Analysis of an Oil-Spray Cooling System for an Induction Switched Reluctance Machine Using Computational Fluid Dynamics

Authors

  • Narges Ghandi Department of Electrical Engineering Institute of Artificial Intelligence and Social and Advanced Technologies, Isf.C., Islamic Azad University, Isfahan, Iran
  • Hadi Saghafi Department of Electrical Engineering Institute of Artificial Intelligence and Social and Advanced Technologies, Isf.C., Islamic Azad University, Isfahan, Iran
  • Mohammadali Abbasian Department of Electrical Engineering Institute of Artificial Intelligence and Social and Advanced Technologies, Isf.C., Islamic Azad University, Isfahan, Iran

DOI:

https://doi.org/10.13052/2024.ACES.J.400707

Keywords:

Electric vehicle, Induction Switched Reluctance Machine (ISRM), oil-spray cooling, two-phase thermal management systems

Abstract

The growing interest in electric vehicles has spurred the development of high-performance electric machines. The effective cooling of windings in electric machines is essential as they are the primary site of energy loss. Oil-spray cooling systems have gained popularity due to their ability to reduce temperatures and protect winding insulation. This paper proposes a Computational Fluid Dynamics (CFD) model for the Spray-Cooling Induction Switched Reluctance Machine (ISRM) to enhance the thermal management of electric machines using Ansys Fluent software. The proposed machine demonstrates efficient heat dissipation during transient simulation tests. Oil is applied to both the stator and the rotor during a transient two-phase simulation, enabling effective thermal exchange despite uneven temperature distributions across the components. We first modeled the machine using the finite element method and extracted the losses from ANSYS. This analysis focuses on the energy losses related to the selective oil spray at the end of the rotor. By performing a detailed thermal analysis, we found that increasing the flow rate enhances the Nusselt number, improves heat transfer, and increases the machine losses.

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Author Biographies

Narges Ghandi, Department of Electrical Engineering Institute of Artificial Intelligence and Social and Advanced Technologies, Isf.C., Islamic Azad University, Isfahan, Iran

Narges Ghandi was born in Isfahan, in 1986. She received her B.Sc. degree in Electrical Engineering from Islamic Azad University, Najaf Abad Branch, in 2007, and her M.Sc. degree in the same field from Islamic Azad University, Khomeini Shahr Branch, in 2015. Since 2018, she has been pursuing her Ph.D. in Electrical Engineering at Islamic Azad University, Khorasgan Branch, Isfahan, Iran.

Hadi Saghafi, Department of Electrical Engineering Institute of Artificial Intelligence and Social and Advanced Technologies, Isf.C., Islamic Azad University, Isfahan, Iran

Hadi Saghafi was born in Isfahan, in 1982. He earned his B.Sc. in 2004, M.Sc. in 2007, and Ph.D. in 2014 in power engineering from Isfahan University of Technology. Since 2015, he has been an assistant professor in the Department of Technical Engineering at the Isfahan (Khorasgan) Branch of Islamic Azad University. His research focuses on microgrids, distributed generation, power electronics control, and their applications in power systems.

Mohammadali Abbasian, Department of Electrical Engineering Institute of Artificial Intelligence and Social and Advanced Technologies, Isf.C., Islamic Azad University, Isfahan, Iran

Mohammadali Abbasian received a bachelor’s degree, M.Sc. degree, and a Ph.D. degree in Electrical Engineering from the Isfahan University of Technology. From 2017 to 2018, he was with the Bundeswehr University, Munich, Germany, as a research scientist. He was an assistant professor at the IAU University, Khorasgan, Isfahan, Iran. His research area is Electrical Machines and Drives.

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Published

2025-07-30

How to Cite

[1]
N. . Ghandi, H. . Saghafi, and M. . Abbasian, “Analysis of an Oil-Spray Cooling System for an Induction Switched Reluctance Machine Using Computational Fluid Dynamics”, ACES Journal, vol. 40, no. 07, pp. 627–638, Jul. 2025.

Issue

2025: Vol 40 Iss 7

Section

General Submission

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