The concept of piezohydraulic actuation is to transfer the reciprocal small stroke displacement of piezoceramics into unidirectional motion by frequency rectification through a hydraulic fluid. It takes advantage of the high force capabilities that piezoelectric materials have and couples it with very stiff media such as hydraulic fluid to amplify and create this unidirectional motion. Inlet and outlet valves are connected to a pumping chamber where pressure is built by the displacement of the piezoelectric material and released by the opening of the outlet valve, thus achieving a variable flow rate that is used to push a hydraulic cylinder. Loads may be connected to this hydraulic cylinder for measuring/achieving mechanical power. As part of this research, a benchtop piezohydraulic actuator with active piezohydraulic valves has been developed and the concept of piezohydraulic actuation has been demonstrated. Displacement of a hydraulic cylinder by driving a piezoelectric stack has been achieved while the cylinder was loaded or unloaded. Lumped-parameter state-space models have been developed in order to simulate the dynamics of the active valves and entire actuator system. The model simulates the chamber pressure, displacement of the hydraulic cylinder, and power of the piezohydraulic unit. A four-stage cycle simulation was used to model the pumping operation and dynamic response of the system. Experimental results demonstrate the importance of fluid compressibility, valve timing, and fluid circuit components in the optimization of the output power of the actuation system. An array of different timing tests run on the inlet and outlet valves shows that their timing is crucial to the performance of the system. Also shown is that the optimal timing conditions change slightly while under different loads. When operating at higher frequencies (above 140 Hz), it is shown that the hydraulic fluid circuit does not respond quickly enough for the piston to fully extend against the fluid and loaded cylinder. There is not sufficient time when operating at higher frequencies to push all the fluid from the chamber into the hydraulic cylinder, operation is too fast for the dynamics of the fluid circuit. The four stage lumped-parameter model achieves good approximations of the experimental results when the load inertia was neglected while operating at frequencies below 120 Hz and under loads at or below 12.825 kg. Memory limitations caused the number of elements included in the lumped-parameter model to be limited, and are believed to be the source of the errors for the higher operation frequencies and loads. The model never converged due to the lack of elements, and the simulated system did not respond quickly enough to accurately model the fluid exiting the chamber. When operating at frequencies above the 120 Hz value, this error in modeling the fluid exiting the valves becomes very important. The simulation predicts higher values than the experiment and fails to correlate to the actual results at the higher frequencies and while under the higher loads. The errors at higher loads may also be attributed to the neglected inertia. The most recent tests on the benchtop set-up were all run with a pre-pressure value of 190 psi, a piston duty cycle of 50%, valve duty cycles of 40% for each, and a 5% outlet valve offset. Slightly better operation performance might be achieved at frequencies higher than 140 Hz by increasing the piston duty cycle and varying the valve parameters. Also, increaing the pre-pressure of the fluid may help by stiffening the system to create a faster response, however this will have an adverse effect also by creating more force against piston motion. Lastly, the hydraulic cylinder was built for high pressures and had considerable friction associated with it. Obtaining a different cylinder with less friction may also help the response time of the fluid circuit. / Master of Science

This study highlights the aeroelastic behavior of very flexible truss-braced wing (TBW) aircraft designs obtained through a multidisciplinary design optimization (MDO) framework. Several improvements to previous analysis methods were developed and validated. Firstly, a flutter constraint was developed and the effects of the constraint on the MDO of TBW transport aircraft for both medium-range and long-range missions were studied while minimizing the take-off gross weight (TOGW) and the fuel burn as the objective functions. Results show that when the flutter constraint is applied at 1.15 times the dive speed, it imposes a 1.5% penalty on the take-off weight and a 5% penalty on the fuel consumption while minimizing these two objective functions for the medium-range mission. For the long-range mission, the penalties imposed by the similar constraint on the minimum TOGW and minimum fuel burn designs are 3.5% and 7.5%, respectively. Importantly, the resulting TBW designs are still superior to equivalent cantilever designs for both of the missions as they have both lower TOGW and fuel burn. However, a relaxed flutter constraint applied at 1.05 times the dive speed can restrict the penalty on the TOGW to only 0.3% and that on the fuel burn to 2% for minimizing both the objectives, for the medium-range mission. For the long-range mission, a similar relaxed constraint can reduce the penalty on fuel burn to 2.9%. These observations suggest further investigation into active flutter suppression mechanisms for the TBW aircraft to further reduce either the TOGW or the fuel burn. Secondly, the effects of a variable-geometry raked wingtip (VGRWT) on the maneuverability and aeroelastic behavior of passenger aircraft with very flexible truss-braced wings (TBW) were investigated. These TBW designs obtained from the MDO environment while minimizing fuel burn resemble a Boeing 777-200 Long Range (LR) aircraft both in terms of flight mission and aircraft configuration. The VGRWT can sweep forward and aft relative to the wing with the aid of a Novel Control Effector (NCE) mechanism. Results show that the VGRWT can be swept judiciously to alter the bending-torsion coupling and the movement of the center of pressure of wing. Such behavior of the VGRWT is applied to both achieve the required roll control as well as to increase flutter speed, and thus, enable the operation of TBW configurations which have up to 10% lower fuel burn than comparable optimized cantilever wing designs. Finally, a transonic aeroelastic analysis tool was developed which can be used for conceptual design in an MDO environment. Routine transonic aeroelastic analysis require expensive CFD simulations, hence they cannot be performed in an MDO environment. The present approach utilizes the results of a companion study of CFD simulations performed offline for the steady Reynolds Averaged Navier Stokes equations for a variety of airfoil parameters. The CFD results are used to develop a response surface which can be used in the MDO environment to perform a Leishman-Beddoes (LB) indicial functions based flutter analysis. A reduced-order model (ROM) is also developed for the unsteady aerodynamic system. Validation of the strip theory based aeroelastic analysis with LB unsteady aerodynamics and the computational efficiency and accuracy of the ROM is demonstrated. Finally, transonic aeroelastic analysis of a TBW aircraft designed for the medium-range flight mission similar to a Boeing 737 next generation (NG) with a cruise Mach number of 0.8 is presented. The results show the potential of the present approach to perform a more accurate, yet inexpensive, flutter analysis for MDO studies of transonic transport aircraft which are expected to undergo flutter at transonic conditions. / Ph. D.

Time series analysis is a statistical technique used to analyze sequential data points collected or recorded over time. While traditional models such as autoregressive models and moving average models have performed sufficiently for time series analysis, the advent of artificial neural networks has provided models that have suggested improved performance. In this research, we provide a custom neural network; a shift encoder that can capture the intricate temporal patterns of time series data. We then compare the sparse matrix of the shift encoder to the parameters of the autoregressive model and observe the similarities. We further explore how we can replace the state matrix in a state-space model with the sparse matrix of the shift encoder.

This work describes a framework for simultaneous estimation and modeling (SEAM) of dynamic systems using non-Gaussian belief fusion by first presenting the relevant fundamental formulations, then building upon these formulations incrementally towards a more general and ubiquitous framework. Multi-Gaussian belief fusion (MBF) is introduced as a natural and effective method of fusing non-Gaussian probability distribution functions (PDFs) in arbitrary dimensions efficiently and with no loss of accuracy. Construction of some multi-Gaussian structures for potential use in MBF is addressed. Furthermore, recursive Bayesian estimation (RBE) is developed for linearized systems with uncertainty in model parameters, and a rudimentary motion model correction stage is introduced. A subsequent improvement to motion model correction for arbitrarily non-Gaussian belief is developed, followed by application to observation models. Finally, SEAM is generalized to fully nonlinear and non-Gaussian systems. Several parametric studies were performed on simulated experiments in order to assess the various dependencies of the SEAM framework and validate its effectiveness in both estimation and modeling. The results of these studies show that SEAM is capable of improving estimation when uncertainty is present in motion and observation models as compared to existing methods. Furthermore, uncertainty in model parameters is consistently reduced as these parameters are updated throughout the estimation process. SEAM and its constituents have potential uses in robotics, target tracking and localization, state estimation, and more. / Doctor of Philosophy / The simultaneous estimation and modeling (SEAM) framework and its constituents described in this dissertation aim to improve estimation of signals where significant uncertainty would normally introduce error. Such signals could be electrical (e.g. voltages, currents, etc.), mechanical (e.g. accelerations, forces, etc.), or the like. Estimation is accomplished by addressing the problem probabilistically through information fusion. The proposed techniques not only improve state estimation, but also effectively "learn" about the system of interest in order to further refine estimation. Potential uses of such methods could be found in search-and-rescue robotics, robust control algorithms, and the like. The proposed framework is well-suited for any context where traditional estimation methods have difficulty handling heightened uncertainty.

The power requirements imposed on an active vibration isolation system are quite important to the overall system design. In order to improve the efficiency of an active isolation system we analyze different feedback control strategies which will provide electrical energy regeneration. The active isolation system is modeled in a state-space form for two different types of actuators: a piezoelectric stack actuator and a linear electromagnetic (EM) actuator. During regenerative operation, the power is flowing from the mechanical disturbance through the electromechanical actuator and its switching drive into the electrical storage device (batteries or capacitors). We demonstrate that regeneration occurs when controlling one or both of the flow states (velocity and/or current). This regenerative control strategy affects the closed loop dynamics of the isolator which sees its damping reduced. / Master of Science

Distributed generation for consumer applications is a relatively new field and it is difficult to satisfy both cost and performance targets. High expectations coupled with extreme cost cutting to compete with traditional technologies make converter design difficult. As power electronics mature more opportunities arise for entry into this lucrative area. An excellent understanding of converter dynamics is crucial in producing a well performing and cost competitive system. The six-switch single-phase inverter proposed in this thesis is a prime candidate for use in single households and small businesses. Its compact size and compatibility with existing electrical standards make its integration easy. However, little work is available on characterizing the system from a controls point of view. In particular balancing the two outputs with an uneven load is a concern. This thesis uses nodal and loop analysis to formulate a mathematical model of the six-switch single-phase inverter. A non-linear time invariant model is constructed for circuit simulation; details found in real circuits are added. A hardware-in-the-loop (HIL) configuration is used for more accurate simulation. In fact, its use makes for an almost seamless transition between simulation and hardware experimentation. A detailed explanation of the HIL system developed is presented. The system is simulated under various load conditions. Uneven loads and lightly loaded conditions are thoroughly examined. Controllers are verified in simulation and then are tested on real hardware using the HIL system. DC bus disturbance rejection and non-linear loads are also investigated. Acceptable inverter performance is demonstrated without expensive current sensors or high sampling frequency. / Master of Science

No / This paper presents the details of a methodology for predicting the thermoelastic properties degradation in general symmetric laminates with uniform ply cracks in some or all of the 90° layers. First, a stress transfer method is derived by using the concept of state space equation. The laminate can be subjected to any combination of in-plane biaxial and shear loading, and the uniform thermal loading is also taken into account. The method takes into account all independent material constants and guarantees continuous fields of all interlaminar stresses across interfaces between material layers. By this method, a laminate may be composed of an arbitrary number of monoclinic layers and each layer may have different material property and thickness. Second, the concept of the effective thermoelastic properties of a cracked laminate is introduced. Based on the numerical solutions of specially designed loading cases, the effective thermoelastic constants of a cracked laminate can be obtained. Finally, the applications of the methodology are shown by numerical examples and compared with numerical results from other models and experiment data in the literature. It is found that the theory provides good predictions of the thermoelastic properties degradation in general symmetric laminates.

Avian populations have undergone global declines that have profound implications for biodiversity. The prognosis of avian decline risks has been hindered by a lack of understanding of the endogenous and exogenous determinants of avian fauna declines. I investigated the spatiotemporal population dynamics of 428 North American breeding birds using the Breeding Bird Survey data from 1970 to 2018. I hypothesized life history strategies would determine avian population trends by mediating population regulation and responses to global climate changes (H1). I also hypothesized birds with increasing or stable population trends would have greater within-species spatial variability in their population responses to local climate changes and abundances than species with decreasing trends (H2). Machine learning methods classified 225 species (53%) to a decreasing group and 203 species (47%) to an increasing group. The effects of North Atlantic Oscillation (NAO) and Southern Oscillation (SO) on continentally aggregated populations were significantly greater in the increasing group than the decreasing group. However, neither direct nor delayed density dependence differed between the two groups. Bayesian phylogenetic logistic regression demonstrated that increased fledging age significantly reduced avian population decline risks, suggesting that increased investments of parental care mitigate avian population decline risks. Birds living in open areas had about 50% higher risks of population declines than those associated with densely vegetated ecosystems, signaling alarming avian faunal decline risks caused by converting grasslands and shrublands to agriculture or other land use. Structural equation models demonstrated that life history strategy was a direct causal factor of density dependence and population responses to NAO and SO and an indirect cause of avian population decline via mediating avian responses to SO, supporting H1. In metapopulations of 159 breeding birds from 1985 to 2018, density dependence did not differ significantly between the decreasing and increasing groups; however, bird species in the increasing group had greater within-species spatial variance in population responses to temperature and precipitation than declining species, partially supporting H2. Global changes may homogenize avian life history traits and population responses to climate changes, which in turn increase avian fauna decline risks.

Program analysis techniques play a crucial role in modern software development by helping developers find bugs and verify code behavior. These techniques rely heavily on systematic reasoning about program execution paths. Symbolic execution has emerged as a powerful method for systematic program analysis. However, symbolic execution faces a fundamental challenge known as state-space explosion, where the number of potential execution paths grows exponentially. While backwards symbolic execution (BSE) offers some advantages over forward approaches in managing this explosion, it still struggles with path explosion when handling complex program elements such as loops, pointers, and function calls. This dissertation advances backwards reasoning through several key contributions. We introduce BROIL, an approach that enables handling of loops in backwards reasoning without requiring complete loop unrolling. By developing parameterized loop summaries that capture loop behavior, BROIL significantly reduces the state-space explosion problem common in symbolic execution. We demonstrate BROIL's effectiveness by applying it with incorrectness logic for targeted assertions. Our empirical evaluation then investigates the question of which alias analysis technique best complements BSE. Through experimentation comparing different alias analysis approaches, we demonstrate that demand-driven analysis substantially outperforms whole-program approaches, achieving a 7.29× geometric mean speedup in symbolic execution. Finally, we develop CAMS, a compositional approach for function summarization in backwards analysis. CAMS introduces context-agnostic function summaries that capture pointer and global variable effects while supporting modular composition. By enabling the reuse of summaries across different program units, CAMS achieves significant performance improvements compared to non-compositional approach. / Doctor of Philosophy / Modern software has become increasingly complex, making it challenging for developers to ensure their code works correctly. To help with this, developers use program analysis tools that systematically check code for bugs. One promising approach is symbolic execution, which explores how a program behaves in different program paths. However, this approach struggles because the number of possible program states it needs to check grows too quickly. An approach, backwards symbolic execution, helps manage this problem by working backwards from potential bugs, but it still faces state-space explosion when analyzing common programming constructs like loops and function calls. This dissertation presents three solutions to make program analysis more practical. First, we introduce BROIL, a new approach that efficiently handles program loops by creating summaries of their behavior instead of analyzing them completely. We show how BROIL can be used effectively to find bugs in programs. Second, we investigate how different alias analysis techniques perform when integrated with backwards symbolic execution. Our experiments show that demand-driven alias analysis significantly outperforms traditional whole-program analysis approaches, achieving more than seven times speedup. Finally, we develop CAMS, a technique that creates reusable summaries of program functions. These summaries can be used across different parts of the program, making the analysis significantly faster than traditional approaches. Together, these contributions make program analysis tools more practical for analyzing real-world programs.

State-space modelling is a powerful tool to study ecological systems. The direct inclusion of uncertainty, unification of models and data, and ability to model unobserved, hidden states increases our knowledge about the environment and provides new ecological insights. I extend the state-space framework to create multi-species models, showing that the ability to model ecosystem interactions is limited only by data availability. State-space models are fit using both Bayesian and Frequentist methods, making them independent of a statistical school of thought. Bayesian approaches can have the advantage in their ability to account for missing data and fit hierarchical structures and models with many parameters to limited data; often the case in ecological studies. I have taken a Bayesian model fitting approach in this thesis. The predator-prey interactions between the hen harrier (Circus cyaneus) and red grouse (Lagopus lagopus scoticus) are used to demonstrate state-space modelling’s capabilities. The harrier data are believed to be known without error, while missing data make the cyclic dynamics of the grouse harder to model. The grouse-harrier interactions are modelled in a multi-species state-space model, rather than including one species as a covariate in the other’s model. Finally, models are included for the harriers’ alternate prey. The single- and multi-species state-space models for the predator-prey interactions provide insight into the species’ management. The models investigate aspects of the species’ behaviour, from the mechanisms behind grouse cycles to what motivates harrier immigration. The inferences drawn from these models are applicable to management, suggesting actions to halt grouse cycles or mitigate the grouse-harrier conflict. Overall, the multi-species models suggest that two popular ideas for grouse-harrier management, diversionary feeding and habitat manipulation to reduce alternate prey densities, will not have the desired effect, and in the case of reducing prey densities, may even increase the harriers’ impact on grouse chicks.