An Integral Computational Model for Crack Simulation and Detection via Eddy Currents

Albanese, R.; Rubinacci, G.; Villone, F.

doi:10.1006/jcph.1999.6261

Cited by 59 publications

(23 citation statements)

References 20 publications

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“…For solving general ECT problems, the eddy current density has been formulated by representing it with the help of the volume integral equation method and using the weighted residual approach. ECT problems have been successfully analyzed [9]. In this paper, we employ surface integral equations in order to reduce computational costs compared with the volume integral equation method.…”

Section: Introductionmentioning

confidence: 99%

Eddy current analysis for ECT by BEM utilizing induced currents at 90° edge

Ishibashi

2007

Journal of Materials Processing Technology

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Section: Introductionmentioning

confidence: 99%

Eddy current analysis for ECT by BEM utilizing induced currents at 90° edge

Ishibashi

2007

Journal of Materials Processing Technology

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“…The VI method generally requires to only discretize the defective area within the media. The method is therefore computationally efficient and more likely to be successfully used in inverse problems approaches [15][16][17][18]. However, the model is generally only available for standard EC problem configurations and should be specifically derived for new or complex configurations [19,20].…”

Section: Introductionmentioning

confidence: 99%

A Differential DPSM Based Modeling Applied to Eddy Current Imaging Problems

Boré¹,

Joubert²,

Placko³

2014

PIER

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Abstract-This paper deals with an innovative implementation of a semi-analytical modeling method, called the Distributed Points Source Method (DPSM), in the case of an eddy current problem. The DPSM has already shown great potentialities for the versatile and computationally efficient modeling of complex electrostatic, electromagnetic or ultrasonic problems. In this paper, we report a new implementation of the DPSM, called differential DPSM, which shows interesting prospects for the modeling of complex eddy current problems such as met in the non-destructive imaging of metallic parts. In this paper, the used eddy current imaging device is firstly presented. It is composed of an eddy current (EC) inducer and a magneto optical set-up used to translate the magnetic field distribution appearing at the surface of the imaged part, into a recordable optical image. In this study, the device is implemented for the time-harmonics (900 Hz) imaging of a two-layer aluminum based assembly, featuring surfacebreaking and buried defects. Then, the basics of the time-harmonics DPSM modeling are recalled, and the differential approach is presented. It is implemented for the modeling of the interactions of the eddy current imaging device with the considered flawed assembly in the same operating conditions as the experimental implementation. The comparison between experimental and computed data obtained for millimetric surface and buried defects is presented in the form of complex magnetic cartographies and Lissajous plots. The obtained results show good agreement and open the way to the modeling of complex EC problems. Furthermore, the low computational complexity of the differential DPSM modeling makes it promising for solving EC inverse problems.

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“…5) Since the large problem is solved for only one value of the parameter, the need for repeated preconditioning of large coefficient matrices for each new value of the parameter is avoided, something necessary for convergence in conventional iterative schemes. 6) Since the final solution is constructed through a linear sum of vectors and columns of as shown in (20), it can be updated for different only at a few nodes of interest, by summing the corresponding entries of these vectors. This saves the requirement of storing or computing solutions over the whole domain, and is especially useful in coupled field applications.…”

Section: Major Advantagesmentioning

confidence: 99%

“…The Woodbury formula [18], [19] has been used for solution updating in a number of applications where a large system of equations has to be solved repeatedly after low-rank changes in the coefficient matrix [20]. The method requires that the original (unchanged) system be easy to solve.…”

Section: Relation With the Woodbury Algorithmmentioning

confidence: 99%