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Lookup NU author(s): Muhammad IkhlaqORCiD, Sana Ullah, Daniel Smith, Professor Barrie MecrowORCiD, Dr Xu DengORCiD
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© 2025 Elsevier Ltd. The performance of electric machines heavily depends on the airgap length, as it affects magnetic energy transfer. A larger airgap increases the magnetic circuit reluctance, reducing output power but making heat removal easier. A numerical approach estimates airgap heat transfer and windage loss, validated against analytical correlations based on Taylor-Couette flow, with the inner cylinder rotating and the outer stationary. Heat transfer and windage loss correlations are developed for various airgap ratios (G) and aspect ratios (AR). Skin friction coefficients for different airgap geometries are estimated to calculate windage loss for high Reynolds and Taylor numbers. The airgap ratio significantly impacts heat transfer, while the aspect ratio strongly affects windage loss. Machine Learning Algorithms (MLAs) are trained and tested on 1200 data points from high-fidelity Computational Fluid Dynamics (CFD) and Computational Heat Transfer (CHT). Comparisons of Artificial Neural Network (ANN), Random Forest (RF), and Support Vector Regressor (SVR) performances against CFD data show that ANN predicts skin friction coefficients best, while SVM excels in predicting windage loss and the Nusselt number.
Author(s): Ikhlaq M, Ullah S, Smith DJB, Mecrow B, Deng X, Amjad Raja MN, Shahzad MW
Publication type: Article
Publication status: Published
Journal: Thermal Science and Engineering Progress
Year: 2025
Volume: 64
Print publication date: 01/08/2025
Online publication date: 03/07/2025
Acceptance date: 01/07/2025
ISSN (electronic): 2451-9049
Publisher: Elsevier Ltd
URL: https://doi.org/10.1016/j.tsep.2025.103832
DOI: 10.1016/j.tsep.2025.103832
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