The flapping path of a single airfoil and dual airfoils in a biplane configuration is optimized for maximum thrust and/or propulsive efficiency. Unsteady, low speed viscous flows are computed using a Navier-Stokes solver in a parallel computing environment. A gradient based algorithm and Response Surface Methodology (RSM) are employed for optimization. The evaluation of gradient vector components and the design of experiments for RSM, which require unsteady solutions, are also carried out in parallel. Parallel computations are performed using Parallel Virtual Machine (PVM) library. First, a single airfoil undergoing a combined sinusoidal or non-sinusoidal pitching and plunging motion is studied. The non-sinusoidal flapping motion is described using an elliptic curve or Non-Uniform Rational B-Splines (NURBS). It is shown that the thrust generation may significantly be increased in comparison to the sinusoidal flapping motion. For a high thrust, the airfoil stays at high effective angle of attack values during the upstroke and the downstroke, and the effective pitching occurs at minimum and maximum plunge positions. Secondly, the optimization of sinusoidal and non-sinusoidal flapping paths of dual airfoils is considered. Moving and deforming overset grids are used for computations. The deforming overset grids remove the restrictions on the flapping motion, and improve the optimization results obtained earlier. At low flapping frequencies, an airfoil in a biplane configuration produces more thrust than a single airfoil. Yet, at high frequencies the airfoil in biplane configuration produces less thrust at a significantly lower efficiency than the single airfoil.

Due to its complex and multidisciplinary nature, the conceptual design phase of helicopters becomes critical in meeting customer satisfaction. Statistical (probabilistic) design methods can be employed to understand the design better and target a design with lower variability. In this thesis, a conceptual design and helicopter sizing methodology is developed and shown on a helicopter design for Turkey.

Subsurface flow phenomena characterize many important societal issues in energy and the environment. A key feature of these problems is that subsurface properties are uncertain, due to the sparsity of direct observations of the subsurface. The Bayesian formulation of this inverse problem provides a systematic framework for inferring uncertainty in the properties given uncertainties in the data, the forward model, and prior knowledge of the properties. We address the problem: given noisy measurements of the head, the pdf describing the noise, prior information in the form of a pdf of the hydraulic conductivity, and a groundwater flow model relating the head to the hydraulic conductivity, find the posterior probability density function (pdf) of the parameters describing the hydraulic conductivity field. Unfortunately, conventional sampling of this pdf to compute statistical moments is intractable for problems governed by large-scale forward models and high-dimensional parameter spaces. We construct a Gaussian process surrogate of the posterior pdf based on Bayesian interpolation between a set of "training" points. We employ a greedy algorithm to find the training points by solving a sequence of optimization problems where each new training point is placed at the maximizer of the error in the approximation. Scalable Newton optimization methods solve this "optimal" training point problem. We tailor the Gaussian process surrogate to the curvature of the underlying posterior pdf according to the Hessian of the log posterior at a subset of training points, made computationally tractable by a low-rank approximation of the data misfit Hessian. A Gaussian mixture approximation of the posterior is extracted from the Gaussian process surrogate, and used as a proposal in a Markov chain Monte Carlo method for sampling both the surrogate as well as the true posterior. The Gaussian process surrogate is used as a first stage approximation in a two-stage delayed acceptance MCMC method. We provide evidence for the viability of the low-rank approximation of the Hessian through numerical experiments on a large scale atmospheric contaminant transport problem and analysis of an infinite dimensional model problem. We provide similar results for our groundwater problem. We then present results from the proposed MCMC algorithms. / text

A robust simulation-based optimization approach is proposed for truck-shovel systems in surface coal mines to maximize the expected value of revenue obtained from loading customer trains. To this end, a large surface coal mine in North America is considered as case study. A data-driven modeling framework is developed and then applied to automatically generate a highly detailed simulation model of the mine in Arena. The framework comprises a formal information model based on Unified Modeling Language (UML), which is used to input mine structural as well as production information. Petri net-based model generation procedures are applied to automatically generate the simulation model based on the whole set of simulation inputs. Then, factors encountered in material handling operations that may affect the robustness of revenue are then classified into 1) controllable; and 2) uncontrollable categories. While controllable factors are trucks locked to routes, uncontrollable factors are inverses of summation over truck haul, and shovel loading and truck-dumping times for each route. Historical production data of the mine contained in a data warehouse is used to derive probability distributions for the uncontrollable factors. The data warehouse is implemented in Microsoft SQL, and contains snapshots of historical equipment statuses and production outputs taken at regular intervals in each shift of the mine. Response Surface Methodology is applied to derive an expression for the variance of revenue as a function of controllable and uncontrollable factors. More specifically, 1) first order and second order effects for controllable factors, 2) first order effects for uncontrollable factors, and 3) two factor interactions for controllable and uncontrollable factors are considered. Latin Hypercube Sampling method is applied for setting controllable factors and the means of uncontrollable factors. Also, Common Random Numbers method is applied to generate the sequence of pseudo-random numbers for uncontrollable factors in simulation experiments for variance reduction between different design points of the metamodel. The variance of the metamodel is validated using leave-one-out cross validation. It is later applied as an additional constraint to the mathematical formulation to maximize revenue in the simulation model using OptQuest. The decision variables in this formulation are truck locks only. Revenue is a function of the actual quality of coal delivered to each customer and their corresponding quality specifications for premiums and penalties. OptQuest is an optimization add-on for Arena that uses Tabu search and Scatter search algorithms to arrive at the optimal solution. The upper bound on the variance as a constraint is varied to obtain different sets of expected value as well as variance of optimal revenue. After comparison with results using OptQuest with random sampling and without variance expression of metamodel, it has been shown that the proposed approach can be applied to obtain the decision variable set that not only results in a higher expected value but also a narrower confidence interval for optimum revenue. According to the best of our knowledge, there are two major contributions from this research: 1) It is theoretically demonstrated using 2-point and orthonormal k-point response surfaces that Common Random Numbers reduces the error in estimation of variance of metamodel of simulation model. 2) A data-driven modeling and simulation framework has been proposed for automatically generating discrete-event simulation model of large surface coal mines to reduce modeling time, expenditure, as well as human errors associated with manual development.

Lipases are widely researched enzyme group because of their ability to catalyze a wide range of synthesis reactions. The aim of this doctoral thesis was to study and optimize fatty acid ester synthesis using lipases. The influence of enzyme choice, its preparation (immobilization), choice of substrates and reaction conditions on fat hydrolysis, biodiesel and phenethyloctanoate (flavour ester) synthesis reaction course and yield was studied. Enterobacter aerogenes lipase, which was developed in Lithuania, was immobilized and its properties were determined for the first time. Commercial lipases were used for hydrolysis and synthesis reactions, the influence of reaction parameters was determined using response surface methodology, and so the yield was optimized. Silica gel was found to be a promising reaction additive for acyl group migration catalysis in biodiesel synthesis reaction. Different lipase specificities towards different acylglycerol classes and regioisomers varied greatly; on the basis of this knowledge two-step biodiesel synthesis catalysis was proposed. / Lipazių atranka ir taikymas riebalų rūgščių esterių sintezei Lipazės yra intensyviai tiriama fermentų grupė dėl jų gebos katalizuoti platų spektrą sintezės reakcijų. Disertacinio darbo tikslas buvo ištirti ir optimizuoti riebalų rūgščių esterių sintezės, panaudojant lipazes, būdus. Tirta fermento preparato paruošimo (imobilizavimo), fermento bei substratų pasirinkimo bei reakcijos sąlygų ir priedų įtaka riebalų hidrolizės, biodyzelino, fenetiloktanoato (kvapiojo esterio) sintezės reakcijų eigai ir išeigai. Pirmą kartą ištirtos Lietuvoje paruoštos Enterobacter aerogenes lipazės savybės, ji imobilizuota ant gamtinės kilmės nešiklių. Komercinių lipazių katalizuojamų reakcijų sąlygų įtakai nustatyti ir reakcijos išeigai optimizuoti naudota atsako paviršiaus metodologija. Tiriant reakcijos priedų įtaką acilgrupės migracijai ir biodyzelino sintezės reakcijai, nustatyta, kad perspektyvus priedas yra silikagelis. Lipazių specifiškumas ženkliai skiriasi skirtingoms acilglicerolių klasėms ir regioizomerams, šių žinių pagrindu buvo pasiūlyta dviejų etapų biodyzelino sintezės katalizė.

Lipazės yra intensyviai tiriama fermentų grupė dėl jų gebos katalizuoti platų spektrą sintezės reakcijų. Disertacinio darbo tikslas buvo ištirti ir optimizuoti riebalų rūgščių esterių sintezės, panaudojant lipazes, būdus. Tirta fermento preparato paruošimo (imobilizavimo), fermento bei substratų pasirinkimo bei reakcijos sąlygų ir priedų įtaka riebalų hidrolizės, biodyzelino, fenetiloktanoato (kvapiojo esterio) sintezės reakcijų eigai ir išeigai. Pirmą kartą ištirtos Lietuvoje paruoštos Enterobacter aerogenes lipazės savybės, ji imobilizuota ant gamtinės kilmės nešiklių. Komercinių lipazių katalizuojamų reakcijų sąlygų įtakai nustatyti ir reakcijos išeigai optimizuoti naudota atsako paviršiaus metodologija. Tiriant reakcijos priedų įtaką acilgrupės migracijai ir biodyzelino sintezės reakcijai, nustatyta, kad perspektyvus priedas yra silikagelis. Lipazių specifiškumas ženkliai skiriasi skirtingoms acilglicerolių klasėms ir regioizomerams, šių žinių pagrindu buvo pasiūlyta dviejų etapų biodyzelino sintezės katalizė. / Lipases are widely researched enzyme group because of their ability to catalyze a wide range of synthesis reactions. The aim of this doctoral thesis was to study and optimize fatty acid ester synthesis using lipases. The influence of enzyme choice, its preparation (immobilization), choice of substrates and reaction conditions on fat hydrolysis, biodiesel and phenethyloctanoate (flavour ester) synthesis reaction course and yield was studied. Enterobacter aerogenes lipase, which was developed in Lithuania, was immobilized and its properties were determined for the first time. Commercial lipases were used for hydrolysis and synthesis reactions, the influence of reaction parameters was determined using response surface methodology, and so the yield was optimized. Silica gel was found to be a promising reaction additive for acyl group migration catalysis in biodiesel synthesis reaction. Different lipase specificities towards different acylglycerol classes and regioisomers varied greatly; on the basis of this knowledge two-step biodiesel synthesis catalysis was proposed.

There is always a need to study the properties of complex input–output systems, properties that may be very difficult to determine. Two such properties are the output’s sensitivity to changes in the inputs and the output’s uncertainty if the inputs are uncertain. A system can be formulated as a model—a set of functions, equations and conditions that describe the system. We ultimately want to study and learn about the real system, but with a model that approximates the system well, we can study the model instead, which is usually easier. It is often easier to build a model as a set of combined sub-models, but good knowledge of each sub-model does not immediately lead to good knowledge of the entire model. Often, the most attractive approach to model studies is to write the model as computer software and study datasets generated by that software. Methods for sensitivity analysis (SA) and uncertainty analysis (UA) cannot be expected to be exactly the same for all models. In this thesis, we want to determine suitable SA and UA methods for a road traffic emission model, methods that can also be applied to any other model of similar structure. We examine parts of a well-known emission model and suggest a powerful data-generating tool. By studying generated datasets, we can examine properties in the model, suggest SA and UA methods and discuss the properties of these methods. We also present some of the results of applying the methods to the generated datasets. / Det finns alltid behov av att studera egenskaper hos komplexa input-output-system, egenskaper som kan vara mycket svåra att få fram. Två sådana egenskaper är ut fallets känslighet mot förändringar i ingångsvärdena och utfallets osäkerhet om ingångsvärdena har osäkerhet. Ett system kan formuleras som en modell-en mängd funktioner, ekvationer och betingelser som tillsammans liknar systemet. Vi vill egentligen studera och lära oss det verkliga systemet, men med en modell som approximerar det verkliga systemet bra kan man studera modellen istället, vilket i de flesta fall är enklare. Det är oftast enklare att bygga en modell som en mängd kombinerade delmodeller, men bra kunskap om varje delmodell leder inte omedelbart till bra kunskap om hela modellen. Det enklaste tillvägagångssättet för modellstudier är oftast att studera datamängder som genererats av modellen genom ett datorprogram. Metoder för känslighetsanalys (SA) och osäkerhetsanalys (UA) kan inte förväntas vara likadana för varje modell. I den här avhandlingen ska vi studera SA- och UA-metoder och resultat för en emissionsmodell för vägtrafik, men metoderna kan även användas för andra modeller av liknande struktur. Vi undersöker en välkänd emissionsmodell och föreslår ett kraftfullt verktyg för att generera data. Genom att studera genererade datamängder kan vi undersöka egenskaper i modellen, föreslå SA- och VA-metoder och diskutera metodernas egenskaper. Vi visar också några resultat när man tillämpar metoderna på de genererade datamängderna.

The purpose of this research was to determine the solubility of apricot (Prunus armeniaca L.) oil in supercritical carbon dioxide (SC-CO2), effects of parameters (particle size, solvent flow rate, pressure, temperature and co-solvent (ethanol) concentration) on extraction yield and to investigate the possibility of fractionation. Solubility, increased with pressure and increased with temperature above the crossover pressure, which was found between 200 and 300 bar, and decreased with temperature below the crossover pressure. Appropriate models were fitted to data. Extraction of apricot kernel oil occurred in two extraction periods as fast and slow extraction periods. Most of the oil was extracted in the fast extraction period and the oil recovered in the slow extraction period was negligible. Extraction yield increased with decrease in particle size and recovery of more than 99 % of the oil was possible if particle diameter decreased below 0.425 mm. Extraction rate increased with increase in flow rate, pressure, temperature and ethanol concentration. The volume mass transfer coefficient in the fluid phase changed between 0.6 and 3.7 /min, whereas the volume mass transfer coefficient in the solid phase changed between 0.00009 and 0.00048 /min. Extraction yield at 15 min for particle diameter smaller than 0.85 mm was formulated as a function of solvent flow rate, pressure, temperature, and ethanol concentration by using Response Surface Methodology. According to the model yield was highest (0.26 g /g) at 4 g/min flow rate, 60 oC, 450 bar and 3 % ethanol concentration. Fractionation was not possible at significant levels.

The main objective of this study was to optimize the processing conditions of breads baked in halogen lamp-microwave combination oven by using response surface methodology. It was also aimed to construct neural network models for the prediction of quality parameters of bread as a function of processing conditions. Different baking time and power combinations were used in order to find the optimum baking conditions of bread in halogen lamp-microwave combination oven. The independent variables were the baking time (4, 4.5, 5, 5.5, and 6 min), power of upper and lower halogen lamps (40, 50, 60, 70, and 80%), and power of the microwave (20, 30, 40, 50, and 60%). As control, breads baked in conventional oven at 200&ordm / C for 13 min were used. The measured quality parameters were the weight loss, color change, specific volume, porosity, and texture profile of the breads. Baking time, upper halogen lamp power, and microwave power were found to be significant on affecting most of the quality parameters. On the other hand, lower halogen lamp power was found to be an insignificant factor for all of the responses. For the optimization process, Response Surface Methodology (RSM) was used. The optimum baking conditions were determined as 5 min of baking time at 70% upper halogen lamp power, 50% lower halogen lamp power, and 20% microwave power. Breads baked at the optimum condition had comparable quality with conventionally baked ones. When halogen lamp-microwave combination oven was used, conventional baking time of breads was reduced by 60%. Artificial neural network models were developed for each of the quality parameters in order to observe the effects of the baking time and different oven conditions on the quality of the breads. High regression coefficients were calculated between the experimental data and predicted values showing that this method is capable in predicting quality parameters of breads during halogen lamp-microwave combination baking. In addition, the results were comparable to the RSM study.

The main objective of this study is to evaluate the effects of microwave frying process on the quality of potato slices and to optimize the process by using different statistical optimization techniques. Use of microwave frying for food products may be considered as a new way of improving the quality of the fried foods. In the first part of the study, the effects of microwaves on quality of fried potatoes (moisture content, oil content, color and hardness) were studied and the process was optimized by using Taguchi Technique. Microwave power level (400W, 550W and 700W), frying time (2.0, 2.5, 3.0 minutes) and oil type (sunflower, corn and hazelnut oil) were the parameters used in the study. Moisture content of potatoes decreased whereas oil content, hardness and &amp / #916 / E values of the potatoes increased with increasing frying time and microwave power level. The potatoes with the highest oil content were found to be the ones that were fried in the hazelnut oil. The optimum condition was found as frying at 550W microwave power level, for 2.5 minutes in sunflower oil. The potatoes that were fried at the optimum condition were determined to have lower oil contents compared to the ones fried conventionally.In the second part of the study, osmotic dehydration was applied prior to microwave frying process in order to reduce oil uptake and to evaluate the effect of osmotic dehydration with microwaves on quality of fried potatoes. The process was optimized by using both Taguchi Technique and Response Surface Methodology. Microwave power level (400W, 550W and 700W), frying time (1.5, 2.0, 2.5 minutes) and osmotic dehydration time (15, 30, 45 minutes) were the parameters used in the study. Osmotic dehydration treatment was conducted in a salt solution of 20 % (w/w) at 30oC. Moisture content decreased whereas oil content, hardness and &amp / #916 / E value of potatoes increased with increasing frying time and microwave power level. Dehydration of potatoes osmotically prior to frying reduced the oil content of fried potatoes. The optimum condition was found as frying at 400 W microwave power level for 1.5 min after 30 min of osmotic dehydration time according to Taguchi Technique. Microwave power level and frying time were the same as Taguchi Technique but osmotic dehydration time was 39 min for the optimum condition found using response surface methodology.