Dissertations / Theses on the topic 'Energy efficiency not elsewhere classified'
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Thakore, Renuka. "A strategic engagement model for delivering energy efficiency initiatives in the English housing sector." Thesis, University of Central Lancashire, 2016. http://clok.uclan.ac.uk/18647/.
Full textAzabany, Azad. "Economic analysis and environmental impact of energy usage in microbusinesses in UK and Kurdistan, Iraq." Thesis, University of Central Lancashire, 2014. http://clok.uclan.ac.uk/20475/.
Full textÖhman, Ben Sebastian. "Energy efficiency investments in the commercial real estate business : A study of decision drivers on the Swedish market." Thesis, Uppsala universitet, Industriell teknik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-355254.
Full text(6623699), Juan Carlos Orozco. "Analysis of Energy Efficiency in Truck-Drone “Last Mile” Delivery Systems." Thesis, 2019.
Find full textTruck-drone delivery systems have the potential to improve how the logistics industry approaches the “last mile problem”. For the purposes of this study, the “last mile” refers to the portion of the journey between the last transportation hub and the individual customer that will consume the product. Drones can deliver packages directly, without the need for an underlying transportation network but are limited by their range and payload capacity. Studies have developed multiple truck-drone configurations, each with different approaches to leverage the benefits and mitigate the limitations of drones. Existing research has also established the drone’s reduction to package delivery time over the traditional truck only model. Two key model factors that have not been considered in previous research are the distribution of package demand, and the distribution of package weight. This study analyzes the drone’s impact to the energy efficiency of a package delivery system, which has taken a backseat to minimizing delivery time. Demand distribution dictates the travel distances required for package delivery, as well as the proportion of delivery locations that are in range for drone delivery. Package weight determines the energy consumption of a delivery and further restricts the proportion of drone eligible packages. The major contributions of this study are the development of a truck-drone tandem mathematical model which minimizes energy consumption, the construction of a population-based package demand distribution, a realistic package weight distribution, and a genetic algorithm used to solve the mathematical model developed for problems that are too computationally expensive to be solved optimally using an exact method. Results show that drones can only have a significant impact to energy efficiency in package delivery systems if implemented under the right conditions. Using truck-drone tandem systems in areas with lower package demand density affords the drone the potential for larger energy savings as larger portions of the truck distance can be replaced. Further, the lower density translates to greater differences between the road-restricted driving distance and the flying distance between delivery points. Finally, energy savings are highly dependent on the underlying package weight distribution of the system. A heavier average package weight increases the energy consumption of the system, but more importantly the portion of packages above the drone’s payload capacity severely limit the savings afforded by the incorporation of drones.
(9503810), Jose Adrian Chavez Velasco. "COMPREHENSIVE STUDY OF THE ENERGY CONSUMPTION OF MEMBRANES AND DISTILLATION." Thesis, 2020.
Find full textMolecular
separations are essential in the production of many chemicals and purified
products. Of all the available separation technologies, distillation, which is
a thermally driven process, has been and continues to be one of the most
utilized separation methods in chemical and petrochemical plants. Although
distillation and other commercial technologies fulfilled most of the current
separation needs, the energy-intensive nature of many molecular separations and
the growing concern of reducing CO2 emissions has led to intense research to
seek for more energy-efficient separation processes.
Among the emerging separation technologies alternative to distillation, there is special attention on non-thermally driven methods, such as membranes. The growing interest in non-thermal methods, and particularly in the use of membranes, has been influenced significantly from the widespread perception that they have a potential to be markedly less energy-intensive than thermal methods such as distillation. Even though many publications claim that membranes are more energy-efficient than distillation, except for water desalination, the relative energy intensity between these processes in the separation of chemical mixtures has not been deeply studied in the literature. One of the objectives of this work focuses on introducing a framework for comparative analysis of the energy intensity of membranes and distillation.
A complication generally encountered when comparing the energy consumption of membranes against an alternative process is that often the purity and recovery that can be achieved through a single membrane stage is limited. While using a multi-stage membrane process is a plausible solution to achieve both high purity and recovery, even for a simple binary separation, finding the most suitable multistage membrane process is a difficult task. This is because, for a given separation, there exists multiple cascades that fulfill the separation requirements but consume different amounts of energy. Moreover, the energy requirement of each cascade depends on the operating conditions. The first part of this work is dedicated to the development of a Mixed Integer Non-linear Program (MINLP) which allows for a given gaseous or liquid binary separation, finding the most energy-efficient membrane cascade. The permeator model, which is derived from a combination of the cross-flow model and the solution diffusion theory, and is originally expressed as a differential-algebraic equation (DAE) system, was integrated analytically before being incorporated in the optimization framework. This is in contrast to the common practice in the literature, where the DAE system is solved using various discretization techniques. Since many of the constraints have a non-convex nature, local solvers could get trapped in higher energy suboptimal solutions. While an option to overcome this limitation is to use a global solver such as BARON, it fails to solve the MINLP to the desired optimality in a reasonable amount of time for most of the cases. For this reason, we derive additional cuts to the problem by exploiting the mathematical properties of the governing equations and from physical insights. Through numerical examples, we demonstrate that the additional cuts aid BARON in expediting the convergence of branch-and-bound and solve the MINLP within 5%-optimality in all the cases tested in this work.
The proposed optimization model allows identifying membrane cascades with enhanced energy efficiency that could be potentially used for existing or new separations. In addition, it allows to compare the optimum energy consumption of a multistage membrane process against alternative separations methods and aid in the decision of whether or not to use a membrane system. Nevertheless, it should be noted that when a membrane process or any other non-thermal separation process is compared with a thermal process such as distillation, an additional complication often arises because these processes usually use different types of energies. Non-thermal processes, such as membranes, consume electrical energy as work, whereas thermal processes, such as distillations, usually consume heat, which is available in a wide range of temperatures. Furthermore, the amount of fuel consumed by a separation process strongly depends on how its supplied energy is produced, and how it is energy integrated with the rest of the plant. Unfortunately, common approaches employed to compare the energy required by thermal and non-thermal methods often lead to incorrect conclusions and have driven to the flawed perception that thermal methods are inherently more energy-intensive than non-thermal counterparts. In the second part of this work, we develop a consistent framework that enables a proper comparison of the energy consumption between processes that are driven by thermal and non-thermal energy (electrical energy). Using this framework, we refute the general perception that thermal separation processes are necessarily the most energy-intensive and conclusively show that in several industrially important separations, distillation processes consume remarkably lower fuel than non-thermal membrane alternatives, which have often been touted as more energy efficient.
In order to gain more understanding of the conditions where membranes or distillation are more energy-efficient, we carried out a comprehensive analysis of the energy consumed by these two processes under different operating conditions. The introduced energy comparison analysis was applied to two important separation examples; the separation of p-xylene/o-xylene, and propylene/propane. Our results showed that distillation is more energy favored than membranes when the target purity and recovery of the most volatile (resp. most permeable) component in the distillate (resp. permeate) are high, and particularly when the feed is not too concentrated in the most volatile (resp. most permeable) component. On the other hand, when both the recovery and purity of the most volatile (resp. most permeable) component are required at moderate levels, and particularly when the feed is highly enriched in the most volatile (resp. most permeable) component, membranes show potential to save energy as compared to distillation.
(10506350), Amogh Agrawal. "Compute-in-Memory Primitives for Energy-Efficient Machine Learning." Thesis, 2021.
Find full text(5930180), Ashish Ranjan. "Energy-efficient Memory System Design with Spintronics." Thesis, 2019.
Find full textModern computing platforms, from servers to mobile devices, demand ever-increasing amounts of memory to keep up with the growing amounts of data they process, and to bridge the widening processor-memory gap. A large and growing fraction of chip area and energy is expended in memories, which face challenges with technology scaling due to increased leakage, process variations, and unreliability. On the other hand, data intensive workloads such as machine learning and data analytics pose increasing demands on memory systems. Consequently, improving the energy-efficiency and performance of memory systems is an important challenge for computing system designers.
Spintronic memories, which offer several desirable characteristics - near-zero leakage, high density, non-volatility and high endurance - are of great interest for designing future memory systems. However, these memories are not drop-in replacements for current memory technologies, viz. Static Random Access Memory (SRAM) and Dynamic Random Access Memory (DRAM). They pose unique challenges such as variable access times, and require higher write latency and write energy. This dissertation explores new approaches to improving the energy efficiency of spintronic memory systems.
The dissertation first explores the design of approximate memories, in which the need to store and access data precisely is foregone in return for improvements in energy efficiency. This is of particular interest, since many emerging workloads exhibit an inherent ability to tolerate approximations to their underlying computations and data while still producing outputs of acceptable quality. The dissertation proposes that approximate spintronic memories can be realized either by reducing the amount of data that is written to/read from them, or by reducing the energy consumed per access. To reduce memory traffic, the dissertation proposes approximate memory compression, wherein a quality-aware memory controller transparently compresses/decompresses data written to or read from memory. For broader applicability, the quality-aware memory controller can be programmed to specify memory regions that can tolerate approximations, and conforms to a specified error constraint for each such region. To reduce the per-access energy, various mechanisms are identified at the circuit and architecture levels that yield substantial energy benefits at the cost of small probabilities of read, write or retention failures. Based on these mechanisms, a quality-configurable Spin Transfer Torque Magnetic RAM (STT-MRAM) array is designed in which read/write operations can be performed at varying levels of accuracy and energy at runtime, depending on the needs of applications. To illustrate the utility of the proposed quality-configurable memory array, it is evaluated as an L2 cache in the context of a general-purpose processor, and as a scratchpad memory for a domain-specific vector processor.
The dissertation also explores the design of caches with Domain Wall Memory (DWM), a more advanced spintronic memory technology that offers unparalleled density arising from a unique tape-like structure. However, this structure also leads to serialized access to the bits in each bit-cell, resulting in increased access latency, thereby degrading overall performance. To mitigate the performance overheads, the dissertation proposes a reconfigurable DWM-based cache architecture that modulates the active bits per tape with minimal overheads depending on the application's memory access characteristics. The proposed cache is evaluated in a general purpose processor and improvements in performance are demonstrated over both CMOS and previously proposed spintronic caches.
In summary, the dissertation suggests directions to improve the energy efficiency of spintronic memories and re-affirms their potential for the design of future memory systems.
(6185759), Manish Nagaraj. "Energy Efficient Byzantine Agreement Protocols for Cyber Physical Resilience." Thesis, 2019.
Find full textCyber physical systems are deployed in a wide range of applications from sensor nodes in a factory setting to drones in defense applications. This distributed setting of nodes or processes often needs to reach agreement on a set of values. Byzantine Agreement protocols address this issue of reaching an agreement in an environment where a malicious entity can take control over a set of nodes and deviates the system from its normal operation. However these protocols do not consider the energy consumption of the nodes. We explore Byzantine Agreement protocols from an energy efficient perspective providing both energy resilience where the actions of the Byzantine nodes can not adversely effect the energy consumption of non-malicious nodes as well as fairness in energy consumption of nodes over multiple rounds of agreement.
(8088431), Gopalakrishnan Srinivasan. "Training Spiking Neural Networks for Energy-Efficient Neuromorphic Computing." Thesis, 2019.
Find full textSpiking Neural Networks (SNNs), widely known as the third
generation of artificial neural networks, offer a promising solution to
approaching the brains' processing capability for cognitive tasks. With more
biologically realistic perspective on input processing, SNN performs neural
computations using spikes in an event-driven manner. The asynchronous
spike-based computing capability can be exploited to achieve improved energy
efficiency in neuromorphic hardware. Furthermore, SNN, on account of
spike-based processing, can be trained in an unsupervised manner using Spike
Timing Dependent Plasticity (STDP). STDP-based learning rules modulate the strength
of a multi-bit synapse based on the correlation between the spike times of the
input and output neurons. In order to achieve plasticity with compressed
synaptic memory, stochastic binary synapse is proposed where spike timing
information is embedded in the synaptic switching probability. A bio-plausible
probabilistic-STDP learning rule consistent with Hebbian learning theory is
proposed to train a network of binary as well as quaternary synapses. In
addition, hybrid probabilistic-STDP learning rule incorporating Hebbian and
anti-Hebbian mechanisms is proposed to enhance the learnt representations of
the stochastic SNN. The efficacy of the presented learning rules are
demonstrated for feed-forward fully-connected and residual convolutional SNNs
on the MNIST and the CIFAR-10 datasets.
STDP-based learning is limited to shallow SNNs (<5
layers) yielding lower than acceptable accuracy on complex datasets. This
thesis proposes block-wise complexity-aware training algorithm, referred to as
BlocTrain, for incrementally training deep SNNs with reduced memory
requirements using spike-based backpropagation through time. The deep network
is divided into blocks, where each block consists of few convolutional layers
followed by an auxiliary classifier. The blocks are trained sequentially using
local errors from the respective auxiliary classifiers. Also, the deeper blocks
are trained only on the hard classes determined using the class-wise accuracy
obtained from the classifier of previously trained blocks. Thus, BlocTrain
improves the training time and computational efficiency with increasing block
depth. In addition, higher computational efficiency is obtained during
inference by exiting early for easy class instances and activating the deeper
blocks only for hard class instances. The ability of BlocTrain to provide
improved accuracy as well as higher training and inference efficiency compared
to end-to-end approaches is demonstrated for deep SNNs (up to 11 layers) on the
CIFAR-10 and the CIFAR-100 datasets.
Feed-forward SNNs are typically used for static image recognition while recurrent Liquid State Machines (LSMs) have been shown to encode time-varying speech data. Liquid-SNN, consisting of input neurons sparsely connected by plastic synapses to randomly interlinked reservoir of spiking neurons (or liquid), is proposed for unsupervised speech and image recognition. The strength of the synapses interconnecting the input and liquid are trained using STDP, which makes it possible to infer the class of a test pattern without a readout layer typical in standard LSMs. The Liquid-SNN suffers from scalability challenges due to the need to primarily increase the number of neurons to enhance the accuracy. SpiLinC, composed of an ensemble of multiple liquids, where each liquid is trained on a unique input segment, is proposed as a scalable model to achieve improved accuracy. SpiLinC recognizes a test pattern by combining the spiking activity of the individual liquids, each of which identifies unique input features. As a result, SpiLinC offers comparable accuracy to Liquid-SNN with added synaptic sparsity and faster training convergence, which is validated on the digit subset of TI46 speech corpus and the MNIST dataset.
(8815964), Minsuk Koo. "Energy Efficient Neuromorphic Computing: Circuits, Interconnects and Architecture." Thesis, 2020.
Find full text(11180610), Indranil Chakraborty. "Toward Energy-Efficient Machine Learning: Algorithms and Analog Compute-In-Memory Hardware." Thesis, 2021.
Find full text(6823670), Priyadarshini Panda. "Learning and Design Methodologies for Efficient, Robust Neural Networks." Thesis, 2019.
Find full text(11191896), Chamika M. Liyanagedera. "Intelligent Sensing and Energy Efficient Neuromorphic Computing using Magneto-Resistive Devices." Thesis, 2021.
Find full textWith the Moore’s Law era coming to an end, much attention has been given to novel nanoelectronic devices as a key driving force behind technological innovation. Utilizing the inherent device physics of nanoelectronic components, for sensory and computational tasks have proven to be useful in reducing the area and energy requirements of the underlying hardware fabrics. In this work we demonstrate how the intrinsic noise present in nano magnetic devices can pave the pathway for energy efficient neuromorphic hardware. Furthermore, we illustrate how the unique magnetic properties of such devices can be leveraged for accurate estimation of environmental magnetic fields. We focus on spintronic technologies in particular, due to the low current and energy requirements in contrast to traditional CMOS technologies.
Image segmentation is a crucial pre-processing stage used in many object identification tasks that involves simplifying the representation of an image so it can be conveniently analyzed in the later stages of a problem. This is achieved through partitioning a complicated image into specific groups based on color, intensity or texture of the pixels of that image. Locally Excitatory Globally Inhibitory Oscillator Network or LEGION is one such segmentation algorithm, where synchronization and desynchronization between coupled oscillators are used for segmenting an image. In this work we present an energy efficient and scalable hardware implementation of LEGION using stochastic Magnetic Tunnel Junctions that leverage the fast parallel
nature
of the algorithm. We demonstrate that the proposed hardware is capable of segmenting
binary and gray-scale images with multiple objects more efficiently than
existing
hardware implementations.
It is understood that the underlying device physics
of spin devices can be used for emulating the functionality of a spiking
neuron. Stochastic spiking neural networks based on nanoelectronic spin devices
can be a possible pathway of achieving brain-like compact and energy-efficient
cognitive intelligence. Current computational models attempt to exploit the
intrinsic device stochasticity of nanoelectronic synaptic or neural components
to perform learning and inference. However, there has been limited analysis on
the scaling effect of stochastic spin devices and its impact on the operation
of such stochastic networks at the system level. Our work attempts to explore
the design space and analyze the performance of nanomagnet based stochastic neuromorphic
computing architectures, for magnets with different barrier heights. We illustrate
how the underlying network architecture must be modified to account for the
random telegraphic switching behavior displayed by magnets as they are scaled into
the superparamagnetic regime.
Next we investigate how the magnetic properties
of spin devices can be utilized for real world sensory applications. Magnetic
Tunnel Junctions can efficiently translate variations in external magnetic
fields into variations in electrical resistance. We couple this property of
Magnetic Tunnel Junctions with Amperes law to design a non-invasive sensor to
measure the current flowing through a wire. We demonstrate how undesirable
effects of thermal noise and process variations can be suppressed through novel
analog and digital signal conditioning techniques to obtain reliable and
accurate current measurements. Our results substantiate that the proposed
noninvasive current sensor surpass other state-of-the-art technologies in terms
of noise and accuracy.
(9412388), Maryam Parsa. "Bayesian-based Multi-Objective Hyperparameter Optimization for Accurate, Fast, and Efficient Neuromorphic System Designs." Thesis, 2020.
Find full text(6639257), Matthew Steven Wilfing. "Integration of Solar Microgrids." Thesis, 2019.
Find full text(5930687), Jinglin Jiang. "Investigating How Energy Use Patterns Shape Indoor Nanoaerosol Dynamics in a Net-Zero Energy House." Thesis, 2019.
Find full textResearch on net-zero energy buildings (NZEBs) has been largely centered around improving building energy performance, while little attention has been given to indoor air quality. A critically important class of indoor air pollutants are nanoaerosols – airborne particulate matter smaller than 100 nm in size. Nanoaerosols penetrate deep into the human respiratory system and are associated with deleterious toxicological and human health outcomes. An important step towards improving indoor air quality in NZEBs is understanding how occupants, their activities, and building systems affect the emissions and fate of nanoaerosols. New developments in smart energy monitoring systems and smart thermostats offer a unique opportunity to track occupant activity patterns and the operational status of residential HVAC systems. In this study, we conducted a one-month field campaign in an occupied residential NZEB, the Purdue ReNEWW House, to explore how energy use profiles and smart thermostat data can be used to characterize indoor nanoaerosol dynamics. A Scanning Mobility Particle Sizer and Optical Particle Sizer were used to measure indoor aerosol concentrations and size distributions from 10 to 10,000 nm. AC current sensors were used to monitor electricity consumption of kitchen appliances (cooktop, oven, toaster, microwave, kitchen hood), the air handling unit (AHU), and the energy recovery ventilator (ERV). Two Ecobee smart thermostats informed the fractional amount of supply airflow directed to the basement and main floor. The nanoaerosol concentrations and energy use profiles were integrated with an aerosol physics-based material balance model to quantify nanoaerosol source and loss processes. Cooking activities were found to dominate the emissions of indoor nanoaerosols, often elevating indoor nanoaerosol concentrations beyond 104 cm-3. The emission rates for different cooking appliances varied from 1011 h-1 to 1014 h-1. Loss rates were found to be significantly different between AHU/ERV off and on conditions, with median loss rates of 1.43 h-1 to 3.68 h-1, respectively. Probability density functions of the source and loss rates for different scenarios will be used in Monte Carlo simulations to predict indoor nanoaerosol concentrations in NZEBs using only energy consumption and smart thermostat data.
(10676388), Madeline Sheeley. "Regulation of Energy Metabolism in Extracellular Matrix Detached Breast Cancer Cells." Thesis, 2021.
Find full textBreast cancer is the predominant cancer diagnosed among women, and the second most deadly cancer. The vast majority of cancer-related deaths is caused by the metastatic spread of cancer from the primary tumor to a distant site in the body. Therefore, new strategies which minimize breast cancer metastasis are imperative to improve patient survival. Cancer cells which acquire anchorage independence, or the ability to survive without extracellular matrix attachment, and metabolic flexibility have increased potential to metastasize. In the present studies, the ability to survive detachment and subsequent metabolic changes were determined in human Harvey-ras transformed MCF10A-ras breast cancer cells. Detachment resulted in reduced viability in a time-dependent manner with the lowest cell viability observed at forty hours. In addition, decreased cell viability was observed in both glutamine and glucose depleted detached conditions, suggesting a dependence on both nutrients for detached survival. Compared to attached cells, detached cells had reduced total pool sizes of pyruvate, lactate, α-ketoglutarate, fumarate, malate, alanine, serine, and glutamate, suggesting the metabolic stress which occurs under detached conditions. However, intracellular citrate and aspartate pools were unchanged, demonstrating a preference to maintain these pools in detached conditions. Compared to attached cells, detached cells had suppressed glutamine metabolism, as determined by decreased glutamine flux into the TCA cycle and reduced mRNA abundance of glutamine metabolizing enzymes. Further, detached glucose anaplerosis through pyruvate dehydrogenase activity was decreased, while pyruvate carboxylase (PC) expression and activity were increased. A switch in metabolism was observed away from glutamine anaplerosis to a preferential utilization of PC activity to replenish the TCA cycle, determined by reduced PC mRNA abundance in detached cells treated with a cell-permeable analog of α-ketoglutarate, the downstream metabolite of glutamine which enters the TCA cycle. These results suggest that detached cells elevate PC to increase flux of carbons into the TCA cycle when glutamine metabolism is reduced.
Vitamin D is recognized for its role in preventing breast cancer progression, and recent studies suggest that regulation of energy metabolism may contribute to its anticancer effects. Vitamin D primarily acts on target tissue through its most active metabolite, 1α,25-dihydroxyvitamin D (1,25(OH)2D). The present work investigated 1,25(OH)2D’s effects on viability of detached cells through regulation of energy metabolism. Treatment of MCF10A-ras cells with 1,25(OH)2D resulted in decreased viability of detached cells. While 1,25(OH)2D treatment did not affect many of the glucose metabolism outcomes measured, including intracellular pyruvate and lactate pool sizes, glucose flux to pyruvate and lactate, and mRNA abundance of enzymes involved in glucose metabolism, 1,25(OH)2D treatment reduced detached PC expression and glucose flux through PC. A reduction in glutamine metabolism was observed with 1,25(OH)2D treatment, although no 1,25(OH)2D target genes were identified. Further, PC depletion by shRNA decreased cell viability in detached conditions with no additional effect with 1,25(OH)2D treatment. Moreover, PC overexpression resulted in increased detached cell viability and inhibited 1,25(OH)2D’s negative effects on viability. These results suggest that 1,25(OH)2D reduces detached cell viability through regulation of PC. Collectively this work identifies a key metabolic adaptation where detached cells increase PC expression and activity to compensate for reduced glutamine metabolism and that 1,25(OH)2D may be utilized to reverse this effect and decrease detached cell viability. These results contribute to an increased understanding of metastatic processes and the regulation of these processes by vitamin D, which may be effective in preventing metastasis and improve breast cancer patient survival.
(7480409), RISHIKESH MAHESH BAGWE. "MODELING AND ENERGY MANAGEMENT OF HYBRID ELECTRIC VEHICLES." Thesis, 2019.
Find full text(7878308), Robert E. Warburton. "Interfacial Reactivity Studies of Electrochemical Energy Storage Materials from First Principles." Thesis, 2019.
Find full textSince their commercialization in the early 1990’s, rechargeable lithium ion batteries (LIBs) have become ever-present in consumer electronics, and the share of electric vehicles within the transportation sector has become much more significant. Ab initio modeling techniques - namely density functional theory (DFT) - have played a signifcant role in describing the atomic scale nature of Li+ insertion and removal chemistry in LIB electrode materials, and have been pivotal in accelerating the design of energy dense battery materials based on their bulk properties. Despite these advances, there remains a knowledge gap with respect to understanding the many complex reactions that occur at the surfaces and interfaces of rechargeable battery materials. This work considers several case studies of surface and interfacial reactions in energy storage materials, using DFT modeling techniques to develop strategies that can rationally control the interfacial chemistry for optimal electrochemical performance.
The first portion of this thesis aims to understand the role of interfacial modification strategies toward mitigating Mn dissolution from the spinel LiMn2O4 (LMO) surface. First, a thermodynamic characterization of LMO surface structures is performed in order to develop models of LMO substrates for subsequent computational surface science studies. A subset of these surface models are then used analyze interfacial degradation processes through delithiation-driven stress buildup and crack formation, as well as reaction mechanisms for ethylene carbonate and hydrofluoric acid to form surface Mn2+ ions that are susceptible to dissolution. Surface passivation mechanisms using protective oxide and metallic coatings are then analyzed, which elucidate an electronic structure-based descriptor for structure-sensitive atomic layer growth mechanisms and describe the changes in lithiation reactions of coated electrodes through electronic band alignment at the solid-solid interface. These studies of protective coatings describe previously overlooked physics at the electrode-coating interface that can aid in further development of coated electrode materials. Using the LMO substrate models, a thermodynamic framework for evaluating the solubility limits and surface segregation tendencies of cationic dopants is described in the context of stabilizing LMO surfaces against Mn loss.
Next, solid-solid interfacial models are developed to evaluate the role of nanostructure in catalyzing the lithiation of NiO to form reduced Ni and Li2O as concurrent discharge products. Applying a Ni/NiO multilayer morphology, interfacial energies are evaluated using DFT and implemented into a classical nucleation model at a heterogeneous interface. These calculations, alongside operando X-ray scattering measurements, are used to explain atomic scale mechanisms that reduce voltage hysteresis in metal oxide LIB conversion chemistry.
The structure between a Li metal anode and the lithium lanthanum titanate solid electrolyte are subsequently analyzed as a model system to understand potential inter- facial stabilization mechanisms in solid-state batteries. This analysis combines bulk, surface, and interfacial thermodynamics with ab initio molecular dynamics simulations to monitor the evolution of the interfacial structure over short time scales, which provides insights into the onset of degradation mechanisms. It is shown that the reductive instability of Ti4+ is the primary driving force for interfacial decomposition reactions, and that a lanthanum oxide interlayer coating is expected to stabilize the interface based on both thermodynamic and electronic band alignment arguments.
In the last part of this thesis, charge transfer kinetics are studied for several applications using constrained DFT (cDFT) to account for electronic coupling and reorganization energies between donor and acceptor states. Charge hopping mechanisms to and from dichalcogenide-based electrocatalysts during O2 and CO2 reduction/evolution reactions in Li-O2 and Li-CO2 battery systems are first evaluated. Then, the role of the spatial separation Li+ vacancies and interstitials on hole and electron polaron hopping in the prototypical LixCoO2 cathode is analzyed. The results demonstrate that Marcus rate theories using cDFT-derived parameters can reproduce experimentally observed anisotropies in electronic conductivity, whereas conventional transition state theory analyses of polaron hopping do not. Overall, this proof-of-concept study provides a framework to understand how charged species are transported in battery electrodes and are dependent on charge compensating defects.
Finally, the key insights from these studies are discussed in the context of future directions related to the understanding and design of materials for electrochemical energy conversion and storage.
(8782256), Kyle Whittaker. "A Low Power FinFET Charge Pump For Energy Harvesting Applications." Thesis, 2020.
Find full text(5931200), Francisco Rivera-Abreu. "Dual Band Octagonal Microstrip Patch Antenna Design Method for Energy Harvesting." 2020.
Find full text(10867179), Abigail Jubilee Kragt Finnell. "Wireless Power Transfer: Efficiency, Far Field, Directivity, and Phased Array Antennas." Thesis, 2021.
Find full text(5930495), Zibo Zhao. "DECENTRALIZED PRICE-DRIVEN DEMAND RESPONSE IN SMART ENERGY GRID." Thesis, 2021.
Find full textReal-time pricing (RTP) of electricity for consumers has long been argued to be crucial for realizing the many envisioned benefits of demand flexibility in a smart grid. However, many details of how to actually implement a RTP scheme are still under debate. Since most of the organized wholesale electricity markets in the US implement a two-settlement mechanism, with day-ahead electricity price forecasts guiding financial and physical transactions in the next day and real-time ex post prices settling any real-time imbalances, it is a natural idea to let consumers respond to the day-ahead prices in real-time. However, if such an idea is not controlled properly, the inherent closed-loop operation may lead consumers to all respond in the same fashion, causing large swings of real-time demand and prices, which may jeopardize system stability and increase consumers’ financial risks.
To overcome the potential uncertainties and undesired demand peak caused by “selfish” behaviors by individual consumers under RTP, in this research, we develop a fully decentralized price-driven demand response (DR) approach under game- theoretical frameworks. In game theory, agents usually make decisions based on their belief about competitors’ states, which needs to maintain a large amount of knowledge and thus can be intractable and implausible for a large population. Instead, we propose using regret-based learning in games by focusing on each agent’s own history and utility received. We study two learning mechanisms: bandit learning with incomplete information feedback, and low regret learning with full information feedback. With the learning in games, we establish performance guarantees for each individual agent (i.e., regret minimization) and the overall system (i.e., bounds on price of anarchy).
In addition to the game-theoretical framework for price-driven demand response, we also apply such a framework for peer-to-peer energy trading auctions. The market- based approach can better incentivize the development of distributed energy resources (DERs) on demand side. However, the complexity of double-sided auctions in an energy market and agents’ bounded rationality may invalidate many well-established theories in auction design, and consequently, hinder market development. To address these issues, we propose an automated bidding framework based on multi-armed bandit learning through repeated auctions, and is aimed to minimize each bidder’s cumulative regret. We also use such a framework to compare market outcomes of three different auction designs.
(9033764), Aveek Dutta. "Plasmonics for Nanotechnology: Energy Harvesting and Memory Devices." Thesis, 2020.
Find full text(9745856), Min Wu. "Nanomanufacturing of Wearable Electronics for Energy Conversion and Human-integrated Monitoring." Thesis, 2020.
Find full text