This thesis aims to forecast aircraft demand in the aerospace and defense industry, specifically aircraft orders and deliveries. Orders are often placed by airline companies with aircraft manufacturers, and then suddenly canceled due to changes in plans. Therefore, at some point during the three-year lead time, the number of orders placed and realized deliveries may be quite different. As a result, orders and deliveries are very difficult to predict and are influenced by many different factors. Among these factors are past trends, macroeconomic indicators as well as aircraft sales measures. These predictor variables were analyzed thoroughly, then used with time series and multiple regression forecasting methods to develop different forecasts for quarterly and annual orders and deliveries. The relative accuracies of forecasts were measured and compared through the use of Theil’s U statistic. Finally, a linear program was used to aggregate multiple forecasts to develop an optimal combination of all forecasts. In conclusion, the methods employed in this thesis are quite effective and produce a wholesome aggregate forecast with an error that is generally quite low for a forecasting task as challenging as this one.

This dissertation describes the development and application of a naval ship distributed system architectural framework, Architecture Flow Optimization (AFO), and Dynamic Architecture Flow Optimization (DAFO) to naval ship Concept and Requirements Exploration (CandRE). The architectural framework decomposes naval ship distributed systems into physical, logical, and operational architectures representing the spatial, functional, and temporal relationships of distributed systems respectively. This decomposition greatly simplifies the Mission, Power, and Energy System (MPES) design process for use in CandRE. AFO and DAFO are a network-based linear programming optimization methods used to design and analyze MPES at a sufficient level of detail to understand system energy flow, define MPES architecture and sizing, model operations, reduce system vulnerability and improve system reliability. AFO incorporates system topologies, energy coefficient component models, preliminary arrangements, and (nominal and damaged) steady state scenarios to minimize the energy flow cost required to satisfy all operational scenario demands and constraints. DAFO applies the same principles as AFO and adds a second commodity, data flow. DAFO also integrates with a warfighting model, operational model, and capabilities model that quantify tasks and capabilities through system measures of performance at specific capability nodes. This enables the simulation of operational situations including MPES configuration and operation during CandRE. This dissertation provides an overview of design tools developed to implement this process and methods, including objective attribute metrics for cost, effectiveness and risk, ship synthesis model, hullform exploration and MPES explorations using design of experiments (DOEs) and response surface models. / Doctor of Philosophy / This dissertation describes the development and application of a warship system architectural framework, Architecture Flow Optimization (AFO), and Dynamic Architecture Flow Optimization (DAFO) to warship Concept and Requirements Exploration (CandRE). The architectural framework decomposes warship systems into physical, logical, and operational architectures representing the spatial, functional, and time-based relationships of systems respectively. This decomposition greatly simplifies the Mission, Power, and Energy System (MPES) design process for use in CandRE. AFO and DAFO are a network-based linear programming optimization methods used to design and analyze MPES at a sufficient level of detail to understand system energy usage, define MPES connections and sizing, model operations, reduce system vulnerability and improve system reliability. AFO incorporates system templates, simple physics and energy-based component models, preliminary arrangements, and simple undamaged/damaged scenarios to minimize the energy flow usage required to satisfy all operational scenario demands and constraints. DAFO applies the same principles and adds a second commodity, data flow representing system operation. DAFO also integrates with a warfighting model, operational model, and capabilities model that quantify tasks and capabilities through system measures of performance. This enables the simulation of operational situations including MPES configuration and operation during CandRE. This dissertation provides an overview of design tools developed to implement this process and methods, including optimization objective attribute metrics for cost, effectiveness and risk.

Variable speed motor drives are being rapidly deployed for a vast range of applications in order to increase efficiency and to allow for a higher level of control over the system. One of the important areas within the field of variable speed motor drives is the system's operational boundary. Presently, the operational boundaries of variable speed motor drives are set based on the operational boundaries of single speed motors, i.e. by limiting current and power to rated values. This results in under-utilization of the system, and places the motor at risk of excessive power losses. The constant power loss (CPL) concept is introduced in this dissertation as the correct basis for setting and analyzing the operational boundary of variable speed motor drives. The control and dynamics of the permanent magnet synchronous motor (PMSM) drive operating with CPL are proposed and analyzed. An innovative implementation scheme of the proposed method is developed. It is shown that application of the CPL control system to existing systems results in faster dynamics and higher utilization of the system. The performance of a motor drive with different control strategies is analyzed and compared based on the CPL concept. Such knowledge allows for choosing the control strategy that optimizes a motor drive for a particular application. Derivations for maximum speed, maximum current requirements, maximum torque and other performance indices, are presented based on the CPL concept. High performance drives require linearity in torque control for the full range of operating speed. An analysis of concurrent flux weakening and linear torque control for PMSM is presented, and implementation strategies are developed for this purpose. Implementation strategies that compensate for the variation of machine parameters are also introduced. A new normalization technique is introduced that significantly simplifies the analysis and simulation of a PMSM drive's performance. The concepts presented in this dissertation can be applied to all other types of machines used in high performance applications. Experimental work in support of the key claims of this dissertation is provided. / Ph. D.

In most applications, the structure components have to be tested under different loading conditions before being placed in operation. A reliable and low cost measuring technique is desirable. However, most currently employed measuring approaches can only provide the structural response at several discrete locations. The accuracy of the measurements varies with the location and orientation of the sensors. Practically, it is not possible to place sensors at all the critical locations for different excitations. Therefore, an approach that derives the full field response using a limited set of measured data is desirable. In contrast to experimental full field measurement techniques, the expansion approach involves analytically expanding the limited measurements to all the degrees of freedom of the structure. Among all the analytical methods, the modal expansion method is computationally efficient and thus more suitable for real time expansion of measured data. In this method, the full-field response is approximated by the linear combination of mode shapes. In previous studies, the modal expansion method is limited by errors from mode aliasing, inaccuracy of the calculated mode shapes and the noise in measurements. In order to overcome these limitations, the modal expansion method is enhanced by mode selection and error compensation in this study. First, the key parameters used in modal expansion method were analyzed using a cantilever beam model and a method for optimal placement of sensors was developed. A mode selection method and error compensation method based on operation modal analysis and adaptive compressed sensing techniques were then developed to reduce the effects of mode aliasing, mode shape inaccuracy and measurement noise. The developed approach was further tested virtually using a numerical model of rotor 67. The numerical model was created using a two-way coupled fluid structure interaction technique. By developing these methods, the enhanced modal expansion approach can provide full field response for structures under different load conditions. Compared to the traditional modal expansion method, it can expand the data with high noise and under general dynamic loading. / Doctor of Philosophy / Accurate knowledge of the strain and stress at critical locations of a given structure is crucial when assessing its integrity. However, currently employed measuring approaches can only provide the structural response at several discrete locations. Practically, it is not possible to place sensors at all the critical locations for different excitations. Therefore, an approach that derives the full field response using a limited set of measured data is desirable. Compared to experimental full field measurement techniques, the expansion approach is focused on analytically expanding the limited measurements to all the degrees of freedom of the structure. Among all the analytical methods, the modal expansion method is computationally efficient and thus more suitable for real-time expansion of measured data. The current modal expansion method is limited by errors from mode aliasing, inaccuracy of the mode shapes, and the noise in measurements. Therefore, an enhanced method is proposed to overcome these shortcomings of the modal expansion. The following objectives are accomplished in this study: 1) Develop a method for optimal placement of sensors for modal expansion; 2) Eliminate the mode aliasing effects by determining the significance of participated modes using operational modal analysis techniques; 3) Compensate for the noise in measurements and computational model by implementing the compressed sensing approach. After accomplishing these goals, the developed approach is able to provide full field response for structures under different load conditions. Compared to the traditional modal expansion method, it can expand the data under dynamic loading; it also shows promise in reducing the effects of noise and errors. The developed approach is numerically tested using fluid-structure interaction model of rotor 67 fan blade.

The low frequency (0-500 Hz) automotive noise and vibration behavior is influenced by the rolling dynamics of the tire. Driven by pressing environmental concerns, the automotive industry has strived to innovate fuel-efficient and quieter powertrain systems over the last decade. This has eventually led to the prevalence of hybrid and electric vehicles. With the noise masking effect of the engine orders being absent, the interior structure-borne noise is dominated by the tire pavement interaction under 500 Hz. This necessitates an accurate estimation of rolling tire dynamics. To this date, there is no direct procedure available for modal analysis of rolling tires with tread patterns under realistic operating conditions. The present start-of-art laser vibrometer based non-contact measurements are limited to tread vibration measurement of smooth tires only in a lab environment. This study focuses on devising an innovative strategy to use a wireless Tire Cavity Accelerometer (TCA) and two optical sensors in a tire on drum setup with cleat excitation to characterize dynamics of tread vibration in an appreciably easier, time and cost-effective approach. In this approach, First, the TCA vibration signal in a single test run is clustered into several groups representing an array of virtual sensor position at different circumferential positions. Then modal identification has been performed using both parametric and non-parametric operational modal identification procedures. Furthermore, relevant conclusions are drawn about the observed modal properties of the tire under rolling including the limitations of the proposed method. The method proposed here, as is, can be applied to a treaded tire and can also be implemented in an on-road test setup. / Master of Science / The low frequency(0-500 Hz) interior noise and vibration of an automobile is primarily influenced by the dynamics of the rolling tire. In recent studies, the laser vibrometer with moving mirrors for measurement of vibration on the tread of a rotating tire has been used. However, these are limited to tires without tread pattern. In this study, an innovative experimental way of performing operational modal analysis using the Tire cavity Accelerometer (TCA) and optical sensors is presented. The proposed method is simpler in terms of instrumentation and cost and time-effective. This method, as is, can also be implemented in case of a treaded tire

The built environment puts the greatest pressure on the natural environment out of all human activities, so it has a fundamental obligation to be environmentally sustainable. Carbon dioxide (CO2) or carbon emissions is a significant greenhouse gas that is inevitably associated with energy use when energy is produced via the combustion of fuels. Total life cycle energy, embodied and operational energy over a building's lifetime, creates significant environmental impacts through the production of CO2. By keeping and reusing existing and historic buildings rather than discarding them and building new, the embodied energy, or the energy that is locked up, can help to mitigate future damage. These buildings already exist, which indicates that the energy consumed to build them has been applied and the carbon associated with their construction has been released. The greenest buildings are ones that are already built. They are inherently more sustainable than any new buildings even with green and zero net energy systems and can be retrofitted to become more energy efficient. To demonstrate this thesis specifically, a design project engages with an abandoned late nineteenth-century bank building in Philadelphia and transforms it into a high-performance building that is prepared for long-term use. For the immediate next use, the project creates a work environment and a new vertical expansion of residential units. The preservation field always confronts the challenge of bridging the gap between embodied energy and operational energy. In the abandoned bank, there are some aspects of this building that are near permanent and define its character, such as brick walls with masonry ornament, two bank vaults, Wissahickon Schist foundation wall, and ceiling trusses. This thesis explores new approaches to leverage the embodied energy of the permanent parts of the abandoned bank and transform it into a high-performance building. A lot of energy of the abandoned bank, the building's material, and thermal mass is still actively performing. The building's envelope, the thick masonry wall, provides a moderately good insulating effect that will temper the indoor air that also preserves its historical character both inside and outside. The embodied energy of the building's envelope is leveraged by pairing it with localized heating and cooling using a radiation and conduction system. Other approaches that increase energy performance in the existing building, include the use of phase-change material for cooling the process water, solar hot water, creating drinking water via a solar still in the skylight, and distilled water from radiant cooling surfaces. In the new construction, a thermal switch facade and double-skin facade for the residential units are proposed, along with providing flexible space with thick mobile interior wall units. / Master of Architecture / Global warming as a problem of the twenty-first-century increase concentrations of greenhouse gases in the atmosphere due to human actions like burning fossil fuels. The built environment puts the greatest pressure on the natural environment of all industrial parts, and it has a fundamental role to manage the environment sustainably. Total life cycle energy, embodied and operational energy over the lifetime of the buildings, creates significant environmental impacts through the production of CO2. Embodied energy is the whole amount of energy applied to extract the raw materials, manufacture, transport, install, and use the product across its life cycle. Assessments of the embodied energy of historic and existing buildings are helping to mitigate future damage to resources. These buildings already exist, which indicates that the energy consumed to build them has been applied and the carbon associated with their construction has been released. The greenest buildings are ones that are already built. They are inherently sustainable and can be retrofitted to become more energy efficient. Specifically, this design engages with an abandoned late nineteenth-century bank building in Philadelphia and transforms it into a high-performance building that is prepared for long-term use. For the immediate next use, the project creates a work environment and in a new vertical expansion, residential units. In the abandoned bank, there are some aspects of this building that are near-permanent and define its characters, such as brick walls with masonry ornament, two bank vaults, Wissahickon Schist wall, and ceiling trusses. This thesis explores the new approaches to leverage the embodied energy of the permanent parts of the abandoned bank and transform it into a high-performance building. This is achieved through various means such as providing localized heating and cooling by using a radiation and conduction system, the use of phase-change material for cooling the process water, solar hot water, creating drinking water via a solar still in the skylight and distilled water from radiant cooling surfaces. In the new construction, a thermal switch facade and double-skin facade for the residential units are proposed, along with providing flexible space with thick mobile interior wall units.

Yes / Multi-Stage Flash (MSF) desalination process has been used for decades for making fresh water from seawater and is the largest sector in desalination industries. In this work, dynamic optimisation of MSF desalination is carried out using powerful and robust dynamic simulation and optimisation software called gPROMS model builder. For a fixed freshwater demand, a number of optimal combinations of the factors such as heat transfer area, brine flow rate, cooling water flow rate, steam flow in brine heater, Top Brine Temperature, the number of stages, etc. are determined with the objective of maximising the performance ratio of the process (defined as the amount of fresh water produced per unit of energy input) considering the seasonal variations. An attempt has been made to develop an operational schedule for a particular day using dynamic optimisation.

Civil infrastructure failure is hard to predict, both in terms of occurrence and impact. This is due to combination of many factors, including highly variable environmental and operational conditions, complex construction and materials, and the sheer size of these structures. Often, the mitigation strategy is visual inspection and regular maintenance, which can be time-consuming and may not address root causes of failure. One potential solution to anticipating infrastructure failure and mitigating its consequences is the use of distributed sensors to monitor the physical state of a structure, an area of research known commonly as structural health monitoring, or SHM. This approach can be applied in a variety of contexts: safety during and after natural disasters, evaluation of building construction quality and life-cycle assessment for performance based design frameworks. In one way or another, SHM methods always require a ``baseline,'' a set of physical features which describes the behavior of a healthy structure. Often, the baseline is defined in terms of modal parameters: natural frequencies, damping ratios, and mode shapes. Although changes in modal parameters are indicative of structural damage, they are also indicative of a slew of non-damage factors, such as signal-to-noise ratio, environmental conditions, and the characteristics of forces exciting the structure. In many cases, the degree of observed modal parameter changes due to non-damage factors can be much greater than that due to damage itself. This is especially true of low-frequency modal parameters. For example, the fundamental frequency of a building is more sensitive to global influences like temperature than local structural changes like a cracked column. It has been proposed that extracting modal parameters at higher frequencies may be the key to improving the damage-sensitivity of SHM methods. However, for now, modal analysis of civil structures has been limited to low frequency ambient excitation and sparse sensor networks, due to practical limitations. Two key components for high-frequency modal analysis have yet to be studied: 1) Sufficient excitation at high frequencies and 2) high-resolution (high sensor density) measurements. The unifying goal of this work is to expand modal analysis in these two areas by applying novel instrumentation and experimental methods to two full-scale buildings, Goodwin Hall and Ernest Cockrell Jr. Hall. This enables realistic, practical insights into the limitations and benefits of the high-frequency SHM approach. Throughout, analyses are supported through the novel integration of uncertainty quantification techniques which so far has been under-utilized in the field. This work is divided into three experimental areas, with approaches centering on the identification of modal parameters. The first area is the application of high spacial resolution sensor networks in combination to ambient vibration testing. The second is the implementation of a robust automation and monitoring strategy for complex dynamic structures. The third is the testing of a novel method for performing experimental modal analysis on buildings emph{in situ}. The combination of results from these experiments emphasizes key challenges in establishing reliable high-frequency, high-resolution modal parameter ``baselines'' for structural health monitoring (SHM) of civil infrastructure. The first study presented in this work involved the identification of modal parameters from a five-story building, Goodwin Hall, using operational modal analysis (OMA) on ambient vibration data. The analysis began with a high spacial density network of 98 accelerometers, later expanding this number to 117. A second extensional study then used this data as reference to implement a novel automation method, enabling the identification of long-term patterns in the building's response behavior. Three dominant sources of ambient excitation were identified for Goodwin Hall: wind, human-induced loading, and consistent low-level vibrations from machinery, etc. It was observed that the amplitude of excitation, regardless of source, had significant effects on the estimated natural frequencies and damping ratios. Namely, increased excitation translated to lower natural frequencies and higher damping. In addition, the sources had different characteristics in terms of excitation direction and bandwidth, which contributed to significantly different results depending on the ambient excitation employed. This has significant implications for ambient-based methods that assume that all ambient vibrations are broadband random noise. The third and final study demonstrated the viability of emph{in situ} seismic testing for controlled excitation of full-scale civil structures, also known as experimental modal analysis (EMA). The study was performed by exciting Ernest Cockrell Jr. Hall in Austin, Texas with both vertical and lateral ground waves from seismic shaker truck, T-Rex. The EMA results were compared to a standard operational modal analysis (OMA) procedure which relies on passive ambient vibrations. The study focused on a frequency bandwidth from 0 to 11 Hz, which was deemed high frequency for such a massive structure. In cases were coherence was good, the confidence comparable or better than OMA, with the added advantage that the EMA tests took only a fraction of the time. The ability to control excitation direction in EMA enabled the identification of new structural information that was not observed OMA. It is proposed that the combination of high spacial resolution instrumentation and emph{in situ} excitation have the potential to achieve reliable high-frequency characterization, which are not only more sensitive to local damage but also, in some cases, less sensitive to variations in the excitation conditions. / Ph. D. / Civil structures, like buildings and bridges, become weaker as they age, increasing their risk of collapse due to sever weather, earthquakes, and heavy traffic. Engineers regularly inspect civil structures to ensure they are in good shape, but it is difficult do a full assessment by eye since many defects can be hidden. Structural Health Monitoring, or SHM for short, is an approach that uses permanent vibration sensors to continuously inspect civil structures. Any activity, like blowing wind or moving traffic on bridge, produces small vibrations which can be analyzed to assess the “health” of the structure. This approach can detect some invisible defects, but there is still debate about whether it can detect them when they are small and early on in the life of a structure. If SHM can’t issue early warnings, then there is little incentive to spend large amounts of money on a sensor system. To capture small defects, a sensor system needs a large number of sensors, hence the term high-resolution in the title. In addition, the structure being tested needs to vibrate rapidly (that is at high-frequencies) in order for the high-resolution information to be useful. So far, there have been no tests of this kind on civil structures, especially buildings. Instead, most sensor systems have contained a relatively low number of sensors tested with low-frequency vibrations. This works fills in this gap by testing two different buildings with SHM sensor systems. The first experiment uses a very high number of sensors to analyze the vibrations of Goodwin Hall on the Virginia Tech campus. The vibrations in this building are produced by wind and people walking inside. The second experiment uses a standard number of sensors, but explores a new method of vibrating buildings. This method uses a truck with a large hydraulic piston to shake the ground near the E. Cockrell Jr. building (University of Austin-Texas), essentially creating a tiny earthquake. The experiments show that both testing techniques provide more useful information than standard ones alone. For the first experiment, using more sensors meant the analysis could better distinguish the structural characteristics of the building. For the second, the artificial “earthquake” enabled the measurement of high-frequency vibrations, something which was not possible by relying on wind or people to vibrate the building. Although these new approaches are not used to inspect for damage, they have laid the foundation for improving the early-warning capabilities of SHM systems. This could mean that buildings and other structures can be repaired sooner, remain in operation longer, and cost the owners less money in the end!

The United Nations currently adopts media policies for peacekeeping missions on an ad-hoc basis, often relying on US or NATO rules. Some have suggested that a standardized media policy for all peacekeeping missions should be the norm. This project examines that proposition with an eye to the tension between the right to know and operational security. Looking at UN intervention in the Gulf War, Somalia, and Rwanda, the problems with such a rigid media policy appear significant. Instead, a dual-level approach is advocated, allowing broad principles of independent coverage as well as in-country negotiations to take place. / Master of Arts

Highly Automated Vehicles (AVs) have the capability to revolutionize the transportation system. These systems have the possibility to make roads safer as AVs do not have limitations that human drivers do, many of which are common causes of vehicle crashes (e.g., distraction or fatigue) often defined generically as human error. The deployment of AVs is likely to be very gradual however, and there will exist situations in which the AV will be driving in close proximity with human drivers across the foreseeable future. Given the persistent crash problem in which the makority of crashes are attributed to driver error, humans will continue to create potential collision scenarios that an AV will be expected to try and avoid or mitigate if developed appropriately. The absence of unreasonable risk in an AVs ability to comprehend and react in these situations is referred to as operational safety. Unlike advanced driver assistance systems (ADAS), highly automated vehicles are required to perform the entirety of the dynamic driving task (DDT) and have a greater responsibility to achieve a high level of operational safety. To address this concern, scenario-based testing has increasingly become a popular option for evaluating AV performance. On a functional level, an AV typically consists of three basic systems: the perception system, the decision and path planning system, and vehicle motion control system. A minimum level of performance is needed in each of these functional blocks to achieve an adequate level of operational safety. The goal of this study was to investigate the effects that perception system performance (i.e., target object state errors) has on vehicle operational safety in collision scenarios similar to that created by human drivers. In the first part of this study, recent annual crash data was used to define a relevant crash population of possible scenarios involving intersections that an AV operating as an urban taxi may encounter. Common crash maneuvers and characteristics were combined to create a set of testing scenarios that represent a high iii percentage of the overall crash population. In the second part of this study, each test scenario was executed using an AV test platform during closed road testing to determine possible real-world perception system performance. This provided a measure of the error in object detection measurements compared to the ideal (i.e., where a vehicle was detected to be compared to where it actually was). In the third part of this study, a set of vehicle simulations were performed to assess the effect of perception system performance on crash outcomes. This analysis simulated hypothetical crashes between an AV and one other collision partner. First an initial worst-case impact configuration was defined and was based on injury outcomes seen in crash data. The AV was then simulated to perform a variety of evasive maneuvers based on an adaptation of a non-impaired driver model. The impact location and orientation of the collision partner was simulated as two states: one based on the object detection of an ideal perception system and the other based on the object detection of the perception system from the AV platform used during the road testing. For simulations in which the two vehicles contacted each other, a planar momentum-impulse model was used for impact modeling and injury outcomes were predicted using an omni-directional injury model taken from recent literature. Results from this study indicate that errors in perception system measurements can change the perceived occupant injury risk within a crash. Sensitivity was found to be dependent on the specific crash type as well as what evasive maneuver is taken. Sensitivities occurred mainly due to changes in the principal direction of force for the crash and the interaction within the injury risk prediction curves. In order to achieve full operational safety, it will likely be important to understand the influence that each functional system (perception, decision, and control) may have on AV performance in these crash scenarios. / Master of Science / Highly Automated Vehicles (AVs) have the capability to revolutionize the transportation system. These systems have the possibility to make roads safer as AVs do not have many of the limitations that human drivers do, many of which are common causes of vehicle crashes (e.g., distraction or fatigue). AVs will be expected to drive alongside human drivers, and so these drivers are likely to continue to be at fault in causing crashes. As part of ensuring safety, AVs will reasonably be expected to try and avoid or help reduce the severity of these crashes. AVs operate using three main systems: the perception system which consists of sensors that see the objects around the AV, the decision and path planning system, which makes decision on what the AV will do, and the vehicle motion control system. Due to the nature of the real-world, these systems may not work exactly as intended which may affect the ability of the AV to react to possible crash scenarios. Because of this, the goal of this study was to investigate the effects that perception system performance (i.e., target object state errors) has on the ability of an AV to react to crash scenarios similar to those created by human drivers. This study first defined crash scenarios using real-world crash data. A real-world perception system was then tested in these scenarios to determine object detection performance. Based on this performance, effects on safety were assessed through vehicle crash simulations. Results from this analysis showed that safety can vary based on both perception system performance and crash scenario. This highlights that it will be important to address system performance in order to achieve high levels of driving safety.