Methodology for wind turbine blade geometry optimization

Nowadays, the upwind three bladed horizontal axis wind turbine is the leading player on the market. It has been found to be the best industrial compromise in the range of different turbine constructions. The current wind industry innovation is conducted in the development of individual turbine components. The blade constitutes 20-25% of the overall turbine budget. Its optimal operation in particular local economic and wind conditions is worth investigating. The blade geometry, namely the chord, twist and airfoil type distributions along the span, responds to the output measures of the blade performance. Therefore, the optimal wind blade geometry can improve the overall turbine performance. The objectives of the dissertation are focused on the development of a methodology and specific tool for the investigation of possible existing wind blade geometry adjustments. The novelty of the methodology presented in the thesis is the multiobjective perspective on wind blade geometry optimization, particularly taking simultaneously into account the local wind conditions and the issue of aerodynamic noise emissions. The presented optimization objective approach has not been investigated previously for the implementation in wind blade design. The possibilities to use different theories for the analysis and search procedures are investigated and sufficient arguments derived for the usage of proposed theories. The tool is used for the test optimization of a particular wind turbine blade. The sensitivity analysis shows the dependence of the outputs on the provided inputs, as well as its relative and absolute divergences and instabilities. The pros and cons of the proposed technique are seen from the practical implementation, which is documented in the results, analysis and conclusion sections.

Stochastic approximation methods in stochastic optimization

Stochastic approximation methods for stochastic optimization are considered. Reviewed the main methods of stochastic approximation: stochastic quasi-gradient algorithm, Kiefer-Wolfowitz algorithm and adaptive rules for them, simultaneous perturbation stochastic approximation (SPSA) algorithm. Suggested the model and the solution of the retailer's profit optimization problem and considered an application of the SQG-algorithm for the optimization problems with objective functions given in the form of ordinary differential equation.

Optimization of strength and toughness on the effect of the weldable high strength steels (hss) used in offshore structures

Optimization of high strength and toughness combination on the effect of weldability is very vital to be considered in offshore oil and gas industries. Having a balanced and improved high strength and toughness is very much recommended in offshore structures for an effective production and viable exploration of hydrocarbons. This thesis aims to investigate the possibilities to improve the toughness of high strength steel. High carbon contents induce hardness and needs to be reduced for increasing toughness. The rare combination of high strength with high toughness possibilities was examined by determining the following toughening mechanism of: Heat treatment and optimal microstructure, Thermomechanical processing, Effect of welding parameters on toughness and weldability of steel. The implementation of weldability of steels to attain high toughness for high strength in offshore structures is mostly in shipbuilding, offshore platforms, and pipelines for high operating pressures. As a result, the toughening mechanisms suggested have benefits to the aims of the effect of high strength to high toughness of steel for efficiency, production and cost reduction.

Optimization of parameters for fiber laser-MAG hybrid welding in shipbuilding applications

The Arctic region becoming very active area of the industrial developments since it may contain approximately 15-25% of the hydrocarbon and other valuable natural resources which are in great demand nowadays. Harsh operation conditions make the Arctic region difficult to access due to low temperatures which can drop below -50 °C in winter and various additional loads. As a result, newer and modified metallic materials are implemented which can cause certain problems in welding them properly. Steel is still the most widely used material in the Arctic regions due to high mechanical properties, cheapness and manufacturability. Moreover, with recent steel manufacturing development it is possible to make up to 1100 MPa yield strength microalloyed high strength steel which can be operated at temperatures -60 °C possessing reasonable weldability, ductility and suitable impact toughness which is the most crucial property for the Arctic usability. For many years, the arc welding was the most dominant joining method of the metallic materials. Recently, other joining methods are successfully implemented into welding manufacturing due to growing industrial demands and one of them is the laser-arc hybrid welding. The laser-arc hybrid welding successfully combines the advantages and eliminates the disadvantages of the both joining methods therefore produce less distortions, reduce the need of edge preparation, generates narrower heat-affected zone, and increase welding speed or productivity significantly. Moreover, due to easy implementation of the filler wire, accordingly the mechanical properties of the joints can be manipulated in order to produce suitable quality. Moreover, with laser-arc hybrid welding it is possible to achieve matching weld metal compared to the base material even with the low alloying welding wires without excessive softening of the HAZ in the high strength steels. As a result, the laser-arc welding methods can be the most desired and dominating welding technology nowadays, and which is already operating in automotive and shipbuilding industries with a great success. However, in the future it can be extended to offshore, pipe-laying, and heavy equipment industries for arctic environment. CO2 and Nd:YAG laser sources in combination with gas metal arc source have been used widely in the past two decades. Recently, the fiber laser sources offered high power outputs with excellent beam quality, very high electrical efficiency, low maintenance expenses, and higher mobility due to fiber optics. As a result, fiber laser-arc hybrid process offers even more extended advantages and applications. However, the information about fiber or disk laser-arc hybrid welding is very limited. The objectives of the Master’s thesis are concentrated on the study of fiber laser-MAG hybrid welding parameters in order to understand resulting mechanical properties and quality of the welds. In this work only ferrous materials are reviewed. The qualitative methodological approach has been used to achieve the objectives. This study demonstrates that laser-arc hybrid welding is suitable for welding of many types, thicknesses and strength of steels with acceptable mechanical properties along very high productivity. New developments of the fiber laser-arc hybrid process offers extended capabilities over CO2 laser combined with the arc. This work can be used as guideline in hybrid welding technology with comprehensive study the effect of welding parameter on joint quality.

Towards sustainable Iron- and steelmaking with economic optimization

The iron and steelmaking industry is among the major contributors to the anthropogenic emissions of carbon dioxide in the world. The rising levels of CO2 in the atmosphere and the global concern about the greenhouse effect and climate change have brought about considerable investigations on how to reduce the energy intensity and CO2 emissions of this industrial sector. In this thesis the problem is tackled by mathematical modeling and optimization using three different approaches. The possibility to use biomass in the integrated steel plant, particularly as an auxiliary reductant in the blast furnace, is investigated. By pre-processing the biomass its heating value and carbon content can be increased at the same time as the oxygen content is decreased. As the compression strength of the preprocessed biomass is lower than that of coke, it is not suitable for replacing a major part of the coke in the blast furnace burden. Therefore the biomass is assumed to be injected at the tuyere level of the blast furnace. Carbon capture and storage is, nowadays, mostly associated with power plants but it can also be used to reduce the CO2 emissions of an integrated steel plant. In the case of a blast furnace, the effect of CCS can be further increased by recycling the carbon dioxide stripped top gas back into the process. However, this affects the economy of the integrated steel plant, as the amount of top gases available, e.g., for power and heat production is decreased. High quality raw materials are a prerequisite for smooth blast furnace operation. High quality coal is especially needed to produce coke with sufficient properties to ensure proper gas permeability and smooth burden descent. Lower quality coals as well as natural gas, which some countries have in great volumes, can be utilized with various direct and smelting reduction processes. The DRI produced with a direct reduction process can be utilized as a feed material for blast furnace, basic oxygen furnace or electric arc furnace. The liquid hot metal from a smelting reduction process can in turn be used in basic oxygen furnace or electric arc furnace. The unit sizes and investment costs of an alternative ironmaking process are also lower than those of a blast furnace. In this study, the economy of an integrated steel plant is investigated by simulation and optimization. The studied system consists of linearly described unit processes from coke plant to steel making units, with a more detailed thermodynamical model of the blast furnace. The results from the blast furnace operation with biomass injection revealed the importance of proper pre-processing of the raw biomass as the composition of the biomass as well as the heating value and the yield are all affected by the pyrolysis temperature. As for recycling of CO2 stripped blast furnace top gas, substantial reductions in the emission rates are achieved if the stripped CO2 can be stored. However, the optimal recycling degree together with other operation conditions is heavily dependent on the cost structure of CO2 emissions and stripping/storage. The economical feasibility related to the use of DRI in the blast furnace depends on the price ratio between the DRI pellets and the BF pellets. The high amount of energy needed in the rotary hearth furnace to reduce the iron ore leads to increased CO2 emissions.

On the use of convex underestimators in global optimization

Många kvantitativa problem från vitt skilda områden kan beskrivas som optimeringsproblem. Ett mått på lösningens kvalitet bör optimeras samtidigt som vissa villkor på lösningen uppfylls. Kvalitetsmåttet kallas vanligen objektfunktion och kan beskriva kostnader (exempelvis produktion, logistik), potentialenergi (molekylmodellering, proteinveckning), risk (finans, försäkring) eller något annat relevant mått. I min doktorsavhandling diskuteras speciellt icke-linjär programmering, NLP, i ändliga dimensioner. Problem med enkel struktur, till exempel någon form av konvexitet, kan lösas effektivt. Tyvärr kan inte alla kvantitativa samband modelleras på ett konvext vis. Icke-konvexa problem kan angripas med heuristiska metoder, algoritmer som söker lösningar med hjälp av deterministiska eller stokastiska tumregler. Ibland fungerar det här väl, men heuristikerna kan sällan garantera kvaliteten på lösningen eller ens att en lösning påträffas. För vissa tillämpningar är det här oacceptabelt. Istället kan man tillämpa så kallad global optimering. Genom att successivt dela variabeldomänen i mindre delar och beräkna starkare gränser på det optimala värdet hittas en lösning inom feltoleransen. Den här metoden kallas branch-and-bound, ungefär dela-och-begränsa. För att ge undre gränser (vid minimering) approximeras problemet med enklare problem, till exempel konvexa, som kan lösas effektivt. I avhandlingen studeras tillvägagångssätt för att approximera differentierbara funktioner med konvexa underskattningar, speciellt den så kallade alphaBB-metoden. Denna metod adderar störningar av en viss form och garanterar konvexitet genom att sätta villkor på den perturberade Hessematrisen. Min forskning har lyft fram en naturlig utvidgning av de perturbationer som används i alphaBB. Nya metoder för att bestämma underskattningsparametrar har beskrivits och jämförts. I sammanfattningsdelen diskuteras global optimering ur bredare perspektiv på optimering och beräkningsalgoritmer.

Scalable algorithms for height field illumination

Global illumination algorithms are at the center of realistic image synthesis and account for non-trivial light transport and occlusion within scenes, such as indirect illumination, ambient occlusion, and environment lighting. Their computationally most difficult part is determining light source visibility at each visible scene point. Height fields, on the other hand, constitute an important special case of geometry and are mainly used to describe certain types of objects such as terrains and to map detailed geometry onto object surfaces. The geometry of an entire scene can also be approximated by treating the distance values of its camera projection as a screen-space height field. In order to shadow height fields from environment lights a horizon map is usually used to occlude incident light. We reduce the per-receiver time complexity of generating the horizon map on N N height fields from O(N) of the previous work to O(1) by using an algorithm that incrementally traverses the height field and reuses the information already gathered along the path of traversal. We also propose an accurate method to integrate the incident light within the limits given by the horizon map. Indirect illumination in height fields requires information about which other points are visible to each height field point. We present an algorithm to determine this intervisibility in a time complexity that matches the space complexity of the produced visibility information, which is in contrast to previous methods which scale in the height field size. As a result the amount of computation is reduced by two orders of magnitude in common use cases. Screen-space ambient obscurance methods approximate ambient obscurance from the depth bu er geometry and have been widely adopted by contemporary real-time applications. They work by sampling the screen-space geometry around each receiver point but have been previously limited to near- field effects because sampling a large radius quickly exceeds the render time budget. We present an algorithm that reduces the quadratic per-pixel complexity of previous methods to a linear complexity by line sweeping over the depth bu er and maintaining an internal representation of the processed geometry from which occluders can be efficiently queried. Another algorithm is presented to determine ambient obscurance from the entire depth bu er at each screen pixel. The algorithm scans the depth bu er in a quick pre-pass and locates important features in it, which are then used to evaluate the ambient obscurance integral accurately. We also propose an evaluation of the integral such that results within a few percent of the ray traced screen-space reference are obtained at real-time render times.

Optimization and validation of the solid-liquid extraction technique for determination of picloram in soils by high performance liquid chromatography

The objective of this study was to optimize and validate the solid-liquid extraction (ESL) technique for determination of picloram residues in soil samples. At the optimization stage, the optimal conditions for extraction of soil samples were determined using univariate analysis. Ratio soil/solution extraction, type and time of agitation, ionic strength and pH of extraction solution were evaluated. Based on the optimized parameters, the following method of extraction and analysis of picloram was developed: weigh 2.00 g of soil dried and sieved through a sieve mesh of 2.0 mm pore, add 20.0 mL of KCl concentration of 0.5 mol L-1, shake the bottle in the vortex for 10 seconds to form suspension and adjust to pH 7.00, with alkaline KOH 0.1 mol L-1. Homogenate the system in a shaker system for 60 minutes and then let it stand for 10 minutes. The bottles are centrifuged for 10 minutes at 3,500 rpm. After the settlement of the soil particles and cleaning of the supernatant extract, an aliquot is withdrawn and analyzed by high performance liquid chromatography. The optimized method was validated by determining the selectivity, linearity, detection and quantification limits, precision and accuracy. The ESL methodology was efficient for analysis of residues of the pesticides studied, with percentages of recovery above 90%. The limits of detection and quantification were 20.0 and 66.0 mg kg-1 soil for the PVA, and 40.0 and 132.0 mg kg-1 soil for the VLA. The coefficients of variation (CV) were equal to 2.32 and 2.69 for PVA and TH soils, respectively. The methodology resulted in low organic solvent consumption and cleaner extracts, as well as no purification steps for chromatographic analysis were required. The parameters evaluated in the validation process indicated that the ESL methodology is efficient for the extraction of picloram residues in soils, with low limits of detection and quantification.

Currency Carry Trades – Excess Returns and Performance Optimization of G10 Currency Carry Trade Strategies during the Euro Era

This study examines the excess returns provided by G10 currency carry trading during the Euro era. The currency carry trade has been a popular trade throughout the past decades offering excess returns to investors. The thesis aims to contribute to existing research on the topic by utilizing a new set of data for the Euro era as well as using the Euro as a basis for the study. The focus of the thesis is specifically on different carry trade strategies’ performance, risk and diversification benefits. The study finds proof of the failure of the uncovered interest rate parity theory through multiple regression analyses. Furthermore, the research finds evidence of significant diversification benefits in terms of Sharpe ratio and improved return distributions. The results suggest that currency carry trades have offered excess returns during 1999-2014 and that volatility plays an important role in carry trade returns. The risk, however, is diversifiable and therefore our results support previous quantitative research findings on the topic.

Algorithmic Techniques in Gene Expression Processing. From Imputation to Visualization

The amount of biological data has grown exponentially in recent decades. Modern biotechnologies, such as microarrays and next-generation sequencing, are capable to produce massive amounts of biomedical data in a single experiment. As the amount of the data is rapidly growing there is an urgent need for reliable computational methods for analyzing and visualizing it. This thesis addresses this need by studying how to efficiently and reliably analyze and visualize high-dimensional data, especially that obtained from gene expression microarray experiments. First, we will study the ways to improve the quality of microarray data by replacing (imputing) the missing data entries with the estimated values for these entries. Missing value imputation is a method which is commonly used to make the original incomplete data complete, thus making it easier to be analyzed with statistical and computational methods. Our novel approach was to use curated external biological information as a guide for the missing value imputation. Secondly, we studied the effect of missing value imputation on the downstream data analysis methods like clustering. We compared multiple recent imputation algorithms against 8 publicly available microarray data sets. It was observed that the missing value imputation indeed is a rational way to improve the quality of biological data. The research revealed differences between the clustering results obtained with different imputation methods. On most data sets, the simple and fast k-NN imputation was good enough, but there were also needs for more advanced imputation methods, such as Bayesian Principal Component Algorithm (BPCA). Finally, we studied the visualization of biological network data. Biological interaction networks are examples of the outcome of multiple biological experiments such as using the gene microarray techniques. Such networks are typically very large and highly connected, thus there is a need for fast algorithms for producing visually pleasant layouts. A computationally efficient way to produce layouts of large biological interaction networks was developed. The algorithm uses multilevel optimization within the regular force directed graph layout algorithm.