Samir Sahyoun

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Explicit model reduction for nonlinear systems with no prior information about the type of nonlinearity involved is difficult and challenging. It is easier to reduce nonlinear systems which nonlinearity is known. In this paper we introduce two nonlinear model reduction techniques for quadratic nonlinear systems. The first technique is nonlinear balanced(More)
For a vast variety of fluid flows the dynamics are governed by the Navier-Stokes equations which are highly nonlinear. In particular, the corresponding Galerkin models involve a quadratic type nonlinear-ity. The latter incudes the Burgers' equation as well. In this paper, a computational algorithm for nonlin-ear balanced truncation of the Galerkin models is(More)
— Large electrical power networks viewed as continuum systems have been studied under constant voltage magnitude assumptions. The continuum system phase behavior was proved to follow the dynamics of a second order nonlinear wave equation. The latter represents electromechanical wave propagation in large electric power networks. In this paper, we generalize(More)
— In this paper, we investigate new methods that make the Proper Orthogonal Decomposition (POD) more accurate in reducing the order of large scale nonlinear systems. The general framework is to apply POD locally to clusters instead of applying it to the global system. Each cluster contains relatively close in distance behavior within itself, and(More)
—The Theater Positioning System (TPS), which can perform in GPS-denied environments and can work with, or independently of, GPS systems, is used as a backup to GPS in military. The principal difficulty in optimally combining this new system and GPS is caused by the somewhat unpredictable signal propagation of the TPS groundwave signal and thus results in(More)
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