Browsing by Author "Malki, Heidar A."

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A Novel Demand Response Management Model to Reduce Smart Grid Costs
(2017) Ahmadian, Saeed; Malki, Heidar A.; Barati, Masoud
DRM means leveling demand curve based on electricity prices. Indeed, using variable price rates, electricity consumers are encouraged to use electricity in periods with cheap electricity price ranges. Therefore, total system cost would decrease efficiently. Motivations: To model dynamics between electricity end-users and utility companies. To present a model with simple implementation on smart households. To reduce electricity production costs and to maximize social welfare.
Advanced Sliding Mode Controllers and Their Innovative Applications Using Smart Materials
(2012-08) Singla, Mithun 1983-; Song, Gangbing; Shieh, Leang-San; Malki, Heidar A.; Chen, Ji; Franchek, Matthew A.; Grigoriadis, Karolos M.
This dissertation focuses on the following two research topics involving smart materials: 1) the advanced sliding mode controllers and their applications and 2) the development of an automatic de-icing system for roads by the electrical heating of embedded carbon fiber. Sliding mode control has widely been used in many different applications. In this dissertation, the active sliding mode control behavior was realized through analyzing the vibration suppression of vortex induced vibrations (VIV) of a jumper pipe structure via pounding tuned mass damper (PTMD) integrated with viscoelastic material. The force generated by the PTMD is analogous to the active sliding mode control. Comparison between simulation and experimental results demonstrated the similarity between the PTMD and the active sliding mode control. Sliding mode controllers are robust to uncertainties and immune to disturbances, but suffer chattering problems due to discontinuities in the control law. In this dissertation, an advanced sliding mode control using the continuous sign function and LQR approach to alleviate chattering is proposed. The desired sliding surface was designed using the stable eigenvectors of the controlled system. Simulation results show that the proposed approach is effective in disturbance rejection and chattering reduction. The robustness of the proposed optimal controller was demonstrated through the implementation of active vibration control on a flexible beam with mass uncertainty. The experimental results show that the vibrations of the beam with mass uncertainty can be well controlled by the proposed approach. Due to the inability to guarantee stability of a system with unmatched uncertainties, the proposed approach is improved by replacing the LQR approach with the H∞ approach. The stability of the proposed approach was verified with the H∞ approach. The simulation results show that the control input generated by the proposed robust approach was very smooth compared to conventional sliding mode controllers. The experimental implementation for vibration control of a base-isolated structure equipped with an MR damper, where the nonlinear force generated by the MR damper acted as an uncertainty to the system, showing the effectiveness of the approach. Lastly, an innovative de-icing system using carbon fiber as the heating element was developed. A test sidewalk was prepared by embedding electrically powered carbon fiber frames into the concrete pavement. A LabVIEW interface controlled the de-icing process through two sidewalk surface temperature controllers (ON-OFF and Fuzzy Logic) and enabled the user to keep track of the environmental conditions. The experimental results showed that the proposed technique effectively prevented the formation of ice on the pavement surface and that the advanced temperature controller was 80% more power efficient compared to a manual on-off switch.
An Integrated Approach to Maximize Efficiency and Reliability of Grid Connected Distributed Energy Sources
(2014-12) Kotti, Radhakrishna; Shireen, Wajiha; Malki, Heidar A.; Cline, Raymond E., Jr.; Provence, Robert S.; Barati, Masoud
The emergence of local-generation as a technically sound solution for the reliability concerns of the existing power grid has lead in recent times to the concept of microgrid. The microgrid is a network of low-voltage (LV) interconnected loads and distributed-energy resources (DERs) with clearly-deﬁned electrical boundaries. The distributed generation majority rely on either photovoltaic (PV) or wind turbines producing imperfectly-predictable variable power, making it harder for the system operators to match generation and load at every instant. This has created a signiﬁcant interest in optimal grid integration and control of DER units in terms of efﬁciency maximization and cost minimization by implementing maximum power point tracking (MPPT) control. A new scanning method of MPPT control for grid-connected PV systems is proposed and studied. This technique displays high tracking efﬁciency with low tracking time. The control tracks the global maxima under partial shading conditions and provides a non-oscillatory response under steady-state operation. The performance of the proposed control is compared with perturb and observe (P&O) and incremental conductance (INC) control under varying operating conditions. The simulation and experimental results clearly prove the effectiveness of the proposed MPPT control. For wind energy conversion systems (WECS) a new adaptive sensor-less MPPT control is proposed. The controller overcomes the trade-off between step size and tracking accuracy, providing a fast tracking response along with avoiding the inconsistent generator-converter efﬁciency problems. The performance of the proposed control is validated through simulation and experimental results in comparison with P&O MPPT control under varying wind conditions. The DERs are connected to the microgrid with the help of power electronics interface such as AC-DC, DC-DC and DC-AC converters. The DC-DC and DC-AC converters are coupled with the help of large electrolytic capacitors. The reliability of the system is maximized by reducing the required DC link capacitance and replacing the electrolytic capacitors with high cost and reliable ﬁlm capacitors. A new DC link reduction control to reduce the required DC link capacitance is proposed. The performance of the proposed control is validated through simulation results for grid connected PV and WECS under reduced DC link capacitance.
Attitude determination of GPS satellite vehicles
(2014-08) Arcot, Mrinalini; Malki, Heidar A.; Provence, Robert S.; Shireen, Wajiha
There is an increasing demand for navigation systems that has led to rapid development of Global Positioning System (GPS) across industries. Apart from position and speed, precise attitude measurements are needed for many GPS applications. This thesis presents techniques for attitude determination of satellite vehicles in both real-time and stand-alone positioning applications. The GPS system used is a differential GPS system that estimates the body frame baselines using at least four receivers. The attitude information is obtained using these baselines and projecting them onto a local level frame. Integer ambiguity is a major constraint in attitude determination. Least Squares Ambiguity Deco-relation method is implemented to fix the ambiguities prior to baseline estimation. Estimation techniques such as Least Squares and Kalman Filter are implemented for deriving baseline components. Finally, this system will compute body frame coordinates and attitude components in reference to the desired coordinate frames.
Complex Image Theory and Applications in Boundary Detection in Geo-Steering Using Data from a Directional Resistivity LWD Tool
(2014-05) Wang, Jing; Liu, Richard C.; Wilton, Donald R.; Jackson, David R.; Holley, Thomas K.; Malki, Heidar A.
Geo-steering is the process of controlling and adjusting the direction of the drilling bit in a horizontal or deviated well, in real time to keep the drilling in the desired zone. One of the most challenging steps of Geo-steering is the boundary detection, which is to calculate the distance from the bit to upper or lower boundary based on the measured data from an LWD tool. Generally, calculating the distance from bit to boundary is an inversion problem. To speed up the inversion process, a fast forward modeling algorithm is critical. In this study, the Complex Image Theory applied in finite conductivity layered media is derived to speed up the forward modeling of the geo-steering system. Two approximation results are shown in detail in dealing with the two general cases of dipole radiation. The first one is when the dipole is placed in the relative high resistive layer. The second one is when the dipole is placed in the relative high conductive layer. The algorithm is tested in both two-layer and three-layer cases and in high deviated well. Compared with the results from the full solution (the result from INDTRI), the Complex Image Theory has satisfactory accuracy and when the number of logging points is 600,000, it is 160 times faster. Error only exists in area two ft. or three ft. away from boundary. By considering the power of real source, the possibility of real application is investigated. The tolerance in different frequencies, spacing and conductivity combinations is discussed too. The simulation results show that the Complex Image Theory works in most geo-steering situations. The proposed method reduces the simulation time and improves the real-time performance of the control system. The distance inversion is developed for two-layer formation. The inversion results show that the algorithm works well even at the position 10 ft. away from the boundary. The anti-noise capacity of the propose method is measured by further involving random white noise in the simulation scenario. The relative error of simulation is as low as 5% in the area six ft. away from the boundary. With higher conductivity contrast formation, the proposed method is even more robust.
Design and Controllability of Plug-in Hybrid Electric Vehicle (PHEV) Charging Facilities Integrated with Renewable Energy Resources
(2014-12) Goli, Preetham; Shireen, Wajiha; Chen, Yuhua; Han, Zhu; Malki, Heidar A.; Cline, Raymond E., Jr.
Electricity generation and transportation account for most of the global primary energy demand. The majority of the world’s coal demand is for electricity generation and the majority of the world’s oil demand is for transportation. This has triggered an increase in the deployment of renewable energy sources such as photovoltaic (PV) and wind throughout the globe. Likewise, alternative vehicle technologies, such as plug-in hybrid electric vehicles (PHEVs), are being developed to reduce the world’s dependence on oil for transportation and to limit transportation-related greenhouse gas emissions. A major barrier for the wide penetration of PHEVs in the market is the underdeveloped charging infrastructure. Another emerging issue is that a large number of PHEVs connected to the grid simultaneously may pose a huge threat to the quality and stability of the overall power system. Since the initial penetration of PHEVs is expected to be confined to a particular neighborhood, charging them simultaneously might cause serious issues to the distribution transformers. In view of the above issues this dissertation proposes a PHEV charging station architecture for workplace-based parking facilities using renewable energy sources (wind and/or PV) coupled with smart grid technologies. The proposed control algorithm will reduce the stress imposed on the grid at the distribution level during peak load hours. The proposed architecture consists of a DC microgrid that allows three-way interaction between the distributed energy sources, PHEVs and the grid, ensuring optimal usage of available power, charging time and grid stability. It consists of a photovoltaic and/or wind power source, power conditioning unit (PCU) along with an energy storage unit (ESU). The PCU consists of power converters with an intermediate DC-link. A unique control algorithm, based on the variation in DC link voltage level and the priority charging levels of PHEVs, facilitates the energy management and scheduling of PHEVs in the charging facility. As the DC link voltage is the only criterion used for switching between various modes, the overall complexity of the system is reduced in comparison to other existing methods.
Design and Fabrication of a Controller for a Digital Phase Locked Loop
(2012-12) Troha, Donald; Charlson, Earl J.; Trombetta, Leonard P.; Chen, Ji; Malki, Heidar A.; Pillai, Rajeev Rajan
A controller for an all digital phase locked loop which operates by pulse addition and removal is investigated. Being a first order system, the digital phase locked loop is more limited in regard to parameter controls than its second order analog counterpart. A loop with a fast lock time generally has poor phase/frequency accuracy, while a loop programmed for high accuracy will have slow lock time. Given that the digital phase locked loop is digitally programmable, a set of parameters may be selected which will minimize the lock time of the loop. Once the loop is locked, the parameters may be changed to alter the loop bandwidth and increase the loop accuracy. A controller circuit has been designed to adjust loop parameters in such a manner thereby optimizing loop performance. The exclusive-OR phase detector which is commonly used with the pulse addition/removal type digital phase locked loop has a phase lock range of plus or minus a quarter of a cycle. This work investigates the loop response to an incoming signal which is outside of the phase lock range of phase detector and inside the frequency lock range of the loop. A sub-circuit is proposed to improve the lock time of the loop when it encounters an incoming signal with these characteristics. The proposed circuits were designed using integrated circuit layout tools and submitted to a semiconductor manufacturer for fabrication. The controller concept and results of simulations and prototype experiments are presented.
Detecting Cyber-attacks to Smart Grids and Increasing Resiliency Using Data Driven Algorithms
(2020-08) Ahmadian, Saeed; Malki, Heidar A.; Han, Zhu; Pan, Miao; Rajashekara, Kaushik; Wang, Jianhui
Data driven algorithms can be generally divided into two main categories including optimization methods and machine learning approaches. Optimization methods try to find the optimal decision states by finding the feasible boundaries of the problem. On the other hand, machine learning algorithms aim to find the solutions by iterating via small steps toward the optimal answer following the gradient descents. These two data-driven algorithms are widely deployed in many science and engineering fields and in this dissertation, we use both of these methods to address cyber-security issues of smart grids. We first use the optimization algorithm to present two bi-level problems to address the bidding problem in electricity markets and cyber-attack detection in virtual bidding process in electricity markets. We investigate False Data Injection (FDI) problem in smart grids and the approaches the detect attacks. Both models are solved using mathematical programming with equality constraint (MPEC) and the possible cyber-attack's locations and malicious data are identified. We then study the machine learning abilities to learn the cyber-attacker's behavior using real data. We use the Day-ahead (DA) and Real-time (RT) electricity price and demand to create our initial model of the cyber-attacker. Then, we apply a zero-sum game between the cyber-attacker and system defender using novel machine learning method known as Generative Adversarial Networks (GANs). Then, we present a new deep learning structure to model both cyber-attacker and system defender and aslo flexibility of the system defender to learn different possible attacks. We also use another machine learning approach to mitigate the cyber-attacks effects. Particularly, we use Reinforcement Learning (RL) to investigate the optimal possible actions after the cyber-attack happens in the system. In order to model the possible attack's locations we use multi-stage game between the cyber-attacker and system defender. To model the attacker's moves, we use the Hamiltonian Markov Chain Monte Carlo (H-MCMC) and sample from the posterior distribution of the attack's locations. we then train a deep RL network to learn the optimal actions regarding given game stage and possible future game stages.
Fault-Tolerant High-Density Power Converters and In-Situ Health Prediction for Offshore MVDC Distribution
(2021-05) Sadat, Amin; Krishnamoorthy, Harish S.; Rajashekara, Kaushik; Malki, Heidar A.; Mayerich, David; Gökdere, Levent U.
A fault-tolerant solid-state transformer (SST) structure to combine the benefits of higher power density and robustness in medium-voltage DC (MVDC) electric distribution systems is proposed in this dissertation. A SiC-MOSFET-based 6 MW, 36/6 kV ISOSP (input series output series-parallel) modular stacked DC/DC SST is proposed using medium frequency (MF) transformer isolation. This structure renders the system with fault tolerance and the capability to operate normally even in a partial fault condition. Small-signal modeling, simulations, and Typhoon-HiL real-time system were performed to verify the operation of the converter. Experimental results from a scaled-down laboratory prototype prove the feasibility of the proposed isolated DC/DC structure and control system. While replacing a low-frequency transformer (LFT) with an SST for a certain application, the design of the transformer must be primarily optimized for size and efficiency. In this work, the transient model of a single-phase E-core transformer using a Multi-Turn Coil Domain was used to analyze the electromagnetic field. A medium frequency (MF) transformer with a ferrite core is designed and simulated in COMSOL© based on hardware prototype specifications, and outcomes from 3-D finite element analysis (FEA) matched the 20 kHz MF transformer design used in the hardware. The model includes the analysis of the nonlinear B-H curve, including saturation effects in the core to simulate the magnetic behavior of the soft-iron core. A pulsating voltage, including with up to 7th harmonic, to simulate the effects of a near-square wave is applied to the model. The FEA design was done before manufacturing and to confirm the behavior of the designed transformer, especially for peak flux density. It was also possible to compare the volume of the proposed MF transformer with LF transformers using FEA simulation. Overall, the proposed system can lead to significant improvement in power density in mission-critical MVDC applications such as subsea electrification. The reliability prediction and survival indices calculation are required for the sensitive operation of the proposed fault-tolerant converter. A better understanding of the failure mechanisms and barriers to the utilization of electronic devices in extreme environments leads to reliable power converters in offshore applications. It is well known that each component's reliability in a power converter affects the reliability of the overall system. Due to the advancements in computing infrastructure and sensor technologies, data-driven approaches for predicting the health of power converters in real-time are slowly becoming popular. This research proposes a new statistical approach using probability density functions (PDFs) and associated concepts in measure theory to predict the probability of system failure using individual components’ degradation data. For this purpose, remaining-useful-life (RUL) is estimated for each power component (or sub-system) using qualification data, followed by an evaluation of a cumulative probability of survival for the converter. An artificial neural network (ANN) is then trained to quickly estimate in real-time the probability of survival of the power converter in the future. While the algorithm involves multiple computation steps, the RUL prediction accuracy using the proposed method will be high due to the data-driven approach. Moreover, the machine learning-based model resulting from this approach to predict the probability of survival is low on memory utilization. It is envisioned that this approach can be used to create digital twins of power converters in practical circuits, optimize performance, and predict RUL. This dissertation explains the theory followed by scaled-down hardware of an isolated modular DC-DC converter. An experimental qualification setup for device degradation test and system-level RUL measurement methods are provided.
Lithium-Ion Battery Prognostic and Health Prediction Using Machine Learning Models
(2020-12) Nsaka, Ozioma John; Malki, Heidar A.; Pan, Miao; Li, Xingpeng
Lithium-Ion battery prognostic and health prediction is an essential part of our modern world today. Reliable predictions of the current state and remaining useful life are critical to a wide range of industrial applications’ safe operations. There have been existing and widely studied battery state prediction models, the equivalent circuit, and physics-based models. The emergence and advancements of machine learning algorithms, such as support vector machine (SVM), have led to a new era of data-driven models. In this research, we first establish a baseline prediction using traditional machine learning algorithms and then utilize advance deep learning algorithms. We analyze the battery’s degradation pattern based on the State of Health (SOH) prediction and the Remaining Useful Life (RUL), which aims to predict the degradation from a specific threshold cycle to the end of life (EOL) of the battery. One of the challenges this paper aims to solve is having a comparable baseline for several machine learning-based prognostic predictions. We solve this by performing several experiments on the same data set using traditional algorithms and then perform further experiments using the neural network model. The classification accuracy agrees with several benchmarks established in the research literature. This research further proves the viability of using Long Short Term Memory (LSTM) models for RUL prediction and advancing the role of data-driven models in prognostic and health management for critical engineering applications.
MEG-Based Functional Connectivity Biomarkers of Dyslexia
(2014-12) Iraola Goiburu, Inigo; Zouridakis, George; Malki, Heidar A.; Lent, Ricardo
Dyslexia a learning disability related to reading, often characterized by difficulty with accurate word recognition, decoding, and spelling. The disorder affects approximately 10% of the population and it is typically diagnosed using neuropsychological evaluation. The main objective of this thesis has been the development of unique measures based on fast neurophysiological recordings that may used to improve detection and allow intervention at an earlier age, with improved outcomes. We used functional connectivity analysis to identify brain connectivity networks in task-free, resting-state Magnetoencephalographic recordings of brain activity obtained in two groups of participants, namely 21 dyslexia patients and 20 age-matched normal controls. In an attempt to quantify interaction among brain regions and understand how brain networks are affected by dyslexia, we used Granger causality, which can estimate cause-and-effect relationships both in terms of strength and direction. A Granger connectivity matrix was computed for each subject individually, and then group templates were estimated by averaging all matrices in each group. Furthermore, we performed classification of the subjects using support vector machines and Fisher's criterion to rank the features and identify the best subset for maximum separation of the two groups. Our results show that a combined model based on connectivity matrices and graph theory measures can provide 100% classification accuracy in separating the two groups, with 100% sensitivity and specificity. These findings suggest that analysis of functional connectivity patterns may provide a valuable tool for the early detection of dyslexia.
Modelling Cyber Attacks on Electricity Market Using Mathematical Programming With Equilibrium Constraints
(IEEE Access, 2/25/2019) Ahmadian, Saeed; Tang, Xiao; Malki, Heidar A.; Han, Zhu
With the development of communication infrastructure in smart grids, cyber security reinforcement has become one of the most challenging issues for power system operators. In this paper, an attacker is considered a participant in the virtual bidding procedure in the day-ahead (DA) and real-time (RT) electricity markets to maximize its profit. The cyber attacker attempts to identify the optimal power system measurements to attack along with the false data injected into measurement devices. Towards the maximum profit, the attacker needs to specify the relation between manipulated meters, virtual power traded in the markets, and electricity prices. Meanwhile, to avoid being detected by the system operator, the attacker considers the physical power system constraints existing in the DA and RT markets. Then, a bi-level optimization model is presented which combines the real electricity market state variables with the attacker decision-variables. Using the mathematical problem with equilibrium constraints, the presented bi-level model is converted into a single level optimization problem and the optimal decision variables for the attacker are obtained. Finally, simulation results are provided to demonstrate the performance of the attacker, which also provides insights for security improvement.
Predictive Energy Management Methods for Smart Grids
(2012-12) Hooshmand, Ali 1982-; Malki, Heidar A.; Mohammadpour, Javad; Shieh, Leang-San; Han, Zhu; Chen, Yuhua
In this dissertation, we propose energy management methods for power systems in the context of smart grids. In this regard, we consider new management problems for various conﬁgurations of smart grids, microgrids, as well as the power system generation. For diﬀerent scenarios, we consider grid connection and distributed generations such as photovoltaic cells, wind turbine, and microgas turbines as energy sources. In addition, the eﬀects and advantages of storage devices in smart grids operation are investigated by including them as one of the system components. For microgrids operation, we consider a microgrid both in islanded mode and grid-tied mode of operation. In these modes, we develop and solve new optimization problems which aim to minimize the cost of energy within a microgrid to supply the load and maximize the lifetime of battery units simultaneously. Next, we extend the concept and consider a network of microgrids which are able to collaborate with each other. By proposing a cooperative optimization problem for microgrids network, we will show that the total cost of energy would be minimized. On the generation side, we investigate the economic dispatch problem for power systems which include renewable sources among energy providers. In this case, we will illustrate that conventional approaches for considering renewable energy sources in the dispatching problem will not be functional anymore. In addition, we will develop a new method which can be an appropriate alternative for conventional approach. Finally, we will investigate the advantages of storage devices in the aforementioned economic dispatch problem. Model predictive control (MPC) policies, in both deterministic and stochastic forms, are employed to solve the underlying optimization problems. Several solution methods such as stochastic dynamic programming, linear programming, etc., will be employed to solve the MPC optimization problems. Numerous testbeds and experimental data including IEEE 14-bus system and California ISO data will be utilized to demonstrate the eﬃciency and optimality of the proposed energy management methods.
Real-Time Management of Smart Grid Using Multi-Agent Systems
(2016-08) Khan, Tariq; Abolhassani, Mehdi T.; Shireen, Wajiha; Malki, Heidar A.; Fu, Xin
In this thesis, we explore the following three areas: Design of a multi-agent system (MAS) and a microgrid, implementation of MAS in a microgrid, and real-time resource management in smart grid networks. This includes a detailed anatomy of the multi-agent system, a complete description of various components of the microgrid, a thorough analysis of the communication between these two independent systems and, finally two case studies to experience the usage in actual real-life conditions. The microgrid has been simulated in a Matlab/Simulink environment with standard distribution elements. The microgrid consists of one Solar Farm as the Distributed Energy Resources (DER) with main grid feeding the residential areas during normal operation and DER during fault condition. JADE is chosen as the multi-agent system framework and the communication between the multi-agent system and the microgrid model in Simulink is established using MACSimJX, a third-party interface for protocol conversion.
Simulation of Triaxial Induction Tool Response in Biaxial Anisotropic Formation
(2014-12) Zhou, Mengsu; Liu, Richard C.; Wolfe, John C.; Malki, Heidar A.; Davydycheva, Sofia
A fast 1D electromagnetic method for simulating triaxial induction tool responses is used to model the field distribution in fractured anisotropic formations. Fractures are a specific feature of geological formations. Using the bucking coil technique, we interpreted the simulation results as apparent conductivity of the formation and modeled the practical wireline induction tool response. The method is validated versus other independent modeling software. Multiple examples are presented to compare the apparent conductivity of transversely isotropic and biaxial anisotropic formations. Sensitivity to anisotropic conductivity, bed boundaries, dip angle and layer thickness were observed, which proves that the failure to consider the biaxial anisotropy would have great impact on the interpretation of triaxial induction tool responses. We discuss particular features which enable visual log interpretation for the presence of possible fractures.
Small Spacecraft Design & Machine Learning-based Approaches To Lunar Robotics Navigation
(2023-05-08) Tanaka, Toshiki; Malki, Heidar A.; Becker, Aaron T.; Song, Gangbing; Cescon, Marzia; Provence, Robert S.
Since human exploration of the Moon in the 1960s, the lunar community has benefited from a series of successful missions, including flybys, orbiters, landers (crewed and robotic), rovers, and impactors. The next generation of lunar exploration will include a cis-lunar station, crewed missions, and in-situ resource utilization (ISRU)-based missions that will generate significant amounts of data to help answer questions about how the Moon formed and evolved, what its surface processes and resources are, and the nature of the chemical composition of its surface and deep interior. Complete utilization of the currently available technologies is vital to effectively plan and execute future missions. This can be facilitated by two key technologies: small satellites and machine learning (ML). Nowadays, satellite technologies have progressed to the point where off-the-shelf components can be purchased for small-satellite missions, greatly reducing the time and cost needed to prepare a new mission. The rapid escalation of the production and launch of small satellites has revolutionized the space industry, proving that small satellites in constellations are more useful than fewer, larger ones for some scientific missions and radio relay missions on a large scale. ML and artificial intelligence also play an increasingly important role in aerospace applications, particularly for automated systems, including space robotics guidance, navigation, and control. This dissertation aims to demonstrate three potential components that small satellites and ML could help accelerate in view of future exploration of the Moon and other planetary bodies. The discussion is divided into three topics: 1) renewal of lunar navigation systems with small spacecraft, 2) a machine learning-based approach to lunar hopper control, and 3) a machine learning-based approach to small rover path planning. In the first topic, a new triangulation theory that enables the creation of lunar global navigation satellite systems with just two small satellites is introduced. In the second topic, a new ML-based methodology for lunar hopper obstacle avoidance, descent, and landing is presented. In the third topic, a new ML-based global path planning methodology for small lunar rovers is proposed.
Study of Dielectric Constant Logging Tools
(2012-08) Lin, Chang-Ming; Liu, Richard C.; Wilton, Donald R.; Malki, Heidar A.
One of the important issues of well logging is to determine the water saturation from the oil bearing formation. The induction resistivity tool may finish the job if the water is fairly saline. In fresh water, the induction tool is difficult to figure out the exact water saturation and oil/gas contents. Therefore, it is crucial that there be a method to determine water saturation that is less dependent upon the knowledge of water salinity thus making it of great use in fresh water zones. Dielectric constant logging tools offer an attractive new method of formation evaluation, which is relatively independent of water salinity. In this thesis, a dipole model is developed in that it consists of three infinitesimal dipoles: one for the transmitter and the other two for the receivers. The model, then, is used to simulate the dielectric constant tool responses by measuring the amplitude ratios and phase shifts in both homogeneous and inhomogeneous layered formation. The results show a good agreement with the experimental data and theoretical results published in previous studies. Moreover, the numerical results portray that the dipole model accurately provides a decent estimation of the formation dielectric properties compared to other models.
Study of Dielectric Tools and Dielectric Property of Rocks
(2014-12) Zhang, Yinxi; Liu, Richard C.; Wilton, Donald R.; Jackson, David R.; Holley, Thomas K.; Malki, Heidar A.
Dielectric tools have mainly been used for identifying freshwater zones in oil- or gas-bearing formations. New-generation dielectric tools are also used for detecting shale reservoirs, heavy oil, and residue oil in invasion zones. However, currently commercialized tools either lack information on frequency dispersive behavior or offer redundant logging curves. This dissertation investigates both the design and simulation of novel array dielectric tools; meanwhile, it throws light on the dielectric properties of rocks through lab measurements. A multi-component, multi-spacing array dielectric tool working at 5 different frequencies in the range of 10 MHz to 1 GHz was studied. It covered the frequency gap between propagation tools and conventional dielectric tools. Tool sensitivity was carefully investigated to demonstrate tool capability in exploring formation properties, including permittivity, conductivity, dipping, and anisotropy. Meanwhile, tool simulations with COMSOL provided comprehensive evaluations of tool performance. From the simulation results, it was found that the size of the tool pad has an influence on tool response, especially when lower frequency channels are fired. However, the impact from borehole mud can be negligible since the tool is pushed against the borehole wall. Moreover, the existence of mud cakes and invasions affect the measurements and depth of investigation of the tool as well. In addition, vertical resolution was studied for different formation conditions. The designed tool was proven to be able to detect thin conductive beds or beds with high dielectric constants. In the past few decades, dielectric dispersion has been observed from core data. Practical core measurements for dielectric constant and conductivity were also conducted in this dissertation to study the dielectric properties of sediment rocks. A parallel plate system was used for the study. The relative dielectric constant and conductivity were measured in a frequency range from 10 KHz to 1GHz. Measured data agreed well with the resistivity log inversion results, and large dielectric enhancement at the induction frequencies was observed. Dielectric and conductivity corrections were applied to the original log to correct the errors caused by dielectric dispersions. The results lead to the conclusion that dielectric correction should be added to the resistivity inversion routine to avoid misinterpretation.
Study of Downhole Electromagnetic Boundary-Detection Methods Using Numerical Simulations
(2014-05) Gong, Bo; Liu, Richard C.; Wilton, Donald R.; Jackson, David R.; Holley, Thomas K.; Malki, Heidar A.
Logging-while-drilling (LWD) technology has been used as a real-time aid in directional drilling. In this dissertation, a full investigation is conducted on the use of LWD resistivity tools in geosteering, especially the application in detecting remote bed boundaries. By looking into the electromagnetic field of multiple tool configurations using numerical simulations, an independent evaluation is provided on the downhole boundary detection capability of different resistivity logging tools, as well as their applicability in various drilling environments. In order to explore the potential of predicting formation properties in front of the drill bit, tool responses are first modeled with different downhole electromagnetic transmitters in homogeneous formation, where ahead-of-the-bit field distribution is investigated. Field attenuation rates are compared among different tools, and the influence of borehole conductivity is studied. Next, tool responses are modeled in two-layer formation models to evaluate their boundary detection capabilities. Look-ahead capabilities are compared between tools with axially symmetrical antennas when boundaries are perpendicular to the tool axis. Also, the feasibility of using cross-component measurements to detect horizontal boundaries is studied for tools using orthogonal antennas. After that, the deep-looking capability of a new directional resistivity tool using ultra-long spacings and low frequencies is explored. Tool responses for different configuration parameters and drilling environments are calculated and discussed. At last, an inversion algorithm based on the Gauss-Newton method is developed to recover the boundary distance from the tool response. This dissertation presents a comprehensive summary for the first time on the use of LWD resistivity tools in predicting formation anomalies ahead of or around the drill bit. It is found that the conventional resistivity logging tools using axially symmetrical antennas can only penetrate the formation ahead of the bit by a limited range, which is restricted by the borehole dimension and power supplies, but a deep-looking capability can be acquired by using cross-component measurements in high angle and horizontal wells. The detailed comparisons between tools of different types establish a missing link in the research of deep resistivity logging tools, and provide a natural guide for the future development of downhole boundary detection methods.
Study of Time Domain Logging Tools Using Finte Difference Time Domain Method
(2013-05) Yu, Boyuan 1985-; Liu, Richard C.; Vipulanandan, Cumaraswamy; Wilton, Donald R.; Charlson, Earl J.; Malki, Heidar A.
In the well logging industry, before designing any kind of logging tools, the tool response needs to be simulated and analyzed in different circumstances. Those kinds of logging tools can be generally categorized into two main genres: frequency domain tools and time domain tools. This dissertation presents a numerical way to simulate the time domain tool response. The 3-D finite difference time method is employed in this dissertation. In this dissertation I used 3D FDTD method to simulate the logging tools response in complicated logging circumstance to see how the response will appear with different formation. A few examples were included in this dissertation and shown very decent results. As it is known that the frequency is set relatively low so the tool can detect further when dealing with well logging problems. In order to improve the efficiency of the entire simulation process, two main techniques are introduced in this dissertation: perfectly matched layer boundary conditions and artificially high dielectric constant. With those techniques combined together, the specific tool (Look-Ahead time do main logging tool) response is studied such as the distance to bed boundary response, sea-bed logging tool response, and reservoir effect. With those cases study in detail, the response patterns are investigated while the formation parameters are varied. One thing that needs to be pointed out is that the source excited in all the examples through this dissertation is a Differential Gaussian wave with different maximum operating frequency chosen based on different needs and different examples.