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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: 138
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Abstract: ) Modeling and characterization of ductile fracture in metals is still a challenging task in the field of computational mechanics. Experimental testing offers specific responses in the form of crack-mouth (CMOD) and crack-tip (CTOD) opening displacement related to applied force or crack growth. The main aim of this paper is to develop a phase-field-based Finite Element Method (FEM) implementation for modeling of ductile fracture in stainless steel. (2) A Phase-Field Damage Model (PFDM) was coupled with von Mises plasticity and a work-densities-based criterion was employed, with a threshold to propose a new relationship between critical fracture energy and critical total strain value. In addition, the threshold value of potential internal energy—which controls damage evolution—is defined from the critical fracture energy. (3) The material properties of AISI 316L steel are determined by a uniaxial tensile test and the Compact Tension (CT) specimen crack growth test. The PFDM model is validated against the experimental results obtained in the fracture toughness characterization test, with the simulation results being within 8% of the experimental measurements.
Keywords: phase-field damage modeling, ductile fracture, crack-tip opening displacement, crack growth, resistance curve, finite element method, simulations
Published in DKUM: 19.09.2024; Views: 0; Downloads: 12
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Abstract: A material inhomogeneity in the direction of crack extension causes a difference between the near-tip crack driving force, Jtip, and the nominally applied far-field crack driving force, Jfar. This difference can be quantified by a material inhomogeneity term, Cinh, which is evaluated by a post-processing procedure to a conventional finite element stress analysis. The magnitude of the material inhomogeneity term is evaluated for cracks in an inhomogeneous welded joint made of a high-strength low-alloy steel. Both a crack proceeding from the under-matched (UM) to the over-matched (OM) and from the OM to the UM weld metal are treated. The effects of the inhomogeneity of the different material parameters (modulus of elasticity, yield strength, and strain hardening exponent) on Cinh and Jtip are systematically studied. The results demonstrate that the material inhomogeneity term is primarily influenced by the inhomogeneity of the yield strength. A crack growing towards an OM/UM interface experiences an accelerated crack growth rate or a pop-in, an UM/OM interface leads to a reduced crack growth rate or a crack arrest. The application of global assessment methods of the mismatch effect which are included in the Engineering Treatment Model (ETM) or in the Structural Integrity Assessment Procedures for European Industry (SINTAP) is discussed.
Keywords: crack driving force, material inhomogeneity, mismatched weld, interface, J-integral, finite element modeling
Published in DKUM: 31.05.2012; Views: 1754; Downloads: 91
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