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2019-11-21
Advanced EMC Assessment of Composites Material: Monte Carlo Statistical Description with Spherical Inclusions and Improvement with SROM
By
Progress In Electromagnetics Research Letters, Vol. 88, 9-14, 2020
Abstract
This article proposes an advanced methodology to deal with the complexity of composite materials modeling up to 60 GHz. For radiofrequency (RF) requirements, it has been demonstrated that the distribution of conductive inclusions plays a major role. Since their locations are intrinsically subject to uncertain assumptions, the Monte Carlo (MC) technique is considered as a golden standard. Unfortunately, the computational costs involved by coupling full-wave electromagnetic (EM) simulations and MC remains prohibitive. The aim of this proposal is to demonstrate the interest of stochastic reduced order method (SROM) to tackle computational constraints, jointly with the statistical precision needed for a realistic description of RF composites.
Citation
Sebastien Lallechere, "Advanced EMC Assessment of Composites Material: Monte Carlo Statistical Description with Spherical Inclusions and Improvement with SROM," Progress In Electromagnetics Research Letters, Vol. 88, 9-14, 2020.
doi:10.2528/PIERL19080904
References

1. Preault, V., et al. "Shielding effectiveness of composite materials: Effect of inclusion shape," IEEE Trans. on Magn., Vol. 49, 1941-1944, 2013.
doi:10.1109/TMAG.2013.2244865

2. Liao, Y., et al. "Equivalent modeling of the microwave dielectric properties for fiber reinforced shielding composites," Proc. APEMC 2016, Shenzhen, China, 2016.

3. Njoku, C., et al. "Effective permittivity of heterogeneous substrates with cubes in a 3-d lattice," IEEE Ant. Prop. Wir. Prop. Lett., Vol. 10, 1480-1483, 2011.
doi:10.1109/LAWP.2011.2182597

4. Boubekeur, M., et al. "Modeling of thin heterogeneous sheets in the Discontinuous Galerkin method for 3D transient scattering problems," Eur. Phys. J. APhys., Vol. 73, 1-5, 2016.

5. Lallechere, S., "Advanced statistical 3D models of composite materials for microwave electromagnetic compatibility applications," ACES J., Vol. 32, No. 12, 1113-1116, 2017.

6. Zhang, Z. and Y. B. Yi, "Monte Carlo simulations of effective electrical conductivity in short-fiber composites," Journal of Applied Physics, Vol. 103, 014910, 2008.
doi:10.1063/1.2828180

7. Koledintseva, M., et al. "Maxwell Garnett rule for dielectric mixtures with statistically distributed orientations of inclusions," Progress In Electromagnetics Research, Vol. 99, 131-148, 2009.
doi:10.2528/PIER09091605

8. Bastianelli, L., et al. "Shielding effectiveness statistical evaluation of random concrete composites," Proc. IEEE Metrology for Aerospace 2016, Florence, Italy, 2016.

9. De Menezes, L. R. A. X., et al. "Efficient computation of stochastic electromagnetic problems using unscented transforms," IET Sci. Meas. Technol., Vol. 2, No. 2, 88-95, 2008.
doi:10.1049/iet-smt:20070050

10. De Medeiros, J. E. G., et al. "Extended formulation for unscented transform and its application as Monte Carlo alternative," Electronics Letters, Vol. 52, No. 22, 1842-1843, 2016.
doi:10.1049/el.2016.2867

11. Grigoriu, M., "Reduced order models for random functions. Application to stochastic problems," Appl. Math. Model., Vol. 33, No. 1, 161-175, 2009.
doi:10.1016/j.apm.2007.10.023

12. Zhuang, Y., et al. "Design of multioctave high-efficiency power amplifiers using stochastic reduced order models," IEEE Trans. on MTT, Vol. 66, No. 2, 1015-1023, 2018.
doi:10.1109/TMTT.2017.2750164