This dissertation is focused on the quantitative metrics of performance, cost, and reliability for future launch vehicles. Methods are developed that hold performance constant for a required mission and payload so that cost and reliability can be traded. Reliability strategies such as reducing the number of engines, increasing the thrust-to-weight ratio, and adding redundant subsystems all increase launch vehicle reliability. However, there are few references that illustrate the cost of increasing launch vehicle reliability in a disciplined, integrated approach. For launch vehicle design, integrated performance, cost, and reliability disciplines are required to show the sensitivity of cost to different reliability strategies. A methodology is presented that demonstrates how to create the necessary launch vehicle reliability models and integrate them with the performance and cost disciplines. An integrated environment is developed for conceptual design that can rapidly assess thousands of launch vehicle configurations. The design process begins with a feasible launch vehicle configuration and its mission objectives. The performance disciplines, such as trajectory analysis, propulsion, and mass estimation are modeled to include the effects of using different reliability strategies. Reliability models are created based upon the launch vehicle configuration. Engine reliability receives additional attention because engines are historically one of the leading causes of launch vehicle failure. Additionally, the reliability of the propulsion subsystem changes dynamically when a launch vehicle design includes engine out capability. Cost estimating techniques which use parametric models are employed to capture the dependencies on system cost of increasing launch vehicle reliability. Uncertainty analysis is included within the cost and reliability disciplines because of the limited historical database for launch vehicles. Optimization is applied within the integrated design environment to find the best launch vehicle configuration based upon a particular weighting of cost and reliability. The results show that both the Saturn V and future launch vehicles could be optimized to be significantly cheaper, be more reliable, or have a compromise solution by illustrating how cost and reliability are coupled with vehicle configuration changes.

Breeding objectives in the turkey industry are heavily weighted towards improving growth traits. This thesis focused on methods to efficiently select for other important production traits such as reproduction, feed efficiency, and meat yield. Based on bivariate and random regression modeling it was determined that egg production, fertility, and hatchability were influenced by genotype by environment interactions and, as a result, the regulation of reproductive traits is by some unique genes in the summer and winter. This may be due to changes in day length and temperature. Feed efficiency is another important consideration in a breeding objective and feed conversion ratio and residual feed intake were both moderately heritable. Residual feed intake was also more independent of production traits than feed conversion. Feed intake, body weight, and weight gain were moderately heritable and progress can be made in feed efficiency by appropriately weighting these traits in an index. Infrared measures of surface temperature were then investigated to determine if they can be used to select for feed efficiency. Temperatures of the distal metatarsus, eye, neck, and head did not show a strong relationship to feed efficiency and therefore offer limited advantages to a breeding program. Selection for breast meat yield (BMY) is important and it was determined that breast muscle depth, measured with ultrasound technology, is heritable and highly correlated to the carcass trait. As a result, ultrasound traits can compliment conformation scoring and sibling testing in a breeding program to increase the accuracy of selection for BMY and increase response to selection. A deterministic model was also developed and could be used to determine optimum slaughter weight. This would optimise profits in an integrated system, enabling the industry to account for and capitalize on genetic gains. Overall, the population parameters and selection criteria identified for reproduction, efficiency, and meat yield traits identified in the present thesis could be used to increase selection efficiency in turkey breeding programs. Further, the developed production model can be used by the industry to slaughter turkeys at a time that maximizes profits, based on performance levels.

No description available.

A strategy for beneficiation of wastewater streams from fissure and process water developed for a gold mine operation in the west of Johannesburg was tested for viability in a pilot study. The investigation was aimed at evaluating the compliance of the finally discharged effluent streams with the current Water Use Licence (WUL). The core of the water recovery process consisted of softening to remove divalent cationic species, followed by ion exchange processes employing Strong Acid Cationic (SAC) resins and Weak Acid Cationic (WAC) resins. An operational design limitation was that the crystalactor used in the softening stage had a minimum capacity of 20 000 L/h, whereas the rest of the system could be operated at flow rates of as low as 2 000 L/h. For this reason, the softening step was done in semi-batch mode. Calcium hardness was decreased from 70 mg/L to values lower than 40 mg/L (as Ca2+). During the ion exchange (water recovery) process, columns using SAC resin produced better quality water than the WAC resins. The SAC columns produced water compatible with South African Water Quality Standards. Additionally, the use of SAC proved to be a more financially favourable option, since the regenerant stream contained high concentrations of calcium nitrate, magnesium nitrate and sodium nitrate fertilizer. The latter could be sold as a liquid fertilizer to farmers. In addition to the above findings, the pilot system reduced the concentration of toxic and radiotoxic metals such as uranium. The final concentration of the uranium in the effluent (0.01 mg/L) was below the regulation limit 0.07 mg/L. The selective removal of uranium is crucial in order to produce high-quality fertilizer from the ion exchange regeneration streams. Copyright / Dissertation (MEng)--University of Pretoria, 2013. / Chemical Engineering / unrestricted

In this research, a focus is on the development of simplified models to represent the behavior of electric machinery within the time-domain models of power systems. Toward this goal, a generator model is considered in which the states include the machine’s active and reactive power. In the case of the induction machine, rotor slip is utilized as a state and the steady-state equivalent circuit of the machine is used to calculate active and reactive power. The power network model is then configured to accept the generator and induction machine active and reactive power as inputs and provide machine terminal voltage amplitude and angle as outputs. The potential offered by these models is that the number of dynamic states is greatly reduced compared to traditional machine models. This can lead to increased simulation speed, which has potential benefits in model-based control. A potential disadvantage is that the relationship between the reactive power and terminal voltage requires the solution of nonlinear equations, which can lead to challenges when attempting to predict system dynamics in real-time optimal control. In addition, the accuracy of the generator model is greatly reduced with variations in rotor speed. Evaluation of the models is performed by comparing their predictions to those of traditional machine models in which stator dynamics are included and neglected.

Diploma thesis is focused on information system assessment and subsequent proposal for its modification. These changes are partly concerned on company requests, where system implemented is, but on the other hand also of development of processes in the time horizon. These proposals of changes and optimization will be afterwards presented and recommended for realization.

Indiana University-Purdue University Indianapolis (IUPUI) / The following thesis looks into modeling a chilled water system equipped with variable speed drives on different piece of equipment and optimization of system setpoints to achieve energy savings. The research was done by collecting data from a case-study and developing a system of component models that could be linked to simulate the overall system operation.

Multi-objective optimization tools using genetic algorithms (GAs) are being increasingly used for improving building performances and sustainability. However, few research studies focus on district-scale solutions. In the present project, a multi-objective optimization method using genetic algorithms was applied in order to help decision makers find the optimal energy mix of a district energy system in the preliminary design phase.   A case study consisting of the new campus Albano in Stockholm (comprising lecture buildings and student residences) was used for the analysis. A wide range of energy systems was included as a design variable: wind turbines, solar thermal collectors and photovoltaic cells, ground-source heat pumps, biomass boilers, combined cooling, heating and power, district heating and district cooling. The energy provided by the chosen technologies and the district energy balances are simulated on an annual basis using a steady-state method with an hourly resolution.   Three objectives functions were to be minimized: (1) the life-cycle costs; (2) the greenhouse gas emissions; and (3) the annual non-renewable primary energy consumption of the district. The optimization process was implemented on MOBO, a multi-objective optimization tool based on genetic algorithms.   The findings include understanding the trade-offs among the three objectives and a selection of alternatives of energy supply systems to be further investigated in the detailed design phase.

This thesis provides a discrete-event simulation framework that can be used to analyze  and  dimension  computing  systems.  The  simulation  framework  can define  and  parametrize  the  flexible  queueing  system.  We  use  the  simulation framework to explore the data collected from the real-world system. We analyze the metrics, including waiting time and server utilization of single-server and multi-server  queueing  systems.  In  particular,  we  study  the  impact  of  the number of servers on waiting time and server utilization. The experiments show it  is  possible  to  increase  server  utilization  and  decrease  the  server  number without  significantly  increasing  waiting  time,  and  flexible  architectures  canlead to significant gains. / Detta  examensarbete  tillhandahåller  ett  ramverk  som  kan  användas  för  att analysera och dimensionera dator-system. Simuleringsramverket kan definera och parameterisera ett flexibelt kösystem baserat på data från ett system i drift. Vi använder simuleringsramverket för att undersöka datat insamlat från skarpa system.  Vi  analyserar  prestandatal,  såsom  väntetid  och  utnyttjandegrad  för system  med  en  och  flera  betjänare.  Framför  allt  undersöker  vi  hur  antalet betjänare  påverkar  väntetid  och  utnyttjandegrad.   Försöken  visar  att  det  är möjligt  att  öka  uttnyttjandegraden  och  minska  antalet  betjänare  utan  att märkbart öka väntetiden, och att en flexibel arkitektur kan leda till märkbaraförbättringar. / <p>Industrial Advisors: Olga Grinchtein and Johan Karlsson </p>

<p dir="ltr">The energy consumption in the building sector is responsible for over 36% of the total energy consumption across the globe. Of all the energy-consumer devices within a building, heating, ventilation, and air conditioning (HVAC) systems account for over 50% of the total energy consumed. This makes HVAC systems a source of preventable and unexplored energy waste that can be tackled by incorporating intelligent operations. Since its inception, model predictive control (MPC) has been one of the prospective solutions for HVAC management systems to reduce both costs and energy usage. Additionally, MPC is becoming increasingly practical as the processing capacity of building automation systems increases and a large quantity of monitored building data becomes available. MPC also provides the potential to improve the energy efficiency of HVAC systems via its capacity to consider limitations, to predict disruptions, and to factor in multiple competing goals such as interior thermal comfort and building energy consumption. In this regard, the opening chapter delves into the evolving landscape of the HVAC industry. It explores how rapid advancements in technology, growing concerns about climate change, and the ever-present need for energy efficiency are driving innovation. The chapter highlights the shift from static to dynamic HVAC systems, where buildings become sensor-rich networks enabling advanced control strategies like Model Predictive Control (MPC) and Fault Detection and Diagnosis (FDD). we first provide a comprehensive review of the literature concerning the application of MPC in HVAC systems. Detailed discussions of modeling approaches and optimization algorithms are included. Numerous design aspects such as prediction horizon, time step, and cost function, that impact MPC performance are discussed in detail. The technical characteristics, advantages, and disadvantages of various types of modeling software are discussed. Next, a thorough, real-world case study for the design and implementation of a generalized data-collection and control architecture for HVAC systems in an educational building is proposed. The proposed MPC method adds a supervisory control layer on top of the current BMS by delivering temperature setpoints to the legacy controller. This means that the technique may be used to a variety of current HVAC systems in different commercial buildings. In addition, the utilization of remote web services to host the cloud-based architecture significantly minimizes the amount of technical expertise generally necessary to create such systems. In addition, we provide significant lessons learned from the installation process and we list indicative prices, therefore minimizing uncertainty for other researchers and promoting the use of comparable solutions. Chapter two focuses on Fault Detection and Diagnosis (FDD), a critical component of maintaining optimal HVAC performance and minimizing energy waste. HVAC systems are susceptible to malfunctions over time, leading to increased energy consumption and higher maintenance costs. FDD techniques play a vital role in identifying and diagnosing these faults early on, allowing for timely repairs and preventing further deterioration. This chapter introduces a novel bi-level machine learning framework for diagnosing faults in air handling units. This framework addresses key challenges associated with FDD. A bi-level machine learning framework is developed for diagnosing faults in air handling units (AHUs) and rooftop units (RTUs) based on principal component analysis (PCA), time series anomaly detection, and random forest (RF). By proposing this framework, we address three persistent challenges in this field: (I) minimizing false positives; (II) accounting for data imbalance; and (III) normal condition monitoring of equipment. It is shown that PCA can reduce the dataset dimension with one principal component accounting for 95% of data variance. Also, the random forest could classify the faults with 89% precision for single-zone AHU, 85% precision for RTU, and 79% for multi-zone AHU. Chapter three tackles the practical implementation of Model Predictive Control (MPC) in a real-world commercial building setting. It details the development, implementation, and cost analysis of a universally applicable cloud-based MPC framework for HVAC control systems. This chapter offers valuable insights into the feasibility and effectiveness of MPC in achieving energy efficiency goals while maintaining occupant comfort. The chapter delves into the hardware and software components used for data acquisition and MPC implementation. It emphasizes the use of cloud-based microservices to ensure seamless integration with existing building management systems, promoting wider adoption of this advanced control strategy. Three innovative control strategies are presented and evaluated in this chapter. The chapter presents compelling evidence for the effectiveness of these strategies, showcasing significant energy savings of up to 19.21%. Chapter four focuses on Occupancy-based Demand Controlled Ventilation (DCV) as a means to optimize indoor air quality (IAQ) while minimizing energy consumption. This chapter highlights the growing importance of IAQ in the wake of the COVID-19 pandemic and its impact on occupant health and well-being. Current ventilation standards often rely on static occupancy assumptions, which can lead to over-ventilation during unoccupied pe riods and wasted energy. This chapter proposes a dynamic occupant behavior model using machine learning algorithms to predict CO2 concentrations within buildings. The chapter investigates the performance of various machine learning algorithms, ultimately identify ing a Multilayer Perceptron (MLP) as the most effective in predicting CO2 levels under dynamic occupancy conditions. This model allows for real-time modulation of ventilation rates, ensuring adequate IAQ while minimizing energy consumption. The concluding chapter presents experimental findings on the effectiveness of adaptive Variable Frequency Drive (VFD) control strategies in optimizing HVAC energy consump tion. Variable Frequency Drives allow for adjusting the speed of electric motors, including those powering HVAC fans. This chapter explores the potential of using real-time occu pancy predictions to optimize VFD operation. The proposed control strategy demonstrates impressive energy savings, achieving a 51.4% reduction in HVAC fan energy consumption while adhering to ASHRAE IAQ standards. This chapter paves the way for occupant-centric ventilation strategies that prioritize both human health and energy efficiency. These results underscore the potential of predictive control systems to transform building operations to ward greater sustainability and efficiency. The chapter acknowledges the need for further validation through extended monitoring and analysis. In summary, this thesis contributes significantly to the advancement of smart building technologies by proposing practical frameworks for implementing advanced control strategies in HVAC systems. The findings presented here offer valuable insights for building designers, engineers, facility managers, and policymakers interested in creating sustainable, energy efficient, and occupant-centric buildings. The developed frameworks have the potential to be applied across a wide range of building types and climatic conditions, promoting broader adoption of smart building technologies and contributing to a more sustainable built environment.</p>