<p>  </p> <p>Present barriers to electric vehicle (EV) adoption include cost and range anxiety. Dynamic wireless power transfer (DWPT) systems, which send energy from an in-road transmitter to a vehicle in motion, offer potential remedies to both issues. Specifically, they reduce the size and charging needs of the relatively expensive battery system by supplying the power required for vehicle motion and operation. Recently, Purdue researchers have been exploring the development of inductive DWPT systems for Class 8 and 9 trucks operating at highway speeds. This research has included the design of transmitter/receiver coils as well as compensation circuits and power electronics that are required to efficiently transmit 200 kW-level power across a large air gap.</p> <p>In this thesis, a focus is on the derivation of electromagnetic and thermal models that are used to support the design and validation of DWPT systems. Specifically, electromagnetic models have been derived to predict the volume and loss of ferrite-based AC inductors and film capacitor used in compensation circuits. A thermal equivalent circuit of the transmitter has been derived to predict the expected coil and pavement temperatures in DWPT systems that utilize either single- or three-phase transmitter topologies. A description of these models, along with their validation using finite element-based simulation and their use in multi-objective optimization of DWPT systems is provided.</p>

In this dissertation, we develop a new framework for 3D trajectory planning of Autonomous Underwater Vehicles (AUVs) in realistic ocean scenarios. The work is divided into three parts. In the first part, we provide a new approach for deterministic trajectory planning in steady current, described using Ocean General Circulation Model (OGCM) data. We apply a Non-Linear Programming (NLP) to the optimal-time trajectory planning problem. To demonstrate the effectivity of the resulting model, we consider the optimal time trajectory planning of an AUV operating in the Red Sea and the Gulf of Aden. In the second part, we generalize our 3D trajectory planning framework to time-dependent ocean currents. We also extend the framework to accommodate multi-objective criteria, focusing specifically on the Pareto front curve between time and energy. To assess the effectiveness of the extended framework, we initially test the methodology in idealized settings. The scheme is then demonstrated for time-energy trajectory planning problems in the Gulf of Aden. In the last part, we account for uncertainty in the ocean current field, is described by an ensemble of flow realizations. The proposed approach is based on a non-linear stochastic programming methodology that uses a risk-aware objective function, accounting for the full variability of the flow ensemble. We formulate stochastic problems that aim to minimize a risk measure of the travel time or energy consumption, using a flexible methodology that enables the user to explore various objectives, ranging seamlessly from risk-neutral to risk-averse. The capabilities of the approach are demonstrated using steady and transient currents. Advanced visualization tools have been further designed to simulate results.

Indiana University-Purdue University Indianapolis (IUPUI) / Design optimization of composite structures is of importance in the automotive, aerospace, and energy industry. The majority of optimization methods applied to laminated composites consider linear or simplified nonlinear models. Also, various techniques lack the ability to consider the composite failure criteria. Using artificial neural networks approximates the objective function to make it possible to use other techniques to solve the optimization problem. The present work describes an optimization process used to find the optimum design to meet crashworthiness requirements which includes minimizing peak crushing force and specific energy absorption for a square tube. The design variables include the number of plies, ply angle and ply thickness of the square tube. To obtain an effective approximation an artificial neural network (ANN) is used. Training data for the artificial neural network is obtained by crash analysis of a square tube for various samples using LS DYNA. The sampling plan is created using Latin Hypercube Sampling. The square tube is considered to be impacted by the rigid wall with fixed velocity and rigid body acceleration, force versus displacement curves are plotted to obtain values for crushing force, deceleration, crush length and specific energy absorbed. The optimized values for the square tube to fulfill the crashworthiness requirements are obtained using an artificial neural network combined with Multi-Objective Genetic Algorithms (MOGA). MOGA finds optimum values in the feasible design space. Optimal solutions obtained are presented by the Pareto frontier curve. The optimization is performed with accuracy considering 5% error.

Porozumění chování silniční dopravy je klíčem pro její efektivní řízení a organizaci. Tato úloha se stává čím dál více důležitou s rostoucími požadavky na dopravu a počtem registrovaných vozidel. Informace o dopravní situaci je důležitá pro řidiče a osoby zodpovědné za její řízení. Naštěstí v posledních několika dekádách došlo k značnému rozvoji technologií pro monitorování dopravní situace. Stacionární senzory, jako jsou indukční smyčky, radary, kamery a infračervené senzory, mohou být nainstalovány na důležitých místech. Zde jsou schopny měřit různé mikroskopické a makroskopické dopravní veličiny. Bohužel mnohá měření obsahují nekorektní data, která není možné použít při dalším zpracování, například pro predikci dopravy a její inteligentní řízení. Tato nekorektní data mohou být způsobena poruchou zařízení nebo problémy při přenosu dat. Z tohoto důvodu je důležité navrhnout obecný framework, který je schopný doplnit chybějící data. Navíc by tento framework měl být také schopen poskytovat krátkodobou predikci budoucího stavu dopravy. Tato práce se především zabývá vybranými problémy v oblasti doplnění chybějících dopravních dat, predikcí dopravy v krátkém časovém horizontu a predikcí dojezdových dob. Navrhovaná řešení jsou založena na kombinaci současných metod strojového učení, například Support vector regression (SVR) a multikriteriálních evolučních algoritmů. SVR má mnoho meta-parametrů, které je nutné dobře nastavit tak, aby byla dosažena co nejkvalitnější predikce. Kvalita predikce SVR dále silně závisí na výběru vhodné množiny vstupních proměnných. V této práci používáme multiktriteriální optimalizaci pro optimalizaci SVR meta-parametrů a množiny vstupních proměnných. Multikriteriální optimalizace nám umožňuje získat mnoho Pareto nedominovaných řešení. Mezi těmito řešeními je možné dynamicky přepínat dle toho, jaká data jsou aktuálně k dispozici tak, aby bylo dosaženo maximální kvality predikce. Metody navržené v této práci jsou především vhodné pro prostředí s velkým množstvím chybějících hodnot v dopravních datech. Tyto metody jsme ověřili na reálných datech a porovnali jejich výsledky s metodami, které jsou v současné době používány. Navržené metody poskytují lepší výsledky než stávající metody, a to především ve scénářích, kde se vyskytuje mnoho chybějících hodnot v dopravních datech.

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.

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.