FEA Based Traction Converter Thermal Design Method for Railway Application Using Mission Profile Definition

Klints, Audars Karlis; Bogdanovs, Arturs; Zarembo, Jaroslavs

doi:10.1109/rtucon56726.2022.9978854

Cited by 2 publications

(1 citation statement)

References 12 publications

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“…The adoption of AI and advanced communication technologies has brought up huge potential in terms of optimization, learning and adaptation, but also raised several dependability concerns [36] MVDC railway system Real-time model Offers a thorough insight into the modelling of every part of the MVDC railway system [37] Optimizing traction converter FEM/FEA Leverages DT to strategically position temperature sensors and power modules for proactive maintenance and breakdown causes identification. [38] Automating intermodal transportation AnyLogics software (Agentbased software)…”

Section: Namentioning

confidence: 99%

Digital Twin for Railway: A Comprehensive Survey

Ghaboura,

Ferdousi,

Laamarti

et al. 2023

IEEE Access

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Digital transformation has been prioritized in the railway industry to bring automation to railway operations. Digital Twin (DT) technology has recently gained attention in the railway industry to fulfill this goal. Contemporary researchers argue that DT can be advantageous in Railway manufacturing logistics to planning and scheduling. Although underlying technologies of DT, e.g., modelling, computer vision, and the Internet of Things, have been studied for various railway industry applications, the DT has been least explored in the context of railways. Thus, in this paper, we aim to understand the state-of-the-art of DT for railway (DTR), for advanced railway systems. Besides, this survey clarifies how DT can serve the railway twin system designers and developers. As DTR is still in its early adoption stage, there is hardly any clear direction to identify the technologies for specific DTR applications. Therefore, based on our findings we present a taxonomy for DTR for designers and developers. Finally, we describe potential challenges, pitfalls, and opportunities in DTR for future researchers.

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Section: Namentioning

confidence: 99%