Optimization of a magnetic pole face using linear constraints to avoid jagged contours

Subramaniam, S.; Arkadan, A.A.; Hoole, S. Ratnajeevan H.

doi:10.1109/20.312682

Cited by 19 publications

(11 citation statements)

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“…In this paper, the GIPM derivations are Manuscript received July 10, 1995. e.mai1: ArkadanA@vms.csd.mu.edu Presented and are applied to two case studies. It should be noted here that, as one of the examples involves the shape optimization of a PM pole face, the current work is different from an earlier work presented in [2]. The difference is due to the fact that the current example involves a PM pole face, where any change in the pole shape during the optimization procedure results in a corresponding change in the excitation (equivalent Ampere -turn).…”

Section: Introductionmentioning

confidence: 67%

“…The difference is due to the fact that the current example involves a PM pole face, where any change in the pole shape during the optimization procedure results in a corresponding change in the excitation (equivalent Ampere -turn). This is in contrary to the case of an electromagnet pole face where the Ampere-turn is independent of the pole shape and is kept constant throughout the optimization procedure [2]. Full details are given below.…”

Section: Introductionmentioning

confidence: 95%

“…The first objective can be mathematically formulated as follows in the least squares sense by choosing n points, which are termed as measurement points, along the air gap [2]: where h, is the area of a triangular element in the PM region of the FE model. For a design optimization of a PM device with these three objectives an object function can be formulated as with the assumption that all the three objectives have equal weights.…”

Section: Introductionmentioning

confidence: 99%

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Shape optimization of PM devices using constrained gradient based inverse problem methodology

Arkadan

Subramaniam-Sivanesan

Demerdash

1996

IEEE Trans. Magn.

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Permanent Magnets (PMs) are widely used in a variety of industrial equipments and devices. In magnet design, the shape of a PM plays an important role and minimization of the leakage flux improves the performance of the device. In this paper, shape optimization of PM devices using Gradient based Inverse Problem Methodology (GIPM) is presented. This paper describes for the first time the use OC analytical sensitivities for shape optimization of PM devices. Furthermore, the adjoint method of the direct differentiation approach is used for the computation of the sensitivities of the object function.Two case studies are presented. The first involves a magnetic circuit with an air gap and PM excitation, the second is that of a PM pole face. In both cases, design optimization is employed to obtain a desired flux density profile i n the air gap with a minimum leakage flux and minimum size of the PM material.

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Section: Introductionmentioning

confidence: 67%

Section: Introductionmentioning

confidence: 95%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Shape optimization of PM devices using constrained gradient based inverse problem methodology

Arkadan

Subramaniam-Sivanesan

Demerdash

1996

IEEE Trans. Magn.

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“…Along with the device is shown the mesh by a problem-specific mesh generator modeling the pole-face by the principles of Fig. 1 [4].…”

Section: Mesh Changes Preserving Materials Interfacementioning

confidence: 99%

“…1, if a particular part with a dimension p is changing during optimization, the special mesh generator adjusts the mesh according to mathematical formula preserving scales so that C 1 continuity is preserved. This has been used to study the shape of a pole-face to produce a constant flux density in the air gap [4]. The method has been generalized to 0924 split a domain into 10 rectangular parts [5].…”

Section: Introductionmentioning

confidence: 99%

A common algorithm for various parametric geometric changes in finite element design sensitivity computation

Krishnakumar

Hoole

2005

Journal of Materials Processing Technology

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Mathematical Optimization Techniques

Russenschuck¹

2010

Field Computation for Accelerator Magnets

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From the beginning the ROXIE program was structured such that mathematical optimization techniques can be applied to the design of the superconducting magnets. With the concept of features it is possible to create the complex coil assemblies in 2 and 3 dimensions with only a small number of engineering data which can then be addressed as design variables of the optimization problem. In this chapter some background information on the application of mathematical optimization techniques is given. Historical overviewMathematical optimization including numerical techniques such as linear and nonlinear programming, integer programming, network flow theory and dynamic optimization has its origin in operations research developed in world war II, e.g., Morse and Kimball 1950 [45]. Most of the real-world optimization problems involve multiple conflicting objectives which should be considered simultaneously, so-called vector-optimization problems. The solution process for vector-optimization problems is threefold, based on decision-making methods, methods to treat nonlinear constraints and optimization algorithms to minimize the objective function.Methods for decision-making, based on the optimality criterion by Pareto in 1896 [48], have been introduced and applied to a wide range of problems in economics by Marglin 1966 [42], Geoffrion 1968 [18] and Fandel 1972. The theory of nonlinear programming with constraints is based on the optimality criterion by Kuhn and Tucker, 1951 [37] -magnet. Girdinio, Molfino, Molinari and Viviani 1983 [20] optimized a profile of an electrode. These attempts tended to be application-specific, however. Only since the late 80 th, have numerical field calculation packages for both 2d and 3d applications been placed in an optimization environment. Reasons for this delay have included constraints in computing power, problems with discontinuities and nondifferentiabilities in the objective function arising from FE meshes, accuracy of the field solution and software implementation problems. A small selection of papers can be found in the references.The variety of methods applied shows that no general method exists to solve nonlinear optimization problems in computational electromagnetics in the same way that the simplex algorithm exists to 60

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Optimization of a magnetic pole face using linear constraints to avoid jagged contours

Cited by 19 publications

References 10 publications

Shape optimization of PM devices using constrained gradient based inverse problem methodology

Shape optimization of PM devices using constrained gradient based inverse problem methodology

A common algorithm for various parametric geometric changes in finite element design sensitivity computation

Mathematical Optimization Techniques

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