We introduced a method presented in Information Field Theory (IFT) [Abramovich et al., 2007] to improve interpolation in Smoothed Particle Hydrodynamics (SPH) in cases of less smooth flow. The method makes use of wavelet theory combined with B-splines for interpolation. The idea is to identify any jumps a function may have and then reconstruct the smoother segments between the jumps. The results of our work demonstrated superior capability when compared to a particular challenging SPH application, to better conserve jumps and more accurately interpolate the smoother segments of the function. The results of our work also demonstrated increased computational efficiency with limited loss in accuracy as number of multiplications and execution time were reduced. Similar benefits were observed for functions with spikes analyzed by the same method. Lesser, but similar effects were also demonstrated for real life data sets of less smooth nature. SPH is widely used in modeling and simulation of flow of matters. SPH presents advantages compared to grid based methods both in terms of computational efficiency and accuracy, in particular when dealing with less smooth flow. The results we achieved through our research is an improvement to the model in cases of less smooth flow, in particular flow with jumps and spikes. Up until now such improvements have been sought through modifications to the models' physical equations and/or kernel functions and have only partially been able to address the issue. This research, as it introduced wavelet theory and IFT to a field of science that, to our knowledge, not currently are utilizing these methods, did lay the groundwork for future research ideas to benefit SPH. Among those ideas are further development of criteria for wavelet selection, use of smoothing splines for SPH interpolation and incorporation of Bayesian field theory. Improving the method's accuracy, stability and efficiency under more challenging conditions such as flow with jumps and spikes, will benefit applications in a wide area of science. Just in medicine alone, such improvements will further increase real time diagnostics, treatments and training opportunities because jumps and spikes are often the characteristics of significant physiological and anatomic conditions such as pulsatile blood flow, peristaltic intestine contractions and organs' edges appearance in imaging.

<p>Recent experiment in our laboratory demonstrates that an increase in slag foamingwith carbon injection rate is limited by slag volume. The current work has identified arelationship between foam height, carbon injection rate and slag volumes, whichpredicts the critical injection rate above which foaming become inefficient. Theprediction of critical injection rate employs an extension of understanding mechanismof bubble movement in the foam by estimating average/steady-state bubble size andwall thickness. The carbon gasification model developed in our laboratory by King etal., which has been extended to include greater consideration of gas bubble burstingwhen to predict bubble size, and further improvement for calculating how fast bubblecan burst instantaneously in carbon-gas-slag halo system, has found that has importantinfluence on the predicting foaming parameters in King’s model, which is crucial taskfor continuous development in future.</p> / Master of Applied Science (MASc)

This study was directed toward the development of a bistable wall attachment Flip-Flop device which was capable of directionally controlling particle flow. The particles were transported by a fluid stream which under the influence of wall attachment. The dominant criteria in the development of the device was the achievement of the highest recovery of particles at the active output, without destroying the wall attachment of the fluid stream The experiment was conducted in several distinct stages; each of which was concerned with at least one aspect of wa1l attachment or particle flow. Results derived from one test were used to develop the criteria for the next experimental arrangement. Two experimental models were constructed: one of plywood with only one attachment wall, and one of plexiglas which had two attachment walls and was bistable. The plywood model was used in testing wall attachment and particle recovery as a function of the attachment wall angle. From these tests it was concluded that the optimum wall angle was 18 degrees from the center line of the device. Observations of particle action in the plywood model led to the incorporation of additional features in the plexiglas model. They were: an extended nozzle, the elimination of the separation bubble, and the development of smooth transitions at the corners. The plexiglas model was used to investigate optimum splitter location, the effect of jet velocity on recovery efficiency, the effect of vents on the performance of the device, and the performance of the device using a water jet. In the last stages of testing, moving parts and additional output features were used in conjunction with the bistable device to improve the collection efficiency. Some observations resulting from the data gathered in the various tests include: 1. The higher the jet velocity, the greater the wall attachment. 2. The higher the density and viscosity of the fluid stream the greater the recovery of particles at the active output. 3. Particles with large inertial forces were controlled less by the attached jet stream. The addition 0f vents in the device may produce greater particle recovery. 5. The use of moving parts and variations in the output leg design can produce 100 percent particle recovery. This study indicated that it was possible to control the directional flow of particles with the bistable wall attachment device which was developed. However, the pure fluid bistable device could not achieve 100 percent recovery of particles. The addition of moving parts or variations in the output leg design can produce 100 percent recovery of the particles. The use of a bistable device could provide simplicity, reliability and adaptability in transporting materials for industrial processes.

The revolution of autonomous vehicles has led to the development of robots with abundant sensors, actuators with many degrees of freedom, high-performance computing capabilities, and high-speed communication devices. These robots use a large volume of information from sensors to solve diverse problems. However, this usually leads to a significant modeling burden as well as excessive cost and computational requirements. Furthermore, in some scenarios, sophisticated sensors may not work precisely, the real-time processing power of a robot may be inadequate, the communication among robots may be impeded by natural or adversarial conditions, or the actuation control in a robot may be insubstantial. In these cases, we have to rely on simple robots with limited sensing and actuation, minimal onboard processing, moderate communication, and insufficient memory capacity. This reality motivates us to model simple robots such as bouncing and underactuated robots making use of the dynamical system techniques. In this dissertation, we propose a four-pronged approach for solving tasks in resource-constrained scenarios: 1) Combinatorial filters for bouncing robot localization; 2) Bouncing robot navigation and coverage; 3) Stochastic multi-robot patrolling; and 4) Deployment and planning of underactuated aquatic robots. First, we present a global localization method for a bouncing robot equipped with only a clock and contact sensors. Space-efficient and finite automata-based combinatorial filters are synthesized to solve the localization task by determining the robot’s pose (position and orientation) in its environment. Second, we propose a solution for navigation and coverage tasks using single or multiple bouncing robots. The proposed solution finds a navigation plan for a single bouncing robot from the robot’s initial pose to its goal pose with limited sensing. Probabilistic paths from several policies of the robot are combined artfully so that the actual coverage distribution can become as close as possible to a target coverage distribution. A joint trajectory for multiple bouncing robots to visit all the locations of an environment is incrementally generated. Third, a scalable method is proposed to find stochastic strategies for multi-robot patrolling under an adversarial and communication-constrained environment. Then, we evaluate the vulnerability of our patrolling policies by finding the probability of capturing an adversary for a location in our proposed patrolling scenarios. Finally, a data-driven deployment and planning approach is presented for the underactuated aquatic robots called drifters that creates the generalized flow pattern of the water, develops a Markov-chain based motion model, and studies the long- term behavior of a marine environment from a flow point-of-view. In a broad summary, our dynamical system approach is a unique solution to typical robotic tasks and opens a new paradigm for the modeling of simple robotics systems

Thesis (Ph. D.)--University of Chicago, Dept. of Physics, December 1997. / Includes bibliographical references. Also available on the Internet.

This thesis focuses on the comprehensive study of controlling a customized Active Magnetic Bearing (AMB) installed on Bently Nevada’s RK4 rotor kit in Cal Poly’s Vibrations and Rotordynamics Lab. The AMB was uniquely designed and manufactured by a Cal Poly senior project team to fit Bently’s rotor kit and the results of this research are distinctive to the custom system. To achieve practical functionality of the AMB system, we designed a controller a Virtual Instrument (VI) using the National Instrument software, LabVIEW. From the experimental study, we calibrated the programming to find unknown parameters of the AMB system and validated the design using a well-established industrial rotordyanmics software, Bentley Nevada System 1. The development of the control programming consists of theoretical analysis (MATLAB/ Simulink) and simulation validation (MSC ADAMS View). Both linear and non-linear models were implemented in MATLAB and Simulink to effectively tune a Proportional-Integral-Derivative (PID) control developed for various AMB models. To validate the theoretical results, we compared them to results from a co-simulation using MSC Adams VIEW, a multi-body dynamics simulation, and Simulink. From experimental trial and error at a shaft rotational speed of 2800 rpm, a 16% decrease in shaft orbit was achieved. These results demonstrate the practicality of the control program and custom AMB rotor kit that can be used for further research.

Energy consumption has increased drastically during the last century. Currently, the worldwide energy consumption is about 17.4 TW and is predicted to reach 25 TW by 2035. Solar energy has emerged as one of the potential renewable energy sources. Since its first physical recognition in 1887 by Adams and Day till nowadays, research in solar energy is continuously developing. This has lead to many achievements and milestones that introduced it as one of the most reliable and sustainable energy sources. Recently, the International Energy Agency declared that solar energy is predicted to be one of the major electricity production energy sources by 2035. Enhancing the efficiency and lifecycle of photovoltaic (PV) modules leads to significant cost reduction. Reducing the temperature of the PV module improves its efficiency and enhances its lifecycle. To better understand the PV module performance, it is important to study the interaction between the output power and the temperature. A model that is capable of predicting the PV module temperature and its effects on the output power considering the individual contribution of the solar spectrum wavelengths significantly advances the PV module designs toward higher efficiency. In this work, a thermoelectrical model is developed to predict the effects of the solar spectrum wavelengths on the PV module performance. The model is characterized and validated under real meteorological conditions where experimental temperature and output power of the PV module measurements are shown to agree with the predicted results. The model is used to validate the concept of active optical filtering. Since this model is wavelength-based, it is used to design an active optical filter for PV applications. Applying this filter to the PV module is expected to increase the output power of the module by filtering the spectrum wavelengths. The active filter performance is optimized, where different cutoff wavelengths are used to maximize the module output power. It is predicted that if the optimized active optical filter is applied to the PV module, the module efficiency is predicted to increase by about 1%. Different technologies are considered for physical implementation of the active optical filter.

It is well known that when a flexible or flexibly-mounted structure is placed perpendicular to the flow of a Newtonian fluid, it can oscillate due to the shedding of separated vortices at high Reynolds numbers. If the same flexible object is placed in non-Newtonian flows, however, the structure's response is still unknown. The main objective of this thesis is to introduce a new field of viscoelastic fluid-structure interactions by showing that the elastic instabilities that occur in the flow of viscoelastic fluids can drive the motion of a flexible structure placed in its path. Unlike Newtonian fluids, the flow of viscoelastic fluids can become unstable at infinitesimal Reynolds numbers due to the onset of a purely elastic flow instability. This instability occurs in the absence of nonlinear effects of fluid inertia and the Reynolds number of the flows studied here are in the order of 10-4. When such an elastic flow instability occurs in the vicinity of a flexible structure, the fluctuating fluid forces exerted on the structure grow large enough to cause a structural instability which in turn feeds back into the fluid resulting in a flow instability. Nonlinear periodic oscillations of the flexible structure are observed which have been found to be coupled to the time-dependent growth and decay of viscoelastic stresses in the wake of the structure. Presented in this thesis are the results of an investigation of the interaction occurring in the flow of a viscoelastic wormlike micelle solution past a flexible rectangular sheet. The structural geometries studied include: flexible sheet inclinations at 20°, 45° and 90° and flexible sheet widths of 5mm and 2.5mm. By varying the flow velocity, the response of the flexible sheet has been characterized in terms of amplitude and frequency of oscillations. Steady and dynamic shear rheology and filament stretching extensional rheology measurements are conducted in order to characterize the viscoelastic wormlike micelle solution. Bright field images show the deformation of the flexible sheet during an unstable oscillation while flow-induced birefringence images highlight the viscoleastic fluid stresses produced in the wake of the flexible sheet.

Reservoir Computing (RC) is an emerging machine learning paradigm where a fixed kernel, built from a randomly connected "reservoir" with sufficiently rich dynamics, is capable of expanding the problem space in a non-linear fashion to a higher dimensional feature space. These features can then be interpreted by a linear readout layer that is trained by a gradient descent method. In comparison to traditional neural networks, only the output layer needs to be trained, which leads to a significant computational advantage. In addition, the short term memory of the reservoir dynamics has the ability to transform a complex temporal input state space to a simple non-temporal representation. Adaptive real-time systems are multi-stage decision problems that can be used to train an agent to achieve a preset goal by performing an optimal action at each timestep. In such problems, the agent learns through continuous interactions with its environment. Conventional techniques to solving such problems become computationally expensive or may not converge if the state-space being considered is large, partially observable, or if short term memory is required in optimal decision making. The objective of this thesis is to use reservoir computers to solve such goal-driven tasks, where no error signal can be readily calculated to apply gradient descent methodologies. To address this challenge, we propose a novel reinforcement learning approach in combination with reservoir computers built from simple Boolean components. Such reservoirs are of interest because they have the potential to be fabricated by self-assembly techniques. We evaluate the performance of our approach in both Markovian and non-Markovian environments. We compare the performance of an agent trained through traditional Q-Learning. We find that the reservoir-based agent performs successfully in these problem contexts and even performs marginally better than Q-Learning agents in certain cases. Our proposed approach allows to retain the advantage of traditional parameterized dynamic systems in successfully modeling embedded state-space representations while eliminating the complexity involved in training traditional neural networks. To the best of our knowledge, our method of training a reservoir readout layer through an on-policy boot-strapping approach is unique in the field of random Boolean network reservoirs.

The focus of this document is on the design, modeling, and control of a self-balancing two wheel robot, hereafter referred to as the balance bot, driven by independent brushless DC (BLDC) motors. The balance bot frame is composed of stacked layers allowing a lightweight, modular, and rigid mechanical design. The robot is actuated by a pair of brushless DC motors equipped with Hall effect sensors and encoders allowing determination of the angle and angular velocity of each wheel. Absolute orientation measurement is accomplished using a full 9-axis IMU consisting of a 3-axis gyroscope, a 3-axis accelerometer, and a 3-axis magnetometer. The control algorithm is designed to minimize deviations from a set point specified by an external radio remote control, which allows the remote operator to steer and drive the bot wirelessly while it remains balanced. Multiple dynamic models are proposed in this analysis, and the selected model is used to develop a linear-quadratic regulator based state-feedback controller to perform reference tracking. Controller tracking performance is improved by incorporating a prefilter stage between the setpoint command from the remote control and the state-feedback controller. Modeling of the actuator dynamics is considered brie y and is discussed in relation to the control algorithm used to balance the robot. Electrical and software design implementations are also presented with a focus on effective implementation of the proposed control algorithms. Simulated and physical testing results show that the proposed balance bot and controller design are not only feasible but effective as a means of achieving robust performance under dynamic tracking profiles provided by the remote control.