Browsing by Author "Mutluer, Mumtaz"

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Analysis and Design of a Permanent Magnet Linear Synchronous Motor Based on Inductance Calculation
(Polska Akad Nauk, Polish Acad Sciences, 2025) Yucel, Enes; Mutluer, Mumtaz; Cunkas, Mehmet
This paper presents a comprehensive design and analysis methodology for a Permanent Magnet Linear Synchronous Motor (PMLSM), with a focus on evaluating different inductance modeling approaches. The motor design begins with analytical dimensioning based on defined design parameters. A two-dimensional finite element analysis follows this in ANSYS Maxwell to verify magnetic saturation, back-EMF, flux linkage, and electromagnetic performance under full load conditions. The inductance parameters are calculated using both conventional and look-up table (LUT) based models. In the conventional model, seven different methods are tested under static and dynamic conditions, as well as in non-salient and salient scenarios, and their results are compared. In the LUT model, current-dependent inductance values are extracted from flux linkage maps. The motor designed in Maxwell, along with the calculated inductance data, is integrated into a dynamic cooperative simulation (co-sim) model controlled by an inverter in Simplorer to analyze the thrust force. The results show that the LUT model provides outputs that are closer to the co-sim reference than the traditional model. Furthermore, performance curves based on the Maximum Torque Per Ampere strategy are generated, and the force-speed and power-speed characteristics derived from both inductance models are compared. The findings emphasize the importance of accurate inductance modeling in capturing the actual electromagnetic behaviour of PMLSM under dynamic operating conditions.
A Review of BLDC Motors: Types, Application, Failure Modes and Detection
(MDPI, 2025) Sen, Mehmet; Mutluer, Mumtaz
Brushless DC (BLDC) motors are widely used in many engineering fields such as transportation, industrial automation, pumping systems, household devices, and renewable energy applications. Their popularity mainly arises from advantages like high power density, low noise, long service life, and high efficiency. This study contributes to the literature by comprehensively addressing the types, applications, faults, and diagnostic methods of BLDC motors. This review systematically examines recent studies to identify and classify common mechanical, electrical, magnetic, thermal, and sensor-related faults. Diagnostic approaches reported in these studies are then analyzed and compared. The methods are grouped into several categories, including signal processing, model-based, data driven, artificial intelligence-supported, and thermal or magnetic monitoring techniques. The review results show that hybrid and intelligent diagnostic strategies, which combine different analysis methods, significantly improve the accuracy of fault detection and enable earlier fault identification. These improvements also contribute to higher reliability and safer operation of BLDC systems. In the discussion, attention is given to the growing use of artificial intelligence and data fusion in fault diagnosis. These trends are likely to guide the next generation of condition monitoring systems for BLDC motors. Overall, this study emphasizes the importance of developing reliable and sustainable diagnostic frameworks to enhance energy efficiency and system performance. The results can provide a useful reference for researchers and engineers working on BLDC motor technologies.