Analyzing modeled configuration using finite element analysis for performance prediction of LSRM
Author:
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Software
Link
https://link.springer.com/content/pdf/10.1007/s00521-022-07598-3.pdf
Reference27 articles.
1. Vatani M, Mirsalim M (2020) The modular and crooked-tooth translator linear switched reluctance motor with a high-thrust per weight. IEEE Trans Transp Elect. https://doi.org/10.1109/TTE.2020.3047516
2. Cao R, Su E, Lu M (2020) Comparative study of permanent magnet assisted linear switched reluctance motor and linear flux switching permanent magnet motor for railway transportation. IEEE Trans Appl Supercond 30(4):1–5
3. Li X, Wang X, Yu S (2020) Design and analysis of a novel transverse-flux tubular linear switched reluctance machine for minimizing force ripple. IEEE Trans Transp Elect 7(2):741–753
4. Masoudi S, Mehrjerdi H, Ghorbani A (2020) Adaptive control strategy for velocity control of a linear switched reluctance motor. IET Electr Power Appl 14(8):1496–1503
5. Frank M, Drikakis D, Charissis V (2020) Machine-learning methods for computational science and engineering. Computation 8(1):15
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