Towards Digital Twins: a Novel Model-Data Fusion Method for Simulating a Train Passing Through Curved Tracks
The use of digital twin technology eases the integration of physical and virtual spaces, thus offering significant potential for simulation of the dynamics of trains passing through curved tracks and enhancement of train operational safety when compared with the previous emphasis on traditional dyna...
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| Format: | Article |
| Language: | English |
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SAGE Publishing
2025-04-01
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| Series: | Advances in Mechanical Engineering |
| Online Access: | https://doi.org/10.1177/16878132251332069 |
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| author | Shaodi Dong Tengfei Jing Wei Xia |
| author_facet | Shaodi Dong Tengfei Jing Wei Xia |
| author_sort | Shaodi Dong |
| collection | DOAJ |
| description | The use of digital twin technology eases the integration of physical and virtual spaces, thus offering significant potential for simulation of the dynamics of trains passing through curved tracks and enhancement of train operational safety when compared with the previous emphasis on traditional dynamic research. This paper introduces a novel model-data fusion method that uses extended Kalman filtering (MDF-EKF) to simulate the dynamics of trains passing through curved tracks, with the ultimate goal of advancing development of digital twins. By integrating the three components of interaction, data fusion, and model-data fusion, this framework ensures that data from both static and dynamic sources are captured and used accurately to create a comprehensive digital twin that can then be used to map physical train operating states accurately. Furthermore, by using the extended Kalman filtering method, the proposed MDF-EKF method can estimate the operating state variables of the train. When compared with other model-data fusion methods, the MDF-EKF method exhibits minimal deviations throughout the entire process of a train passing through curved tracks. Experimental results indicate that the MDF-EKF method reduces the state description errors caused by system uncertainty and reflects the real-time operating states of trains passing through curves accurately. |
| format | Article |
| id | doaj-art-3a0ef2a6eef34b8296c7bb2b0b8eeb0a |
| institution | OA Journals |
| issn | 1687-8140 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | Advances in Mechanical Engineering |
| spelling | doaj-art-3a0ef2a6eef34b8296c7bb2b0b8eeb0a2025-08-20T02:19:51ZengSAGE PublishingAdvances in Mechanical Engineering1687-81402025-04-011710.1177/16878132251332069Towards Digital Twins: a Novel Model-Data Fusion Method for Simulating a Train Passing Through Curved TracksShaodi Dong0Tengfei Jing1Wei Xia2School of Automation, Chongqing Industry Polytechnic College, Chongqing, PR ChinaSchool of Vehicle Engineering, Chongqing Industry Polytechnic College, Chongqing, PR ChinaChongqing Jianshe Vehicle System Co., Ltd., Chongqing, PR ChinaThe use of digital twin technology eases the integration of physical and virtual spaces, thus offering significant potential for simulation of the dynamics of trains passing through curved tracks and enhancement of train operational safety when compared with the previous emphasis on traditional dynamic research. This paper introduces a novel model-data fusion method that uses extended Kalman filtering (MDF-EKF) to simulate the dynamics of trains passing through curved tracks, with the ultimate goal of advancing development of digital twins. By integrating the three components of interaction, data fusion, and model-data fusion, this framework ensures that data from both static and dynamic sources are captured and used accurately to create a comprehensive digital twin that can then be used to map physical train operating states accurately. Furthermore, by using the extended Kalman filtering method, the proposed MDF-EKF method can estimate the operating state variables of the train. When compared with other model-data fusion methods, the MDF-EKF method exhibits minimal deviations throughout the entire process of a train passing through curved tracks. Experimental results indicate that the MDF-EKF method reduces the state description errors caused by system uncertainty and reflects the real-time operating states of trains passing through curves accurately.https://doi.org/10.1177/16878132251332069 |
| spellingShingle | Shaodi Dong Tengfei Jing Wei Xia Towards Digital Twins: a Novel Model-Data Fusion Method for Simulating a Train Passing Through Curved Tracks Advances in Mechanical Engineering |
| title | Towards Digital Twins: a Novel Model-Data Fusion Method for Simulating a Train Passing Through Curved Tracks |
| title_full | Towards Digital Twins: a Novel Model-Data Fusion Method for Simulating a Train Passing Through Curved Tracks |
| title_fullStr | Towards Digital Twins: a Novel Model-Data Fusion Method for Simulating a Train Passing Through Curved Tracks |
| title_full_unstemmed | Towards Digital Twins: a Novel Model-Data Fusion Method for Simulating a Train Passing Through Curved Tracks |
| title_short | Towards Digital Twins: a Novel Model-Data Fusion Method for Simulating a Train Passing Through Curved Tracks |
| title_sort | towards digital twins a novel model data fusion method for simulating a train passing through curved tracks |
| url | https://doi.org/10.1177/16878132251332069 |
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