Master of Science in Engineering, University of Akron, 2016, Electrical Engineering

An observer-controller pair was designed to regulate the fuel flow rate and the flue-gas oxygen ratio of a combustion boiler. The structure of the observer was a proportional-integral state estimator. The designed controller was composed of a combination of two common controller structures: state-feedback with reference tracking and proportional-integral-derivative(PID). A discrete-time, linear state-space model of the combustion system was developed such that the linear controller and observer could be designed. This required establishing separate models pertaining to the combustion process, actuators, and sensors. The complete model of the combustion system incorporated all three models. The combustion model, which related the flue-gas oxygen ratio to the fuel and oxygen flow rates, was obtained using the mathematical formulas corresponding to combustion of natural gas. The actuators were modeled using measured fuel and oxygen flow rate data for various actuator signals, and fitting the data to a parametric model. The established nonlinear models for the combustion process and actuators required linearization about a specified operating point. The sensors model was then obtained using the predictive error identification technique based on batch input-output data. For the acquired model of the combustion system, a linear quadratic regulator was used to calculate the optimal state feedback gain. The classical controller gains were determined by tuning the gains and evaluating the simulation of the closed-loop response. Computer-aided simulations provided evidence that the controller and state estimator could regulate the desired set point in the presence of moderate disturbances. The observer-controller pair was implemented and verified on an experimental boiler system by means of an embedded system. Even in the presence of a disturbance resulting from a 50% blockage of the surface area of the air intake duct, the closed-loop system was capable of regulating t (open full item for complete abstract)

Committee: Nathan Ida Dr. (Advisor); Robert Veillette Dr. (Committee Member); Kye-Shin Lee Dr. (Committee Member) Subjects: Electrical Engineering; Engineering

Master of Science, The Ohio State University, 2020, Mechanical Engineering

The increasing penetration of renewable sources of energy has resulted in an increased likelihood of power over-generation and ramp rate requirements at the electricity supplier end. By incorporating temporally varying costs of electricity provided to the customer, the grid supplier may choose to offer demand-response programs that encourage the customer to defer high load activities to periods of low grid load, effectively overcoming these challenges and increasing machine life. Smart homes optimally activate appliances at the appropriate time with an objective to minimize load at high-price periods, so that at the user end, the total electricity price is lowered. The work presented in this thesis focuses first on the development of models for energy demand and generation associated with electric vehicle (EV) charging and solar power generation, and their integration in an existing residential energy modeling framework. For this enhanced residential power demand model, machine learning (ML) techniques are used to develop a prediction of the user activities for single-resident and multi-resident households. The predicted power demand can be integrated into the smart home algorithm to enhance the optimal activation of appliances to minimize electricity cost and inconvenience.

Committee: Stephanie Stockar (Advisor); Manoj Srinivasan (Committee Member) Subjects: Alternative Energy; Artificial Intelligence; Energy; Engineering; Mechanical Engineering

Master of Science, University of Toledo, 2009, Civil Engineering

Prediction of future pavement conditions is one of the important functions of pavement management systems. They are helpful in determining the rate of roadway network deterioration both at the network-level and project-level management, which forms a major part of engineering decision making and reporting. Network-level management focuses on determination and allocation of funds to maintain the pavement network above a specified operational standard and does not give importance to how the individual pavement sections deteriorate. Therefore, a survival time analysis is determined to predict the remaining service life. At the project-level, engineers make decisions on which pavement to repair, when and how to repair. Therefore, it requires more condition accuracy than network-level. The two adjustment methods proposed by Shahin (1994) and Cook and Kazakov (1987) are often used to obtain more condition prediction at the project-level. Both the Shahin and the Cook and Kazakov models take into account a family average curve in predicting deterioration of individual pavement sections. This prediction is done through the latest available condition-age point of an individual pavement section and does not consider all available data points. This study considers the most commonly used pavement condition prediction models viz. linear regression, polynomial constrained least squares, S-shape and power curve. The prediction accuracy of these four models is compared. Further the prediction accuracy of each of the four models is compared with their respective the Shahin's and the Cook's models to determine whether is it possible to further improve the prediction accuracy error for each of the four models.

Committee: Eddie Y. Chou PhD (Committee Chair); George J. Murnen PhD (Committee Member); Andrew G. Heydinger PhD (Committee Member) Subjects: Civil Engineering; Engineering; Transportation

Doctor of Philosophy (PhD), Ohio University, 2018, Electrical Engineering & Computer Science (Engineering and Technology)

The lack of aircraft state awareness has been one of the leading causal and contributing factors in aviation accidents. Many of these accidents were due to flight crew's inability to understand the automation modes and properly monitor the aircraft energy and attitude state. The capability of providing flight crew with improved aircraft state awareness is essential in ensuring aviation safety. This dissertation describes predictive alerting methods that apply algorithms such as Multiple Hypothesis Prediction (MHP) on aircraft avionics outputs to predict and prevent hazardous conditions. Simulation and Human-In-The-Loop (HITL) studies results are presented to show the effectiveness of the predictive alerting method in aiding flight crew's aircraft state awareness during some of the most confusing aircraft automate modes.

Committee: Maarten Uijt de Haag (Advisor); Frank van Graas (Committee Member); Michael Braasch (Committee Member); Chris Bartone (Committee Member) Subjects: Electrical Engineering; Engineering

Doctor of Philosophy, Case Western Reserve University, 2017, EECS - System and Control Engineering

We introduce the “Forward and Backward Extended Prony” (FBEP) method that identifies the parameters of complex exponential signals using a new strategy for finding true pole locations. The performance of the proposed method is investigated theoretically using statistical analysis and experimentally by simulation. Initial validation is accomplished using time series data without additive noise and with the help of singular value decomposition (SVD), the advantages of this method in accurately identifying both growing and decaying modes in moderate noise is then demonstrated by adding noise to the time series data with different signal-to-noise ratios (SNRs). The FBEP method is compared with the TLS-Prony method and the subspace-based methods by illustrating the Mean Squared Errors (MSEs) of the frequency and damping factor estimates given by each method with comparisons to the corresponding Cramer-Rao (CR) bounds. The computational time of FBEP and the subspace-based methods is compared. The performance of the FBEP method using the pseudoinverse and Total Least Squares (TLS) approaches below the threshold SNR is also studied. The FBEP method can be applied in cases where the poles of some modes are known a priori in complex exponential signals. The FBEP method is applied to power system small-signal stability analysis. Its effectiveness in identifying system eigenvalues from the output signal is validated by experiments on a test system model. Using a four-machine-two-area power system model the identification of the dominant modes contained in oscillatory signals given by the FBEP method is compared with that given by the SVD-TLS method, the Prony-SR method and Trudnowski's algorithm at different SNR levels. The computational cost of the FBEP method, the Prony-SR method, and Trudnowski's algorithm is evaluated. The results from multi-signal analysis and sliding window analysis using the FBEP method are also presented.

Committee: Kenneth Loparo (Committee Chair); Vira Chankong (Committee Member); Marc Buchner (Committee Member); Richard Kolacinski (Committee Member); Mingguo Hong (Committee Member) Subjects: Engineering

Doctor of Philosophy, University of Akron, 2016, Civil Engineering

Self-centering concentrically braced frame (SC-CBF) systems have been developed to increase the drift capacity of braced frame systems prior to damage to reduce post-earthquake damages in braced frames. However, due to special details required by the SC-CBF system, the construction cost of an SC-CBF is expected to be higher than that of a conventional CBF. While recent experimental research has shown better seismic performance of SC-CBF system subjected to design basis earthquakes, superior seismic performance of this system needs to be demonstrated for both structural and nonstructural components in all ground motion levels and more building configurations. Moreover, Stakeholders would be attracted to utilize SC-CBF if higher construction cost of this system can be paid back by lower earthquake induced losses during life time of the building. In this study, the seismic performance and economic effectiveness of SC-CBFs are assessed and compared with CBF system in three building configurations. First, probabilistic demand formulations are developed for engineering demand parameters (inter-story drift, residual drift and peak floor acceleration) using results of nonlinear time history analysis of the buildings under suites of ground motions. Then, Seismic fragility curves, engineering demand (inter-story drift, peak floor acceleration and residual drift) hazard curve and annual probabilities of exceeding damage states are used to evaluate and compare seismic performance of two systems. Finally, expected annual loss and life cycle cost of buildings are evaluated for prototype buildings considering both direct and indirect losses and prevailing uncertainties in all levels of loss analysis. These values are used evaluate economic benefit of using SC-CBF system instead of CBF system and pay-off time (time when the higher construction cost of SC-CBF system is paid back by the lower losses in earthquakes) for building configurations. Additionally, parametric study is per (open full item for complete abstract)

Committee: Qindan Huang Dr. (Advisor); Qindan Huang Dr. (Committee Chair); David Roke Dr. (Committee Member); Craig Menzemer Dr. (Committee Member); Akhilesh Chandra Dr. (Committee Member); Hamid Bahrami Dr. (Committee Member) Subjects: Civil Engineering; Economics; Engineering; Finance; Mechanical Engineering

MS, University of Cincinnati, 2015, Engineering and Applied Science: Computer Science

UCII has designed and built the Weather monitoring system for Bridges, at multiple locations in North America. The Weather Monitoring System collects data from the local weather stations, airports around the bridge and the sensors installed on the bridge. This data is analyzed for inclement weather conditions on and around the bridge and whenever an abnormal behavior is detected, “Alarms” are sent out via email. Using the large amount of data collected from various stations and sensors correlation between every variable is studied. This detailed study is later used to build Eight Hour Ahead Temperature Prediction system and a Four Hours Ahead Hourly Precipitation Prediction System. The Prediction system uses machine learning techniques to predict new data based on prior data. The Atmospheric variables collected such as temperature, pressure, humidity, hourly precipitation, wind speed, wind direction, solar radiation is analyzed. The correlation between the afore-mentioned variables on time scale is also analyzed and is discussed in detail which aided in building the Prediction models. The prediction system uses an Artificial Neural network to train and provide predictions. The prediction system was designed and built to be used alongside Weather Monitoring system built by UCII and to further its intelligence coefficient in predicting inclement weather condition.

Committee: Arthur Helmicki Ph.D. (Committee Chair); Victor Hunt Ph.D. (Committee Member); Paul Talaga Ph.D. (Committee Member) Subjects: Engineering

Master of Science, University of Akron, 2014, Applied Mathematics

A stochastic initiation and propagation model is developed to predict the effects of pitting corrosion on susceptible metals. The model relies upon an inhomogeneous Markov chain system in order to describe the propagation of pit depths throughout a discretized set of states. This work mainly examines the flexibility of the model with respect to the probabilistic transition rates λi used in the Markov system. Depending on the form of λi, either an analytical or a numerical solution procedure can be used to solve the Markov system, with the numerical form of λi being able to simulate a wider variety of systems, especially for dynamically changing environments. By modifying the expression of λi, increases or decreases, cyclical changes, or abrupt shifts in environmental corrosivity are studied. Simplifications to the model are also suggested for the sake of computational efficiency. A tool in the form of a Mathematica Computational Document is offered as an example of the model's possible use in industry. Additionally, suggestions are made in regard to metastable pitting and situations where the metal begins in a corroded state. The model is flexible enough to handle these scenarios as long as appropriate data is available.

Committee: Nao Mimoto Dr. (Advisor); Curtis Clemons Dr. (Advisor); Kevin Kreider Dr. (Advisor); Gerald Young Dr. (Advisor) Subjects: Applied Mathematics

Doctor of Philosophy, The Ohio State University, 2009, Civil Engineering

The present work focuses on the development of a Modular Multi-Component Coastal Ocean Prediction System (mmcops) that incorporates the full 3D wave-current interactions for a better representation of the entrainment and transport mechanics in complex deep and shallow water coastal environments. The system incorporates wind, temperature and atmospheric pressure forcing that drive the circulation, wave, sediment and bottom boundary layer model components. The effects of the wind generated surface waves on the water column and bottom layer dynamics are parametrized by the inclusion of the Stokes drift, and the wave radiation stress terms that quantify the excess of mass and momentum flux produced by the waves. Coupled wave-hydrodynamic models traditionally incorporate the radiation stress terms only into the vertically integrated momentum. Considering the fact that currents are 3D structures, the vertical variation of the radiation stress should be also considered. In the present work the 3D momentum equations are re-derived to include the full 3D impact of the radiation stresses on the currents. As a preliminary test, the system is applied to Lake Michigan with a twofold purpose: a to conduct an initial testing of the model prognostic variables with and without the effect of the waves; and b to develop a methodology required to answer whether the annually observed Spring turbidity nearshore plume in Southern Lake Michigan is transporting material from its origin in one continuous transport mode or as generated by a series of local deposition, resuspension and transport activities. To this end data collected during the EEGLE project are fully analyzed; shoreline erosion rates and texture of the eroded material were collected from various sources and via various methods and are presented for 34 shoreline segments in a uniform format; an Eulerian Particle Tracking formulation that identifies the source and origin of the various particle sizes (open full item for complete abstract)

Committee: Keith Bedford W (Advisor); Carolyn Merry J (Committee Member); Gil Bohrer (Committee Member) Subjects: Civil Engineering; Geophysics; Ocean Engineering; Oceanography

Doctor of Philosophy (PhD), Ohio University, 2006, Electrical Engineering & Computer Science (Engineering and Technology)

This dissertation is focused on the development of system level architectures and models of dynamically self-reconfigurable systems for machine intelligence. This research is significant for building brain-like intelligent systems. Although the development of deep submicron very large scale integration (VLSI) system, nanotechnology and bioinformatics facilitate building such intelligent systems, yet it is very challenging to study how these kinds of complex, reconfigurable systems can self-develop their connectivity structures, accumulate knowledge, make associations and predictions, dynamically interact with environment, and self-control to accomplish desired tasks. A new framework of “learning-memory-prediction” for machine intelligence is proposed in this research, and it serves as the foundation for building intelligent systems through learning in dynamic value systems, memorizing in self-organizing networks, and predicting in hierarchical structures. These systems are characterized by on-line data driven learning, distributed structure of processing components with local and sparse interconnections, dynamic reconfigurability, self-organization, and active interaction with environment. Learning is the fundamental element for biologically intelligent systems. The proposed online value system is able to learn and dynamically estimate the value of any multi-dimensional data set, and such value system can be used in reinforcement learning. Feedback mechanism is introduced in the self-organizing learning system to allow the machine to be able to memorize information in its distributed processing elements and make associations. After the information is learned and stored in the associative memory, a biologically-inspired anticipation-based temporal sequence learning architecture is proposed. All systems proposed in this research are hardware-oriented. A novel computing paradigm that can achieve low power consumption for designing large scale, high density intelligent (open full item for complete abstract)

Committee: Janusz Starzyk (Advisor) Subjects: