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An improved technique for interface shape identification in magnetic fluid dynamics

An improved technique for interface shape identification in magnetic fluid dynamics Purpose – The interface between two conducting fluids in a magnetic fluid dynamics (MFD) problem was identified by means of external magnetic field measurements. Genetic algorithms (GA) were applied to solve the inverse problem.The principal component analysis (PCA) was used to speed up the process of interface reconstruction. Design/methodology/approach – With respect to the experimental results we have designed a general technique for mode identification and/or interface reconstruction. Two main procedures are available to solve the inverse problem, the full interface reconstruction and the principle component analysis (PCA) mode. In the case of full reconstruction, it can be decided whether an algorithm for fast identification of the dominant modes applying a FFT module should be performed or not. The full interface reconstruction applies stochastic optimization methods ((GA) or evolution strategies (ES)) for the estimation of the interface shape characteristics. The main goal of the PCA mode is to find the dominant mode of the interface shape and its amplitude. The PCA mode is realized by means of stochastic optimization methods (GA, ES) and a simple direct searching (DS) using the golden section technique. Findings – PCA with GA procedure enables the identification of the dominant mode of the interface shape between two conducting fluids with sufficient accuracy for simulated magnetic fields. Time of identification is strongly reduced due to a redefinition of the genotype representations in the PCA mode. Accuracy of reconstruction depends on the noise level, i.e. signal to noise ratio and a geometrical model used in the reconstruction phase. The correlation between the noise level and values of cost function for identified modes has been found if a proper geometry modelling is applied. Originality/value – The paper describes a new, fast technique for solving an inverse field problem of a MFD problem where the interface between two conducting fluids has to be identified using a magnetic field tomography measuring system. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png COMPEL: The International Journal for Computation and Mathematics in Electrical and Electronic Engineering Emerald Publishing

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Publisher
Emerald Publishing
Copyright
Copyright © 2005 Emerald Group Publishing Limited. All rights reserved.
ISSN
0332-1649
DOI
10.1108/03321640510598175
Publisher site
See Article on Publisher Site

Abstract

Purpose – The interface between two conducting fluids in a magnetic fluid dynamics (MFD) problem was identified by means of external magnetic field measurements. Genetic algorithms (GA) were applied to solve the inverse problem.The principal component analysis (PCA) was used to speed up the process of interface reconstruction. Design/methodology/approach – With respect to the experimental results we have designed a general technique for mode identification and/or interface reconstruction. Two main procedures are available to solve the inverse problem, the full interface reconstruction and the principle component analysis (PCA) mode. In the case of full reconstruction, it can be decided whether an algorithm for fast identification of the dominant modes applying a FFT module should be performed or not. The full interface reconstruction applies stochastic optimization methods ((GA) or evolution strategies (ES)) for the estimation of the interface shape characteristics. The main goal of the PCA mode is to find the dominant mode of the interface shape and its amplitude. The PCA mode is realized by means of stochastic optimization methods (GA, ES) and a simple direct searching (DS) using the golden section technique. Findings – PCA with GA procedure enables the identification of the dominant mode of the interface shape between two conducting fluids with sufficient accuracy for simulated magnetic fields. Time of identification is strongly reduced due to a redefinition of the genotype representations in the PCA mode. Accuracy of reconstruction depends on the noise level, i.e. signal to noise ratio and a geometrical model used in the reconstruction phase. The correlation between the noise level and values of cost function for identified modes has been found if a proper geometry modelling is applied. Originality/value – The paper describes a new, fast technique for solving an inverse field problem of a MFD problem where the interface between two conducting fluids has to be identified using a magnetic field tomography measuring system.

Journal

COMPEL: The International Journal for Computation and Mathematics in Electrical and Electronic EngineeringEmerald Publishing

Published: Sep 1, 2005

Keywords: Fluid dynamics; Magnetic fields; Programming and algorithm theory

References