Browsing by Author "Chen, Zheng"
Now showing 1 - 20 of 25
- Results Per Page
- Sort Options
Item A Comprehensive Integrity Monitoring System for Bolted Flange Connections(2020-08) Jiang, Jinwei; Song, Gangbing; Franchek, Matthew A.; Grigoriadis, Karolos M.; Chen, Zheng; Mo, Yi-LungBolted flange connections are commonly utilized to link pressure vessels and pipeline systems. Although there exists a series of design codes and standards to specify the design of flange joints, multiple adverse factors, such as unexpected loads and excessively high pressures and temperatures, can directly cause leakage failures of the flange connections in service. Leakage failures, if undetected, may result in crippling economic losses and sometimes irreversible environmental damages, especially for offshore applications. Therefore, integrity monitoring and inspection of both onshore and offshore bolted flange connections is necessary. In this dissertation, a comprehensive integrity monitoring system is proposed and developed through the implementation of cutting-edge sensing technologies to thoroughly investigate the integrity of a bolted flange assembly under tensile loads, internal pressure loads and a combination of both. API 6A flanges (4-1/16”, Type 6B, 2000 psi) were selected to perform the proposed research. Fiber Bragg grating (FBG) -enabled bolts offer a direct measurement of the bolt strains in the flange assembly. The piezo-based active sensing method and the electromechanical impedance (EMI) method provide different approaches to monitor the characteristic variations in the metal-to-metal sealing condition. Upon seal failure, the acoustics generated by the sudden release of the escaping pressurized nitrogen gas were readily detected by the acoustic emission (AE) system. Meanwhile, the internal pressure of the flange assembly was simultaneously recorded as a reference for other measurements. Through the data analysis, this comprehensive integrity monitoring system provides useful insights of flange connection behaviors under different internal pressures and tensile loads and a preliminary understanding of a leakage failure envelope considering the bolt torques and tensile loads. Moreover, a touch-based sensing mechanism was also explored and further applied on a specifically designed robotic manipulator, which was incorporated with an undersea remotely operated vehicle (ROV) to comprise a remote inspection system for subsea bolted connections. Its undersea inspection performance was demonstrated through field testing in a local marine environment. Thus, the proposed comprehensive integrity monitoring system offers potential solutions for assuring the performance and integrity of onshore and offshore bolted flange joints in practical applications.Item A Percussion Method to Detect Erosion of Elbow Using Machine Learning Algorithms(2022-12-13) Cao, Lan; Song, Gangbing; Chen, Zheng; Chen, Xuemin; Franchek, Matthew A.Elbows are widely used in many industries, especially in oil and gas industry. The purpose of elbow is to change the flow direction in pipeline systems. In some severe applications, elbows are employed to transport abrasive high-pressure multiphase flow medium. With the increase of the service time, the wall thickness of the elbow will become thinner due to erosion and wear, which may lead to piercing or bursting of the high-pressure piping system and cause negative impacts on both the economy and the environment. A novel method of using percussion and machine learning to detect the rate of elbow’s erosion was developed and discussed in this thesis. Three sets of elbow and pipe assembly were used as test specimens. Then, six different erosion levels were simulated by grinding off mass from the internal wall of the elbows. The elbow bottom location, where the simulated erosion was, was tapped to generate the percussion sound, which was recorded by a smart phone. The power spectral density (PSD) and mel-frequency cepstral coefficient (MFCC) were employed to extract features from the percussion sound. The k-nearest neighbor (KNN), the decision tree (DT), and the support vector machine (SVM) were implemented with PSD features to learn the training samples and predict test samples. By using the above three basic machine learning methods, the experiment achieved an average of 90% accuracy on training data and 80% on testing data. Then, the recurrent neural network (RNN), a deep learning method, was implemented with MFCC features to learn and train the data. This method achieved 100% accuracy on training data and 97% on testing data. Finally, the unsupervised clustering algorithms, k-means and Gaussian mixture model (GMM), were implemented with transformed MFCC features. The accuracy of k-means algorithm varied in a range from 49% to 68%, while the GMM clustering method achieved an accuracy of 76%. The results of this work have demonstrated the feasibility of the novel method of percussion and machine learning to detect the level of erosion of elbow in pipeline. Compared with the conventional method, the proposed method does not require installation of sensors or extra signal acquisition instruments. The erosion detection using percussion and machine learning brings great potential contribution to pipeline operating safety assurance.Item Damage detection of fiber reinforced polymer plate repaired steel structure using percussion and machine learning(2022-05-12) Xu, Yong; Song, Gangbing; Chen, Zheng; Chen, XueminThe recent collapse of the Fern Hollow Bridge in Pittsburgh, Pennsylvania, brings attention to the structurally deficient infrastructure. The fiber reinforced polymer (FRP) has been proven to be a cost-effective, efficient, and reliable method for structure rehabilitation or reinforcement. Damage detection is an important measure to ensure the integrity and performance of such repairs. A novel method of using percussion and machine learning to detect the damage of FRP plate repaired steel structure was developed and discussed in this work. A steel beam with bonded carbon fiber reinforced polymer (CFRP) and known bonding defects was used as a test specimen. Then, different locations with different bonding conditions on the beam were tapped to generate the percussion sound, which was recorded by an iPhone. The mel-frequency cepstral coefficient (MFCC) algorithm was employed to extract features from percussion sound. The support vector machine (SVM) and recurrent neural network (RNN) method were implemented to learn the training samples and achieved high accuracy when predicting the healthy status of new test samples. The SVM used a new way of feature transformation, which is based on mean and standard deviation of MFCC. The high accuracy of 98.5% demonstrated the new feature transformation method is effective for SVM in percussion application. Then, the unsupervised clustering algorithms, k-means and Gaussian mixture model (GMM), were implemented on the sample data. The accuracy of k-means algorithm varies in a wide range from 51.5% to 70.4%, while the GMM clustering uses transformed MFCC and manually selected features, achieves an accuracy of 93.8%, The results of this work have demonstrated that the novel method of percussion and machine learning is reliable for damage detection of the FRP repaired steel structure. Compared with the conventional method, the proposed method does not require installation of sensors or implementing data acquisition systems or test instruments. The damage detection using percussion and machine learning brings great potential contribution to restoration of the deteriorated structure.Item DIELECTRIC ELASTOMER TUBULAR ACTUATORS: MODELING, CONTROL, AND BIOMEDICAL APPLICATIONS(2023-12) Kaaya, Theophilus Ssebalabye; Chen, Zheng; Becker, Aaron T.; Franchek, Matthew A.; Grigoriadis, Karolos M.; Song, GangbingDielectric elastomers, a class of electro active polymers, have found applications in a vast array of fields such as soft robotics, haptic devices, biomedical devices, energy harvesting, tunable lenses, soft sensing, microfluidics, and textile electronics. This work explores the multifaceted domain of dielectric elastomers, encompassing physics-based modeling, state boundary avoidance control for safety assurance and real time control, and diverse applications including a dielectric elastomer-enabled cuff device, and a prosthetic finger. The first section delves into the intricate physics-based modeling techniques employed to simulate the behavior of dielectric elastomers under various conditions. In particular, a tubular dielectric actuator is discussed. By leveraging fundamental principles of electromechanics and material science, researchers have developed sophisticated models that enhance our understanding of the material’s response to electrical stimuli. The second focal point of this work is the implementation of state boundary avoidance control for ensuring the internal state safety of dielectric elastomer-based devices. As these materials can undergo substantial deformations in response to electric fields, preventing undesired states and ensuring controlled actuation is crucial. A state boundary avoidance control strategy is discussed as an effective mechanism to mitigate potential risks and enhance the reliability of dielectric elastomer systems. The work also highlights two innovative applications of dielectric elastomer technology. Firstly, a dielectric elastomer-enabled cuff device is presented, showcasing the material’s potential in wearable technology for therapeutic or assistive purposes. Secondly, the development of a dielectric elastomer-enabled prosthetic finger is explored, emphasizing the adaptability and precision achievable through the integration of these materials in bioengineering applications. Finally, the work provides a concise summary of the discussed topics and outlines potential future directions in dielectric elastomer research. Considerations for further advancements in modeling techniques, safety assurance protocols, and novel applications are addressed. The integration of dielectric elastomers into diverse fields holds promise for transformative technological advancements, with ongoing research poised to unlock new possibilities and refine existing applications.Item Electroactive Polymer Artificial Muscles Enabled RoboFish(2019-10-23) Chen, ZhengAutonomous underwater robots are highly demanded in environmental monitoring, intelligent collection, and deep water exploration. Recent years have witnessed significant effort in development of bio-inspired underwater robots to mimic aquatic animals, such as robotic fish, robotic jelly fish, and robotic manta ray, to achieve high energy propulsion efficiency and maneuvering capabilities. Novel actuating materials, which are lightweight, soft, and capable of generating large flapping motion under electrical stimuli, are highly desirable to build such bio-inspired robotic fish. Electroactive polymers (EAPS) are emerging smart materials that can generate large deformations under electrical stimuli. As an important category of ionic EAPs, Ionic Polymer-Metal Composites (IPMCs) can work under wet conditions with low actuation voltages, which shows their great potential as artificial muscles in bio-inspired underwater robots. In this talk, a systems perspective is taken, from modeling, control, fabrication, and bio-inspired design, which addresses the most challenges in this research area. Three types of bio-inspired underwater robots using artificial muscles will be presented in this talk, including robotic fish, robotic manta ray, and artificial swimming bladder. Advantages and challenges of using IPMC artificial muscles in bio-inspired robots will be concluded at the end.Item From 2D to 3D Maneuverable Robotic Fish: A Systems Perspective(2022-05-12) Zuo, Wenyu; Chen, Zheng; Franchek, Matthew A.; Pan, Miao; Song, Gangbing; Grigoriadis, Karolos M.Robotic fish, as an emerging member of marine robots, have received lots of attention in recent years. Because of its unique propulsion mechanism, a large amount of research work today focuses on robotic fish design. Due to the complex hydrodynamics, the modeling of the robotic fish has become a challenging topic, and the research on control and application is still in its beginning. This study systematically introduces the development and application of a robotic fish from the perspective of design, modeling, and control. A three-joint robotic fish propelled by a Double-Slider-Crank (DSC) mechanism, which uses one DC motor to achieve oscillating foil propulsion, is designed. From the design aspect, DSC helps the robotic fish in mimicking a real fish's two-dimensional free-swimming. The robotic fish's top speed is 0.35 m/s at 3 Hz, equivalent to 0.98 body length (BL) per second. DSC also benefits the control of the robotic fish by independently adjusting its steering and swimming speed. This characteristic is studied in a hydrodynamic model that derives the thrust within a DSC frame. A semi-physics-based and data-driven linear model is established to connect the bias angle to the robotic fish's steering. A linear model is used to design a controller, called event-trigger-control, to overcome the adverse effects of communication drop-off. Furthermore, the work is extended to a robotic fish application study that uses robotic fish to estimate the flow field. Besides, the three-dimensional maneuverability is also addressed by developing a buoyancy control device to change depth. Overall, the proposed robotic fish has an excellent performance in free-swimming and shows great application value in environmental surveys.Item INTELLIGENT DETECTION OF BOLT LOOSENESS USING STRUCTURAL HEALTH MONITORING METHODS AND PERCUSSION APPROACH(2020-12) Wang, Furui; Song, Gangbing; Franchek, Matthew A.; Grigoriadis, Karolos M.; Chen, Zheng; Mo, Yi-LungBolted joints have been widely used to connect different components across multiple engineering fields, while the bolt looseness detection is an urgent issue to be solved. Recently, several piezo-enabled structural health monitoring (SHM) methods have been utilized to detect bolt looseness, including the active sensing method, electromechanical impedance (EMI) method, and the vibro-acoustic modulation (VAM) method. However, current approaches mostly focus on single-bolt looseness detection, and there is still a lack theoretical investigation to explore the principle of these methods. In this dissertation, several in-depth studies to advance the development of research in the bolt looseness detection are presented. First, a numerical model, a semi-analytical model, and an analytical model of the active sensing method for single-bolt looseness detection is proposed. Then, several new entropy-based indices are developed to replace the current index, i.e., signal energy. Via these entropy-based indices and machine learning (ML) technique, the detection of multi-bolt looseness is achieved for the first time. Second, a model to describe the relationship between bolt preload and EMI signal is theoretically developed, providing a better understanding of the EMI method. Third, in terms of the VAM method, swept sine waves as inputs are employed to improve practicability, and a new entropy-based index is developed to enable the VAM method to detect multi-bolt looseness. Moreover, considering that the above methods depend on permanent contact between transducers and structures, a new percussion-based approach is proposed. By tapping the bolted joint and analyzing the percussion-induced sound signals, the bolt looseness can be detected without contact-type sensors. First, an analytical model to research the mechanism of the percussion-based approach for bolt looseness detection is proposed. Then, by using deep learning (DL) based techniques to process and classify the percussion-induced sound signals under different bolt preloads, two practical percussion-based approaches to detect bolt looseness detection were developed. In summary, several in-depth investigations of SHM methods and a new percussion-based approach for bolt looseness detection have been conducted in this dissertation. It is believed that these methods have great potential for future industrial applications.Item Jellyfish-inspired Robots Enabled by Soft and Hard Actuators(2022-12-14) Wang, Shengbin; Chen, Zheng; Song, Gangbing; Chen, Jiefu; Grigoriadis, Karolos M.; Cescon, MarziaDielectric elastomer (DE) materials, a category of electroactive polymers, can convert an electrical input into mechanical work. They can be used to design Dielectric elastomer actuators (DEAs) that are flexible, resilient, lightweight, and durable and provide such properties without suffering high financial costs, which makes them particularly promising for soft robotic applications. Unfortunately, severe technical deficiencies limit the development of DEAs: hard-to-embed algorithms for self-sensing, low output forces, and challenging integration with other actuators. This thesis provides some approaches to address these limitations by combining various DEAs applications. First, a tubular DEA is designed to develop a self-sensing algorithm by building a model between the capacitance and measured displacement. Fast Fourier Transform (FFT) is used to filter a given frequency of the probing current and voltage, and then calculate the capacitance at the probing frequency with the probing current and voltage during each time window. With the relationship between displacement and the capacitance of the DE tube, the movement of the DE actuator can be estimated online and achieve self-sensing without an external sensor. Second, a DEA-enabled robotic jellyfish robot is developed based on contracting the muscle-like behavior of DE material. It combines a DE diaphragm actuator with a transmission mechanism, which can provide a compliant thrust force to propel the jellyfish robot to transit through the water. A data-driven model is developed to capture the vibration in the first step. The process of contracting the bell and producing thrust force is captured by a physical model in the second step. Lastly, a novel 2D maneuverable jellyfish robot fabricated with multiple DE membranes and IPMC is introduced, which uses the DE membrane to generate a periodic contraction on its eight fins to provide propulsion. The robot utilizes an IPMC to generate a bending moment that directs the heading angle of its swimming. Lastly, a biomimetic jellyfish robot driven by the DC motor is fabricated and activated to mimic real locomotive behavior, which has a higher speed and better controllability compared with the previous two robotic jellyfish. Due to the nonlinear fluid dynamics and electromechanical coupling, a model-free control method based on reinforcement learning (RL) is employed to offer powerful algorithms to search for optimal controllers of systems.Item Linear Parameter Varying Control of Uncertain Time-Delay Systems with Applications to Automated Blood Pressure Regulation(2020-12) Tasoujian, Shahin; Grigoriadis, Karolos M.; Franchek, Matthew A.; Faghih, Rose T.; Song, Gangbing; Chen, ZhengThis dissertation examines the problem of real-time estimation and automated control of mean arterial blood pressure (MAP) response of a critical patient subject to the vasoactive drug infusion in emergency resuscitation scenarios. The proposed methodologies rely on the wealth of the system identification and feedback control theory and can provide reliable and efficient patient resuscitation tools via computerized drug administration. Therefore, such advanced resuscitation methods can reduce emergency care costs and significantly increase the survival chances by improving the patient's MAP regulation in an intensive care unit. In order to derive an appropriate mathematical description, a dynamic first-order linear time-varying model structure with varying parameters and time delay is employed to characterize the patient's complex physiological MAP response dynamics. In the first part of the dissertation, real-time estimation of the varying model parameters and delay is performed via a Bayesian-based multiple-model square-root cubature Kalman filtering (MMSRCKF) approach. The estimation results substantiate the effectiveness of the utilized identification method using experimental data. Next, two classical frequency-domain control design methods, namely, IMC-PID and parameter-varying loop-shaping approaches, are proposed and implemented to achieve desired MAP regulation in various simulation scenarios. The second part of the dissertation is devoted to the analysis and control synthesis of time-delayed linear parameter-varying (LPV) systems with norm-bounded parametric and/or time-delay uncertainties. LPV time-delay systems are linear dynamical systems whose dynamic characteristics rely on a measurable scheduling parameter vector, where the scheduling parameter vector is used systematically to capture the dynamics of time-varying and nonlinear systems. In order to reduce the design conservatism and handle the varying delay uncertainties, a Lyapunov-Krasovskii based approach is exercised, and by utilizing an improved parameter-dependent Lyapunov Krasovskii functional (LKF) candidate and applying an efficient cross-term bounding technique, the affine Jensen's inequality, sufficient stability and performance conditions are derived and formulated in terms of convex linear matrix inequality (LMI) framework. The final relaxed synthesis conditions are obtained to design a robust delay-dependent gain-scheduled controller, which guarantees closed-loop stability and minimizes disturbance amplification in terms of the induced L2-norm performance specification. The effectiveness of the proposed control design algorithms is assessed through the automated MAP regulation task, and the results are compared with the conventional control approaches in the literature. The final closed-loop simulation results confirm the potential and superiority of the adopted LPV methodologies.Item Magnetic Methods for Underwater Localization and Navigation(2023-12) Garcia, Javier; Becker, Aaron T.; Chen, Jiefu; Chen, Zheng; Leclerc, Julien; Pan, MiaoUnderwater robotics is a rapidly expanding field with many important applications. From exploration and maintenance of oil and gas installations, to search and rescue, to defense, robotic agents are desirable for efficiency and safety reasons. However, performing tasks with only one agent usually necessitates an increase in size and complexity, which in turn exacerbates the power requirements that can drastically reduce run times. Instead, this dissertation examines localization between a group of agents, so that they can cooperate and perform tasks. My work on this project focused on the practical implementation of underwater localization and navigation. For robotic agents to cooperate and efficiently work together, they must be able to efficiently locate each other and communicate. While many technologies have been employed for communication in underwater environments, they have drawbacks that we try to address through the use of magnetic induction (MI) communications. The first part of this dissertation focuses on implementing MI so different agents, with limited prior knowledge of the swarm, can find each other and communicate at short distances, where collision between agents is most probable. The theoretical basis and validation, as well as hardware design and implementation are covered. The use of magneto-statics to find ferrous objects underwater, such as pipes, is presented in the second part. This is important for inspection and maintenance tasks, as it can complement vision-based methods in finding objects in waters with low visibility or even partially covered by the ocean floor. Detection of ferrous structures is also a necessity to avoid collision when navigating around them.Item Magnetoelectromechanical Coupling Mechanisms in Soft Materials(2018-08) Alameh, Zeinab; Sharma, Pradeep; Kulkarni, Yashashree; Yu, Cunjiang; Chen, Zheng; Ardebili, HalehSoft materials are everywhere around and inside us. Because of their abundance, low cost and ease of fabrication along with interesting physical properties, they have numerous promising applications such as artificial muscles, sensors and actuators, smart materials and energy harvesters to name a few. Soft materials deform easily under the presence of external stimuli due to their low mechanical stiffness. This response makes them ideal candidates for the design of multifunctional smart materials. Recently, it was shown that soft materials can produce apparent piezoelectric and magnetoelectric behavior even in the absence of those intrinsic properties provided certain conditions apply. In this dissertation, we will highlight some characteristics and implications of magneto-electro-elastic coupling behavior in specific soft materials structures: The emergence of apparent magnetoelectric behavior in soft materials and its stability: We explore the interplay between elastic strain, electric voltage and magnetic field and its effect on the maximum stretch and voltage that the material can sustain. We present physical insights to support the design of wireless energy harvesters that can be remotely activated with an external magnetic field. Engineering concurrent magnetoelectricity and piezoelectricity in soft materials using electret structure: We prove that by embedding charges in an elastically heterogeneous soft dielectric structure, it is possible to obtain simultaneous piezoelectricity and magnetoelectricity even in the absence of these intrinsic properties. We show that the coupling coefficients in this case are large and compare to some of the well-known ceramic composites. Enhanced electromagnetomechanical response in solid and liquid inclusions: We design a composite made of a dielectric elastomer as the matrix and a spherical inclusion made of iron or ferrofluid while accounting for capillary effects. The beauty of this composite resides on its ability to respond to external electric and magnetic stimuli. We investigate the effect of surface energy at the inclusion/matrix interface on the effective response of this composite. Microscopic mechanisms underpinning flexoelectricity in soft materials: We prove that the existence of frozen dipoles and their thermal fluctuations contribute to the flexoelectric response of the dielectric material. We also predict a temperature dependence of the coupling coefficient.Item Managing Blood Glucose Concentration in Type 1 Diabetes with Deep Learning-Based Methodologies(2023-08) Jaloli, Mehrad; Cescon, Marzia; Grigoriadis, Karolos M.; Song, Gangbing; Chen, Zheng; Prasad, SaurabhThis thesis presents groundbreaking research on advanced models and automated systems designed to revolutionize Blood Glucose (BG) management for individuals with type 1 diabetes (T1D). Integrating cutting-edge deep learning algorithms, phys- iological modeling, and RL techniques, the primary objective is to improve diabetes care, optimize glucose control, and enhance the overall quality of life for T1D patients. The journey commences with the development of state-of-the-art BG predictive mod- els, proposing a powerful deep learning-based model to achieve remarkable accuracy in glucose predictions. Additionally, the impact of behavioral factors, such as physical activity and stress, on BG fluctuations, is explored, further enhancing model accuracy and generalizability. Furthermore, innovative BG testing platforms are introduced, including a data- driven model predictive control algorithm integrated with a BG predictor, showcasing superior glucose control compared to traditional linear control models, and deriving BG dynamic models in response to physical activity, providing invaluable insights for the design of automated closed-loop systems aimed at improved glucose control in daily life. The final frontier lies in RL-based automated insulin delivery systems, with two pivotal papers presenting closed-loop insulin administration frameworks dynamically adjusting insulin dosages based on real-time glucose readings and meal intakes, show- casing significant reductions in glucose variability and improvements in time spent within the target glucose range. In conclusion, this comprehensive thesis showcases innovative models and systems to elevate BG management in T1Ds, offering hope for a brighter future for those living with this condition as advancements continue to reshape diabetes care through personalized and automated approaches.Item Mimicking the Motion of a Seahorse(2019) Carretero Murillo, JavierBio-inspired robotics focuses on the physics learned from animals in nature and applied to artificially design machines to perform specific functions. Although a plethora of marine animals have been used as the template bio-inspired robotic design, there is little to no research on the applications of seahorses in robotics. Applying the mechanical features of a seahorse to a robot would make the robot stable and would allow for a 360o control over the robot. In this project various materials were tested to create prototypes that could mimic a sea-horse’s buoyancy and fin and tail movements. Ionic Polymer-Metallic Composites (IPMCs) were used to create and test the buoyancy mechanism. A test was conducted to see how much voltage various resistors would give while being connected to the IPMC, thus testing their efficiency in reverse electrolysis. Nitinol wire, a Shape Memory Alloy (SMA), was used to create a prototype of the sea-horse tail. We tested the amount of opposing force needed to deform the wire to an undesirable shape. Finally, a magnetic actuator was built to mimic the fin motion. Results from the IPMC tests showed that the 220K resistor gave the highest voltage, thus should provide the highest rate of electrolysis. Additionally, it was noted that the nitinol wire retained its shape up to a force four times its weight. Furthermore, the buoyancy and precise fin and tail movements of the sea-horse robot could allow for its use in underwater explorations and missions.Item Modeling and Control of Hydrogen Systems(2021-08) Keow, Alicia Li Jen; Chen, Zheng; Becker, Aaron T.; Franchek, Matthew A.; Grigoriadis, Karolos M.; Song, GangbingA metal hydride (MH) hydrogen storage can be charged directly using a proton exchange membrane (PEM) water electrolyzer. The electrolysis process regulates the hydrogen pressure during the charging process. A hierarchical control approach is taken where a higher-level controller determines the desired gas rate for charging, while a lower-level controller tracks this gas generation rate. The low-level proportional-integral (PI) controller is tuned using the relay-feedback auto-tuning approach to adapt to the nonlinear and time-varying dynamics of the electrolyzer. A self-assessment algorithm determines when to activate the autotuner and gain scheduling reduces tuning frequency. This controller is validated on a PEM electrolyzer setup, showing desirable transient behavior at varying operating conditions. The high-level controller adopts the active disturbance rejection control (ADRC) paradigm. ADRC consists of a pressure profile generator, an observer that estimates unmeasurable states, and a controller that produce a suitable gas rate to track the pressure profile. The observer also predicts the state-of-charge (SOC) of the tank. Simulation results show that the ADRC provides good disturbance and noise rejection. Further, this work develops two types of buoyancy control devices. The first system varies its buoyancy using the collection of gases from water electrolysis and the release of stored gases using solenoid valves. The second system varies buoyancy via the gas generation and consumption of reversible fuel cells (RFC), allowing for improved energy efficiency. A dynamic model is constructed for both buoyancy varying systems. The first device is controlled by a proportional-integral-derivative-acceleration (PIDA) controller with its gain identified via the pole placement method. The second device employs a proportional-derivative-acceleration (PDA) controller with gains identified via a relay-feedback auto-tuning method. Finally, the effectiveness of both controllers is confirmed with experiments.Item Modeling, Simulation and Optimization of Electromagnetic Systems(2019-12) Mallek, Moadh; Franchek, Matthew A.; Grigoriadis, Karolos M.; Song, Gangbing; Chen, Zheng; Provence, Robert S.Permanent magnet machines having Halbach Array exhibit a number of attractive features. Therefore, they have been increasingly applied to different market sectors, including aerospace, industrial, domestic, renewable, and healthcare. The need for fast global optimization, cost-effective design, and physical understanding of the relationship between parameters and performance requires a powerful analytical model. This Thesis develops a two-dimensional mathematical model estimating the torque of a Halbach Array surface permanent magnet motor. The magnetic field domain for the 2-D motor model is divided into five regions: slots, slot openings, air gap, rotor magnets and rotor back iron. Applying the separation of variable method, an expression of magnetic vector potential distribution can be represented as the Fourier series. By considering the interface and boundary conditions connecting the proposed regions, the Fourier series constants are determined. The proposed model offers a computationally efficient approach to analyzing SPM motor designs including those having a Halbach Array. Furthermore, design for traction Halbach Array motor is performed using the analytical model developed beforehand, according to two different duty cycles. In addition, global optimization via Analysis led Design procedure is proposed to evaluate the most effective design areas. The Neodymium Iron Baron magnet has been become more expensive over the last few years, increasing the awareness of cost-effective design. Meanwhile, the needs of high electromagnetic performance, including lower torque ripple and sinusoidal air-gap flux density, are also critically required. In order to meet such demands, magnet poles with unequal thickness, main design parameters, and Halbach magnetization are proposed in this thesis. Other than permanent magnet motors, Halbach Array configuration is proposed in double-sided magnetic lead screw (MLS). An analytical model is developed based on the subdomain method to calculate the magnetic field distribution. This allows calculating the electromagnetic performances such as the generated thrust force. The analytical model is deployed in early stage design process of a MLS. It has been incorporated within a simulation based analysis process. With the aid of developed analytical models and finite element analysis, the findings provide useful guidelines for design and analysis of permanent magnet machine and magnetic lead screw device having Halbach Arrays.Item NON-DIMENSIONAL DATA-DRIVEN MODELS OF COMPUTATIONAL FLUID DYNAMICS (CFD) OF MULTI-PHASE FLOW IN ANNULUS(2022-08-15) Hao, Yitong; Franchek, Matthew A.; Grigoriadis, Karolos M.; Chen, Zheng; Cescon, Marzia; Nikolaou, Michael; Tang, YingjieIn the offshore drilling process of an oil and gas well, the blowout preventer (BOP) serves as a critical safety device to monitor and control wells to prevent blowouts. However, it is usually subject to the high-temperature and high-pressure (HTHP) conditions. These extreme working conditions challenge the BOP’s robustness and threaten its liability. For example, the cavitation damage the elastomer sealing surface and lead to BOP malfunction or failure. To understand the flow characteristics under extreme working conditions and ultimately enable the estimation of the BOP lifetime, experimental approaches and multi-physics numerical simulations can be employed. However, the full-scale experiments are very costly, and the coupled full-scale simulations are time-consuming. To overcome these challenges, a scalable model is required to connect the cavitation criterion, pressure conditions, and geometric parameters of the BOP sealing. In the present work, the BOP sealing was simplified as a converging-diverging annulus. Various annulus geometries necessary to quantify annulus flow were identified and investigated using computational fluid dynamics (CFD) tools. Based on the simulation results, the criterion of thin or thick annulus was established. In addition, two non-dimensional data-driven models were developed to describe the cavitation conditions in terms of downstream/upstream pressure ratio and non-dimensional geometric parameters. The first model evaluated the critical downstream/upstream pressure ratio to cause cavitation, and the corresponding discharge coefficient of the annulus system under two-phase (liquid, vapor) flow condition. The second model extended to the three-phase (liquid, vapor, air) situation, closer to the actual offshore BOP working conditions. Using the obtained non-dimensional models, the annulus flow rate of either two-phase or three-phase flow can be estimated under both choked and unchoked flow conditions across multi-scales. The study results can provide numerical guidance for small-scale annulus flow experiments, build connections between small-scale experiment results and full-scale applications, and ultimately enhance future governing standards to evaluate flows in blowout preventer (BOP) and its potential failure in the oil and gas industry.Item Nonlinear Mechanics of Deformable Soft Electret Materials and the Emergent Phenomena of Pyroelectricity, Electrocaloric Effect, and the Kerr Effect(2019-12) Darbaniyan, Faezeh; Sharma, Pradeep; Gunaratne, Gemunu H.; Chen, Zheng; Baxevanis, Theocharis; Kulkarni, YashashreeSoft materials are capable of undergoing large deformations. This is essential in several applications such as soft robotics, sensors, actuators, energy harvesting, biomimetics among other. In addition, there is a strong impetus to study \emph{multifunctional} soft materials i.e. those that show coupling between two or more fields such a deformation, electrical, magnetic, pH, temperature to name just a few. Unfortunately, most soft matters do not intrinsically possess such multifunctional couplings. In this dissertation, we outline a theoretical framework that exploits nonlinear deformation to design a soft material that exhibits an emergent pyroelectric and electrocaloric behavior. In addition, we present a nonlinear homogenization framework to design the nonlinear Kerr effect. Specifically, in this dissertation, we develop the nonlinear theory that includes the mechanical, thermal, and electrical energy of the system and address the following problems: i) Soft pyroelectric and electrocaloric materials: Pyroelectric and electrocaloric materials are restricted to hard materials or crystals. We propose the notion of using electrets and design soft composite that can \emph{behave} as pyroelectric and electrocaloric materials; ii) Pit-bearing snakes (vipers, pythons, and boas) have an extraordinary ability to ``see” and accurately locate their prey and predators in total darkness. These animals use the infrared radiation emanating from objects that are warmer relative to the background environment to form a thermal image. Building on our theoretical work on pyroeletric materials, we elucidate the central mechanism underpinning the infrared vision of snakes. Despite the exceptional simplicity of our proposed mechanism and model, we are able to explain many of central experimental results pertaining to the transduction process; iii) Soft Material and the Kerr effect: A nonlinear homogenization approach is developed to design the so-called Kerr effect in soft materials.Item Numerical Simulations of Oil Spills and Wind Turbine Flows for Applications in Offshore Environmental Sustainability(2020-08) Xiao, Shuolin; Yang, Di; Liu, Dong; Ostilla-Mónico, Rodolfo; Chen, Zheng; Momen, MostafaEnvironmental sustainability has currently become one of the biggest issues faced by the mankind. The high demand of fossil fuel has resulted in considerable offshore oil drilling activities, significantly increasing the possibility of offshore oil spill accident. Specifically, the 2010 Deepwater Horizon accident has shocked the world with its severe adverse impact on the ocean ecosystem and the human society around the Gulf of Mexico. On the other hand, the technological advancement of floating turbines has made offshore wind a feasible resource to help supply the high energy demand. This doctoral dissertation covers three research topics related to offshore environmental sustainability, including the dynamics of multiphase buoyant plumes related to subsea hydrocarbon blowout, the effects of surface oil plume on upper-ocean radiative transfer related to the adverse impact of offshore oil spill on the ocean ecosystem, and the fluid dynamics of offshore floating wind farm for energy harvesting. A number of high-fidelity numerical simulation models are developed and applied to tackle these challenging problems, including the large-eddy simulation model for the oceanic and atmospheric turbulent flows, the Eulerian large-eddy simulation model for particle plume dispersion in ocean environment, the high-order spectral method for ocean waves, the Monte Carlo photon transport model for ocean radiative transfer, and the actuator disk model for wind turbines. The results show that the inherent properties of oil droplets including their size and rising velocity, under the background of cross flow, will significantly affect their temporal-spatial distribution and the resulting photosynthesis within the ocean mixed layer. Besides, pitch motions will be induced on offshore wind turbines via their interaction with ocean surface wave, leading to modified wake flow statistics and power extraction rate compared with that on shore. Therefore, these findings can give guidance for fast-response strategy when oil spills happen and design of offshore wind turbines.Item Observer-Based Parameter Estimation for Linear Uncertain Discrete-Time Systems(2018-12) Hattab, Oussama; Franchek, Matthew A.; Grigoriadis, Karolos M.; Song, Gangbing; Chen, Zheng; Provence, Robert S.Presented is an observer-based parameter estimation solution for a class of linear, discrete-time systems. The proposed formulation embeds the problem of parameter estimation within a parametric uncertain observer formulation where the state and output matrices are expressed as and . The methodology is developed by creating general solutions for the uncertainty matrices and . A unique solution for each is recovered by parameterizing the general solution subject to a rank condition. The primary advantage of the proposed method is that individual parameters within the linear state equation matrices can be estimated using input/output data. The methodology is well suited for parameter estimation problems involving multi-energy-domain systems where intermediate measurements between fields are not available. Simulation examples are provided to demonstrate the utility of the proposed parameter estimation method. This result has broad applications to robust feedback solutions and system health monitoring (system diagnostics and prognostics). The methodology is applied to a double mass-spring-damper system with both constant and varying uncertainty cases. The approach is demonstrated to be able to adapt to the changes in parameters of the system in real-time. The observer-based parameter estimation method is applied to an adaptive model-based approach for structural health monitoring (SHM) of multistory buildings. Fault detection, isolation and estimation (FDIE) are accomplished through the integration of reduced-order physics models with the methodology that directly estimates changes in structural stiffness and damping. In the proposed method, each building floor is connected to its adjoining floors using springs and dampers (i.e., structure columns) to capture the planar motion of the system. The novelty in this method is that the structure features of stiffness and damping are directly estimated from the observer model states. To demonstrate the proposed method, a finite element analysis of a scaled digital building is used to generate dynamic structural data. The simulated data will be corrupted to emulate sensor noise. It will be shown that for this numerical study, a 15% stiffness change in one of the nine columns between the floors that produces 1.67% decrease in overall stiffness is detected. It will be also shown that the proposed approach is able to detect, isolate and estimate faults of different magnitudes at single and multiple locations. Finally, the identification approach is applied to a blowout preventer annular health-monitoring problem. The methodology is used to adapt the annular model coefficients and track their changes with cycles. The approach is able to separate graphically between early, middle, and aged cycles. Hence, health-monitoring decisions are achievable based on simple graphical representation of the results.Item Reachable Set for a drone(2022-12-14) Sultan, Mohammad M.; Becker, Aaron T.; Jackson, David R.; Chen, Zheng; Cescon, Marzia; Li, XingpengQuadcopters are increasingly popular for robotics applications. Being able to efficiently calculate the set of positions reachable by a quadcopter within a time budget enables collision avoidance and pursuit-evasion strategies. This research computes the set of positions reachable by a quadcopter within a specified time limit using a simplified 2D model for quadcopter dynamics. This popular model is used to determine the set of candidate optimal control sequences to build the full 3D reachable set at final time T in (x,z,θ) phase space or rotated to form the set in (x,y,z) Cartesian space. We calculate the analytic equations that exactly bound the set of positions reachable in a given time horizon for all initial conditions. We use these bounds to: escape a bounded region, avoid a collision, find the collision set, determine the closest point to the reachable set, reach a goal (x,z) in the reachable set, and for drone countermeasures.