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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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
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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| Summary: | 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. |
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| ISSN: | 1687-8140 |