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Transport of sediment in coastal regions directly impacts mine countermeasure operations and naval construction efforts. Wave induced shear stress in the surf zone is responsible for entraining sediment particles into suspension within the combined wave and current boundary layer, where momentum is imparted through highly nonlinear processes. Therefore, a detailed understanding of sediment flux processes in the surf zone is essential to accurately model net sediment transport. This study examines the use of acoustic backscatter inversion as a means of measuring sediment concentration profiles. Measurements of sediment concentration and velocity profiles were made by a high frequency Doppler velocity profiler deployed on Blackâ s Beach during the Nearshore Canyon Experiment, NCEX. Profiles of sediment flux were compared with hourly mean current measurements from a cross-shore/long-shore array of PUV sensors and two-dimensional planner images of the morphological evolution provided by a three camera Argus video suite. Observations from a seven day period containing the development and evolution of a weak rip channel demonstrated that acoustic backscatter inversion techniques, when calibrated with in situ sediment samples, provide high spatial and temporal resolution estimates of sediment concentration and fluxes into the thin wave boundary layer. These sediment transport measurements were correlated with observed mean currents and rip channel evolution, and show a strong morphological response to the sediment flux.

This thesis is focused on the dynamics and control of nonlinear engineering systems. A developed approach is applied to three specific problems: suppression of torsional vibrations occurring in a drill-string, lateral vibrations on an unbalanced rotor and vibrational energy extraction from rotating pendulum systems. The first problem deals with drill-string torsional vibrations while drilling, which is conducted in the experimental drilling rig developed at University of Aberdeen. A realistic model of the experimental setup is then constructed, taking into account the dynamics of the drill-string and top motor. Physical parameters of the experimental drilling rig are estimated in order to calibrate the model to ensure the correspondence of the research results to the experimental conditions. Consequently, a control method is introduced to suppress torsional and stick-slip oscillations exhibited in the experimental drilling rig. The experimental and numerical results considering delay of the actuator are shown to be in close agreement, including the success of the controller in significantly reducing the vibrations. In the second problem a soft impact oscillator approach is used to study the dynamics of the asymmetric Jeffcott rotor. A realistic model of the experimental setup is developed, taking into account an asymmetric physical configuration in rotor part as well as snubber rig. Several experimental bifurcation diagrams are conducted with different conditions in range around the grazing point. Experimental and numerical results based on the proposed model are compared and shown to be in close agreement. The last problem relates to initiating and maintaining the rotational motion of a parametric pendulum as an energy harvesting system. Several possible control methods to initiate and maintain the rotational motion of a harmonically-excited pendulum are proposed and then verified experimentally. The time-delayed feedback method is shown to maintain quite well the rotational motion of a sinusoidally excited parametric pendulum, even in the presence of noise. A control method for the wave-excited pendulum system is then suggested and tested in order to increase the probability of its rotational motion. This proposed control method succeeds in significantly raising the probability of rotational motion of the wave-excited pendulum.

Continuous-time systems are usually modelled by differential equations arising from physical laws. However, the use of these models in practice requires discretisation. In this thesis we consider sampled-data models for linear and nonlinear systems. We study some of the issues involved in the sampling process, such as the accuracy of the sampled-data models, the artifacts produced by the particular sampling scheme, and the relations to the underlying continuous-time system. We review, extend and present new results, making extensive use of the delta operator which allows a clearer connection between a sampled-data model and the underlying continuous-time system. In the first part of the thesis we consider sampled-data models for linear systems. In this case exact discrete-time representations can be obtained. These models depend, not only on the continuous-time system, but also on the artifacts involved in the sampling process, namely, the sample and hold devices. In particular, these devices play a key role in determining the sampling zeros of the discrete-time model. We consider robustness issues associated with the use of discrete-time models for continuous-time system identification from sampled data. We show that, by using restricted bandwidth frequency domain maximum likelihood estimation, the identification results are robust to (possible) under-modelling due to the sampling process. Sampled-data models provide a powerful tool also for continuous-time optimal control problems, where the presence of constraints can make the explicit solution impossible to find. We show how this solution can be arbitrarily approximated by an associated sampled-data problem using fast sampling rates. We also show that there is a natural convergence of the singular structure of the optimal control problem from discrete- to continuous-time, as the sampling period goes to zero. In Part II we consider sampled-data models for nonlinear systems. In this case we can only obtain approximate sampled-data models. These discrete-time models are simple and accurate in a well defined sense. For deterministic systems, an insightful observation is that the proposed model contains sampling zero dynamics. Moreover, these correspond to the same dynamics associated with the asymptotic sampling zeros in the linear case. The topics and results presented in the thesis are believed to give important insights into the use of sampled-data models to represent linear and nonlinear continuous-time systems. / PhD Doctorate

David A. Chesler. / "February 15, 1960." Issued also as a thesis, M.I.T. Dept. of Electrical Engineering, January 11, 1960. / Bibliography: p. 76. / Army Signal Corps Contract DA36-039-sc-78108. Dept. of the Army Task 3-99-20-001 and Project 3-99-00-000.

Donald A. George. / "July 24, 1959." Based on thesis submitted to M.I.T. Dept. of Electrical Engineering, July 24, 1959. / Bibliography: p. 102. / Army Signal Corps Contract DA36-039-sc-78108. Dept. of the Army Task 3-99-20-001 and Project 3-99-00-000.

In chemical processes, online measurements of all the process variables and parameters required for process control, monitoring and optimization are seldom available. The use of soft sensors or observers is, therefore, highly significant as they can estimate unmeasured state variables from available process measurements. However, for reliable estimation by a soft sensor, the process measurements have to be placed at locations that allow reconstruction of process variables by the soft sensors. This dissertation presents a new technique for computing an optimal measurement structure for state and parameter estimation of stable nonlinear systems. The methodology can compute locations for individual sensors as well as networks of sensors where a trade-off between process information, sensor cost, and information redundancy is taken into account. The novel features of the approach are (1) that the nonlinear behavior that a process can exhibit over its operating region can be taken into account, (2) that the technique is applicable for systems described by lumped or by distributed parameter models, (3) that the technique reduces to already established methods, if the system is linear and only some of the objectives are examined, (4) that the results obtained from the procedure can be easily interpreted, and (5) that the resulting optimization problem can be decomposed, resulting in a significant reduction of the computational effort required for its solution. The other issue addressed in this dissertation is designing soft sensors for a given measurement structure. In case of high-dimensional systems, the application of conventional soft sensor or observer designs may not always be practical due to the high computational requirements or the resulting observers being too sensitive to measurement noise. To address these issues, this dissertation presents reduced-order observer design techniques for state estimation of high-dimensional chemical processes. The motivation behind these approaches is that subspaces, which are close to being unobservable, cannot be correctly reconstructed in a realistic setting due to measurement noise and inaccuracies in the model. The presented approaches make use of this observation and reconstruct the parts of the system where accurate state estimation is possible.

In chemical processes, online measurements of all the process variables and parameters required for process control, monitoring and optimization are seldom available. The use of soft sensors or observers is, therefore, highly significant as they can estimate unmeasured state variables from available process measurements. However, for reliable estimation by a soft sensor, the process measurements have to be placed at locations that allow reconstruction of process variables by the soft sensors. This dissertation presents a new technique for computing an optimal measurement structure for state and parameter estimation of stable nonlinear systems. The methodology can compute locations for individual sensors as well as networks of sensors where a trade-off between process information, sensor cost, and information redundancy is taken into account. The novel features of the approach are (1) that the nonlinear behavior that a process can exhibit over its operating region can be taken into account, (2) that the technique is applicable for systems described by lumped or by distributed parameter models, (3) that the technique reduces to already established methods, if the system is linear and only some of the objectives are examined, (4) that the results obtained from the procedure can be easily interpreted, and (5) that the resulting optimization problem can be decomposed, resulting in a significant reduction of the computational effort required for its solution. The other issue addressed in this dissertation is designing soft sensors for a given measurement structure. In case of high-dimensional systems, the application of conventional soft sensor or observer designs may not always be practical due to the high computational requirements or the resulting observers being too sensitive to measurement noise. To address these issues, this dissertation presents reduced-order observer design techniques for state estimation of high-dimensional chemical processes. The motivation behind these approaches is that subspaces, which are close to being unobservable, cannot be correctly reconstructed in a realistic setting due to measurement noise and inaccuracies in the model. The presented approaches make use of this observation and reconstruct the parts of the system where accurate state estimation is possible.

Thesis (M.S.)--Ohio State University, 2008. / Title from first page of PDF file. Includes bibliographical references (p. 106).

Thesis (Ph. D.)--Electrical and Computer Engineering, Georgia Institute of Technology, 2004. / J. Stevenson Kenney, Committee Member ; G. Tong Zhou, Committee Chair ; W. Marshall Leach, Committee Member ; Ye (Geoffrey) Li, Committee Member ; Jianmin Qu, Committee Member. Includes bibliographical references (leaves 100-103).