This dissertation develops and demonstrates a new physics-based approach that provides computational stability of subgrid stress models in large eddy simulations while producing far smaller changes in the original subgrid stress and subgrid production fields than do current \textit{ad hoc} stabilization methods. A pseudo-spectral code that is shown here to be almost entirely non-dissipative yet inherently stable without any subgrid model is used to conduct simulations with stable and unstable subgrid stress models. Results show that initial instability, subsequent exponential growth, and eventual machine overflow occur via a highly localized dynamical process that results from interactions among terms in the kinetic energy and enstrophy transport equations. This process begins first at one material point and then occurs at increasingly more material points, with local exponential growth rates of kinetic energy and enstrophy being the same for all points, until machine overflow eventually occurs at the material point where the process began first. A Lagrangian backtracking scheme is developed and applied to this material point, allowing backward-in-time tracking of all terms in the kinetic energy and enstrophy transport equations. This gives insights into the dynamics that produce this local instability and its subsequent exponential growth, with the initial instability shown to result from interactions between the subgrid production and subgrid redistribution terms. Elementary backscatter limiting based on locally reducing individual subgrid stress components that contribute to local kinetic energy backscatter is shown to stabilize any stress model, but still produces substantial changes in the stress and production fields. The rational Boolean stabilization method developed here instead uses the local subgrid production and subgrid redistribution rates to determine where and how individual subgrid stress components must be rescaled to provide local backscatter limiting and/or forward scatter amplification. This stabilizes all subgrid stress models while producing only small changes in the subgrid stress and production fields. Rational Boolean stabilization is computationally fast, and can be generalized to stabilize models for other subgrid terms in large eddy simulations while producing only small changes in their resulting fields. This solves a key problem that has previously limited the accuracy of large eddy simulations.

Laser radars or lidar’s have been used extensively to remotely study winds within the atmospheric boundary layer and atmospheric transport. Lidar sensors have become an important tool within the meteorology and the wind energy community. For example, Doppler lidars are used frequently in wind resource assessment, wind turbine control as well as in atmospheric science research. A Time of Flight based (ToF) direct detection lidar sensor is used in vehicles to navigate through complex and dynamic environments autonomously. These optical sensors are used to map the environment around the car accurately for perception and localization tasks that help achieve complete autonomy.

This thesis begins with a detailed discussion on the fundamentals of a Doppler lidar system. The laser signal flow path to and from the target, the optics of the system and the core signal processing algorithms used to extract velocity information, were studied to get closer to the hardware of a Doppler lidar sensor. A Doppler lidar simulator was built to study the existing signal processing algorithms to detect and estimate doppler frequency, and radial velocity information. Understanding the sensor and its processing at the hardware level is necessary to develop new algorithms to detect and track specific flow structures in the atmosphere. For example, the aircraft vortices have been a topic of extensive research and doppler lidars have proved to be a valuable sensor to detect and track these coherent flow structures. Using the lidar simulator a physics based doppler lidar vortex algorithm is tested on simulated data to track a pair of counter rotating aircraft vortices.



At a system level the major components of a time of flight lidar is very similar to a Doppler lidar. The fundamental physics of operation is however different. While doppler lidars are used for radial velocity measurement, ToF sensors as the name suggests provides precise depth measurements by measuring time of flight between the transmitted and the received pulses. The second part of this dissertation begins to explore the details of ToF lidar system. A system level design, to build a ToF direct detection lidar system is presented. Different lidar sensor modalities that are currently used with sensors in the market today for automotive applications were evaluated and a 2D MEMS based scanning lidar system was designed using off-the shelf components.

Finally, a range of experiments and tests were completed to evaluate the performance of each sub-component of the lidar sensor prototype. A major portion of the testing was done to align the optics of the system and to ensure maximum field of view overlap for the bi-static laser sensor. As a laser range finder, the system demonstrated capabilities to detect hard targets as far as 32 meters. Time to digital converter (TDC) and an analog to digital converter (ADC) was used for providing accurate timing solutions for the lidar prototype. A Matlab lidar model was built and used to perform trade-off studies that helped choosing components to suit the sensor design specifications.

The size, weight and cost of these lidar sensors are still very high and thus making it harder for automotive manufacturers to integrate these sensors into their vehicles. Ongoing research in this field is determined to find a solution that guarantees very high performance in real time and lower its cost over the next decade as components get cheaper and can be seamlessly integrated with cars to improve on-road safety.

Just for a moment! Imagine you live in Arizona without air-conditioning systems!

Air-conditioning and refrigeration systems are one of the most crucial systems in anyone’s house and car these days. Energy resources are becoming more scarce and expensive. Most of the currently used refrigerants have brought an international concern about global warming. The search for more efficient cooling/refrigeration systems with environmental friendly refrigerants has become more and more important so as to reduce greenhouse gas emissions and ensure sustainable and affordable energy systems. The most widely used air-conditioning and refrigeration system, based on the vapor compression cycle, is driven by converting electricity into mechanical work which is a high quality type of energy. However, these systems can instead be possibly driven by heat, be made solid-state (i.e., thermoelectric cooling), consist entirely of a gaseous working fluid (i.e., reverse Brayton cycle), etc. This research explores several thermally driven cooling systems in order to understand and further overcome some of the major drawbacks associated with their performance as well as their high capital costs. In the second chapter, we investigate the opportunities for integrating single- and double-stage ammonia-water (NH3–H2O) absorption refrigeration systems with multi-effect distillation (MED) via cascade of rejected heat for large-scale plants. Similarly, in the third chapter, we explore a new polygeneration cooling-power cycle’s performance based on Rankine, reverse Brayton, ejector, and liquid desiccant cycles to produce power, cooling, and possibly fresh water for various configurations. Different configurations are considered from an energy perspective and are compared to stand-alone systems. In the last chapter, a new simple, inexpensive, scalable, environmentally friendly cooling system based on an adsorption heat pump system and evacuated tube solar collector is experimentally and theoretically studied. The system is destined as a small-scale system to harness solar radiation to provide a cooling effect directly in one system.

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.

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.