Lithium-aluminium casting alloys and their associated metal-mould reactions

Aluminium - lithium alloys are specialist alloys used exclusively by the aerospace industry. They have properties that are favourable to the production of modern military aircraft. The addition of approximately 2.5 percent lithium to aluminium increases the strength characteristics of the new alloys by 10 percent. The same addition has the added advantage of decreasing the density of the resulting alloy by a similar percentage. The disadvantages associated with this alloy are primarily price and castability. The addition of 2.5 weight percent lithium to aluminium results in a price increase of 100% explaining the aerospace exclusivity. The processability of the alloys is restricted to ingot casting and wrought treatment but for complex components precision casting is required. Casting the alloys into sand and investment moulds creates a metal - mould reaction, the consequences of which are intolerable in the production of military hardware. The primary object of this project was to investigate and characterise the reactions occurring between the newly poured metal and surface of the mould and to propose a method of counteracting the metal - mould reaction. The constituents of standard sand and investment moulds were pyrolised with lithium metal in order to simplify the complex in-mould reaction and the products were studied by the solid state techniques of powder X-Ray diffraction and magic angle spinning nuclear magnetic resonance spectroscopy. The results of this study showed that the order of reaction was: Organic reagents> > Silicate reagents> Non silicate reagents Alphaset and Betaset were the two organic binders used to prepare the sand moulds throughout this project. Studies were carried out to characterise these resins in order to determine the factors involved in their reaction with lithium. Analysis revealed that during the curing process the phenolic hydroxide groups are not reacted out and that a redox reaction takes place between these hydroxides and the lithium in the molten alloys. Casting experiments carried out to assess the protection afforded by various hydroxide protecting agents showed that modern effective, protecting chemicals such as bis-trimethyl silyl acetamide and hexamethyldisilazane did not inhibit the metal - mould reaction to a sufficiently high standard and that tri-methylchlorosilane was consistently the best performer. Tri-methyl chlorosilane has a simple functionalizing mechanism compared to other hydroxide protecting reagents and this factor is responsible for its superior inhibiting qualities. Comparative studies of 6Li and 7Li N.M.R. spectra (M.A.S. and `off angle') establish that, for solid state (and even solution) analytical purposes 6Li is the preferred nucleus. 6Li M.A.S.N.M.R. spectra were obtained for thermally treated laponite clay. At temperatures below 800oC both dehydrated and rehydrated samples were considered. The data are consistent with mobility of lithium ions from the trioctahedral clay sites at 600oC. The superior resolution achievable in 6Li M.A.S.N.M.R. is demonstrated in the analysis of a microwave prepared lithium exchanged clay where 6Li spectroscopy revelaed two lithium sites in comparison to 7Li M.A.S.N.M.R. which gave only a single lithium resonance.

Modelling and simulation of biomass gasification in a circulating fluidized bed reactor

Computational Fluid Dynamics (CFD) has found great acceptance among the engineering community as a tool for research and design of processes that are practically difficult or expensive to study experimentally. One of these processes is the biomass gasification in a Circulating Fluidized Bed (CFB). Biomass gasification is the thermo-chemical conversion of biomass at a high temperature and a controlled oxygen amount into fuel gas, also sometime referred to as syngas. Circulating fluidized bed is a type of reactor in which it is possible to maintain a stable and continuous circulation of solids in a gas-solid system. The main objectives of this thesis are four folds: (i) Develop a three-dimensional predictive model of biomass gasification in a CFB riser using advanced Computational Fluid Dynamic (CFD) (ii) Experimentally validate the developed hydrodynamic model using conventional and advanced measuring techniques (iii) Study the complex hydrodynamics, heat transfer and reaction kinetics through modelling and simulation (iv) Study the CFB gasifier performance through parametric analysis and identify the optimum operating condition to maximize the product gas quality. Two different and complimentary experimental techniques were used to validate the hydrodynamic model, namely pressure measurement and particle tracking. The pressure measurement is a very common and widely used technique in fluidized bed studies, while, particle tracking using PEPT, which was originally developed for medical imaging, is a relatively new technique in the engineering field. It is relatively expensive and only available at few research centres around the world. This study started with a simple poly-dispersed single solid phase then moved to binary solid phases. The single solid phase was used for primary validations and eliminating unnecessary options and steps in building the hydrodynamic model. Then the outcomes from the primary validations were applied to the secondary validations of the binary mixture to avoid time consuming computations. Studies on binary solid mixture hydrodynamics is rarely reported in the literature. In this study the binary solid mixture was modelled and validated using experimental data from the both techniques mentioned above. Good agreement was achieved with the both techniques. According to the general gasification steps the developed model has been separated into three main gasification stages; drying, devolatilization and tar cracking, and partial combustion and gasification. The drying was modelled as a mass transfer from the solid phase to the gas phase. The devolatilization and tar cracking model consist of two steps; the devolatilization of the biomass which is used as a single reaction to generate the biomass gases from the volatile materials and tar cracking. The latter is also modelled as one reaction to generate gases with fixed mass fractions. The first reaction was classified as a heterogeneous reaction while the second reaction was classified as homogenous reaction. The partial combustion and gasification model consisted of carbon combustion reactions and carbon and gas phase reactions. The partial combustion considered was for C, CO, H2 and CH4. The carbon gasification reactions used in this study is the Boudouard reaction with CO2, the reaction with H2O and Methanation (Methane forming reaction) reaction to generate methane. The other gas phase reactions considered in this study are the water gas shift reaction, which is modelled as a reversible reaction and the methane steam reforming reaction. The developed gasification model was validated using different experimental data from the literature and for a wide range of operating conditions. Good agreement was observed, thus confirming the capability of the model in predicting biomass gasification in a CFB to a great accuracy. The developed model has been successfully used to carry out sensitivity and parametric analysis. The sensitivity analysis included: study of the effect of inclusion of various combustion reaction; and the effect of radiation in the gasification reaction. The developed model was also used to carry out parametric analysis by changing the following gasifier operating conditions: fuel/air ratio; biomass flow rates; sand (heat carrier) temperatures; sand flow rates; sand and biomass particle sizes; gasifying agent (pure air or pure steam); pyrolysis models used; steam/biomass ratio. Finally, based on these parametric and sensitivity analysis a final model was recommended for the simulation of biomass gasification in a CFB riser.

A computational approach to measuring the correlation between expertise and social media influence for celebrities on microblogs

Existing approaches of social influence analysis usually focus on how to develop effective algorithms to quantize users' influence scores. They rarely consider a person's expertise levels which are arguably important to influence measures. In this paper, we propose a computational approach to measuring the correlation between expertise and social media influence, and we take a new perspective to understand social media influence by incorporating expertise into influence analysis. We carefully constructed a large dataset of 13,684 Chinese celebrities from Sina Weibo (literally 'Sina microblogging'). We found that there is a strong correlation between expertise levels and social media influence scores. In addition, different expertise levels showed influence variation patterns: high-expertise celebrities have stronger influence on the 'audience' in their expertise domains.