Authors: Joe Wiart 1,2, Pierric Kersaudy 1,3, Amal Ghanmi 3 , Nadege Varsier1,2 , Abdelhamid Hadhem 1,2 , Picon Odile 2 , Bruno Sudret 4 and Raj Mittra5
Abstract: In this paper we propose a novel approach which combines computational electromagnetics with statistics to statistically characterize the variations of the Radio Frequency (RF) exposure induced by inputs and affected by variability or uncertainty. Conventional numerical techniques such as the Monte Carlo Method, typically used to solve such a problem, are not useful in this case from a practical point of view since the computation time needed to assess the effect of the exposure is inordinately long for this type of problem. This novel approach consists of characterizing the statistical distribution of the output using a surrogate model which is employed in the numerical method. The bottleneck is encountered in the process of building a surrogate model by using a parsimonious approach, because an extensive set of computations are required by the Finite Difference in Time Domain (FDTD) method, despite the fact that the FDTD is a proven computationally efficient technique for modeling problems in bio-electromagnetism. The proposed method employs a truncated Generalized Polynomial Chaos Expansion scheme in conjunction with regression and Least Angle Regression (LARS) algorithms to identify the polynomial which has a significant influence and then to calculate the polynomial coefficient. The accuracy assessment of the surrogate model is carried out with the Leave-One-Out Cross Validation (LOOCV). In this paper this method is used to characterize the variation of the Specific Absorption Rate (SAR) induced in the head by a mobile phone having a variable position relative to the head.
Index Terms: Human exposure, Specific Absorption Rate (SAR), Dosimetry, radiofrequency (RF), Finite Difference in Time Domain (FDTD), Generalized Polynomial Chaos Expansion, Least Angle Regression, Leave One Out Cross Validation.
View PDFStochastic Dosimetry to manage Uncertainty in Numerical EMF Exposure Assessment