Search

The calculation of the probability of detection in non-destructive eddy-current testing requires the solution of a stochastic model requiring numerous calls of a numerical model leading to a huge computation time. To reduce this computation time, we propose in this paper to combine either the use of a stochastic metamodel or a mapping which avoids the remeshing step. The stochastic metamodel is constructed using the least-angle regression method. This approach is tested on an axisymmetric problem with six random input paramters which shows its efficiency and its accuracy. [ABSTRACT FROM AUTHOR]

Model order reduction methods enable reduction of the computation time when dealing with parametrized numerical models. Among these methods, the proper orthogonal decomposition method seems to be a good candidate because of its simplicity and its accuracy. In the literature, the offline/online approach is generally applied but is not always required especially if the study focuses on the device without any coupling with others. In this paper, we propose a method to adaptively construct the reduced model while it limits the evaluation of the full model when appropriate. A stochastic magnetostatic example with 14 uncertain parameters is studied by applying the Monte Carlo simulation method to illustrate the proposed procedure. In that case, it appears that the complexity of this method does not depend on the number of input parameters and so is not affected by the curse of dimensionality. [ABSTRACT FROM AUTHOR]

In mass production, fabrication processes of electrical machines are not perfectly repeatable with time, leading to dispersions on the dimensions which are not equal to their nominal values. The issue is then to link the dispersions on the dimensions, which are uncertain to the performance of electrical machines in order to evaluate their influence. To deal with uncertainty, there is a growing interest in the stochastic approach, which consists of modeling the uncertain parameters with random variables. In fact, this approach enables us to quantify the influence of the variability of the uncertain parameters on the variability of the quantities of interest. In this paper, a stochastic approach coupled with a 3-D finite-element model is used to study the influence of the fabrication process imperfections, such as the rotor eccentricity and the stator deformation, on the performance of a claw pole synchronous machine. [ABSTRACT FROM AUTHOR]

To solve stochastic static field problems, a discretization by the finite-element method can be used. A system of equations is obtained with the unknowns (scalar potential at nodes, for example) being random variables. To solve this stochastic system, the random variables can be approximated in a finite-dimension functional space—a truncated polynomial chaos expansion. The error between the exact solution and the approximated one depends not only on the spatial mesh, but also on the discretization along the stochastic dimension. In this paper, we propose an a posteriori estimation of the error due to the discretization along the stochastic dimension. [ABSTRACT FROM AUTHOR]

A nondestructive testing procedure is currently used to estimate the clogging of tube support plates in French nuclear powerplant steam generators. A stochastic approach has been applied to the finite-element electromagnetic field simulation to evaluate the impact of material properties uncertainties on the monitoring signal. The polynomial chaos expansion method makes it possible to easily derive the Sobol decomposition which measures how much the variability of each input parameter affects the model output. [ABSTRACT FROM AUTHOR]

To take account of the uncertainties introduced on the soft magnetic materials properties (magnetic behavior law, iron losses) during the manufacturing process, the present work deals with the stochastic modeling of the magnetic behavior law B-H and iron losses of claw pole stator generator. Twenty-eight samples of slinky stator (SS) coming from the same production chain have been investigated. The used approaches are similar to those used in mechanics. The accuracy of existing anhysteretic models has been tested first using cross validation techniques. The well known iron loss separation model has been implemented to take into account the variability of the losses. Then, the multivariate Gaussian distribution is chosen to model the variability and dependencies between identified parameters, for both behavior law and iron loss models. The developed stochastic models allow predicting a 98% confidence interval for the considered samples. [ABSTRACT FROM PUBLISHER]

The Non-Intrusive Spectral Projection (NISP) method is widely used for uncertainty quantification in stochastic models. The determination of the expansion of the solution on the polynomial chaos requires the computation of multidimensional integrals. An automatic adaptive algorithm based on nested sparse grids has been developed to evaluate those integrals. The adapted algorithm takes into account the weight of each random variable with respect to the output of the model. To achieve that it constructs anisotropic sparse grid of the mean, leading to a reduction of the number of numerical simulations. Furthermore, the spectral form of the solution is explicitly identified from the constructed quadrature scheme. Numerical results obtained on an industrial application in NDT demonstrate the efficiency of the proposed method. [ABSTRACT FROM PUBLISHER]