An adaptive multi-element probabilistic collocation method for statistical EMC/EMI characterization
KAUST DepartmentPhysical Sciences and Engineering (PSE) Division
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Electrical Engineering Program
Computational Electromagnetics Laboratory
Permanent link to this recordhttp://hdl.handle.net/10754/563131
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AbstractAn adaptive multi-element probabilistic collocation (ME-PC) method for quantifying uncertainties in electromagnetic compatibility and interference phenomena involving electrically large, multi-scale, and complex platforms is presented. The method permits the efficient and accurate statistical characterization of observables (i.e., quantities of interest such as coupled voltages) that potentially vary rapidly and/or are discontinuous in the random variables (i.e., parameters that characterize uncertainty in a system's geometry, configuration, or excitation). The method achieves its efficiency and accuracy by recursively and adaptively dividing the domain of the random variables into subdomains using as a guide the decay rate of relative error in a polynomial chaos expansion of the observables. While constructing local polynomial expansions on each subdomain, a fast integral-equation-based deterministic field-cable-circuit simulator is used to compute the observable values at the collocation/integration points determined by the adaptive ME-PC scheme. The adaptive ME-PC scheme requires far fewer (computationally costly) deterministic simulations than traditional polynomial chaos collocation and Monte Carlo methods for computing averages, standard deviations, and probability density functions of rapidly varying observables. The efficiency and accuracy of the method are demonstrated via its applications to the statistical characterization of voltages in shielded/unshielded microwave amplifiers and magnetic fields induced on car tire pressure sensors. © 2013 IEEE.
SponsorsThis work was supported by the National Science Foundation under Grant DMS 0713771, AFOSR/NSSEFF Program Award FA9550-10-1-0180, Sandia Grant "Development of Calderon Multiplicative Preconditioners with Method of Moments Algorithms," KAUST Grant 399813, ONR BRC Grant "Randomized Algorithms for Reduced Representations," and Center for Uncertainty Quantification in Computational Science and Engineering at KAUST.