Wind measurements are fundamental inputs for the evaluation of potential energy yield and performance of wind farms. Three-dimensional scanning coherent Doppler lidar (CDL) may provide a new basis for wind farm site selection, design, and control. In this research, CDL measurements obtained from multiple wind energy developments are analyzed and a novel wind farm control approach has been modeled. The possibility of using lidar measurements to more fully characterize the wind field is discussed, specifically, terrain effects, spatial variation of winds, power density, and the effect of shear at different layers within the rotor swept area. Various vector retrieval methods have been applied to the lidar data, and results are presented on an elevated terrain-following surface at hub height. The vector retrieval estimates are compared with tower measurements, after interpolation to the appropriate level. CDL data is used to estimate the spatial power density at hub height. Since CDL can measure winds at different vertical levels, an approach for estimating wind power density over the wind turbine rotor-swept area is explored. Sample optimized layouts of wind farm using lidar data and global optimization algorithms, accounting for wake interaction effects, have been explored. An approach to evaluate spatial wind speed and direction estimates from a standard nested Coupled Ocean and Atmosphere Mesoscale Prediction System (COAMPS) model and CDL is presented. The magnitude of spatial difference between observations and simulation for wind energy assessment is researched. Diurnal effects and ramp events as estimated by CDL and COAMPS were inter-compared. Novel wind farm control based on incoming winds and direction input from CDL's is developed. Both yaw and pitch control using scanning CDL for efficient wind farm control is analyzed. The wind farm control optimizes power production and reduces loads on wind turbines for various lidar wind speed and direction inputs, accounting for wind farm wake losses and wind speed evolution. Several wind farm control configurations were developed, for enhanced integrability into the electrical grid. Finally, the value proposition of CDL for a wind farm development, based on uncertainty reduction and return of investment is analyzed.

Multi-touch tablets and smart phones are now widely used in both workplace and consumer settings. Interacting with these devices requires hand and arm movements that are potentially complex and poorly understood. Experimental studies have revealed differences in performance that could potentially be associated with injury risk. However, underlying causes for performance differences are often difficult to identify. For example, many patterns of muscle activity can potentially result in similar behavioral output. Muscle activity is one factor contributing to forces in tissues that could contribute to injury. However, experimental measurements of muscle activity and force for humans are extremely challenging. Models of the musculoskeletal system can be used to make specific estimates of neuromuscular coordination and musculoskeletal forces. However, existing models cannot easily be used to describe complex, multi-finger gestures such as those used for multi-touch human computer interaction (HCI) tasks. We therefore seek to develop a dynamic musculoskeletal simulation capable of estimating internal musculoskeletal loading during multi-touch tasks involving multi digits of the hand, and use the simulation to better understand complex multi-touch and gestural movements, and potentially guide the design of technologies the reduce injury risk. To accomplish these, we focused on three specific tasks. First, we aimed at determining the optimal index finger muscle attachment points within the context of the established, validated OpenSim arm model using measured moment arm data taken from the literature. Second, we aimed at deriving moment arm values from experimentally-measured muscle attachments and using these values to determine muscle-tendon paths for both extrinsic and intrinsic muscles of middle, ring and little fingers. Finally, we aimed at exploring differences in hand muscle activation patterns during zooming and rotating tasks on the tablet computer in twelve subjects. Towards this end, our musculoskeletal hand model will help better address the neuromuscular coordination, safe gesture performance and internal loadings for multi-touch applications.

In this study, two novel sorbents (zeolite 4A and sodium polyacrylate) are tested to investigate if utilizing ultrasonic acoustic energy could decrease the amount of time and overall energy required to regenerate these materials for use in cooling applications. To do this, an experiment was designed employing a cartridge heater and a piezoelectric element to be simultaneously providing heat and acoustic power to a custom designed desorption bed while measuring the bed mass and sorbent temperature at various locations. The results prove to be promising showing that early in the desorption process ultrasound may expedite the desorption process in zeolite by as much as five times and in sodium polyacrylate as much as three times in comparison to providing heat alone. The results also show that in zeolite desorption utilizing ultrasound may be particularly beneficial to initiate desorption whereas in sodium polyacrylate ultrasound appears most promising in the after a temperature threshold is met. These are exciting results and may prove to be significant in the future as more novel heat-based cooling cycles are developed.

The operating temperature of photovoltaic (PV) modules is affected by external factors such as irradiance, wind speed and ambient temperature as well as internal factors like material properties and design properties. These factors can make a difference in the operating temperatures between cells within a module and between modules within a plant. This is a three-part thesis.

Part 1 investigates the behavior of temperature distribution of PV cells within a module through outdoor temperature monitoring under various operating conditions (Pmax, Voc and Isc) and examines deviation in the temperature coefficient values pertaining to this temperature variation. ANOVA, a statistical tool, was used to study the influence of various factors on temperature variation. This study also investigated the thermal non-uniformity affecting I-V parameters and performance of four different PV technologies (crystalline silicon, CdTe, CIGS, a-Si). Two new approaches (black-colored frame and aluminum tape on back-sheet) were implemented in addition to the two previously-used approaches (thermally insulating the frame, and frame and back sheet) to study temperature uniformity improvements within c-Si PV modules on a fixed latitude-tilt array. This thesis concludes that frame thermal insulation and black frame help reducing thermal gradients and next best viable option to improve temperature uniformity measurements is by using average of four thermocouples as per IEC 61853-2 standard.

Part 2 analyzes the temperature data for two power plants (fixed-tilt and one-axis) to study the temperature variation across the cells in a module and across the modules in a power plant. The module placed in the center of one-axis power plant had higher temperature, whereas in fixed-tilt power plant, the module in north-west direction had higher temperatures. Higher average operating temperatures were observed in one-axis tracking as compared to the fixed-tilt PV power plant, thereby expected to lowering their lifetime.

Part 3 focuses on determination of a thermal model coefficients, using parameters similar to Uc and Uv thermal loss factors used in PVsyst, for modules of four different PV technologies experiencing hot-desert climate conditions by statistically correlating a year-long monitored data. Thermal models help to effectively quantity factors influencing module temperatures to estimate performance and energy models.

Recently, nanostructured metamaterials have attracted lots of attentions due to its tunable artificial properties. In particular, nanowire
anohole based metamaterials which are known of the capability of large area fabrication were intensively studied. Most of the studies are only based on the electrical responses of the metamaterials; however, magnetic response, is usually neglected since magnetic material does not exist naturally within the visible or infrared range. For the past few years, artificial magnetic response from nanostructure based metamaterials has been proposed. This reveals the possibility of exciting resonance modes based on magnetic responses in nanowire
anohole metamaterials which can potentially provide additional enhancement on radiative transport. On the other hand, beyond classical far-field radiative heat transfer, near-field radiation which is known of exceeding the Planck’s blackbody limit has also become a hot topic in the field.

This PhD dissertation aims to obtain a deep fundamental understanding of nanowire
anohole based metamaterials in both far-field and near-field in terms of both electrical and magnetic responses. The underlying mechanisms that can be excited by nanowire
anohole metamaterials such as electrical surface plasmon polariton, magnetic hyperbolic mode, magnetic polariton, etc., will be theoretically studied in both far-field and near-field. Furthermore, other than conventional effective medium theory which only considers the electrical response of metamaterials, the artificial magnetic response of metamaterials will also be studied through parameter retrieval of far-field optical and radiative properties for studying near-field radiative transport. Moreover, a custom-made AFM tip based metrology will be employed to experimentally study near-field radiative transfer between a plate and a sphere separated by nanometer vacuum gaps in vacuum. This transformative research will break new ground in nanoscale radiative heat transfer for various applications in energy systems, thermal management, and thermal imaging and sensing.

One the major problems of this modern industrialized world is its dependence on fossil fuels for its energy needs. Burning of fossils fuels generates green-house gases which have adverse effects on global climate contributing to global warming. According to Environmental Protection Agency (EPA), carbon dioxide makes up 80 percent of green-house gases emitted in USA. Electrochemical reduction of carbon dioxide is an approach which uses CO2 emissions to produce other useful hydrocarbons which can be used in many ways.

In this study, primary focus was on optimizing the operating conditions, determining the better catalyst material, and analyzing the reaction products for the process of electrochemical reduction of carbon dioxide (ERC). Membrane electrode assemblies (MEA’s) are developed by air bushing the metal particles with a spray gun on to Nafion-212 which is a solid polymer based electrolyte (SPE), to support the electrodes in the electrochemical reactor gas diffusion layers (GDL) are developed using porous carbon paper. Anode was always made using the same material which is platinum but cathode material was changed as it is the working electrode.

The membrane electrode assembly (MEA) is then placed into the electrochemical reactor along with gas diffusion layer (GDL) to assess the performance of the catalyst material by techniques like linear sweep voltammetry and chronoamperometry. Performance of MEA was analyzed at 4 different potentials, 2 different temperatures and for 2 different cathode catalyst materials. The reaction products of the process are analyzed using gas chromatography (GC) which has thermal conductivity detector (TCD) used for detecting hydrogen (H2), carbon monoxide (CO) and flame ionization detector (FID) used for detecting hydrocarbons. The experiments performed at 40o C gave the better results when compared with the experiments performed at ambient temperature. Also results suggested that copper oxide cathode catalyst has better durability than platinum-carbon. Maximum faradaic efficiency for methane was 5.3% it was obtained at 2.25V using copper oxide catalyst. Furthermore, experiments must be carried out to make the electrochemical reactor more robust to withstand all the operating conditions like higher potentials and to make it a solar powered reactor.

Photovoltaic modules degrade in the field. This thesis aims to answer two questions: 1. Do photovoltaic modules degrade linearly or not? 2. Do soiled modules operate at lower temperatures than clean modules? Answers to these questions are provided in part 1 and part 2 of this thesis respectively.

Part 1: Linearity determination in degradation: The electricity output from PV power plants degrades every year. Generally, a system’s life is considered to last for 20-25 years and rate of degradation is commonly assumed as 1% per year. PV degradation can be found out using Performance Ratio (PR), Performance Index (PI) and raw kWh output. The rate of degradation is considered linear for simplicity of calculations. In this thesis, statistical methods are used to check whether systems in Arizona are degrading linearly or not. Time series modeling such as Winters’ method and ARIMA are used to model the data. Winters’ method and Seasonal ARIMA consider the seasonality component and perform well for small data sets of about 10 years. Rate of degradation is found out as linear for all the evaluated systems.

Part 2: Temperature analysis of clean and soiled modules: Soiling and temperature are important parameters in performance of PV modules. In this paper, an analysis is carried out on a soiling station located in Mesa, Arizona. The soiling station consists of 10 different c-Si coupons with tilt angles varying from 0° to 45° with the difference of 5°. These coupons are cut in half, one is cleaned periodically and the other is remained soiled naturally. The analysis involves data worth for 19 months. 6 dry spells in all four seasons within 19 months were analyzed. The temperature difference between a clean module and a soiled module (ΔT) is compared with the soiling loss factor (SLF). The analysis concludes stating in which season a soiled module is hotter or cooler than a clean module.

This thesis outlines the development of a vector retrieval technique, based on data assimilation, for a coherent Doppler LIDAR (Light Detection and Ranging). A detailed analysis of the Optimal Interpolation (OI) technique for vector retrieval is presented. Through several modifications to the OI technique, it is shown that the modified technique results in significant improvement in velocity retrieval accuracy. These modifications include changes to innovation covariance portioning, covariance binning, and analysis increment calculation. It is observed that the modified technique is able to make retrievals with better accuracy, preserves local information better, and compares well with tower measurements. In order to study the error of representativeness and vector retrieval error, a lidar simulator was constructed. Using the lidar simulator a thorough sensitivity analysis of the lidar measurement process and vector retrieval is carried out. The error of representativeness as a function of scales of motion and sensitivity of vector retrieval to look angle is quantified. Using the modified OI technique, study of nocturnal flow in Owens' Valley, CA was carried out to identify and understand uncharacteristic events on the night of March 27th 2006. Observations from 1030 UTC to 1230 UTC (0230 hr local time to 0430 hr local time) on March 27 2006 are presented. Lidar observations show complex and uncharacteristic flows such as sudden bursts of westerly cross-valley wind mixing with the dominant up-valley wind. Model results from Coupled Ocean/Atmosphere Mesoscale Prediction System (COAMPS®) and other in-situ instrumentations are used to corroborate and complement these observations. The modified OI technique is used to identify uncharacteristic and extreme flow events at a wind development site. Estimates of turbulence and shear from this technique are compared to tower measurements. A formulation for equivalent wind speed in the presence of variations in wind speed and direction, combined with shear is developed and used to determine wind energy content in presence of turbulence.

The ability to shift the photovoltaic (PV) power curve and make the energy accessible during peak hours can be accomplished through pairing solar PV with energy storage technologies. A prototype hybrid air conditioning system (HACS), built under supervision of project head Patrick Phelan, consists of PV modules running a DC compressor that operates a conventional HVAC system paired with a second evaporator submerged within a thermal storage tank. The thermal storage is a 0.284m3 or 75 gallon freezer filled with Cryogel balls, submerged in a weak glycol solution. It is paired with its own separate air handler, circulating the glycol solution. The refrigerant flow is controlled by solenoid valves that are electrically connected to a high and low temperature thermostat. During daylight hours, the PV modules run the DC compressor. The refrigerant flow is directed to the conventional HVAC air handler when cooling is needed. Once the desired room temperature is met, refrigerant flow is diverted to the thermal storage, storing excess PV power. During peak energy demand hours, the system uses only small amounts of grid power to pump the glycol solution through the air handler (note the compressor is off), allowing for money and energy savings. The conventional HVAC unit can be scaled down, since during times of large cooling demands the glycol air handler can be operated in parallel with the conventional HVAC unit. Four major test scenarios were drawn up in order to fully comprehend the performance characteristics of the HACS. Upon initial running of the system, ice was produced and the thermal storage was charged. A simple test run consisting of discharging the thermal storage, initially ~¼ frozen, was performed. The glycol air handler ran for 6 hours and the initial cooling power was 4.5 kW. This initial test was significant, since greater than 3.5 kW of cooling power was produced for 3 hours, thus demonstrating the concept of energy storage and recovery.

As one of the most promising materials for high capacity electrode in next generation of lithium ion batteries, silicon has attracted a great deal of attention in recent years. Advanced characterization techniques and atomic simulations helped to depict that the lithiation/delithiation of silicon electrode involves processes including large volume change (anisotropic for the initial lithiation of crystal silicon), plastic flow or softening of material dependent on composition, electrochemically driven phase transformation between solid states, anisotropic or isotropic migration of atomic sharp interface, and mass diffusion of lithium atoms. Motivated by the promising prospect of the application and underlying interesting physics, mechanics coupled with multi-physics of silicon electrodes in lithium ion batteries is studied in this dissertation. For silicon electrodes with large size, diffusion controlled kinetics is assumed, and the coupled large deformation and mass transportation is studied. For crystal silicon with small size, interface controlled kinetics is assumed, and anisotropic interface reaction is studied, with a geometry design principle proposed. As a preliminary experimental validation, enhanced lithiation and fracture behavior of silicon pillars via atomic layer coatings and geometry design is studied, with results supporting the geometry design principle we proposed based on our simulations. Through the work documented here, a consistent description and understanding of the behavior of silicon electrode is given at continuum level and some insights for the future development of the silicon electrode are provided.