This paper provides and compares two alternative solutions for the simulation of cables and interconnects with the inclusion of the effects of parameter uncertainties, namely the Polynomial Chaos (PC) method and the Response Surface Modeling (RSM). The problem formulation applies to the telegraphers equations with stochastic coefficients. According to PC, the solution requires an expansion of the unknown parameters in terms of orthogonal polynomials of random variables. On the contrary, RSM is based on a least-square polynomial fitting of the system response. The proposed methods offer accuracy and improved efficiency in computing the parameter variability effects on system responses with respect to the conventional Monte Carlo approach. These approaches are validated by means of the application to the stochastic analysis of a commercial multiconductor flat cable. This analysis allows us to highlight the respective advantages and disadvantages of the presented methods.

Comparison of Stochastic Methods for the Variability Assessment of Technology Parameters / Manfredi, Paolo; Fontana, Michele; Stievano, IGOR SIMONE; Canavero, Flavio. - ELETTRONICO. - (2011), pp. 1-4. (Intervento presentato al convegno XXX General Assembly and Scientific Symposium of the International Union of Radio Science tenutosi a Istanbul (Turkey) nel August 13-20) [10.1109/URSIGASS.2011.6050749].

Comparison of Stochastic Methods for the Variability Assessment of Technology Parameters

MANFREDI, PAOLO;FONTANA, MICHELE;STIEVANO, IGOR SIMONE;CANAVERO, Flavio
2011

Abstract

This paper provides and compares two alternative solutions for the simulation of cables and interconnects with the inclusion of the effects of parameter uncertainties, namely the Polynomial Chaos (PC) method and the Response Surface Modeling (RSM). The problem formulation applies to the telegraphers equations with stochastic coefficients. According to PC, the solution requires an expansion of the unknown parameters in terms of orthogonal polynomials of random variables. On the contrary, RSM is based on a least-square polynomial fitting of the system response. The proposed methods offer accuracy and improved efficiency in computing the parameter variability effects on system responses with respect to the conventional Monte Carlo approach. These approaches are validated by means of the application to the stochastic analysis of a commercial multiconductor flat cable. This analysis allows us to highlight the respective advantages and disadvantages of the presented methods.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2456987
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