Use of Adaptive Kriging Metamodeling in Reliability Analysis of Radiated Susceptibility in Coaxial Shielded Cables

Abstract

In this paper, reliability analysis is applied to assess electromagnetic risk due to external electromagnetic waves in a coaxial shielded cable. Generally, the classical sampling method-Monte Carlo (MC)-is used to conduct thousands or millions of runs of the numerical model. As this latter is time-demanding, the computational cost becomes prohibitive. As an alternative, an advanced metamodeling method based on Kriging is proposed for efficiently assessing small failure probabilities. This method is called AK-MCS. It consists in an active learning reliability method combining Kriging and MC simulation. Metamodeling reduces the computational run time by replacing the true numerical model by an inexpensive surrogate model. The interpolated surrogate model presents the advantage to be rapidly handled by MC simulations to calculate the failure probability. AK-MCS method is based on an adaptive strategy to enrich the design of experiments (DOE) with significant sampling points near the failure region, and hence, Kriging model is ameliorated and so is the assessment of failure probability. Three numerical examples of radiated susceptibility in a shielded cable are conducted to demonstrate the efficiency of the underlined Kriging approach. Reliable metamodels can be obtained through a small number of runs of the true numerical model, and it is found that the computed failure probabilities are very accurate compared to crude MC simulation results.