Power Magnetic Devices. Scott D. Sudhoff. Читать онлайн. Newlib. NEWLIB.NET

Автор: Scott D. Sudhoff
Издательство: John Wiley & Sons Limited
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Жанр произведения: Техническая литература
Год издания: 0
isbn: 9781119674634
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Akhil Prasad, and Harshita Singh contributed in performing many of the FEA and/or experimental results in the book. I would also especially thank Dionysios Aliprantis for sparking my interest in genetic algorithms.

      A variety of U.S. government agencies have contributed to research efforts which contributed to this book, including the Army, Navy, DOE, Sandia National Labs, NREL, ORNL, and NASA. The Office of Naval Research in particular has provided steady support for my entire career which directly and indirectly supported this work, and without which this work would not have been possible. The support of the Grainger Foundation has also been very important to the program at Purdue.

      I would thank my colleagues at Purdue. A key attribute of any institution is the people your work with. With this regard, my colleagues at Purdue, namely Oleg Wasynczuk, Steve Pekarek, and Dionysios Aliprantis, are a great group.

      About the Companion Site

      This book is accompanied by a companion website:

       www.wiley.com/go/sudhoff/Powermagneticdevices

      The website includes the following materials: Powerpoint slides, Solutions manual, Matlab code

      We will begin our study of power magnetic device design with a general consideration of the design process. A case will be made to approach the design process rather formally by converting the design problem into an optimization problem. Next, single‐objective optimization is discussed, with particular emphasis on optimization using genetic algorithms (GAs). This is followed by a discussion of multi‐objective optimization. Practical aspects of formulating design problems as optimization problems are then considered. The chapter concludes with a design example that focuses on a UI‐core inductor.

      Using the design process in Figure 1.1, our first step would be to perform a detailed mathematical analysis of the device. Typically, when we analyze a device, our analysis predicts device performance (mass, loss, force) in terms of the device parameters (geometry, materials) rather than directly addressing the design problem by deriving expressions for what the device parameters should be in terms of the device specifications (allowed loss, required force). Therefore, we must manipulate our detailed analysis into a set of design equations that are used to calculate the design parameters as a function of device specifications. However, going from detailed analysis to design equations invariably requires numerous assumptions and approximations, even beyond the ones found in our original “detailed” analysis. As a result, we check our design, either against our original analysis or using some numerical tool such as a finite element analysis. Based on the results from the numerical analysis, we will revise the design and repeat the numerical analysis until specifications are met, at which point we have arrived at a final design. Of course, we often use a more involved design process; for example, another iteration of the design may be made based on physical prototypes.

      At the outermost level of this design process, an optimization engine will select the parameters of the design (geometry, materials, etc.) so as to maximize the objective function. In terms of computational algorithm, Figure 1.2 depicts an optimization engine at the outer level. This engine operates on an objective function that is calculated based on the detailed analysis.

      There are several advantages of this approach. First, it is unnecessary to formulate design equations. This is beneficial in that it reduces the number of approximations and assumptions made and reduces the amount of design experience needed for a good design. Second, the design is formally optimized with regard to the design metrics, potentially leading to better designs, at least in terms of the design metrics. Third, since the engineer is out of the optimization loop, less engineering time is generally required. There are some disadvantages of the procedure. First, the process can be numerically intense and require significant computing time, sometimes on the order of hours and, in extreme