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AI-Enhanced Thermal Analysis of SRM Components: A Fuzzy Logic Design Approach

Nidamanuri Sreenivasa Babu1*, Krishna Kumar Koyyala1, Nidamanuri Surekha2, Kondaiah Seku1, Raja Govindan1, Manikandan Kadirvel1

1University of Technology and Applied Sciences-Shinas, College of Engineering and Technology, Engineering Department, Sultanate of Oman.

2Accenture Solutions, Block 7, Outer Ring Rd, Hobli, Bellandur, Varthur, Bengaluru, Karnataka, India.

Corresponding Author E-mail:kondareddyseku@gmail.com

DOI : http://dx.doi.org/10.13005/ojc/410537

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ABSTRACT:

This study investigates thermal effects in synchronous reluctance motors (SRM), focusing on rotor component analysis. The research evaluates temperature distribution across nodes/elements and examines heat flux distribution, deformations, Von-Mises stresses, and strains. Comparative analysis demonstrates that 1060 aluminium alloy exhibits superior thermal stability, maintaining 98% stress resistance against Ti-3Al-8V-4Mo alloy and 99% against Copper-Al-Bronze alloy. Thermal simulations reveal critical heat flux patterns and deformation characteristics under operational temperatures, providing essential data for SRM design optimization. The findings establish 1060 aluminium as the most thermally stable material for SRM rotors among the tested alloys. A Mamdani Type-1 fuzzy logic design methodology has been employed to create a Fuzzy Inference System (FIS). This system is designed to predict rotor behavior by analyzing input parameters, including speed, temperature, and the coefficient of thermal expansion. These results allow us to predict rotor behavior under various operating conditions.

KEYWORDS:

Flux contours; Fuzzy logic designer; Heat flux; SRM rotor; Stress resistance; Temperature;

Introduction

The Switched Reluctance Motor (SRM) operates by utilizing reluctance torque, which arises from variations in the resistance of the magnetic circuit.1 The stator and rotor feature salient pole structures manufactured from laminated non-oriented electrical steel. The concentrated winding coils are exclusively installed in the stator.2 The structure of the motor is simpler in design than those compared to that of induction motors or synchronous motors, since its rotor has its features either winding coils or permanent magnets on its rotor.3 Therefore, SRMs have the possibility of withstanding high-speed rotations.4-7 SRMs operating at high temperatures and under inferior road surface conditions by absorbing impact and vibrations.8-12 Abunike et al., analyzed the switched reluctance motor (SRM) using finite element technique (FET) to improve its performance. Specifically, the study focused on assessing the impact of important geometrical parameters on torque and efficiency.5-7 The transient analysis has demonstrated that the motor exhibits enhanced starting torque and reduced torque ripple. Accordingly, it is advisable to consider modifying the control and switching circuit of the 12/8 SRM in order to further enhance the starting torque.18

Seok et al., conducted electromagnetic analysis to reduce torque ripple by optimizing the mechanical geometry and electric parameters. Furthermore, the dynamic operating conditions for achieving uniform torque are examined through an evaluation of the dynamic characteristics based on switching time and input current.19 The analytical approach of SRM, through inductance estimation and dynamic simulation, as well as the consideration of the inductance profile, is a crucial factor for torque ripple reduction and determining the output power.20 The dynamic characteristics of the designed and manufactured SRM are associated with the act of switching on and off [8-9]. Guiying Song et al., performed a novel Direct Torque Control (DTC) methodology is employed to directly regulate the torque of the SR motor.23 This is achieved by controlling the magnitude of the flux linkage and the change in speed (acceleration or deceleration) of the stator flux vector.24 The simulation results demonstrate that this scheme effectively regulates the torque output of the motor within the hysteresis band.25 Moreover, this scheme is characterized by its simplicity and can be implemented using cost-effective microprocessor hardware. AI can assist in reducing intricacies of electromagnetic models by autonomously selecting significant features or reducing the dimensionality of the problem.26 Consequently, this can result in expedited simulations while upholding accuracy.27 A novel approach incorporating a Fuzzy Logic designer has been adopted to predict rotor behavior. This method leverages parameters including speed, temperature, and the coefficient of thermal expansion to derive its predictions.

However, our investigation was focused on a comprehensive evaluation of temperature impacts on the structural dynamics of the SRM rotor across various materials. Utilizing Solid Works software, the simulations provided critical insights into deformation patterns, as well as detailed analysis of Von-Mises stresses and strains. This high-precision analysis is crucial for understanding material performance under varying thermal conditions. The approach is as shown in the block diagram Fig 1.

Figure 1: Block diagram of the analysisClick here to View Figure

Research Methodology

The structural analysis of switched reluctance motors.28 study that examines the deformations, stresses, and acoustic conditions of the rotor to optimize performance in the machine[29]. In this context, different materials are chosen to perform the analysis and to evaluate the feasibility of sustainable materials from the chosen, as shown in Table 1.32

Table 1: Material properties

S.No. Material Young’s modulus, E in N/mm2 Poisson’s ratio1/m Density     in Kg/m3 Yield strength, in N/m2x105 Thermal expansion Co-efficient/⁰K
1 Ti-3 Al-8V-4Mo alloy 0.39×1011 0.33 4820 10342 8×10-6
2 1060 Aluminum alloy 0.69×1011 0.33 2700 275.7 2.4×10-5
3 Copper- Al-Bronze alloy 1.1×1011 0.3 7400 2757.4 1.7×10-5

The model carries out thermal analysis with three different materials and a nodal temperature of 55⁰C at the centre and the surfaces are applied with a heat flux of 9 Watts/m2.31 The model is discretised to the optimal number of elements.32 and the results are tabulated. For predicting rotor behavior, the fuzzy logic design model was configured with three inputs, each assigned four membership functions. The output is defined by three functions, indicating low, medium, and high-performance activity. The rules were established using the Mamdani Type-1 methodology, considering all possible combinations through the “AND” operation. Fig: 2 illustrates this schematic approach.

Figure 2: Systematic approach of Mandani Type-I FISClick here to View Figure

Results and discussion

The typical SRM rotor is analyzed for thermal analysis, and the results are verified to enhance its performance at elevated temperatures. An 8-fin SRM rotor with the adopted fixed geometry at the motor shaft location as a primary boundary condition, and thermal analysis is conducted. A standard solid mesh is used to develop discretized models, and 11021 elements are created, with 20852 nodes generated during the process. A solid, Blended curvature-based mesh is used with 16 Jacobian points that were chosen by maintaining 3.0238 aspect ratio. A steady-state thermal analysis with the FFE Plus solver type is adopted. The results of aspect ratio can be seen in Fig 3 (a), the meshed model can be viewed in Fig: 3 (b), and the boundary conditions can be observed in Fig: 3 (c). The temperature distribution and heat flux distribution values for the selected three materials are compared as in Fig 4(a) and Fig 4(b).

Figure 3: (a) Aspect ratio of the model (b) Meshed model (c) Boundary conditionsClick here to View Figure
Figure 4: (a) Temperature distribution curve (b) Heat flux distribution curveClick here to View Figure

On the other hand, the model is analyzed for its structural dynamics with and without the effect of temperature. The results are tabulated and compared with their sustainability against the application of temperature environment with direct loads. The temperature distribution is uniformly distributed at lower nodal points below 1000 and above 5000 nodal points from Fig. 4 (a). The heat flux distribution keeps on increasing from 5050 nodal points to 5600 nodal points in Fig. 4 (b). In the same process, the structural dynamics viz., deformation, stresses and strains of the model is performed from the structural analysis and the results are tabulated in Table 2(a), Table 2(b) and Table 2(c) respectively.

Table 2(a): Deformations of the model

Ti-3 Al-8V-4Mo alloy 1060 Aluminum alloy Copper- Al-Bronze alloy
StructuralPoints Without Temp10-5 mm With Temp 10-5 mm Without Temp10-5 mm With Temp 10-5 mm Without Temp10-5 mm WITH TEMP x10-5mm
1 0 0 0 0 0 0
2 1.409 11.83 2.123 35.32 1.302 23.4
3 2.817 23.67 4.247 70.64 2.505 46.8
4 4.226 35.55 6.37 106 3.907 70.2
5 5.635 47.33 8.493 141.3 5.21 93.6
6 7.044 59.16 1.062 176.6 6.512 117
7 8.452 71 12.74 211.9 7.814 140.4
8 9.861 82.83 14.86 247.2 9.117 163.8
9 11.27 94.6 16.99 282.5 10.42 187.2
10 12.68 106.5 19.11 317.9 11.72 210.6
11 14.09 118.3 21.23 353.2 13.02 234

Table 2(b): Von Mises stresses of the model

Ti-3 Al-8V-4Mo alloy 1060 Aluminum alloy Copper- Al-Bronze alloy
 StructuralPoints Without Temp x105 N/m2 With  Temp x105 N/m2 Without Tempx105 N/m2  With Temp  x105 N/m2 Without Temp x105 N/m2 With Temp x105  N/m2
1 0 0 0 0 0 0
2 0.1735 29.24 17.35 57.91 0.1738 62.58
3 0.3471 58.48 34.71 115.8 0.3475 125.2
4 0.5206 87.73 52.06 173.7 0.5213 187.7
5 0.6942 117 69.42 231.6 0.6951 250.3
6 0.8677 146.2 86.77 289.6 0.8689 312.9
7 1.041 175.5 104.1 347.5 1.043 375.5
8 1.251 204.7 121.5 405.4 1.216 138
9 1.388 233.9 138.8 463.3 1.39 500.6
10 1.562 263.2 156.2 521.2 1.564 563.2
11 1.735 292.4 173.5 579.1 1.738 625.8

Table 2(c): Strains of the model

Ti-3 Al-8V-4Mo alloy 1060 Aluminum alloy Copper- Al-Bronze alloy
StructuralPoints Without Temp x10-5  With Temp x10-5 Without Temp x10-5 With Temp x10-5 Without Temp x10-5 With Tempx10-5
1 0 0 0 0 0 0
2 0.01307 1.199 0.01969 3.578 0.01214 2.352
3 0.02613 2.398 0.03939 7.156 0.0242 4.705
4 0.0392 3.597 0.05908 10.73 0.03641 7.057
5 0.05227 4.796 0.07878 14.31 0.04854 9.41
6 0.06533 5.995 0.09847 17.89 0.06068 11.76
7 0.0784 7.195 0.1182 21.47 0.07281 14.11
8 0.09146 8.394 0.1379 25.04 0.08495 16.47
9 0.1045 9.593 0.1576 28.62 0.09708 18.82
10 0.1176 10.73 0.1772 32.2 0.1092 21.17
11 0.1307 11.99 0.1969 35.78 0.1214 23.52

In a 1060 aluminium alloy material, the stress limits exceeded the value of 275.57 N/m2 yield strength specified in the Table: 1 from 6 th spectrum level and all remaining results are well within the specified limit values. The values obtained from the tables are plotted and the results can be seen for deformations in Fig: 5(a), 5(b) and 5(c). The Von-Mises stresses can be observed from the plotted graphs Fig: 6(a), 6(b) and 6(c). In the same way, the strain in the model is plotted in Fig 7(a), 7(b) and 7(c).

Figure 5: Deformation curves (a) Ti-3 Al-8V-4Mo alloy, (b) 1060 Aluminium alloy and (c) Copper-Al-Bronze alloyClick here to View Figure
Figure 6: Von-Mises stress curves (a) Ti-3 Al-8V-4Mo alloy (b) 1060 Aluminium alloy and (c) Copper- Al-Bronze alloy.Click here to View Figure
Figure 7: Strain curves (a) Ti-3 Al-8V-4Mo alloy, (b) 1060 Aluminium alloy and (c) Copper- Al-Bronze alloyClick here to View Figure

It is observed that the deformation is drastically increased with temperature at every structural point by 16.63 times maximum in 1060 Aluminum alloy but whereas for Ti-3 Al-8V-4Mo alloy is increased by 8.3 times and for Copper- Al-Bronze alloy is increased by 17.97 times. The Von-Mises stresses are also increased by 3.3 times in 1060 aluminium alloy, Ti-3 Al-8V-4MO alloy is increased by 168.53 times and copper-al-bronze alloy is increased by 360.06 times with the application of temperature. Finally, the strains are increased by 182 times in 1060 Aluminium alloy, Ti-3 Al-8V-4Mo alloy increased by 91.73 times and Copper- Al-Bronze alloy is increased by 193.73 times with the application of temperature. It is predicted the result from the Fis to observe the performance of the rotor using Fuzzy logic designer at different conditions and the output varies from 0.159 to 0.5 indicating low and medium performance activity for (speed temperature Thermal expansion co-efficient) as shown in Fig: 8(a) and 8(b).

Figure 8: FSI response for the output prediction for (a) (750 30 0.5) (b) (979.73 46.6364 0.5) conditionsClick here to View Figure

Conclusions

The three different materials of the rotor are investigated for their performance against the temperature and proved that the 1060 Aluminium alloy is good at its best for stresses but moderate in deformations and strains. It has been observed that the Copper-Aluminium-Bronze alloy exhibits the greatest increase in deformation, Von-Mises stresses, and strains with the application of temperature, while Ti-3Al-8V-4Mo shows the least increase in deformation and strains, but the greatest increase in Von-Mises stresses.  It may also be recommended that the resonance frequency test evaluate the temperature. Therefore, it is critical to choose an alloy that minimizes deformation, stress, or strain when designing for high-temperature applications.

Future prospects of this work include conducting further tests to explore the long-term durability and fatigue resistance of the 1060 aluminium alloy under varying operational conditions. Additionally, research can be expanded to explore alternative materials or composites that might offer improved performance or cost-effectiveness. Integrating advanced simulation techniques to predict the behavior of these materials in real-world applications could also enhance the understanding and optimization of rotor designs.

Acknowledgement

Thanks to the University of Technology and Applied Sciences, Shinas, for supporting facility.

Funding Sources

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Conflict of Interest

The author(s) do not have any conflict of interest.

Data Availability

Data will be made available upon reasonable request.

Author’s Contribution Statement

Nidamanuri Sreenivasa Babu: Problem Identification, Methodology, Writing -Original Draft;

Krishna Kumar Koyyala: Materials selection and Formal Analysis;

Kondaiah Seku: Results and Data Analysis, Editing of Original draft.

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Article Publishing History
Received on: 12 Jul 2025
Accepted on: 05 Sep 2025
Article Review Details
Reviewed by: Dr. Naresh Batham
Second Review by: Dr. Amit Sharma
Final Approval by: Dr. Ioana Stanciu
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