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Thesis (Ph. D.)--University of Texas at Austin, 2001. / Vita. Includes bibliographical references (leaves 115-121). Available also in a digital version from Dissertation Abstracts.

Thesis (Ph.D.)--Aberdeen University, 2008. / Title from web page (viewed on July 1, 2009). Includes bibliographical references.

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This thesis is concerned with long-range sound propagation in deep water.  The main area of interest is the stability of acoustic ray paths in wave guides in which there is a transition from single to double duct sound speed profiles, or vice-versa.  Sound propagation is modelled within a ray theoretical framework, which facilitates a dynamical systems approach of understanding long-range propagation phenomena, and the use of its tools of analysis. Alternative reduction techniques to the Poincaré sections are presented, by which the stability of acoustic rays can be graphically determined.  Beyond periodic driving, these techniques prove to be useful in case of the simplest quasiperiodic driving of the ray equations.  One of the techniques facilitates a special representation of ray trajectories for periodic driving. Namely, the space of sectioned trajectories is partitioned into nonintersecting regular and chaotic regions as with the Poincaré sections, when quasiperiodic and chaotic trajectories are represented by curve segments and area filling points, respectively.  In case of the simplest quasiperiodic driving – speaking about the same technique – regular trajectories are represented by curves similar to Lissajous curves, which are opened or closed depending on whether the two driving frequencies involved make relative primes or not. It is confirmed for a perturbed canonical profile that the background sound speed structure controls ray stability. It is also demonstrated for a particular double duct profile, when the singularity of the nonlinearity parameter for the homoclinic trajectory associated with this profile refers to the strong instability of corresponding perturbed trajectories.

This paper presents techniques that actively exploit chaotic behavior to accomplish otherwise-impossible control tasks. The state space is mapped by numerical integration at different system parameter values and trajectory segments from several of these maps are automatically combined into a path between the desired system states. A fine-grained search and high computational accuracy are required to locate appropriate trajectory segments, piece them together and cause the system to follow this composite path. The sensitivity of a chaotic system's state-space topology to the parameters of its equations and of its trajectories to the initial conditions make this approach rewarding in spite of its computational demands.

Columns subjected to time varying axial load may exhibit dynamic instability due to parametric resonance. This type of instability is inherent in structures; it is not due to material or geometrical imperfections, and can occur even in perfectly constructed structures. This characteristic makes parametric resonance a very difficult to predict and therefore dangerous phenomenon. In this thesis the stability of a structural column under bounded noise axial load is studied by use of Lyapunov exponents. Bounded noise is especially useful as a loading because it may be used to represent both wide and narrow band processes, making the stability equations developed general enough to handle a wide variety of real world probabilistic loadings. The equation of motion of the first mode of vibration for this system is a second-order nonlinear stochastic ordinary differential equation. The nonlinearity makes the system exhibit bifurcating behaviour where stability shifts from the trivial solution to a non-zero mean stationary solution. The stability of the trivial and non-trivial solutions is important in obtaining a complete picture of the dynamical behaviour of the system. The effect that damping, the amplitude of noise, and the level of nonlinearity have on the stability of a structural column is studied using both analytical and numerical approaches. The largest Lyapunov exponent of the trivial solution is determined analytically by using time averaged versions of the original equation of motion. The validity of the analytical time averaged equation of motion is also verified with Monte Carlo simulations. Due to the mathematical complexity the largest Lyapunov exponent of the non-trivial stationary solutions is obtained using Monte Carlo simulation only.

Columns subjected to time varying axial load may exhibit dynamic instability due to parametric resonance. This type of instability is inherent in structures; it is not due to material or geometrical imperfections, and can occur even in perfectly constructed structures. This characteristic makes parametric resonance a very difficult to predict and therefore dangerous phenomenon. In this thesis the stability of a structural column under bounded noise axial load is studied by use of Lyapunov exponents. Bounded noise is especially useful as a loading because it may be used to represent both wide and narrow band processes, making the stability equations developed general enough to handle a wide variety of real world probabilistic loadings. The equation of motion of the first mode of vibration for this system is a second-order nonlinear stochastic ordinary differential equation. The nonlinearity makes the system exhibit bifurcating behaviour where stability shifts from the trivial solution to a non-zero mean stationary solution. The stability of the trivial and non-trivial solutions is important in obtaining a complete picture of the dynamical behaviour of the system. The effect that damping, the amplitude of noise, and the level of nonlinearity have on the stability of a structural column is studied using both analytical and numerical approaches. The largest Lyapunov exponent of the trivial solution is determined analytically by using time averaged versions of the original equation of motion. The validity of the analytical time averaged equation of motion is also verified with Monte Carlo simulations. Due to the mathematical complexity the largest Lyapunov exponent of the non-trivial stationary solutions is obtained using Monte Carlo simulation only.