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Abstract: Improving the power density of an interior permanent magnet synchronous machine requires a complex and comprehensive approach that includes electromagnetic and thermal aspects. To achieve that, a multi-objective optimization of the machine’s geometry was performed according to selected key performance indicators by using numerical and analytical models. The primary objective of this research was to create a computationally efficient and accurate alternative to a direct finite element method-based optimization. By integrating artificial neural networks as meta-models, we aimed to demonstrate their performance in comparison to existing State-of-the-Art approaches. The artificial neural network approach achieved a nearly 20-fold reduction compared with the finite element method-based approach in computation time while maintaining accuracy, demonstrating its effectiveness as a computationally efficient alternative. The obtained artificial neural network can also be reused for different optimization scenarios and for iterative fine-tuning, further reducing the computation time. To highlight the advantages and limitations of the proposed approach, a multi-objective optimization scenario was performed, which increased the power-to-mass ratio by 16.5%.
Keywords: interior permanent magnet synchronous machine, artificial neural network, metamodel, multi-objective optimization, finite element method
Published in DKUM: 08.08.2025; Views: 0; Downloads: 20
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Abstract: This Dissertation focuses on the optimization of an Interior Permanent Magnet (IPM) machine for handheld battery-powered tools, aiming to enhance performance and efficiency. The research integrates multi-physics modeling, including electromagnetic Finite Element Method (FEM) and thermal models, to evaluate machine performance under various operating conditions. The performance is evaluated according to selected Key Performance Indicators (KPIs). Further, different control methods, such as Field Oriented Control and Square-Wave Control, impact the performance significantly and are incorporated into the optimization process. Due to the computational challenges of FEM-based performance evaluations in Multi-Objective Optimization (MOO), this work utilizes Artificial Neural Network (ANN)-based meta-models, to accelerate the optimization process while preserving accuracy. The developed meta-models capture nonlinear machine characteristics from the FEM model. These meta-models are then used to evaluate machine performance through a combination of analytical and numerical post-processing methods. Four MOO scenarios are presented, each aimed at optimizing the cross-sectional design of IPM machines, to enhance performance and efficiency while reducing mass and cost. Additionally, these scenarios modify the machine’s electromagnetic behavior, to ensure better alignment with the selected control method. By comparing the optimization process of Scenario 1, which uses direct FEM-based evaluation without time reduction measures, to the approach incorporating Artificial Neural Network based meta-models, the total number of individual FEM evaluations decreased from 2.35×10^9 to 2.03×10^5, without almost any loss of accuracy. This reduced the computation time from 297 years to 9.07 days on our standard desktop computer. The obtained ANN-base meta-models can be used further for other optimizations without the need for additional FEM evaluations. In all four optimization scenarios, the use of meta-models enabled the generation of a Pareto front of the optimal solutions, leading to improved KPIs compared to the reference design. The highest relative improvement occurred in Scenario 1, where the selected optimized machine design achieved a 30% increase in power density compared to the reference design.
Keywords: Interior Permanent Magnet (IPM) Machine, Artificial Neural Network (ANN), Meta-Modeling, Multi-Objective Optimization (MOO), Finite Element Method (FEM), Multi-Physics Modeling, Field Oriented Control (FOC), Square-Wave Control (SWC)
Published in DKUM: 15.05.2025; Views: 0; Downloads: 160
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Abstract: This paper presents an experimental and numerical analysis of the mechanical behaviour of orthopaedic implants with crack-type defects, considering the principles and advantages of the modern X-FEM method, which was used due to limitations of traditional FEM in terms of crack growth simulation, especially for complex geometries. In X-FEM, the finite element space is enriched with discontinuity functions and asymptotic functions at the crack tip, which are integrated into the standard finite element approximation using the unity division property. Though rare, femoral component failures are well-documented complications that can occur after hip prosthetic implantation. Most stem fractures happen in the first third of the implant due to the loosening of the proximal stem and fixation of the distal stem, leading to bending and eventual fatigue failure. The main goal of this paper was to obtain accurate and representative models of such failures. Experimental analyses of the mechanical behaviour of implants subjected to physiological loads, according to relevant standards, using a new combined approach, including both experiments and numerical simulations was presented. The goal was to verify the numerical results and obtain a novel, effective methodology for assessing the remaining fatigue life of hip implants. For this purpose, the analysis of the influence of Paris coefficients on the total number of cycles was also considered. Hence, this simulation involved defining loads to closely mimic real-life scenarios, including a combination of activities such as ascending stairs, stumbling, and descending stairs. The tensile properties of the titanium alloy were experimentally determined, along with the Paris law coefficients C and m. The finite element software ANSYS 2022R2 version was used to develop and calculate the three-dimensional model with a crack, and the resulting stresses, stress intensity factors, and the number of cycles presented in the figures, tables, and diagrams. The results for the fatigue life of a partial hip implant subjected to various load cases indicated significant differences in behaviour, and this underscores the importance of analysing each case individually, as these loads are heavily influenced by each patient’s specific activities. It was concluded that the use of numerical methods enabled the preliminary analyses of the mechanical behaviour of implants under fatigue loading for several different load cases, and these findings can be effectively used to predict the possibility of Ti-6Al-4V implant failure under variable cyclic loads.
Keywords: structural integrity, fatigue fracture, extended finite element method (XFEM), experimental testing, DIC, numerical simulations, stress intensity factor, orthopaedic implants, crack-type defect
Published in DKUM: 21.03.2025; Views: 0; Downloads: 8
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Keywords: computational simulations, solid mechanics, finite element method, design, numerical methods
Published in DKUM: 28.02.2025; Views: 0; Downloads: 1
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Abstract: This study investigated the sensitivity of maximum torque per ampere (MTPA) control in synchronous reluctance machines (SynRMs) to angle errors, examining specifically how deviations in the reference control trajectory affected performance. Analytical and numerical methods were employed to analyze this sensitivity systematically, including the impact of magnetic saturation. Two MTPA control implementation schemes were evaluated, with torque and current amplitude as the reference variables, using a template SynRM from the open-source simulation tool SyR-e. The results indicated that performance sensitivity to angle errors was moderately low near the MTPA trajectory, allowing for significant angle deviations with minimal performance loss. Although magnetic saturation increased this sensitivity slightly, reducing the allowable error range by up to 25%, the maximum angle deviation for up to 1% of the performance decrease still corresponded to approximately ±3∘ around the MTPA trajectory. The findings of this study suggest potential for simplifying control implementations, reducing component costs through less precise position determination (sensor-based or sensorless), and achieving additional control objectives such as torque ripple reduction.
Keywords: control angle error, finite element method, MTPA control, sensitivity to performance decrease, suboptimal operation, synchronous reluctance machines
Published in DKUM: 07.02.2025; Views: 0; Downloads: 13
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