<p>Usavršavanje aerodinamike privrednim motornih vozila putem optimizacije aerodinamičkih dodataka u svrhu postizanja boljih aerodinamičkih performansi u vidu umanjenja sile otpora vazduha. Prostiranje vazdušne struje oko modela je unapređeno optimizacionim dodacima što se sve ogleda u smanjenoj potrošnji goriva privrednog motornog vozila. Osim virtuelnih simulacija računarske dinamike fluida, u radu je prikazana validacija rezultata putem eksperimentalnog merenja u vazdušnom tunelu.</p> / <p>The improvement of commercial motor vehicle&#39;s aerodynamics through optimization process of aerodynamic equpments in order to achieve better aerodynamic performance in the form of drag force reduction. The expansion of the air flow around the model is enhanced by optimization accessories, which is all reflected in the reduced of fuel consumption of the commercial motor vehicle. In addition to virtual simulations of computational fluid dynamics, the paper presents the validation of results by experimental measurement in the wind tunnel.</p>

abstract: Optimal design theory provides a general framework for the construction of experimental designs for categorical responses. For a binary response, where the possible result is one of two outcomes, the logistic regression model is widely used to relate a set of experimental factors with the probability of a positive (or negative) outcome. This research investigates and proposes alternative designs to alleviate the problem of separation in small-sample D-optimal designs for the logistic regression model. Separation causes the non-existence of maximum likelihood parameter estimates and presents a serious problem for model fitting purposes. First, it is shown that exact, multi-factor D-optimal designs for the logistic regression model can be susceptible to separation. Several logistic regression models are specified, and exact D-optimal designs of fixed sizes are constructed for each model. Sets of simulated response data are generated to estimate the probability of separation in each design. This study proves through simulation that small-sample D-optimal designs are prone to separation and that separation risk is dependent on the specified model. Additionally, it is demonstrated that exact designs of equal size constructed for the same models may have significantly different chances of encountering separation. The second portion of this research establishes an effective strategy for augmentation, where additional design runs are judiciously added to eliminate separation that has occurred in an initial design. A simulation study is used to demonstrate that augmenting runs in regions of maximum prediction variance (MPV), where the predicted probability of either response category is 50%, most reliably eliminates separation. However, it is also shown that MPV augmentation tends to yield augmented designs with lower D-efficiencies. The final portion of this research proposes a novel compound optimality criterion, DMP, that is used to construct locally optimal and robust compromise designs. A two-phase coordinate exchange algorithm is implemented to construct exact locally DMP-optimal designs. To address design dependence issues, a maximin strategy is proposed for designating a robust DMP-optimal design. A case study demonstrates that the maximin DMP-optimal design maintains comparable D-efficiencies to a corresponding Bayesian D-optimal design while offering significantly improved separation performance. / Dissertation/Thesis / Doctoral Dissertation Industrial Engineering 2019

abstract: Developing countries suffer from various health challenges due to inaccessible medical diagnostic laboratories and lack of resources to establish new laboratories. One way to address these issues is to develop diagnostic systems that are suitable for the low-resource setting. In addition to this, applications requiring rapid analyses further motivates the development of portable, easy-to-use, and accurate Point of Care (POC) diagnostics. Lateral Flow Immunoassays (LFIAs) are among the most successful POC tests as they satisfy most of the ASSURED criteria. However, factors like reagent stability, reaction rates limit the performance and robustness of LFIAs. The fluid flow rate in LFIA significantly affect the factors mentioned above, and hence, it is desirable to maintain an optimal fluid velocity in porous media. The main objective of this study is to build a statistical model that enables us to determine the optimal design parameters and ambient conditions for achieving a desired fluid velocity in porous media. This study mainly focuses on the effects of relative humidity and temperature on evaporation in porous media and the impact of geometry on fluid velocity in LFIAs. A set of finite element analyses were performed, and the obtained simulation results were then experimentally verified using Whatman filter paper with different geometry under varying ambient conditions. Design of experiments was conducted to estimate the significant factors affecting the fluid flow rate. Literature suggests that liquid evaporation is one of the major factors that inhibit fluid penetration and capillary flow in lateral flow Immunoassays. The obtained results closely align with the existing literature and conclude that a desired fluid flow rate can be achieved by tuning the geometry of the porous media. The derived statistical model suggests that a dry and warm atmosphere is expected to inhibit the fluid flow rate the most and vice-versa. / Dissertation/Thesis / Masters Thesis Electrical Engineering 2019

No description available.

The use of nanoreinforcements in automotive structural composites has provided promising improvements in their mechanical properties. For the first time, a robust statistical design of experiments approach was undertaken to demonstrate how key formulation and processing factors (nanofiber type, use of dispersing agent, mixing method, nanofiber weight fraction, and temperature) affected the dynamic mechanical properties of vapor-grown carbon nanofiber (VGCNF)/vinyl ester (VE) nanocomposites. Statistical response surface models were developed to predict nanocomposite storage and loss moduli as functions of significant factors. Only ~0.50 parts of nanofiber per hundred parts resin produced a roughly 20% increase in the storage modulus versus that of the neat VE at room temperature. Optimized nanocomposite properties were predicted as a function of design factors employing this methodology. For example, the use of highshear mixing (one of the mixing methods in the design) with the oxidized VGCNFs in the absence of dispersing agent or arbitrarily with pristine VGCNFs in the presence of dispersing agent was found to maximize the predicted storage modulus over the entire temperature range (30-120 °C). To study the key concept of interphase in thermoset nanocomposites, molecular dynamics simulations were performed to investigate liquid VE resin monomer interactions with the surface of a pristine VGCNF. A liquid resin having a mole ratio of styrene to bisphenol A-diglycidyl dimethacrylate monomers consistent with a 33 wt% styrene VE resin was placed in contact with both sides of pristine graphene sheets, overlapped like shingles, to represent the outer surface of a pristine VGCNF. The relative monomer concentrations were calculated in a direction progressively away from the surface of the graphene sheets. At equilibrium, the styrene/VE monomer ratio was higher in a 5 Å thick region adjacent to the nanofiber surface than in the remaining liquid volume. The elevated styrene concentration near the nanofiber surface suggests that a styrene-rich interphase region, with a lower crosslink density than the bulk matrix, could be formed upon curing. Furthermore, styrene accumulation in the immediate vicinity of the nanofiber surface might, after curing, improve the nanofiber-matrix interfacial adhesion compared to the case where the monomers were uniformly distributed throughout the matrix.

In this thesis, support structures of a polymer powder based process called XXXXXXXX™ are examined. These structures are crucial for most additive manufacturing processes. The effects of several factors on five industrially important characteristics of support structures are examined by use of the Design of Experiment (DoE) method. It describes the planning as well as the analysis of the experiments. The experiments are planned in a fractional factorial 211-5 design with 64 specimens, resulting in a resolution of IV. The analysis of the data is done by use of the ANOVA method, with which the significance of effects and interaction effects are checked. / I detta examensarbete undersöks stödstrukturer för en polymer-pulverbaserad process kallad XXXXXXXX. Dessa strukturer är väsentliga för de flesta aditiv tillverkning. Med hjälp av metoden "Design of Experiment" (DoE) undersöks effekten av flera faktorer på fem industriellt viktiga egenskaper för stödstrukturer. DoE beskriver både planeringen och analysen av experiment. Experimenten planeras i en fraktionerad faktoriell 211-5 design med 64 provexemplar vilket resulterar i en upplösning av IV. Dataanalysen genomförs med hjälp av ANOVA-metoden, med vilken signifikansen av effekter och interaktionseffekter kan undersökas.

No description available.

Manufacturing operations generate revenue by adding value to material through machine work and the cost associated with part production hinders the maximum profit available. In order to remain competitive, companies invest in research to maximize profit and reduce waste of manufacturing operations. This results in cheaper products for the customer without sacrificing quality. The purpose of this research was to identify machine settings of an Okuma LC 40 Turning Center and optimize the cost of machining in terms of tool cost and energy consumption while maintaining part quality at a productive cycle time. Studying the relationship between energy consumption, tool life, and cycle time with the speed and feed settings through statistical Analysis of Variance (ANOVA) method will allow the production plant to make profitable financial decisions concerning simultaneous turning operation of forged chrome-alloy steel. The project was divided into three phases; the first phase began with a literature survey of sensors used in current manufacturing research and the adaptation of our sensors to the Okuma LC 40 turning center. Then, phase II used design of experiments to identify spindle speed and feedrate settings that optimize multiple responses related to the turning process. The result was a saving in energy consumption (kWh) by 11.8%, a saving in cutting time by 13.2% for a total cost reduction from $1.15 per tool pass to $1.075 per tool pass. Furthermore, this work provides the foundation for phase III to develop an intelligent monitoring system to provide real-time information about the state of the machine and tool. For a monitoring system to be implemented in production, it should utilize cost effective sensors and be nonintrusive to the cutting operation

An investigation was conducted to assess the usefulness of anti-optimization in the process of validating of aerodynamic codes. Anti-optimization is defined here as the intentional search for regions where the computational and experimental results disagree. Maximizing such disagreements can be a useful tool in uncovering errors and/or weaknesses in both analyses and experiments. The codes chosen for this investigation were an airfoil code and a lifting line code used together as an analysis to predict three-dimensional wing aerodynamic coefficients. The parameter of interest was the maximum lift coefficient of the three-dimensional wing, CL max. The test domain encompassed Mach numbers from 0.3 to 0.8, and Reynolds numbers from 25,000 to 250,000. A simple rectangular wing was designed for the experiment. A wind tunnel model of this wing was built and tested in the NASA Langley Transonic Dynamics Tunnel. Selection of the test conditions (i.e., Mach and Reynolds numbers) were made by applying the techniques of response surface methodology and considerations involving the predicted experimental uncertainty. The test was planned and executed in two phases. In the first phase runs were conducted at the pre-planned test conditions. Based on these results additional runs were conducted in areas where significant differences in CL max were observed between the computational results and the experiment — in essence applying the concept of anti-optimization. These additional runs were used to verify the differences in CL max and assess the extent of the region where these differences occurred. The results of the experiment showed that the analysis was capable of predicting CL max to within 0.05 over most of the test domain. The application of anti-optimization succeeded in identifying a region where the computational and experimental values of CL max differed by more than 0.05, demonstrating the usefulness of anti-optimization in process of validating aerodynamic codes. This region was centered at a Mach number of 0.55 and a Reynolds number of 34,000. Including considerations of the uncertainties in the computational and experimental results confirmed that the disagreement was real and not an artifact of the uncertainties. / Ph. D.

With ever-increasing numbers of engine actuators to calibrate within increasingly stringent emissions legislation, the engine mapping and calibration task of identifying optimal actuator settings is much more difficult. The aim of this research is to evaluate the feasibility and effectiveness of the Multidisciplinary Design Optimisation (MDO) frameworks to optimise the multi-attribute steady state engine calibration optimisation problems. Accordingly, this research is concentrated on two aspects of the steady state engine calibration optimisation: 1) development of a sequential Design of Experiment (DoE) strategy to enhance the steady state engine mapping process, and 2) application of different MDO architectures to optimally calibrate the complex engine applications. The validation of this research is based on two case studies, the mapping and calibration optimisation of a JLR AJ133 Jaguar GDI engine; and calibration optimisation of an EU6 Jaguar passenger car diesel engine. These case studies illustrated that: -The proposed sequential DoE strategy offers a coherent framework for the engine mapping process including Screening, Model Building, and Model Validation sequences. Applying the DoE strategy for the GDI engine case study, the number of required engine test points was reduced by 30 – 50 %. - The MDO optimisation frameworks offer an effective approach for the steady state engine calibration, delivering a considerable fuel economy benefits. For instance, the MDO/ATC calibration solution reduced the fuel consumption over NEDC drive cycle for the GDI engine case study (i.e. with single injection strategy) by 7.11%, and for the diesel engine case study by 2.5%, compared to the benchmark solutions. / UK Technology Strategy Board (TSB)