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As a promising approach for sustainable development, distributed energy systems have receive increasing attention worldwide and have become a key topic explored by researchers in the areas of building energy systems and smart grid. In line with this research trend, this dissertation presents a techno-economic analysis and optimization of distributed energy systems including combined heat and power (CHP), photovoltaics (PV), battery energy storage (BES), and thermal energy storage (TES) for commercial buildings. First, the techno-economic performance of the CHP system is analyzed and evaluated for four building types including hospital, large office, large hotel, and secondary school, located in different U.S. regions. The energy consumption of each building is obtained by EnergyPlus simulation software. The simulation models of CHP system are established for each building type. From the simulation results, the payback period (PBP) of the CHP system in different locations is calculated. The parameters that have an influence on the PBP of the CHP system are analyzed. Second, PV system and integrated PV and BES (PV-BES) system are investigated for several commercial building types, respectively. The effects of the variation in key parameters, such as PV system capacity, capital cost of PV, sell back ratio, battery capacity, and capital cost of battery, on the performance of PV and/or PV-BES system are explored. Finally, subsystems in previous chapters (CHP, PV, and BES) along with TES system are integrated together based on a proposed control strategy to meet the electric and thermal energy demand of commercial buildings (i.e., hospital and large hotel). A multi-objective particle swarm optimization (PSO) is conducted to determine the optimal size of each subsystem with the objective to minimize the payback period and maximize the reduction of carbon dioxide emissions. The results reveal how the key factors affect the performance of distributed energy system and demonstrate the proposed optimization can be effectively utilized to obtain an optimized design of distributed energy systems that can get a tradeoff between the environmental and economic impacts for different buildings.

The ever-growing demand for cyber-physical infrastructures has significantly affected worldwide energy consumption and environmental sustainability over the past two decades. Although the average heat load of the computing infrastructures has increased, the supportive capacity of cooling infrastructures requires further improvement. Consequently, energy-efficient cooling architectures, real-time load management, and waste heat utilization strategies have gained attention in the data center (DC) industry. In this dissertation, essential aspects of cooling system modularization, workload management, and waste-heat utilization were addressed. At first, benefits of several legacy and modular DCs were assessed from the viewpoint of the first and second laws of thermodynamics. A computational fluid dynamics simulation-informed thermodynamic energy-exergy formulation captured equipment-level inefficiencies for various cooling architectures and scenarios. Furthermore, underlying reasons and possible strategies to reduce dominant exergy loss components were suggested. Subsequently, strategies to manage cooling parameters and IT workload were developed for the DCs with rack-based and row-based cooling systems. The goal of these management schemes was to fulfill either single or multiple objectives such as energy, exergy, and computing efficiencies. Thermal models coupled to optimization problems revealed the non-trivial tradeoffs across various objective functions and operation parameters. Furthermore, the scalability of the proposed approach for a larger DC was demonstrated. Finally, a waste heat management strategy was developed for new-age infrastructures containing both air- and liquid-cooled servers, one of the critical issues in the DC industry. Exhaust hot water from liquid-cooled servers was used to drive an adsorption chiller, which in turn produced chilled water required for the air-handler units of the air-cooled system. This strategy significantly reduced the energy consumption of existing compression chillers. Furthermore, economic and environmental assessments were performed to discuss the feasibility of this solution for the DC community. The work also investigated the potential tradeoffs between waste heat recovery and computing efficiencies. / Thesis / Doctor of Philosophy (PhD)

In this work, the effectiveness of multi-objective design optimization using metamodeling techniques and an internal state variable (ISV) plasticity damage material model as a design tool is demonstrated. Multi-objective design optimization, metamodeling, and ISV plasticity damage material models are brought together to provide a design tool capable of meeting the stringent structural design requirements of today and of the future. The process of implementing this tool are laid out, and two case studies using multi-objective design optimization were carried out. The first was the optimization of a Chevrolet Equinox rear subframe. The optimized subframe was 12% lighter and met design requirements not achieved by the heavier initial design. The second case was the optimization of a Formula SAE front upright. The optimized upright meets all the design constraints and is 22% lighter.

System evolvability is vital to the longevity of large-scale complex engineered systems. The need for evolvability in complex systems is a result of their long service lives, rapid advances to their integrated technologies, unforeseen operating conditions, and emerging system requirements. In recent years, quantifiable metrics have been introduced for measuring the evolvability of complex systems based on the amount of excess capability in the system. These metrics have opened opportunities for optimization of systems with evolvability as an objective. However, there are several aspects of such an optimization that require further consideration. For example, there is a trade-off between the cost of excess capability initially built into complex systems and the benefit that is added to the system for future evolution. This trade-off must be represented in the optimization problem formulation. Additionally, uncertainty in future requirements and parameters of complex systems can result in an inaccurate representation of the design space. This thesis addresses these considerations through multi-objective optimization and uncertainty analysis. The resulting analysis gives insight into the effects of designing for evolvability. We show that there is a limit to the value added by increasing evolvability. We also show that accounting for uncertainty changes the optimal amount of evolvability that should be designed into a system. The developed theories and methods are demonstrated on the design of a military ground vehicle.

A haptic interface is a robotic force feedback device that provides a sense of touch to users of virtual reality simulations. This thesis presents a general method for the design optimization of parallel planar haptic devices based on maximizing the manipulability of the interface over its workspace. Manipulability is selected as the key design objective to ensure avoidance of singular configurations within the workspace and to maximize the interface's ability to generate feedback forces and torques in each direction in each handle location and orientation. The optimization approach developed in this thesis results in a set of candidate designs that are found by stepping the design parameters through the range of possible values, and testing the manipulability and other measures (including workspace area and space) at each location and orientation of the interface handle. To find the optimal design, a multi-objective approach is taken to generate a set of Pareto optimal designs. A smart Pareto filter is employed to yield a smaller set of designs representative of the full Pareto frontier. The most desirable design is chosen from this reduced set. The result is a general optimization method applicable to parallel haptic interfaces. The method is demonstrated on the design of a 3-RPR parallel planar interface.

The development of a multi-objective decision making and learning model for the use in unmanned systems is the focus of this project. Starting with traditional game theory and psychological learning theories developed in the past, a new model for machine learning is developed. This model incorporates a no-regret decision making model with a Bayesian learning process which has the ability to adapt to errors found in preconceived costs associated with each objective. This learning ability is what sets this model apart from many others. By creating a model based on previously developed human learning models, hundreds of years of experience in these fields can be applied to the recently developing field of machine learning. This also allows for operators to more comfortably adapt to the machine's learning process in order to better understand how to take advantage of its features. One of the main purposes of this system is to incorporate multiple objectives into a decision making process. This feature can better allow its users to clearly define objectives and prioritize these objectives allowing the system to calculate the best approach for completing the mission. For instance, if an operator is given objectives such as obstacle avoidance, safety, and limiting resource usage, the operator would traditionally be required to decide how to meet all of these objectives. The use of a multi-objective decision making process such as the one designed in this project, allows the operator to input the objectives and their priorities and receive an output of the calculated optimal compromise.

This dissertation presents the theory, design, implementation and successful deployment of a cognitive engine decision algorithm by which a cognitive radio-equipped mobile robot may adapt its motion and radio parameters through multi-objective optimization. This provides a proof-of-concept prototype cognitive system that is aware of its environment, its user's needs, and the rules governing its operation. It is to take intelligent action based on this awareness to optimize its performance across both the mobility and radio domains while learning from experience and responding intelligently to ongoing environmental mission changes. The prototype combines the key features of cognitive radios and autonomous vehicles into a single package whose behavior integrates the essential features of both. The use case for this research is a scenario where a small unmanned aerial vehicle (UAV) is traversing a nominally cyclic or repeating flight path (an â •orbitâ •) seeking to observe targets and where possible avoid hostile agents. As the UAV traverses the path, it experiences varying RF effects, including multipath propagation and terrain shadowing. The goal is to provide the capability for the UAV to learn the flight path with respect both to motion and RF characteristics and modify radio parameters and flight characteristics proactively to optimize performance. Using sensor fusion techniques to develop situational awareness, the UAV should be able to adapt its motion or communication based on knowledge of (but not limited to) physical location, radio performance, and channel conditions. Using sensor information from RF and mobility domains, the UAV uses the mission objectives and its knowledge of the world to decide on a course of action. The UAV develops and executes a multi-domain action; action that crosses domains, such as changing RF power and increasing its speed. This research is based on a simple observation, namely that cognitive radios and autonomous vehicles perform similar tasks, albeit in different domains. Both analyze their environment, make and execute a decision, evaluate the result (learn from experience), and repeat as required. This observation led directly to the creation of a single intelligent agent combining cognitive radio and autonomous vehicle intelligence with the ability to leverage flexibility in the radio frequency (RF) and motion domains. Using a single intelligent agent to optimize decision making across both mobility and radio domains is unified multi-domain decision making (UMDDM). / Ph. D.

京都大学 / 新制・課程博士 / 博士(情報学) / 甲第24264号 / 情博第808号 / 新制||情||136(附属図書館) / 京都大学大学院情報学研究科数理工学専攻 / (主査)教授 山下 信雄, 准教授 福田 秀美, 教授 太田 快人 / 学位規則第4条第1項該当 / Doctor of Informatics / Kyoto University / DFAM

The complete design process for a new nuclear power plant concept is costly, long, complicated, and the work is generally split between several specialized groups. These design groups separately do their best to design the portion of the reactor that falls in their expertise according to the design criteria before passing the design to the subsequent design group. Ultimately, the work of each design group is combined, with significant iteration between groups striving to facilitate the integration of each of the heavily interdependent systems. Such complex interaction between experts leads to three significant problems: (1) the issues associated with knowledge management, (2) the lack of design optimization, and (3) the failure to discover the hidden interdependencies between different design parameters that may exist. Some prior work has been accomplished in both developing common frame of reference (CFR) support systems to aid in the design process and applying optimization to nuclear system design.The purpose of this work is to use multi-objective optimization to address the second and third problems above on a small subset of reactor design scenarios. Multi-objective optimization generates several design optima in the form of a Pareto front, which portrays the optimal trade-off between design objectives. As a major part of this work, a system design optimization tool is created, namely the Optimization and Preference Tool for the Improvement of Nuclear Systems (OPTIONS). The OPTIONS tool is initially applied to several individual nuclear systems: the power conversion system (PCS) of the Integral, Inherently Safe Light Water Reactor (I²S-LWR), the Kalina cycle being proposed as the PCS for a LWR, the PERCS (or Passive Endothermic Reaction Cooling System), and the core loop of the Zion plant. Initial sensitivity analysis work and the application of the Non-dominated Sorting Particle Swarm Optimization (NSPSO) method provides a Pareto front of design optima for the PCS of the I²S-LWR, while bringing to light some hidden pressure interdependencies for generating steam using a flash drum. A desire to try many new PCS configurations leads to the development of an original multi-objective optimization method, namely the Mixed-Integer Non-dominated Sorting Genetic Algorithm (MI-NSGA). With this method, the OPTIONS tool provides a novel and improved Pareto front with additional optimal PCS configurations. Then, the simpler NSGA method is used to optimize the Kalina cycle, the PERCS, and the Zion core loop, providing each problem with improved designs and important objective trade-off information. Finally, the OPTIONS tool uses the MI-NSGA method to optimize the integration of three systems (Zion core loop, PERCS, and Rankine cycle PCS) while increasing efficiency, decreasing costs, and improving performance. In addition, the tool is outfitted to receive user preference input to improve the convergence of the optimization to a Pareto front.