This study analyzes the thermoelectric phenomena of nanoparticle suspensions, which are composed of liquid and solid nanoparticles that show a relatively stable Seebeck coefficient as bulk solids near room temperature. The approach is to explore the thermoelectric character of the nanoparticle suspensions, predict the outcome of the experiment and compare the experimental data with anticipated results. In the experiment, the nanoparticle suspension is contained in a 15cm*2.5cm*2.5cm glass container, the temperature gradient ranges from 20 °C to 60 °C, and room temperature fluctuates from 20 °C to 23°C. The measured nanoparticles include multiwall carbon nanotubes, aluminum dioxide and bismuth telluride. A temperature gradient from 20 °C to 60 °C is imposed along the length of the container, and the resulting voltage (if any) is measured. Both heating and cooling processes are measured. With three different nanoparticle suspensions (carbon nano tubes, Al2O3 nanoparticles and Bi2Te3 nanoparticles), the correlation between temperature gradient and voltage is correspondingly 8%, 38% and 96%. A comparison of results calculated from the bulk Seebeck coefficients with our measured results indicate that the Seebeck coefficient measured for each suspension is much more than anticipated, which indicates that the thermophoresis effect could have enhanced the voltage. Further research with a closed-loop system might be able to affirm the results of this study.

Phase Change Material (PCM) plays an important role as a thermal energy storage device by utilizing its high storage density and latent heat property. One of the potential applications for PCM is in buildings by incorporating them in the envelope for energy conservation. During the summer season, the benefits are a decrease in overall energy consumption by the air conditioning unit and a time shift in peak load during the day. Experimental work was carried out by Arizona Public Service (APS) in collaboration with Phase Change Energy Solutions (PCES) Inc. with a new class of organic-based PCM. This "BioPCM" has non-flammable properties and can be safely used in buildings. The experimental setup showed maximum energy savings of about 30%, a maximum peak load shift of ~ 60 min, and maximum cost savings of about 30%. Simulation was performed to validate the experimental results. EnergyPlus was chosen as it has the capability to simulate phase change material in the building envelope. The building material properties were chosen from the ASHRAE Handbook - Fundamentals and the HVAC system used was a window-mounted heat pump. The weather file used in the simulation was customized for the year 2008 from the National Renewable Energy Laboratory (NREL) website. All EnergyPlus inputs were ensured to match closely with the experimental parameters. The simulation results yielded comparable trends with the experimental energy consumption values, however time shifts were not observed. Several other parametric studies like varying PCM thermal conductivity, temperature range, location, insulation R-value and combination of different PCMs were analyzed and results are presented. It was found that a PCM with a melting point from 23 to 27 °C led to maximum energy savings and greater peak load time shift duration, and is more suitable than other PCM temperature ranges for light weight building construction in Phoenix.

Efficient performance of gas turbines depends, among several parameters, on the mainstream gas entry temperature. At the same time, transport of this high temperature gas into the rotor-stator cavities of turbine stages affects the durability of rotor disks. This transport is usually countered by installing seals on the rotor and stator disk rims and by pressurizing the cavities by injecting air (purge gas) bled from the compressor discharge. The configuration of the rim seals influences the magnitude of main gas ingestion as well as the interaction of the purge gas with the main gas. The latter has aerodynamic and hub endwall heat transfer implications in the main gas path. In the present work, experiments were performed on model single-stage and 1.5-stage axial-flow turbines. The turbines featured vanes, blades, and rim seals on both the rotor and stator disks. Three different rim seal geometries, viz., axially overlapping radial clearance rim seals for the single-stage turbine cavity and the 1.5-stage turbine aft cavity, and a rim seal with angular clearance for the single-stage turbine cavity were studied. In the single-stage turbine, an inner seal radially inboard in the cavity was also provided; this effectively divided the disk cavity into a rim cavity and an inner cavity. For the aft rotor-stator cavity of the 1.5-stage turbine, a labyrinth seal was provided radially inboard, again creating a rim cavity and an inner cavity. Measurement results of time-average main gas ingestion into the cavities using tracer gas (CO2), and ensemble-averaged trajectories of the purge gas flowing out through the rim seal gap into the main gas path using particle image velocimetry are presented. For both turbines, significant ingestion occurred only in the rim cavity. The inner cavity was almost completely sealed by the inner seal, at all purge gas flow rates for the single-stage turbine and at the higher purge gas flow rates for 1.5-stage turbine. Purge gas egress trajectory was found to depend on main gas and purge gas flow rates, the rim seal configuration, and the azimuthal location of the trajectory mapping plane with respect to the vanes.

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.

Among the various end-use sectors, the commercial sector is expected to have the second-largest increase in total primary energy consump¬tion from 2009 to 2035 (5.8 quadrillion Btu) with a growth rate of 1.1% per year, it is the fastest growing end-use sectors. In order to make major gains in reducing U.S. building energy use commercial sector buildings must be improved. Energy benchmarking of buildings gives the facility manager or the building owner a quick evaluation of energy use and the potential for energy savings. It is the process of comparing the energy performance of a building to standards and codes, to a set target performance or to a range of energy performance values of similar buildings in order to help assess opportunities for improvement. Commissioning of buildings is the process of ensuring that systems are designed, installed, functionally tested and capable of being operated and maintained according to the owner's operational needs. It is the first stage in the building upgrade process after it has been assessed using benchmarking tools. The staged approach accounts for the interactions among all the energy flows in a building and produces a systematic method for planning upgrades that increase energy savings. This research compares and analyzes selected benchmarking and retrocommissioning tools to validate their accuracy such that they could be used in the initial audit process of a building. The benchmarking study analyzes the Energy Use Intensities (EUIs) and Ratings assigned by Portfolio Manager and Oak Ridge National Laboratory (ORNL) Spreadsheets. The 90.1 Prototype models and Commercial Reference Building model for Large Office building type were used for this comparative analysis. A case-study building from the DOE - funded Energize Phoenix program was also benchmarked for its EUI and rating. The retrocommissioning study was conducted by modeling these prototype models and the case-study building in the Facility Energy Decision System (FEDS) tool to simulate their energy consumption and analyze the retrofits suggested by the tool. The results of the benchmarking study proved that a benchmarking tool could be used as a first step in the audit process, encouraging the building owner to conduct an energy audit and realize the energy savings potential. The retrocommissioning study established the validity of FEDS as an accurate tool to simulate a building for its energy performance using basic inputs and to accurately predict the energy savings achieved by the retrofits recommended on the basis of maximum LCC savings.

Evacuated tube solar thermal collector arrays have a wide range of applications. While most of these applications are limited in performance due to relatively low maximum operating temperatures, these collectors can still be useful in low grade thermal systems. An array of fifteen Apricus AP-30 evacuated tube collectors was designed, assembled, and tested on the Arizona State University campus in Tempe, AZ. An existing system model was reprogrammed and updated for increased flexibility and ease of use. The model predicts the outlet temperature of the collector array based on the specified environmental conditions. The model was verified through a comparative analysis to the data collected during a three-month test period. The accuracy of this model was then compared against data calculated from the Solar Rating and Certification Corporation (SRCC) efficiency curve to determine the relative performance. It was found that both the original and updated models were able to generate reasonable predictions of the performance of the collector array with overall average percentage errors of 1.0% and 1.8%, respectively.

Energy efficient design and management of data centers has seen considerable interest in the recent years owing to its potential to reduce the overall energy consumption and thereby the costs associated with it. Therefore, it is of utmost importance that new methods for improved physical design of data centers, resource management schemes for efficient workload distribution and sustainable operation for improving the energy efficiency, be developed and tested before implementation on an actual data center. The BlueTool project, provides such a state-of-the-art platform, both software and hardware, to design and analyze energy efficiency of data centers. The software platform, namely GDCSim uses cyber-physical approach to study the physical behavior of the data center in response to the management decisions by taking into account the heat recirculation patterns in the data center room. Such an approach yields best possible energy savings owing to the characterization of cyber-physical interactions and the ability of the resource management to take decisions based on physical behavior of data centers. The GDCSim mainly uses two Computational Fluid Dynamics (CFD) based cyber-physical models namely, Heat Recirculation Matrix (HRM) and Transient Heat Distribution Model (THDM) for thermal predictions based on different management schemes. They are generated using a model generator namely BlueSim. To ensure the accuracy of the thermal predictions using the GDCSim, the models, HRM and THDM and the model generator, BlueSim need to be validated experimentally. For this purpose, the hardware platform of the BlueTool project, namely the BlueCenter, a mini data center, can be used. As a part of this thesis, the HRM and THDM were generated using the BlueSim and experimentally validated using the BlueCenter. An average error of 4.08% was observed for BlueSim, 5.84% for HRM and 4.24% for THDM. Further, a high initial error was observed for transient thermal prediction, which is due to the inability of BlueSim to account for the heat retained by server components.

ABSTRACT The heat recovery steam generator (HRSG) is a key component of Combined Cycle Power Plants (CCPP). The exhaust (flue gas) from the CCPP gas turbine flows through the HRSG − this gas typically contains a high concentration of NO and cannot be discharged directly to the atmosphere because of environmental restrictions. In the HRSG, one method of reducing the flue gas NO concentration is to inject ammonia into the gas at a plane upstream of the Selective Catalytic Reduction (SCR) unit through an injection grid (AIG); the SCR is where the NO is reduced to N2 and H2O. The amount and spatial distribution of the injected ammonia are key considerations for NO reduction while using the minimum possible amount of ammonia. This work had three objectives. First, a flow network model of the Ammonia Flow Control Unit (AFCU) was to be developed to calculate the quantity of ammonia released into the flue gas from each AIG perforation. Second, CFD simulation of the flue gas flow was to be performed to obtain the velocity, temperature, and species concentration fields in the gas upstream and downstream of the SCR. Finally, performance characteristics of the ammonia injection system were to be evaluated. All three objectives were reached. The AFCU was modeled using JAVA - with a graphical user interface provided for the user. The commercial software Fluent was used for CFD simulation. To evaluate the efficacy of the ammonia injection system in reducing the flue gas NO concentration, the twelve butterfly valves in the AFCU ammonia delivery piping (risers) were throttled by various degrees in the model and the NO concentration distribution computed for each operational scenario. When the valves were kept fully open, it was found that it led to a more uniform reduction in NO concentration compared to throttling the valves such that the riser flows were equal. Additionally, the SCR catalyst was consumed somewhat more uniformly, and ammonia slip (ammonia not consumed in reaction) was found lower. The ammonia use could be decreased by 10 percent while maintaining the NO concentration limit in the flue gas exhausting into the atmosphere.

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.

An eco-industrial park (EIP) is an industrial ecosystem in which a group of co-located firms are involved in collective resource optimization with each other and with the local community through physical exchanges of energy, water, materials, byproducts and services - referenced in the industrial ecology literature as "industrial symbiosis". EIPs, when compared with standard industrial resource sharing networks, prove to be of greater public advantage as they offer improved environmental and economic benefits, and higher operational efficiencies both upstream and downstream in their supply chain.

Although there have been many attempts to adapt EIP methodology to existing industrial sharing networks, most of them have failed for various factors: geographic restrictions by governmental organizations on use of technology, cost of technology, the inability of industries to effectively communicate their upstream and downstream resource usage, and to diminishing natural resources such as water, land and non-renewable energy (NRE) sources for energy production.

This paper presents a feasibility study conducted to evaluate the comparative environmental, economic, and geographic impacts arising from the use of renewable energy (RE) and NRE to power EIPs. Life Cycle Assessment (LCA) methodology, which is used in a variety of sectors to evaluate the environmental merits and demerits of different kinds of products and processes, was employed for comparison between these two energy production methods based on factors such as greenhouse gas emission, acidification potential, eutrophication potential, human toxicity potential, fresh water usage and land usage. To complement the environmental LCA analysis, levelized cost of electricity was used to evaluate the economic impact. This model was analyzed for two different geographic locations; United States and Europe, for 12 different energy production technologies.

The outcome of this study points out the environmental, economic and geographic superiority of one energy source over the other, including the total carbon dioxide equivalent emissions, which can then be related to the total number of carbon credits that can be earned or used to mitigate the overall carbon emission and move closer towards a net zero carbon footprint goal thus making the EIPs truly sustainable.