The dwindling resources of fossil fuels coupled with the steady increase in energy consumption have spurred research interest in alternative and renewable energy sources. Biodiesel is one of the most promising alternatives for fossil fuels. It can be made from various renewable sources, including recycled oil, and can be utilized in lieu of petroleum-based diesel. To foster market competitiveness for biodiesel, it is necessary to develop cost-effective and technically sound processing schemes, to identify related key design criteria, and optimize performance. The overall goal of this work was to design and optimize biodiesel (Fatty Acid Methyl Ester “FAME”) production from vegetable oil. To achieve this goal, several interconnected research activities were undertaken. First, a base-case flow sheet was developed for the process. The performance of this flow sheet along with the key design and operating criteria were identified by conducting computer-aided simulation using ASPEN Plus. Various scenarios were simulated to provide sufficient understanding and insights. Also, different thermodynamic databases were used for different sections of the process to account for the various characteristics of the streams throughout the process. Next, mass and energy integration studies were performed to reduce the consumption of material and energy utilities, improve environmental impact, and enhance profitability. Finally, capital cost estimation was carried out using the ICARUS Process Evaluator computer-aided tools linked to the results of the ASPEN simulation. The operating cost of the process was estimated using the key information on process operation such as raw materials, utilities, and labor. A profitability analysis was carried out by examining the ROI (Return of Investment) and PP (Payback Period). It was determined that the single most important economic factor is the cost of soybean oil, which accounted for more than 90% of the total annualized cost. Consequently, a sensitivity analysis was performed to examine the effect of soybean oil cost on profitability. It was determined that both ROI and PP quickly deteriorate as the cost of soybean oil increases.

In this research, we designed and implemented optimization search algorithms to facilitate implementation of optimization search software. We provided the design of module interaction graph including modules, ports, and channels. We can map solving algorithms of sub-problems onto behavioral designs incorresponding modules. Finally, they can integrate module¡¦s with channels. Since optimization search algorithms may evolve one to several solutions at the same time, we planned a solution set organization to support designer-planned search strategy. During the optimization process, solutions or sub-solutions should be evaluated and analyzed. Because excessive executive time as commonly spent in replicated evaluation, we planned dynamic programming for reusing evaluation results to reduce replicated evaluation time. Lastly, when evolving new solutions, usually only a small number of decisions are changed. We planed a hierarchical decision representation and maintenance operations to reduce replication of common parts among solutions to further enhance its execution speed.

Distributed electric propulsion is a promising technology currently being considered for gen- eral aviation-class aircraft that has the potential to increase range and performance without sacrificing low-speed flight characteristics. However, the high-aspect ratio wings enabled by distributed electric propulsion make these designs more susceptible to adverse aeroe- lastic phenomena. This thesis describes the development of a gradient-based optimization framework for aircraft with distributed electric propulsion using structural and aeroelastic constraints. The governing equations for the coupled aeroelastic system form the basis of the static aeroelastic and flutter analysis. In this work, the Doublet-Lattice method is used to evaluate the aerodynamic forces exerted on the wing surface. In order to consider the impact of propeller-induced flow on aerodynamic loading, a one-way propeller-wing coupling is com- puted by superposition of the propeller induced velocity profile calculated using actuator disk theory and the wing flow field. The structural finite-element analysis is performed using the Toolkit for the Analysis of Composite Structures (TACS). The infinite-plate spline method is used to perform load and displacement transfer between the aerodynamic surface and the structural model. Instead of utilizing a conventional flutter analysis, the Jacobi-Davidson method is used to solve the governing eigenvalue problem without a reduction to the lowest structural modes, facilitating the evaluation of the gradient for design optimization. This framework is applied to different configurations with distributed electric propulsion to minimize structural weight subject to structural and aeroelastic constraints. The effect of flutter constraints, wing aspect ratio, and electric propeller quantity are compared through a series of design optimization studies. The results show that larger aspect ratio wings and more electric motors lead to heavier wings that are more susceptible to flutter. This framework can be used to develop lighter aircraft with distributed electric propulsion configuration that satisfy strength and flutter requirements.

Παρουσιάζεται το πρόβλημα της ελαχιστοποίησης μιας συνεχώς διαφορίσιμης συνάρτησης μεταβλητών, χωρίς περιορισμούς και με μη γραμμικούς περιορισμούς. Προτείνονται νέες μέθοδοι επίλυσης: 1) Ένας αλγόριθμος για την ελαχιστοποίηση συναρτήσεων χωρίς περιορισμούς, που στηρίζεται σε μια τροποποιημένη μονοδιάστατη μέθοδο διχοτόμησης. Η μέθοδος δεν απαιτεί υπολογισμό ή εκτίμηση Εσσιανής και εφαρμόζεται και σε προβλήματα, όπου οι τιμές της αντικειμενικής συνάρτησης και των παραγώγων της δεν είναι γνωστές με απόλυτη ακρίβεια. 2) Δύο νέοι κωνικοί αλγόριθμοι για την χωρίς περιορισμούς ελαχιστοποίηση. Αυτοί οι αλγόριθμοι είναι βασισμένοι σε μια κωνική πρότυπη συνάρτηση, η μορφή της οποίας δεν περιλαμβάνει την Εσσιανή της. Στον πρώτο αλγόριθμο, η κωνική μέθοδος συνδυάζεται με ένα μη μονότονο line search τύπου Newton και το βήμα αναζήτησης των Barzilai και Borwein. Στον δεύτερο, χρησιμοποιείται για το line search η μέθοδος ελάττωσης διάστασης DROPT. Και στις δύο περιπτώσεις η κλασική κωνική μέθοδος επιταχύνεται σημαντικά. 3) Μια οικογένεια μεθόδων ελαχιστοποίησης με περιορισμούς, οι οποίες χρησιμοποιούν αντί για ένα διάνυσμα ανίχνευσης, μια καμπύλη ανίχνευσης και συγκεκριμένα μια γεωδαισιακή καμπύλη της επιφάνειας των περιορισμών. / In this work we present the problem of minimizing a continuously differentiable function of n variables under no constraints, as well as under nonlinear constraints. New methods for the solution of this problem are proposed. Namely 1) An algorithm for the unconstrained minimization of nonlinear functions, based on a modified one-dimentional bisection method. This method does not require an estimatoin of the Hessian and can be used for problems, where the objective function values as well as the gradient values are not known exactly. 2) Two new conic algorithms for unconstrained optimization. These algorithms are based on a conic model function, whose form does not include its Hessian. In the first algorithm, the conic method is combined with a non-monotone Newton type line search and the step of Barzilai and Borwein. In the second, we use a dimention reducing method for the line search. In both cases the clasic conic method is accelerated significantly. 3) A class of constrained optimization methods that use, instead of a search vector, the g4eodesic curve of the surface of the constraints.

No description available.

No description available.

Influenza H5N1 poses a significant threat to global health in both agricultural world and general populations due to the highly virulent nature of this virus. The potential of mutations or recombination’s of this virus with other Influenza strains could lead to the generation of pandemic strain of the virus capable of bridging the infectivity divide between avian species to human. Currently, for all Influenza infections, vaccination remains the primary strategy for prevention and control. Many vaccine strategies have been developed in an attempt to combat the threat from avian flu. Codon optimization has been used to improve vaccine efficacy for many vaccines, which rely on in vivo expression of a protein antigen. In this study, two types of vaccine platforms were used to evaluate the differential effect of codon optimization of the hemagglutinin (HA) gene of the highly pathogenic avian influenza H5N1 virus, A/Indonesia/5/05 (Ind05) on the level of protection and immune response provided against lethal challenge. Taking advantage of the degenerate nature of codon usage, a codon optimized gene was synthesized to enhance the use of codons represented by the most abundant tRNAs. The codon optimized HA gene produces a protein, which remains identical to the wild type protein regardless of codon changes but is theoretically expressed at higher level based on codon usage. The synthesized genes were cloned into a DNA plasmid based expression vector as well as a replication competent VSV viral vector. In vitro expression studies, using both a HA-expression plasmid and a recombinant VSV HA-virus, compared expression between optimized- and wild type-HA constructs. Vaccination with both optimized HA and wild type HA DNA vaccine platforms and recombinant VSV HA viruses, followed by lethal challenge with Ind05, was then used to determine the relative efficacy of each vaccine and subsequent immune response in a mouse model.

Coin-flipping is the cryptographic task of generating a random coin-flip between two mistrustful parties. Kitaev discovered that the security of quantum coin-flipping protocols can be analyzed using semidefinite programming. This lead to his result that one party can force a desired coin-flip outcome with probability at least 1/???2. We give sufficient background in quantum computing and semidefinite programming to understand Kitaev's semidefinite programming formulation for coin-flipping cheating strategies. These ideas are specialized to a specific class of protocols singled out by Nayak and Shor. We also use semidefinite programming to solve for the maximum cheating probability of a particular protocol which has the best known security. Furthermore, we present a family of protocols where one party has a greater probability of forcing an outcome of 0 than an outcome of 1. We also discuss a computer search to find specific protocols which minimize the maximum cheating probability.

Many mathematical models exist for the simultaneous optimization of transportation and inventory functions. A simultaneous model, while giving the lowest total cost, may not be easily implemented in a firm with decentralized transportation and inventory departments. As such, this thesis studies sequential models, where the primary department is artificially given the authority to make some set of decisions prior to the decisions made by the secondary department. Some known formulations for simultaneous models are studied in an attempt to create a sequential process for the same environment. Finally, a generalized sequential approach is developed that can be applied to any transportation and inventory model with separable costs. The generalized approach allows for the full optimization of the primary departmental costs, and then sequentially allows the optimization of the secondary departmental costs subject to a maximum allowable increase in the costs of the primary department. The analysis of this sequential approach notably reveals that when the relative deviation from the optimal cost of each department is equal, a reasonable solution with respect to total cost is attained. This balance in relative deviation is defined as the fairness point solution. Differing cost scenarios are thus tested to determine the relationship between the cost ratio among departments and the performance of the fairness point solution. The fairness point solution provides an average deviation of total cost from the total optimal cost of less than 1% in four of the seven scenarios tested. Other sequential approaches are discussed and fairness with respect to these new approaches is considered.

Influenza H5N1 poses a significant threat to global health in both agricultural world and general populations due to the highly virulent nature of this virus. The potential of mutations or recombination’s of this virus with other Influenza strains could lead to the generation of pandemic strain of the virus capable of bridging the infectivity divide between avian species to human. Currently, for all Influenza infections, vaccination remains the primary strategy for prevention and control. Many vaccine strategies have been developed in an attempt to combat the threat from avian flu. Codon optimization has been used to improve vaccine efficacy for many vaccines, which rely on in vivo expression of a protein antigen. In this study, two types of vaccine platforms were used to evaluate the differential effect of codon optimization of the hemagglutinin (HA) gene of the highly pathogenic avian influenza H5N1 virus, A/Indonesia/5/05 (Ind05) on the level of protection and immune response provided against lethal challenge. Taking advantage of the degenerate nature of codon usage, a codon optimized gene was synthesized to enhance the use of codons represented by the most abundant tRNAs. The codon optimized HA gene produces a protein, which remains identical to the wild type protein regardless of codon changes but is theoretically expressed at higher level based on codon usage. The synthesized genes were cloned into a DNA plasmid based expression vector as well as a replication competent VSV viral vector. In vitro expression studies, using both a HA-expression plasmid and a recombinant VSV HA-virus, compared expression between optimized- and wild type-HA constructs. Vaccination with both optimized HA and wild type HA DNA vaccine platforms and recombinant VSV HA viruses, followed by lethal challenge with Ind05, was then used to determine the relative efficacy of each vaccine and subsequent immune response in a mouse model.