Thesis (M.S.)--Lehigh University, 2009. / Adviser: Edward K. Levy.

<p> A simple technique to estimate the hydraulic impacts associated with the deployment of hydrokinetic (HK) devices is introduced. This technique involves representing HK devices with an enhanced bottom roughness. The enhanced bottom roughness is found to be a function of Manning roughness, slope, and water depth of the natural channel as well as device efficiency, blockage ratio, and density of device deployment. This approach was developed using a theoretical relationship between power extracted to power dissipated, and assuming a simple rectangular channel cross-section and uniform distribution of devices. Once the effective bottom roughness is determined, it can be used to determine the hydraulic impact of arbitrary device configurations and flow situations. The enhanced bottom roughness approach was compared to an alternative method of determining the affects of hydrokinetic energy extraction (SNL-EFDC). Both methods were in good agreement. Capabilities and limitations of determining hydraulic impacts by enhanced bottom roughness approach are demonstrated by site-specific simulation for the Tanana site. </p>

Thèse (M.Sc.A.)--Université de Sherbrooke (Canada), 2008. / Titre de l'écran-titre (visionné le 1 février 2007). In ProQuest dissertations and theses. Publié aussi en version papier.

Thèse (M.Sc.A.)--Université de Sherbrooke (Canada), 2008. / Titre de l'écran-titre (visionné le 1 février 2007). In ProQuest dissertations and theses. Publié aussi en version papier.

Thesis (M.S.)--Lehigh University, 2010. / Adviser: Edward K. Levy.

Thèse (M.Sc.A.)--Université de Sherbrooke (Canada), 2007. / Titre de l'écran-titre (visionné le 1 février 2007). In ProQuest dissertations and theses. Publié aussi en version papier.

<p>Heat transfer from building foundations varies significantly in all three spatial dimensions and has important dynamic effects at all timescales, from one hour to several years. With the additional consideration of moisture transport, ground freezing, evapotranspiration, and other physical phenomena, the estimation of foundation heat transfer becomes increasingly sophisticated and computationally intensive to the point where accuracy must be compromised for reasonable computation time. The tools currently available to calculate foundation heat transfer are often either too limited in their capabilities to draw meaningful conclusions or too sophisticated to use in common practices. This work presents Kiva, a new foundation heat transfer computational framework. Kiva provides a flexible environment for testing different numerical schemes, initialization methods, spatial and temporal discretizations, and geometric approximations. Comparisons within this framework provide insight into the balance of computation speed and accuracy relative to highly detailed reference solutions. The accuracy and computational performance of six finite difference numerical schemes are verified against established IEA BESTEST test cases for slab-on-grade heat conduction. Of the schemes tested, the Alternating Direction Implicit (ADI) scheme demonstrates the best balance between accuracy, performance, and numerical stability. Kiva features four approaches of initializing soil temperatures for an annual simulation. A new accelerated initialization approach is shown to significantly reduce the required years of presimulation. Methods of approximating three-dimensional heat transfer within a representative two-dimensional context further improve computational performance. A new approximation called the boundary layer adjustment method is shown to improve accuracy over other established methods with a negligible increase in computation time. This method accounts for the reduced heat transfer from concave foundation shapes, which has not been adequately addressed to date. Within the Kiva framework, three-dimensional heat transfer that can require several days to simulate is approximated in two-dimensions in a matter of seconds while maintaining a mean absolute deviation within 3%.

<p> The building sector consumes about 40% of energy used in the United States and is responsible for nearly 40% of greenhouse gas emissions. Energy reduction in this sector by means of cost-effective and scalable approaches will have an enormous economic, social, and environmental impact. Achieving substantial energy reduction in buildings may require to rethink the entire processes of design, construction, and operation of buildings. This thesis focuses on advanced control system design for energy efficient commercial buildings. </p><p> Commercial buildings are plants that process air in order to provide comfort for their occupants. The components used are similar to those employed in the process industry: chillers, boilers, heat exchangers, pumps, and fans. The control design complexity resides in adapting to time-varying user loads as well as occupant requirements, and quickly responding to weather changes. Today this is easily achievable by over sizing the building components and using simple control strategies. Building controls design becomes challenging when predictions of weather, occupancy, renewable energy availability, and energy price are used for feedback control. Green buildings are expected to maintain occupants comfort while minimizing energy consumption, being robust to intermittency in the renewable energy generation and responsive to signals from the smart grid. Achieving all these features in a systematic and cost-effective way is challenging. The challenge is even greater when conventional systems are replaced by innovative heating and cooling systems that use active storage of thermal energy with critical operational constraints.</p><p> Model predictive control (MPC) is the only control methodology that can systematically take into account future predictions during the control design stage while satisfying the system operating constraints. This thesis focuses on the design and implementation of MPC for building cooling and heating systems. The objective is to develop a control methodology that can 1) reduce building energy consumption while maintaining indoor thermal comfort by using predictive knowledge of occupancy loads and weather information, (2) easily and systematically take into account the presence of storage devices, demand response signals from the grid, and occupants feedback, (3) be implemented on existing inexpensive and distributed building control platform in real-time, and (4) handle model uncertainties and prediction errors both at the design and implementation stage.</p><p> The thesis is organized into six chapters. Chapter 1 motivates our research and reviews existing control approaches for building cooling and heating systems. </p><p> Chapter 2 presents our approach to developing low-complexity control oriented models learned from historical data. Details on models for building components and spaces thermal response are provided. The thesis focuses on the dynamics of both the energy conversion and storage as well as energy distribution by means of heating ventilation and air conditioning (HVAC) systems.</p><p> In Chapter 3, deterministic model predictive control problems are formulated for the energy conversion systems and energy distribution systems to minimize the energy consumption while maintaining comfort requirement and operational constraints. Experimental and simulative results demonstrate the effectiveness of the MPC scheme, and reveal significant energy reduction without compromising indoor comfort requirement.</p><p> As the size and complexity of buildings grow, the MPC problem quickly becomes computationally intractable to be solved in a centralized fashion. This limitation is addressed in Chapter 4. We propose a distributed algorithm to decompose the MPC problem into a set of small problems using dual decomposition and fast gradient projection. Simulation results show good performance and computational tractability of the resulting scheme.</p><p> The MPC formulation in Chapter 3 and 4 assumes prefect knowledge of system model, load disturbance, and weather. However, the predictions in practice are different from actual realizations. In order to take into account the prediction uncertainties at control design stage, stochastic MPC (SMPC) is introduced in Chapter 5 to minimize expected costs and satisfy constraints with a given probability. In particular, the proposed novel SMPC method applies feedback linearization to handle system nonlinearity, propagates the state statistics of linear systems subject to finite-support (non Gaussian) disturbances, and solves the resulting optimization problem by using large-scale nonlinear optimization solvers.</p>

Thèse (Ph.D.)--Université de Sherbrooke (Canada), 2008. / Titre de l'écran-titre (visionné le 1 février 2007). In ProQuest dissertations and theses. Publié aussi en version papier.

<p> With increasing penetrations of solar photovoltaic (PV) power in the electricity grid, the variability of the irradiance, and therefore power, is important to understand because variable resources can challenge grid operations. The smoothing of solar irradiance over the extent of PV power plants is examined using two methods: averaging measurements from many irradiance sensors, and using a model developed by Lave, Kleissl, and Stein (2013) called the Wavelet Variability Model. This thesis utilizes data from a network of 45 solar irradiance sensors which was deployed north of Flagstaff, Arizona. The results show the similarities and differences between two irradiance smoothing methods. These two models both show that the smoothing effect is significant for large PV power plants, which means the power plant output has less variability and is easier to integrate into the electricity grid than might have been expected using a single point sensor measurement to predict variability.</p>