In this thesis structural optimization of castings and thermomechanical analysis of castings are studied. In paper I an optimization algorithm is created by using Matlab. The algorithm is linked to the commercial FE solver Abaqus by using Python script. The optimization algorithm uses the successive response surfaces methodology (SRSM) to create global response surfaces. It is shown that including residual stresses in structural optimization of castings yields an optimal shape that differs significantly from the one obtained when residual stresses are excluded. In paper II the optimization algorithm is expanded to using neural networks. It is tested on some typical bench mark problems and shows very promising results. Combining paper I and II the response surfaces can be either analytical functions, both linear and non-linear, or neural networks. The optimization is then performed by using sequential linear programming or by using a zero-order method called Complex. This is all gathered in a package called StuG-OPT. In paper III and IV focus is on the thermomechanical problem when residual stresses are calculated. In paper III a literature review is performed and some numerical simulations are performed to see where numerical simulations can be used in the industry today. In paper IV simulations are compared to real tests. Several stress lattices are casted and the residual stresses are measured. Simulations are performed by using Magmasoft and Abaqus. In Magmasoft a J2-plasticity model is used and in Abaqus two simulations are performed using either J2-plasticity or the ”Cast Iron Plasticity” available in Abaqus that takes into account the different behavior in tension and compression for grey cast iron. / <p>Report code: LIU-TEK-LIC-2007:34.</p>

No description available.

In this work, an active flow control method is studied numerically by using a synthetic jet over a NACA 0015 airfoil. Unsteady, turbulent flows over the NACA 0015 airfoil are computed using a Navier-Stokes solver. The Spalart-Allmaras turbulence model is employed in all computations. Unsteady flow solutions are computed in parallel using Parallel Virtual Machine library routines in a computer cluster. The synthetic jet is implemented to the flow solver as a boundary condition. Response Surface Methodology is employed for the optimization of synthetic jet parameters at various angles of attack. The synthetic jet parameters / the jet velocity, the jet location, the jet angle and the jet frequency are optimized to maximize the lift to drag ratio. The optimization study is performed for a constant value of jet power coefficient. The jet slot size is used as a dependent parameter in the optimization studies. The optimization study has shown that the jet velocity and the jet location are the dominant synthetic jet parameters. The optimum synthetic jet angle is observed to be increasing as the angle of attack increases. The optimum jet location is observed to be moving through the leading edge as angle of attack increases for the separated flows. It is observed that the application of the synthetic jet delays the flow separation on the suction side of the airfoil and increases the lift to drag ratio significantly especially at post stall angles of attack. The application of the synthetic jet is observed to be less effective for attached flows.

Active flow control by a jet over a 12.5% thick elliptic profile is investigated numerically. Unsteady flowfields are calculated with a Navier Stokes solver. The numerical method is first validated without the jet and with the presence of steady-blowing and pulsating jets. Three jet types, namely steady, pulsating and synthetic jets, are next compared with each other and it is shown that the most drag reduction is achieved by a synthetic jet and the most lift enhancement is achieved by a steady jet. The influences of the jet location, the jet velocity, the jet frequency, the jet slot length and the jet angle on the flowfield is parametrically studied. It is shown that the jet location and the jet velocity are the most effective parameters. The jet parameters are optimized to minimize the drag coefficient while keeping the jet power constant. The drag is reduced by 32.5% for the angle of attack 0 and by 24% for the angle of attack 4.

The main objective of this study was to optimize the roasting conditions of hazelnuts in microwave-infrared and microwave-convective heating combination ovens by using response surface methodology. It was also aimed to construct regression models for the prediction of quality parameters of hazelnuts as a function of processing conditions. The independent variables were microwave power (10, 30, 50, 70 and 90%), upper-lower infrared power (10, 30, 50, 70 and 90%) and roasting time (2, 3, 4, 5 and 6 min) for microwave-infrared combination roasting. Microwave power (70, 140 and 210W), air temperature (90, 150 and 210&deg / C) and roasting time (5, 15 and 25 min) were the independent variables of microwave-convective heating combination oven. As control, hazelnuts roasted in conventional oven at 150&deg / C for 20 min were used. The quality parameters were L* value, a* value, fracture force and moisture content of the hazelnuts for both microwave assisted ovens. The optimum roasting conditions of microwave-infrared combination oven were determined as 2.5 min of roasting time at 613.8W microwave power, 1800W upper infrared power, and 300W lower infrared power. Hazelnuts roasted at the optimum condition had comparable quality with the conventionally roasted ones. When micro- wave infrared combination oven was used, conventional roasting time of hazelnuts was reduced by 87.5%. Optimum roasting conditions of microwave-convective heating combination oven were 140 W microwave power, 150&deg / C air temperature and 20 min roasting time. High regression coefficients were calculated between the experimental data and predicted values showing that RSM is capable in predicting quality parameters of hazelnuts during microwave assisted roasting.

Modern engineering design tends to use computer simulations such as Finite Element Analysis (FEA) to replace physical experiments when evaluating a quality response, e.g., the stress level in a phone packaging process. The use of computer models has certain advantages over running physical experiments, such as being cost effective, easy to try out different design alternatives, and having greater impact on product design. However, due to the complexity of FEA codes, it could be computationally expensive to calculate the quality response function over a large number of combinations of design and environmental factors. Traditional experimental design and response surface methodology, which were developed for physical experiments with the presence of random errors, are not very effective in dealing with deterministic FEA simulation outputs. In this thesis, we will utilize a spatial statistical method (i.e., Kriging model) for analyzing deterministic computer simulation-based experiments. Subsequently, we will devise a sequential strategy, which allows us to explore the whole response surface in an efficient way. The overall number of computer experiments will be remarkably reduced compared with the traditional response surface methodology. The proposed methodology is illustrated using an electronic packaging example.

<p>In this thesis structural optimization of castings and thermomechanical analysis of castings are studied.</p><p>In paper I an optimization algorithm is created by using Matlab. The algorithm is linked to the commercial FE solver Abaqus by using Python script. The optimization algorithm uses the successive response surfaces methodology (SRSM) to create global response surfaces. It is shown that including residual stresses in structural optimization of castings yields an optimal shape that differs significantly from the one obtained when residual stresses are excluded.</p><p>In paper II the optimization algorithm is expanded to using neural networks. It is tested on some typical bench mark problems and shows very promising results. Combining paper I and II the response surfaces can be either analytical functions, both linear and non-linear, or neural networks. The optimization is then performed by using sequential linear programming or by using a zero-order method called Complex. This is all gathered in a package called StuG-OPT.</p><p>In paper III and IV focus is on the thermomechanical problem when residual stresses are calculated. In paper III a literature review is performed and some numerical simulations are performed to see where numerical simulations can be used in the industry today. In paper IV simulations are compared to real tests. Several stress lattices are casted and the residual stresses are measured. Simulations are performed by using Magmasoft and Abaqus. In Magmasoft a J2-plasticity model is used and in Abaqus two simulations are performed using either J2-plasticity or the ”Cast Iron Plasticity” available in Abaqus that takes into account the different behavior in tension and compression for grey cast iron.</p> / Report code: LIU-TEK-LIC-2007:34.

No description available.

Current prices and demand for petroleum hydrocabons have generated an increase of oil spills around the country and the world. Health and environmental impacts associated to these organic pollutants represent a huge concern for the general public, leading the public and private sector to develop new technologies and methods to minimize or eliminate those risks. Ex-Situ bioremediation through biopiles, as a main remediation technique to treat a wide range of hydrocarbons, has been a topic of considerable research interest over the last years. It provides an economical and environmental solution to restore the environment to background levels. Nevertheless, successful bioremediation under cold climate conditions is of considerable concern in countries like Canada, as low temperatures can delay the rate of bioremediation of oil hydrocarbons, thus limiting the operation of soil treatment facilities to certain times of the year. Recent research has found out that bioremediation could be conducted even at low or cold temperatures with larger periods of times. And even more, the addition of petroleum degrading microorganisms (bioaugmentation) and nutrients or biosurfactants (biostimulation) could enhance the process in some cases. In the present study, a comprehensive assessment of bioaugmentation and biostimulation strategies for ex-situ bioremediation of petroleum contaminated soil under cold climate conditions is proposed. Field scale biopiles were constructed and subjected to different concentrations of commercial microbial consortia and mature compost, as bioaugmentation and biostimulation strategies, in a soil treatment facility at Moose Creek, Ontario over a period of 94 days (November 2012 to February 2013). Assessment and comparison of the biodegradation rates of total petroleum hydrocarbons (TPH) and their fractions were investigated. Furthermore, a response surface methodology (RSM) based on a factorial design to investigate and optimize the effects of the microbial consortia application rate and amount of compost on the TPH removal was also assessed. Results showed that biopiles inoculated with microbial consortia and amended with 10:1 soil to compost ratio under aerobic conditions performed the best, degrading 82% of total petroleum hydrocarbons (TPHs) with a first-order kinetic degradation rate of 0.016 d_1, under cold temperature conditions. The average removal efficiencies for TPHs after 94 days for control biopiles, with no amendments or with microbial consortia or compost only treatments were 48%, 55%, and 52%, respectively. Statistical analyses indicated a significant difference (p < 0.05) within and between the final measurements for TPHs and a significant difference between the treatment with combined effect, and the control biopiles. On the other hand, the modeling and optimization statistical analysis of the results showed that the microbial consortia application rate, compost amendment and their interactions have a significant effect on TPHs removal with a coefficient of determination (R2) of 0.88, indicating a high correlation between the observed and the predicted values for the model obtained. The optimum concentrations predicted via RSM were 4.1 ml m-3 for microbial consortia application rate, and 7% for compost amendment to obtain a maximum TPH removal of 90.7%. This research contributes to provide valuable knowledge to practitioners about cost-effective and existing strategies for ex-situ bioremediation under cold weather conditions.

In this thesis, it is aimed to design lithium extraction from boron clays using statistical design of experiments and robust design methodologies. There are several factors affecting extraction of lithium from clays. The most important of these factors have been limited to a number of six which have been gypsum to clay ratio, roasting temperature, roasting time, leaching solid to liquid ratio, leaching time and limestone to clay ratio. For every factor, three levels have been chosen and an experiment has been designed. After performing three replications for each of the experimental run, signal to noise ratio transformation, ANOVA, regression analysis and response surface methodology have been applied on the results of the experiments. Optimization and confirmation experiments have been made sequentially to find factor settings that maximize lithium extraction with minimal variation. The mean of the maximum extraction has been observed as 83.81% with a standard deviation of 4.89 and the 95% prediction interval for the mean extraction is (73.729, 94.730). This result is in agreement with the studies that have been made in the literature. However / this study is unique in the sense that lithium is extracted from boron clays by using limestone directly from the nature, and gypsum as a waste product of boric acid production. Since these two materials add about 20% cost to the extraction process, the results of this study become important. Moreover, in this study it has been shown that statistical design of experiments help mining industry to reduce the need for standardization.