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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Yes / The deodorization of the refined palm oil process is simulated here using ASPEN HYSYS. In the absence of a library molecular distillation (MD) process in ASPEN HYSYS, first, a single flash vessel is considered to represent a falling film MD process which is simulated for a binary system taken from the literature and the model predictions are compared with the published work based on ASPEN PLUS and DISMOL. Second, the developed MD process is extended to simulate the deodorization process. Parameter estimation technique is used to estimate the Antoine’s parameters based on literature data to calculate the pure component vapor pressure. The model predictions are then validated against the patented results of refining edible oil rich in natural carotenes and vitamin E and simulation results were found to be in good agreement, within a 2% error of the patented results. Third, Response Surface Methodology (RSM) is employed to develop non-linear second-order polynomial equations based model for the deodorization process and the effects of various operating parameters on the performance of the process are studied. Finally, an optimization framework is developed to maximize the concentration of beta-carotene, tocopherol and free fatty acid while optimizing the feed flow rate, temperature and pressure subject to process constrains. The optimum results of feed flow rate, temperature, and pressure were determined as 1291 kg/h, 147 C and 0.0007 kPa respectively, and the concentration responses of beta- carotene, tocopherol and free fatty acid were found to be 0.000575, 0.000937 and 0.999840 respectively. / Prince of Songkla University, Songkhla, Thailand for providing financial support (Grant code: PSU2554-022)

Yes / Reverse Osmosis (RO) process can be considered as one of the intensively used pioneering equipment for reusing wastewater of several applications. The recent study presented the development of an accurate model for predicting the dimethylphenol removal from wastewater via RO process. The Response Surface Methodology (RSM) was applied to carry out this challenge based on actual experimental data collected from the literature. The independent variables considered are the inlet pressure (5.83-13.58) atm, inlet temperature (29.5-32) ° C, inlet feed flow rate (2.166-2.583) × 10-4 m3/s, and inlet concentration (0.854-8.049) × 10-3 kmol/m3 and the dimethylphenol removal is considered as the response variable. The analysis of variance showed that the inlet temperature and feed flow rate have a negative influence on dimethylphenol removal from wastewater while the inlet pressure and concentration show a positive influence. In this regard, F-value of 240.38 indicates a considerable contribution of the predicted variables of pressure and concentration against the process dimethylphenol rejection. Also, the predicted R2 value of 0.9772 shows the high accuracy of the model. An overall assessment of simulating the performance of RO process against the operating parameters has been systematically demonstrated using the proposed RSM model.

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.

Doctor of Philosophy / Department of Chemistry / Stefan H. Bossmann / Pancreatic cancer is the fourth leading cause of cancer-related deaths in the USA. Asymptomatic early cancer stages and late diagnosis leads to very low survival rates of pancreatic cancers, compared to other cancers. Treatment options for advanced pancreatic cancer are limited to chemotherapy and/or radiation therapy, as surgical removal of the cancerous tissue becomes impossible at later stages. Therefore, there's a critical need for innovative and improved chemotherapeutic treatment of (late) pancreatic cancers. It is mandatory for successful treatment strategies to overcome the drug resistance associated with pancreatic cancers. Nanotechnology based drug formulations have been providing promising alternatives in cancer treatment due to their selective targeting and accumulation in tumor vasculature, which can be used for efficient delivery of chemotherapeutic agents to tumors and metastases. The research of my thesis is following the principle approach to high therapeutic efficacy that has been first described by Dr. Helmut Ringsdorf in 1975. However, I have extended the use of the Ringsdorf model from polymeric to nanoparticle-based drug carriers by exploring an iron / iron oxide nanoparticle based drug delivery system. A series of drug delivery systems have been synthesized by varying the total numbers and the ratio of the tumor homing peptide sequence CGKRK and the chemotherapeutic drug doxorubicin at the surfaces of Fe/Fe₃O₄-nanoparticles. The cytotoxicity of these nanoformulations was tested against murine pancreatic cancer cell lines (Pan02) to assess their therapeutic capabilities for effective treatments of pancreatic cancers. Healthy mouse fibroblast cells (STO) were also tested for comparison, because an effective chemotherapeutic drug has to be selective towards cancer cells. Optimal Experimental Design methodology was applied to identify the nanoformulation with the highest therapeutic activity. A statistical analysis method known as response surface methodology was carried out to evaluate the in-vitro cytotoxicity data, and to determine whether the chosen experimental parameters truly express the optimized conditions of the nanoparticle based drug delivery system. The overall goal was to optimize the therapeutic efficacy in nanoparticle-based pancreatic cancer treatment. Based on the statistical data, the most effective iron/iron oxide nanoparticle-based drug delivery system has been identified. Its Fe/Fe₃O₄ core has a diameter of 20 nm. The surface of this nanoparticle is loaded with the homing sequence CGKRK (139-142 peptide molecules per nanoparticle surface) and the chemotherapeutic agent doxorubicin (156-159 molecules per surface), This nanoplatform is a promising candidate for the nanoparticle-based chemotherapy of pancreatic cancer.