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S T Trickey

Publications and source records attributed to S T Trickey.

2 recordsLinked to original sources

Detecting nonlinearity in structural systems using the transfer entropy.

The transfer entropy was recently proposed as a means of exploring coupling in dynamical systems. Transfer entropy is an information theoretic that quantifies the degree to which one dynamical process affects the transition probabilities (dynamics) of another. Here we demonstrate how this metric may be utilized to detect the presence of nonlinearity in a system. Using the method of surrogate data, the transfer entropy computed at various lag times are compared to values computed from linearized surrogates. The transfer entropy is shown to be a more sensitive indicator of nonlinearity than is the mutual information for both simulated and experimental data. This technique is particularly applicable to the field of structural health monitoring, where damage is often equated with the presence of a nonlinearity in an otherwise linear system.

Journal Article↗

Controlling system dimension: a class of real systems that obey the Kaplan-Yorke conjecture.

The Kaplan-Yorke conjecture suggests a simple relationship between the fractal dimension of a system and its Lyapunov spectrum. This relationship has important consequences in the broad field of nonlinear dynamics where dimension and Lyapunov exponents are frequently used descriptors of system dynamics. We develop an experimental system with controllable dimension by making use of the Kaplan-Yorke conjecture. A rectangular steel plate is driven with the output of a chaotic oscillator. We controlled the Lyapunov exponents of the driving and then computed the fractal dimension of the plate's response. The Kaplan-Yorke relationship predicted the system's dimension extremely well. This finding strongly suggests that other driven linear systems will behave similarly. The ability to control the dimension of a structure's vibrational response is important in the field of vibration-based structural health monitoring for the robust extraction of damage-sensitive features.

Models, Theoretical↗