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.

The heat and mass transfer phenomena in micro-scale for the mass transfer phenomena on drug in cylindrical matrix system, the simulation of oxygen/drug diffusion in a three dimensional capillary network, and a reduced chemical kinetic modeling of gas turbine combustion for Jet propellant-10 have been studied numerically. For the numerical analysis of the mass transfer phenomena on drug in cylindrical matrix system, the governing equations are derived from the cylindrical matrix systems, Krogh cylinder model, which modeling system is comprised of a capillary to a surrounding cylinder tissue along with the arterial distance to veins. ADI (Alternative Direction Implicit) scheme and Thomas algorithm are applied to solve the nonlinear partial differential equations (PDEs). This study shows that the important factors which have an effect on the drug penetration depth to the tissue are the mass diffusivity and the consumption of relevant species during the time allowed for diffusion to the brain tissue. Also, a computational fluid dynamics (CFD) model has been developed to simulate the blood flow and oxygen/drug diffusion in a three dimensional capillary network, which are satisfied in the physiological range of a typical capillary. A three dimensional geometry has been constructed to replicate the one studied by Secomb et al. (2000), and the computational framework features a non-Newtonian viscosity model for blood, the oxygen transport model including in oxygen-hemoglobin dissociation and wall flux due to tissue absorption, as well as an ability to study the diffusion of drugs and other materials in the capillary streams. Finally, a chemical kinetic mechanism of JP-10 has been compiled and validated for a wide range of combustion regimes, covering pressures of 1atm to 40atm with temperature ranges of 1,200 K - 1,700 K, which is being studied as a possible Jet propellant for the Pulse Detonation Engine (PDE) and other high-speed flight applications such as hypersonic missiles. The comprehensive skeletal mechanism consists of 58 species and 315 reactions including in CPD, Benzene formation process by the theory for polycyclic aromatic hydrocarbons (PAH) and soot formation process on the constant volume combustor, premixed flame characteristics.

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.

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.

Locomotion of microorganisms is commonly observed in nature. Although microorganism locomotion is commonly attributed to mechanical deformation of solid appendages, in 1956 Nobel Laureate Peter Mitchell proposed that an asymmetric ion flux on a bacterium's surface could generate electric fields that drive locomotion via self-electrophoresis. Recent advances in nanofabrication have enabled the engineering of synthetic analogues, bimetallic colloidal particles, that swim due to asymmetric ion flux originally proposed by Mitchell. Bimetallic colloidal particles swim through aqueous solutions by converting chemical fuel to fluid motion through asymmetric electrochemical reactions. This dissertation presents novel bimetallic motor fabrication strategies, motor functionality, and a study of the motor collective behavior in chemical concentration gradients. Brownian dynamics simulations and experiments show that the motors exhibit chemokinesis, a motile response to chemical gradients that results in net migration and concentration of particles. Chemokinesis is typically observed in living organisms and distinct from chemotaxis in that there is no particle directional sensing. The synthetic motor chemokinesis observed in this work is due to variation in the motor's velocity and effective diffusivity as a function of the fuel and salt concentration. Static concentration fields are generated in microfluidic devices fabricated with porous walls. The development of nanoscale particles that swim autonomously and collectively in chemical concentration gradients can be leveraged for a wide range of applications such as directed drug delivery, self-healing materials, and environmental remediation.

Many expect renewable energy technologies to play a leading role in a sustainable energy supply system and to aid the shift away from an over-reliance on traditional hydrocarbon resources in the next few decades. This dissertation develops environmental, policy and social models to help understand various aspects of photovoltaic (PV) technologies. The first part of this dissertation advances the life cycle assessment (LCA) of PV systems by expanding the boundary of included processes using hybrid LCA and accounting for the technology-driven dynamics of environmental impacts. Hybrid LCA extends the traditional method combining bottom-up process-sum and top-down economic input-output (EIO) approaches. The embodied energy and carbon of multi-crystalline silicon photovoltaic systems are assessed using hybrid LCA. From 2001 to 2010, the embodied energy and carbon fell substantially, indicating that technological progress is realizing reductions in environmental impacts in addition to lower module price. A variety of policies support renewable energy adoption, and it is critical to make them function cooperatively. To reveal the interrelationships among these policies, the second part of this dissertation proposes three tiers of policy architecture. This study develops a model to determine the specific subsidies required to support a Renewable Portfolio Standard (RPS) goal. The financial requirements are calculated (in two scenarios) and compared with predictable funds from public sources. A main result is that the expected investments to achieve the RPS goal far exceed the economic allocation for subsidy of distributed PV. Even with subsidies there are often challenges with social acceptance. The third part of this dissertation originally develops a fuzzy logic inference model to relate consumers' attitudes about the technology such as perceived cost, maintenance, and environmental concern to their adoption intention. Fuzzy logic inference model is a type of soft computing models. It has the advantage of dealing with imprecise and insufficient information and mimicking reasoning processes of human brains. This model is implemented in a case study of residential PV adoption using data through a survey of homeowners in Arizona. The output of this model is the purchasing probability of PV.

Passive cooling designs & technologies offer great promise to lower energy use in buildings. Though the working principles of these designs and technologies are well understood, simplified tools to quantitatively evaluate their performance are lacking. Cooling by night ventilation, which is the topic of this research, is one of the well known passive cooling technologies. The building's thermal mass can be cooled at night by ventilating the inside of the space with the relatively lower outdoor air temperatures, thereby maintaining lower indoor temperatures during the warmer daytime period. Numerous studies, both experimental and theoretical, have been performed and have shown the effectiveness of the method to significantly reduce air conditioning loads or improve comfort levels in those climates where the night time ambient air temperature drops below that of the indoor air. The impact of widespread adoption of night ventilation cooling can be substantial, given the large fraction of energy consumed by air conditioning of buildings (about 12-13% of the total electricity use in U.S. buildings). Night ventilation is relatively easy to implement with minimal design changes to existing buildings. Contemporary mathematical models to evaluate the performance of night ventilation are embedded in detailed whole building simulation tools which require a certain amount of expertise and is a time consuming approach. This research proposes a methodology incorporating two models, Heat Transfer model and Thermal Network model, to evaluate the effectiveness of night ventilation. This methodology is easier to use and the run time to evaluate the results is faster. Both these models are approximations of thermal coupling between thermal mass and night ventilation in buildings. These models are modifications of existing approaches meant to model dynamic thermal response in buildings subject to natural ventilation. Effectiveness of night ventilation was quantified by a parameter called the Discomfort Reduction Factor (DRF) which is the index of reduction of occupant discomfort levels during the day time from night ventilation. Daily and Monthly DRFs are calculated for two climate zones and three building heat capacities. It is verified that night ventilation is effective in seasons and regions when day temperatures are between 30 oC and 36 oC and night temperatures are below 20 oC. The accuracy of these models may be lower than using a detailed simulation program but the loss in accuracy in using these tools more than compensates for the insights provided and better transparency in the analysis approach and results obtained.

Processed pyro-gel contains castor oil with solid component of boehmite (Al-OOH). The pyro-gel is synthesized by heat to convert boehmite to gamma-Al2O3 and to a certain extent alpha-Al2O3 nano-particles and castor oil into carbon residue. The effect of heat on pyro-gel is analyzed in a series of experiments using two burning chambers with the initial temperature as the main factor. The obtained temperature distribution profiles are studied and it is observed that the gel behaves very close to the theoretical prediction under heat. The carbon residue with Al2O3 is then processed for twelve hours and then analyzed to obtain the pore distribution of the Al2O3 nano-particles and the relation between the pore volume and the pre-heat temperature is analyzed. The obtained pore distribution shows the pore volume of Al2O3 nano-particles has direct relation to the pre-heat temperature. The experimental process involving the cylindrical reactor is simulated by using a finite rate chemistry eddy-dissipation model in a non-premixed and a porous mesh. The temperature distribution profile of the processed gel for both the meshes is obtained and a comparison is done with the data obtained in the experimental analysis. The temperature distribution obtained from the simulations show they follow a very similar profile to the temperature distribution obtained from experimental analysis, thus confirming the accuracy of both the models. The variation in numerical values between the experimental and simulation analysis is discussed. A physical model is proposed to determine the pore formation based on the temperature distribution obtained from experimental analysis and simulation.