Modified weight function with automatic node generation in element‐free Galerkin method for magnetic field computation

Arehpanahi, Mehdi; Vahedi, Omid

doi:10.1049/iet-smt.2015.0102

Cited by 4 publications

(3 citation statements)

References 17 publications

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“…Usually, this closed curve considers a circle which is called domain of influence that is illustrated in Fig. 2 [16]. By definition a suitable weight function the error of numerical integration will be more decreased.…”

Section: Meshless Methodsmentioning

confidence: 99%

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Time‐varying magnetic field analysis using an improved meshless method based on interpolating moving least squares

Arehpanahi

Jamalifard

2018

IET Science, Measurement & Technology

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Section: Meshless Methodsmentioning

confidence: 99%

“…The IMLS technique is introduced to overcoming the MLS technique problem at desired nodes. The modified MLS technique which is called IMLS technique can pass through the desired nodes by definition of specific weight function [16]. In this paper, a new weight function is proposed as…”

Section: Interpolation Mlsmentioning

confidence: 99%

Time‐varying magnetic field analysis using an improved meshless method based on interpolating moving least squares

Arehpanahi

Jamalifard

2018

IET Science, Measurement & Technology

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“…While meshless methods use node-based approximation without mesh discretization, the efficiency and accuracy of data processing are, therefore, greatly improved. 3 In order to meet the development requirement of different fields, a multitude of meshless methods have been presented and applied, such as the element-free galerkin (EFG), 4 diffuse elements method (DEM), 5 and moving least squares (MLS).…”

Section: Introductionmentioning

confidence: 99%

Surface reconstruction method for measurement data with outlier detection by using improved RANSAC and correction parameter

Luo

Tang

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part B:

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The moving least squares (MLS) and moving total least squares (MTLS) are two of the most popular methods used for reconstructing measurement data, on account of their good local approximation accuracy. However, their reconstruction accuracy and robustness will be greatly reduced when there are outliers in measurement data. This article proposes an improved MTLS method (IMTLS), which introduces an improved random sample consensus (RANSAC) algorithm and a correction parameter in the support domain, to deal with the outliers and random errors. Based on the nodes within the support domain, firstly the improved RANSAC is used to generate a model for establishing the group of pre-interpolation and calculating the residual of each node. Subsequently, the abnormal degree of the node with the largest residual is evaluated by the correction parameter associated with the node residual and random errors. The node with certain abnormal degree will be eliminated and the remaining nodes are used to obtain the approximation coefficients. The correction parameter can be used for data reconstruction without insufficient or excessive elimination. The results of numerical simulation and measurement experiment show that the reconstruction accuracy and robustness of the IMTLS method is superior to the MLS and MTLS method.

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Magnetic Field Calculation of a Novel Electromagnetic Clutch Based on an Interpolating Element-Free Galerkin Method

Yang

2020

IEEE Access

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Modified weight function with automatic node generation in element‐free Galerkin method for magnetic field computation

Cited by 4 publications

References 17 publications

Time‐varying magnetic field analysis using an improved meshless method based on interpolating moving least squares

Time‐varying magnetic field analysis using an improved meshless method based on interpolating moving least squares

Surface reconstruction method for measurement data with outlier detection by using improved RANSAC and correction parameter

Magnetic Field Calculation of a Novel Electromagnetic Clutch Based on an Interpolating Element-Free Galerkin Method

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