A procedure for incorporating a key non-linear aerodynamic characteristic into the design optimization of a high-speed civil transport has been developed. Previously, the tendency of a high-speed aircraft to become uncontrollable (pitch-up) at high angles-of-attack during landing or takeoff for some wing shapes could not be included directly in the design process. Using response surface methodology, polynomial approximations to the results obtained from a computationally expensive estimation method were developed by analyzing a set of statistically selected wing shapes. These response surface models were then used during the optimization process to approximate the effects of wing planform changes on pitch-up. In addition, response surface approximations were used to model the effect of horizontal tail size and wing flaps on the performance of the aircraft. Optimizations of the high-speed civil transport were completed with and without the response surfaces. The results of this study provide insight into the influence of nonlinear and more detailed aerodynamics on the design of a high-speed civil transport. / Master of Science

A novel reliability evaluation method for large nonlinear engineering systems excited by dynamic loading applied in time domain is presented. For this class of problems, the performance functions are expected to be function of time and implicit in nature. Available first-or second-order reliability method (FORM/SORM) will be challenging to estimate reliability of such systems. Because of its inefficiency, the classical Monte Carlo simulation (MCS) method also cannot be used for large nonlinear dynamic systems. In the proposed approach, only tens instead of hundreds or thousands of deterministic evaluations at intelligently selected points are used to extract the reliability information. A hybrid approach, consisting of the stochastic finite element method (SFEM) developed by the author and his research team using FORM, response surface method (RSM), an interpolation scheme, and advanced factorial schemes, is proposed. The method is clarified with the help of several numerical examples. (C) 2016 Faculty of Engineering, Ain Shams University. Production and hosting by Elsevier B.V.

Due to the eminent shortage of 3He, there exists a significant need to develop a new (or optimize an existing) neutron detection system which would reduce the dependency on the current 3He-based detectors for Domestic Nuclear Detection Office (DNDO) applications. The purpose of this research is to develop a novel methodology for optimizing candidate neutron detector designs using multivariate statistical analysis of Monte Carlo radiation transport code (MCNPX) models. The developed methodology allows the simultaneous optimization of multiple detector parameters with respect to multiple response parameters which measure the overall performance of a candidate neutron detector. This is achieved by applying three statistical strategies in a sequential manner (namely factorial design experiments, response surface methodology, and constrained multivariate optimization) to results generated from MCNPX calculations. Additionally, for organic scintillators, a methodology incorporating the light yield non-proportionality is developed for inclusion into the simulated pulse height spectra (PHS). A Matlab® program was developed to post-process the MCNPX standard and PTRAC output files to automate the process of generating the PHS thus allowing the inclusion of nonlinear light yield equations (Birks equations) into the simulation of the PHS for organic scintillators. The functionality of the developed methodology is demonstrated on the successful multivariate optimization of three neutron detection systems which utilize varied approaches to satisfying the DNDO criteria for an acceptable alternative neutron detector. The first neutron detection system optimized is a 3He-based radiation portal monitor (RPM) based on a generalized version of a currently deployed system. The second system optimized is a 6Li-loaded polymer composite scintillator in the form of a thin film. The final system optimized is a 10B-based plastic scintillator sandwiched between two standard plastic scintillators. Results from the multivariate optimization analysis include not only the identification of which factors significantly affect detector performance, but also the determination of optimum levels for those factors with simultaneous consideration of multiple detector performance responses. Based on the demonstrated functionality of the developed multivariate optimization methodology, application of the methodology in the development process of new candidate neutron detector designs is warranted.

I examine the market reaction to the announcement of FFO by REITs using abnormal trading volume as a gauge of investors’ reaction. I also address the question of whether FFO provides more useful information to investors than net income. Lastly, I examine whether the quality of private information among traders prior to the announcement of FFO affects the level of abnormal trading volume. Using three different specifications, I find that even though the announcement of FFO leads to abnormal trading, there is no association between the level of abnormal trading volume and the size of the surprise contained in the FFO announcement. I also find, using abnormal returns as a measure of investor response, that FFO explains significantly more variance in abnormal returns than net income suggesting that FFO provides more useful information than net income. Lastly, I use the proportion of institutional holdings as a proxy for the number of informed traders to predict the amount of abnormal trading volume. I find no significant relation between abnormal trading volume and the proportion of institutional holdings. However, when I break down institutional ownership into two broad classifications, I find that the level of abnormal trading volume is significantly positively related to the holdings by mutual funds and investment advisors but negatively related to the holdings of other institutions (pension funds &.endowments, banks and insurance companies). This raises questions of whether the use of an aggregate measure of institutional ownership is appropriate in studies that examine the effect of institutional holdings.

The growing effect of combustion pollutant emission on the environment and increasing petroleum prices are driving development of design methodologies for clean and efficient industrial combustion technologies. The design optimization methodology employs numerical algorithms to find the optimal solution of a design problem by converting it into a multivariate minimization problem. This is done by defining a vector of design parameters that specifies the design configuration, and an objective function that quantifies the performance of the design, usually so the optimal design outcome minimizes the objective function. A numerical algorithm is then employed to find the design parameters that minimize the objective function; these parameters thus specify the optimal design. However this technique is used in several other fields of research, its application to industrial combustion is fairly new. In the present study, a statistical optimization method called response surface methodology is connected to a CFD solver to find the highest combustion efficiency by changing the inlet air swirl number and burner quarl angle in a furnace. OpenFOAM is used to model the steady-state combustion of natural gas in the 300 KW BERL combustor. The main barrier to applying optimization in the design of industrial combustion equipment is the substantial computational effort needed to carry out the CFD simulation every time the objective function needs to be evaluated. This is intensified by the stiffness of the coupled governing partial differential equations, which can cause instability and divergent simulations. The present study addresses both of these issues by initializing the flow field for each objective function evaluation with the numerical results of the previously converged point. This modification dramatically reduced computation time. The combustion of diesel spray in the GenTex 50M process heater is investigated in the next part of this thesis. Experimental and numerical studies were carried out for both the cold spray and the diesel combustion where the numerical results satisfactorily predicted the observations. The simulation results show that, when carrying out a parametric design of a liquid fuel-fired combustor it is necessary to consider the effect of design parameters on the spray aerodynamic characteristics and size distribution, the air/spray interactions, and the size of the recirculation zones.

The growing effect of combustion pollutant emission on the environment and increasing petroleum prices are driving development of design methodologies for clean and efficient industrial combustion technologies. The design optimization methodology employs numerical algorithms to find the optimal solution of a design problem by converting it into a multivariate minimization problem. This is done by defining a vector of design parameters that specifies the design configuration, and an objective function that quantifies the performance of the design, usually so the optimal design outcome minimizes the objective function. A numerical algorithm is then employed to find the design parameters that minimize the objective function; these parameters thus specify the optimal design. However this technique is used in several other fields of research, its application to industrial combustion is fairly new. In the present study, a statistical optimization method called response surface methodology is connected to a CFD solver to find the highest combustion efficiency by changing the inlet air swirl number and burner quarl angle in a furnace. OpenFOAM is used to model the steady-state combustion of natural gas in the 300 KW BERL combustor. The main barrier to applying optimization in the design of industrial combustion equipment is the substantial computational effort needed to carry out the CFD simulation every time the objective function needs to be evaluated. This is intensified by the stiffness of the coupled governing partial differential equations, which can cause instability and divergent simulations. The present study addresses both of these issues by initializing the flow field for each objective function evaluation with the numerical results of the previously converged point. This modification dramatically reduced computation time. The combustion of diesel spray in the GenTex 50M process heater is investigated in the next part of this thesis. Experimental and numerical studies were carried out for both the cold spray and the diesel combustion where the numerical results satisfactorily predicted the observations. The simulation results show that, when carrying out a parametric design of a liquid fuel-fired combustor it is necessary to consider the effect of design parameters on the spray aerodynamic characteristics and size distribution, the air/spray interactions, and the size of the recirculation zones.

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The success of any racing car hinges on three key factors: its speed, handling, and reliability. In a highly competitive environment where lap times are extremely tight, even slight variations in components can significantly affect performance and, consequently, lap times. At the heart of a race car's performance lies the upright—a critical component of its suspension system. The upright serves to link the suspension arms to the wheels, effectively transmitting steering and braking forces to the suspension setup. Achieving optimal performance requires finding the right balance between lightweight design and ample stiffness, crucial for maintaining precise steering geometry and overall vehicle dynamics, especially under intense loads. Furthermore, there is a need to explore the system of structural optimization and seamlessly integrate Finite Element (FE) Models into the mathematical optimization process. This thesis explores a technique for parametric structural optimization utilizing finite element analysis and response surfaces to minimize the weight of the upright. Various constraints such as frequency, stress, displacement, and fatigue are taken into consideration during this optimization process. A parametric finite element model of the upright was designed, along with the mathematical formulation of the optimization problem as a nonlinear programming problem, based on the design objectives and suspension geometry. By conducting parameter sensitivity analysis, three design variables were chosen from a pool of five, and response surfaces were constructed to represent the constraints and objective function to be used to solve the optimization problem using Sequential Quadratic Programming (SQP). To streamline the process of parameter sensitivity analysis and response surface development, a Python scripting procedure was employed to automate the finite element job analysis and results extraction. The optimized upright design resulted in overall weight reduction of 25.3% from the maximum weight design of the parameterized upright. / Master of Science / The success of any racing car depends on three key factors: its speed, handling and reliability. In a highly competitive environment where lap times are extremely tight, even slight variations in components can significantly affect performance and consequently, lap times. At the heart of a race car's performance lies the upright—a critical component of its suspension system. The upright serves to link the suspension arms to the wheels, effectively transmitting steering and braking forces to the suspension setup. To achieve the best performance, upright must be as light as possible but it needs to be strong enough to ensure that the car is predictable when turning in a corner or while braking. Additionally, there is a need to explore methods of structural optimization and integrate finite element analysis seamlessly into the optimization process. Finite element analysis (FEA) is the use of part models, simulations, and calculations to predict and understand how an object might behave under certain physical conditions. This thesis examines a technique for optimizing the upright by designing it with numerous adjustable features for testing and then utilizing response surfaces to minimize its weight. Throughout this process, factors such as vibration, stress, deformation, and fatigue are carefully considered. A detailed parametric finite element model of the upright was developed, alongside the formulation of the optimization problem as a nonlinear programming problem, based on the objectives of the design and the geometry of the suspension. Through rigorous testing of parameters for optimization potential, design variables are selected for optimization. Response surfaces were then constructed to represent the constraints and objective function necessary to solve the optimization problem using Sequential Quadratic Programming (SQP). To enhance the efficiency of this process, a Python script was created to handle specific tasks within the finite element solver. This automation streamlined the analysis of the finite element model and the extraction of results. Ultimately, the optimized design of the upright yielded a 25.3% reduction in weight compared to its maximum weight configuration.

Many industrial statisticians employ the techniques of Response Surface Methodology (RSM) to study and optimize products and processes. A second-order Taylor series approximation is commonly utilized to model the data; however, parametric models are not always adequate. In these situations, any degree of model misspecification may result in serious bias of the estimated response. Nonparametric methods have been suggested as an alternative as they can capture structure in the data that a misspecified parametric model cannot. Yet nonparametric fits may be highly variable especially in small sample settings which are common in RSM. Therefore, semiparametric regression techniques are proposed for use in the RSM setting. These methods will be applied to an elementary RSM problem as well as the robust parameter design problem. / Ph. D.

The expanding role of electronic nose instrumentation, as a quality-monitoring tool for food-packaging materials, is examined and reviewed. The food industry is interested in determining the applicability of using an electronic nose for odor analysis of retained printing solvent levels in packaging. Three electronic nose systems were optimized for this application and their performance assessed. These include the FOX 3000, the Cyranose 320, and the QMB6. Response surface methodology was used to generate 2nd order models of sensor response as a function of system and experimental parameters for the three electronic nose systems. Forty-seven of 50 sensor models generated were found to be significant at an a-level of 0.05. Optimum settings, that allowed adequate signals to be obtained for the full range of examined retained solvents levels, were selected for the remaining work using these models. Performance analyses of these systems, which use three leading sensor technologies, showed that the conducting polymer sensor technology demonstrated the most discriminatory power. All three technologies proved able to discriminate among different levels of retained solvents. Each complete electronic nose system was also able to discriminate between assorted packaging having either conforming or non-conforming levels of retained solvents. Each system correctly identified 100% of unknown samples. Sensor technology had a greater effect on performance than the number of sensors used. Based on discriminatory power and practical features, the FOX 3000 and the Cyranose 320 were superior. The results indicate that electronic nose instrumentation can be used as a complimentary discriminatory tool in quality control. / Master of Science