Computationally efficient magnetic modeling and material characterization methods for automated ferrimagnetic inductor design
Abstract
Automated, population-based design methods are becoming increasingly popular as design tools. When using these algorithms (e.g., Monte Carlo, genetic algorithms, particle swarm optimization), it is common to require the analysis of 10,000-1,000,000 individual designs. Unique challenges arise when using population-based methods to design magnetic components. Whether the components are to be used in stand-alone applications or in the context of a larger system, several modeling and computational difficulties must be overcome. First, detailed knowledge of intrinsic magnetic material characteristics that will comprise the component must be known. Second, considerable effort is required to obtain an accurate 3D magnetic model that enables rapid calculation of quasi-static magnetizations within the component, as well as accounting for all leakage flux. Third, calculation of hysteresis losses, including frequency effects, in a computationally efficient way is required. This thesis presents several key developments that have been made in the modeling and automated design of ferrimagnetic inductors. A novel procedure was developed to obtain the magnetic characteristics of a ferromagnetic material from commonly available toroidal cores and a new test configuration. A high fidelity equivalent circuit model (HFMEC) was developed that rapidly and accurately predicts the anhysteretic flux vs. current characteristic of an inductor based exclusively on material characteristics and geometry. A static hysteresis loss model (SHM) was then developed that eliminates the need to perform time-domain simulations to calculate magnetic power loss in both zero-bias and dc bias applications. Automated design of a stand-alone inductor, including magnetic core loss, has been performed with the use of the HFMEC, SHM, and a genetic algorithm.
Degree
Ph.D.
Advisors
Sudhoff, Purdue University.
Subject Area
Electrical engineering
Off-Campus Purdue Users:
To access this dissertation, please log in to our
proxy server.