact test. Volume 1 : overview and selected results , 2003 .

[4]  B. Marquis,et al.  Report on a railway Benchmark simulating a single wheelset without friction impacting a rigid track , 2008 .

[5]  Wanming Zhai,et al.  Fundamentals of vehicle–track coupled dynamics , 2009 .

[6]  Abbas Afshar,et al.  Non-dominated archiving multi-colony ant algorithm for multi-objective optimization: Application to multi-purpose reservoir operation , 2009 .

[7]  Deyi Li,et al.  A new cognitive model: Cloud model , 2009, Int. J. Intell. Syst..

[8]  Ping Xu,et al.  Optimization for Crashworthiness of Urban Transit Trains Using Genetic Algorithm , 2011 .

[9]  Minh-Trien Pham,et al.  Multi-Guider and Cross-Searching Approach in Multi-Objective Particle Swarm Optimization for Electromagnetic Problems , 2012, IEEE Transactions on Magnetics.

[10]  Xuesong Jin,et al.  Study on safety boundary for high-speed train running in severe environments , 2013 .

[11]  Xinbiao Xiao,et al.  Development of a simulation model for dynamic derailment analysis of high-speed trains , 2014 .

[12]  Jianqiang Wang,et al.  An Uncertain Linguistic Multi-criteria Group Decision-Making Method Based on a Cloud Model , 2014, Group Decision and Negotiation.

[13]  J. Rezaei Best-worst multi-criteria decision-making method , 2015 .

[14]  Michel Feidt,et al.  Thermo-economic optimization of Stirling heat pump by using non-dominated sorting genetic algorithm , 2015 .

[15]  Jian Li,et al.  Crashworthiness optimisation of the front-end structure of the lead car of a high-speed train , 2016 .

[16]  J. Rezaei,et al.  A supplier selection life cycle approach integrating traditional and environmental criteria using the best worst method , 2016 .

[17]  Haobo Zhang,et al.  A cloud decision framework in pure 2-tuple linguistic setting and its application for low-speed wind farm site selection , 2017 .

[18]  Hu-Chen Liu,et al.  An integrated decision making approach for assessing healthcare waste treatment technologies from a multiple stakeholder. , 2017, Waste management.

[19]  Bo Tang,et al.  Intelligent Fault Diagnosis of the High-Speed Train With Big Data Based on Deep Neural Networks , 2017, IEEE Transactions on Industrial Informatics.

[20]  Ping Xu,et al.  Energy-absorption optimisation of locomotives and scaled equivalent model validation , 2017 .

[21]  Zhiwu Li,et al.  Operation patterns analysis of automotive components remanufacturing industry development in China , 2017 .

[22]  Lóránt Tavasszy,et al.  Evaluation of the external forces affecting the sustainability of oil and gas supply chain using Best Worst Method , 2017 .

[23]  Sen Guo,et al.  Fuzzy best-worst multi-criteria decision-making method and its applications , 2017, Knowl. Based Syst..

[24]  Manicka Dhanasekar,et al.  Dynamic simulation of train–truck collision at level crossings , 2017 .

[25]  Chandan Guria,et al.  The elitist non-dominated sorting genetic algorithm with inheritance (i-NSGA-II) and its jumping gene adaptations for multi-objective optimization , 2017, Inf. Sci..

[26]  Ashkan Hafezalkotob,et al.  A novel approach for combination of individual and group decisions based on fuzzy best-worst method , 2017, Appl. Soft Comput..

[27]  Utkarsh Singh,et al.  Optimal Feature Selection via NSGA-II for Power Quality Disturbances Classification , 2018, IEEE Transactions on Industrial Informatics.

[28]  Haihong Li,et al.  Crashworthiness optimisation of a composite energy-absorbing structure for subway vehicles based on hybrid particle swarm optimisation , 2018, Structural and Multidisciplinary Optimization.

[29]  Pintu Chandra Shill,et al.  New automatic fuzzy relational clustering algorithms using multi-objective NSGA-II , 2018, Inf. Sci..

[30]  Hu-Chen Liu,et al.  Linguistic Petri Nets Based on Cloud Model Theory for Knowledge Representation and Reasoning , 2018, IEEE Transactions on Knowledge and Data Engineering.

[31]  Yang Tang,et al.  High-Dimensional Robust Multi-Objective Optimization for Order Scheduling: A Decision Variable Classification Approach , 2019, IEEE Transactions on Industrial Informatics.

[32]  Sam Kwong,et al.  An Improved Artificial Bee Colony Algorithm With its Application , 2019, IEEE Transactions on Industrial Informatics.

[33]  Qi Lu,et al.  Cutting parameter optimization for machining operations considering carbon emissions , 2019, Journal of Cleaner Production.

[34]  MengChu Zhou,et al.  A Collaborative Resource Allocation Strategy for Decomposition-Based Multiobjective Evolutionary Algorithms , 2019, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[35]  Chengxing Yang,et al.  Further assessment of deceleration-time histories for occupant injury and the damage of protected object in a crash stop , 2019, International Journal of Impact Engineering.

[36]  Q. Estrada,et al.  Effect of radial clearance and holes as crush initiators on the crashworthiness performance of bi-tubular profiles , 2019, Thin-Walled Structures.

[37]  Yong Peng,et al.  A hybrid multi-objective optimization approach for energy-absorbing structures in train collisions , 2019, Inf. Sci..