Damage is briefly defined as the presence and growth of micro-defects in a material. This study serves to describe the computational implementation of the material damage theory adopted for ductile materials. Thus, pays attention to the computational analysis of the physical behaviour of materials under finite deformations — in particular, the stress-strain behaviour, load-deformation behaviour and location of weak zones. Throughout this study, non-linear continuum mechanics is utilised as the mathematical basis of the constitutive and general finite element framework. In continuum mechanics, there exists no requirement to discretely characterise each microcrack that grows in a material, thus making it possible to provide analysis of the stress and strain response affected by micro-defects using material particles, which are localised collections of many atomic-scale particles. The continuum is thus a sum of its material particles. To complement this description of mechanics, constitutive and phenomenological equations are adopted from the non-linear thermodynamic phenomena of elasticity, plasticity, and damage; the laws of thermodynamics will therefore apply and are shown as such. The proposed material damage model is developed and implemented in the backend of the in-house computational mechanics toolbox SESKA, which uses finite element-based discretisation and approximation techniques. Field and scalar quantities, such as stress and strain, are computed with the use of the return-mapping method. The stress measures utilised are the 2nd Piola-Kirchhoff stress S and the Mandel stress Σ. The Newton-Raphson update scheme is applied in the plasticity evolution equations via the plastic multiplier (denoted λ), which innately controls the evolution of all other inelastic phenomena. Damage is a function of plastic evolution and thus plays a role in the plasticity multiplier calculation. Moreover, this proposed model makes the assumption of full isotropy, all material properties at a material point are the same in tension and compression and the same regardless of the dimension. Finally, several examples are utilised to showcase the model and all the intricacies are presented — the problem setup, boundary condition assignment and multi-layered analysis are detailed in the content of this study and the examples perform well under qualitative scrutiny. These examples include a cantilevered beam model, a simply supported bending model and a plane strain example to evaluate whether the material model achieves qualifiable correlation to expected behaviour and to assess whether the damage-related parameters affect the stress and strain behaviour as expected. In brief conclusion, this paper shows that the model achieves qualifiable correlation and all the material parameters function as expected.

Use of CO2 as a chemical feedstock in a wide range of applications has been postulated as a method to reduce its concentrations in the atmosphere, in an effort to combat climate change. An especially attractive use of CO2 is its hydrogenation to hydrocarbon fuels. If coupled with a source of renewably generated H2, this reaction could provide a source of carbon neutral energy that can be readily integrated with current infrastructure. This study looked at the performance of a range of iron-based bimetallic catalysts in promoting CO2 hydrogenation. Specifically, iron-nickel, iron-cobalt and iron-copper supported on βsilicon carbide were studied. It had been reported that these materials were more active and selective towards long chain hydrocarbons than their pure metal counterparts, although the reason was unclear. It was hypothesized that alloy formation in these materials would supresses carbide formation, in turn enhancing CO2 activation and hence reaction performance. The catalysts were synthesized using an ammonium hydroxide modified benzyl alcohol technique, which yielded ferrite nanoparticles below 10 nm with narrow size distribution. These ferrites were supported on silicon carbide via a suspension-deposition technique. In total five catalysts were synthesized – two iron-cobalt, two iron-nickel and one iron-copper. All catalysts were synthesized with a molar ratio of two iron to one counter-metal. The catalysts generally had average particle diameters of 6 nm, with one of the iron-nickel catalysts and the ironcopper catalyst slightly smaller at 3 nm and 2 nm respectively. The supported ferrites were reduced in order to yield the active metallic phase. It was shown via in situ characterization that a body centred cubic (BCC) alloy formed in the iron-cobalt samples (final size of 15 nm), while the iron-nickel samples were comprised of two alloy allotropes, with BCC and face centred cubic (FCC) crystalline structures (final size of 10 nm). The iron-copper sample reduced into pure iron (final size 20 nm) and copper phases. The increased size of the metallic phases compared to the freshly synthesized catalysts was due to sintering of the nanoparticles during reduction. In situ reaction studies showed that the iron-cobalt alloys were remarkably stable, with almost no changes in metallic phase seen. The iron-nickel samples were more readily changed by the reactant gases, with the BCC iron-nickel alloy converted to nickel-containing Hägg carbide. The FCC iron-nickel alloy remained unchanged, however. The iron-copper sample, which demonstrated no alloy formation, had its iron phase completely converted to Hägg carbide. Alloying of iron was thus shown to supress carbide formation. Reaction performance of all catalysts to long-chain hydrocarbons was poor when compared to similar materials tested in the literature, with conversions in the range of 4% - 8%. The product distribution was also undesirable, with the majority of product carbon reporting to CO in all five catalysts. Of the hydrocarbons formed, 80% - 96% reported to undesirable methane, depending on the counter metal used. It seemed that iron carbide in the iron-copper catalyst favoured longer chain hydrocarbon production when compared to the more metallic cobalt- and nickel-containing samples (which produced far more methane), but struggled to activate CO2 past CO. While the iron-cobalt catalysts seemed to facilitate more activation of CO2 to hydrocarbons, they showed less potential in forming longer chain hydrocarbons. The two iron-nickel catalysts behaved differently; one catalyst had a stable FCC phase, while its BCC alloy phase was completely converted to carbides, and favoured mostly methane formation. The other catalyst had a similarly stable FCC phase, but also maintained an appreciable BCC alloy fraction, and showed far more propensity to form longer chain hydrocarbons. This catalyst was still not as successful in promoting chain growth as its ironcopper counterpart, however. When comparing performance of the iron-cobalt and -copper catalysts, it seemed that carbide formation was beneficial in encouraging hydrocarbon chain growth, but detrimental to CO2 activation. On the other hand, the iron-nickel catalysts demonstrated that the BCC alloy phase was required to encourage chain growth, while the carbide resulting from its conversion diminished this. These results indicated that an improvement in the activation of CO2 did not necessarily increase hydrocarbon chain length, and that while carbides may be desirable for encouraging longer chain molecules, the presence of nickel in the carbide spoils the effect, at least in the range of temperatures tested. These results led to rejection of the hypothesis that alloys resulted in bimetallic catalysts' improved performance. They indicated that iron carbides are required for stable conversion of CO2 to longer chain hydrocarbons, but that the carbides alone were not extremely active nor selective. It is therefore likely that the counter metal's role in enhancing activity and selectivity at more dilute concentrations is by modulating the carbide phase. It is thus suggested that the impacts of counter metals in more iron-rich systems be studied, where carbide formation would be more facile. Additionally, the difficulty which the catalysts had in activating CO2 could be mitigated by promotion and use of an active support.

In many countries, railway transportation has been the primary mode of transportation, and engineers have been pushing boundaries to increase productivity and reduce costs for decades. The rail is the most important component of the railway infrastructure because it serves as the driving surface, direction guidance, and force transmission. Continuously Welded Rail (CWR), which is defined as rails that have been welded together, are now used in modern railways. When the rail is built on a bridge, the bridge's and rolling stock's behavior adds additional forces to CWR rails. As a result of the coupling effects of tracks and deformed superstructures, additional rail stresses are superimposed on other forces. These extra stresses are caused primarily by the longitudinal elongation of the superstructure as a result of temperature, braking, traction, and deck movement. The interaction of these forces between the rail and the bridge is therefore known as Track-Bridge-Interaction (TBI). Therefore, the horizontal forces must be precisely managed to prevent rail failure. This research presents a characterization of TBI for heavy haul railways and management of longitudinal forces to minimize the possibility of failure due to superimposed longitudinal forces. The Olifants River Viaduct (ORV), a 1 km long bridge with CWR and two continuous spans of 11 spans at each end and a drop span in the middle, was used as a case study in the research. The ORV has been equipped with monitoring systems to help manage the tracks. Thus, data from these systems were used to categorize the interaction forces. The research focused on categorizing the trains crossing the ORV into six (A-F) categories; the categorization was based on the train length and the commodities being hauled. The research also studied the speed variations of each train crossing the bridge. The speeds were analyzed using python and statistical tools in excel. Lastly, the impact of crossing trains on rail forces, rail temperature, ambient temperature, and deck movement was analyzed using python and statistical tools in excel. The study showed that the most frequent train to cross the bridge are category D trains with six locomotives and 342 wagons, while the train speed is dependent on the train length and the commodities hauled. Thus, the short trains in categories (A, B, and E) cross the bridge at higher constant speeds while the long trains in categories (C and D) cross the bridge at reduced speeds than the short trains but exhibit speed variations and sometimes cross the bridge at speeds exceeding the 50 km/h limit. Therefore, higher dynamic forces should be expected from short trains crossing the bridge at high constant speeds, but no additional forces should be expected on the rails from these trains as they experience no speed variations. At the same time, the long trains experience significant speed variations of both acceleration and decelerations, which imposes additional forces on the rails due to traction and braking. The imposed forces on the rails are predominantly due to crossing trains with significant speed variations of acceleration and deceleration, the acceleration change ranges from 5-30 km/h, and deceleration change ranges from 5-20 km/h. The braking and acceleration effect causes a change in the rail forces, rail temperature, and deck deflection, which in turn imposes additional forces on the rails. Therefore, high speed variation induces additional longitudinal forces on the rails. However, the imposed acceleration forces are higher than the braking forces, but the braking imposed forces are the most critical one as they tend to cause an increase in the tensile and compression forces when the forces are at their peaks, and there is a train present on the bridge, while acceleration causes a decrease in the rail forces at those times. The deck movement forces imposed on the rails were predominantly due to ambient temperature, which showed a positive linear relationship between the two. The deck expands with increasing ambient temperature and contracts with a decrease in ambient temperature. In contrast, the compression forces were within the given limits of 1100 kN, while the tension forces exceeded the rail force limit of 1400 kN when the rail temperature was between 0 − 20℃, and the deck deflection above 83 mm in the negative direction, and a present train on the bridge, making the rail more susceptible to failure during winter

This dissertation is for researching the feasibility of a Low Probability of Intercept (LPI), Air Defence (AD) Noise Radar with high range and doppler resolution. The research is approached by first simulating an S-Band LPI Noise Radar detecting a flying target and determining its range and velocity. The simulated Noise Radar is then implemented in the Universal Software Radio Peripheral (USRP) B210 H/W and tested against flying targets in monostatic mode. The actual results from H/W detection of real airborne targets are finally compared with simulated results.

No description available.

Mineral processing requires large quantities of water for its operations. With the continuing move to reduce freshwater withdrawal and mine water discharge, the mineral industry has been applying water recycling and reuse to improve water use efficiency. Previous studies on the use of remediated water as supplementary point source water and water recycling within the flotation circuit have shown that these practices affect the flotation process performance. It is therefore important to understand the effect that components within recycled and reclaimed water may have on flotation performance. While research on the effects of the abiotic components such as ions on flotation is well represented in literature, the effects of biotic water components, particularly microorganisms, on flotation performance still remain understudied and poorly understood. This study aimed to contribute to our understanding of the effects microbes have on the flotation performance of a PGM bearing ore from the Bushveld IgneousComplex in South Africa. In addition, the effects of xanthate collectors such as Sodium Ethyl Xanthate (SEX) and Sodium Isobutyl Xanthate (SIBX), widely employed in sulphide mineral beneficiation, on microbial growth were also considered. Results suggest that the presence of microbial cells and recycling of flotation waters increase water and solids recovery, while the metal grade recoveries were negatively affected. The microbial community used in this study could proliferate in the presence of up to 240 ppm for SEX and 480 ppm for SIBX, with an increase in the lag phase of growth observed with increasing collector concentrations. The presence of microbes at a concentration of 109 cells/ml resulted in the compete removal of 60 ppm collector from solution, both SEX and SIBX, from solution within a 2-hour time period. Outcomes of this study include a method for the measurement of microbial activity within a mineral slurry, which will further facilitate studying the effects of microorganisms on flotation systems. The work presented in this dissertation revealed that the presence of microbial consortia studied here negatively affected metal (Cu and Ni) grades attesting to the detrimental effect posed by the usage of microbial laden water for flotation operations. Further, the microbial consortium showed that it can facilitate the bioremoval of xanthate from solution which could affect the recovery of valuable minerals during flotation operations. This mechanistic framework, explaining the mechanisms by which the microbes affect flotation and the detrimental effects posed by microbes found in flotation waters is an actionable (fundamental) for the mining industry. From the present study, it is recommended that researchers should assess the microbial load present in flotation waters prior to their usage for flotation operations as high microbial load proved to be detrimental as far as flotation performance is concerned on a laboratory scale. Conclusively, the knowledge generated from this study builds on the ongoing scientific efforts decoding the effects of naturally prevailing microbes in flotation waters.

Building accurate classifiers for predicting group membership is made difficult when data is skewed or imbalanced which is typical of real world data sets. The classifier has the tendency to be biased towards the over represented group as a result. This imbalance is considered a class imbalance problem which will induce bias into the classifier particularly when the imbalance is high. Class imbalance data usually suffers from data intrinsic properties beyond that of imbalance alone. The problem is intensified with larger levels of imbalance most commonly found in observational studies. Extreme cases of class imbalance are commonly found in many domains including fraud detection, mammography of cancer and post term births. These rare events are usually the most costly or have the highest level of risk associated with them and are therefore of most interest. To combat class imbalance the machine learning community has relied upon embedded, data preprocessing and ensemble learning approaches. Exploratory research has linked several factors that perpetuate the issue of misclassification in class imbalanced data. However, there remains a lack of understanding between the relationship of the learner and imbalanced data among the competing approaches. The current landscape of data preprocessing approaches have appeal due to the ability to divide the problem space in two which allows for simpler models. However, most of these approaches have little theoretical bases although in some cases there is empirical evidence supporting the improvement. The main goals of this research is to introduce newly proposed a priori based re-sampling methods that improve concept learning within class imbalanced data. The results in this work highlight the robustness of these techniques performance within publicly available data sets from different domains containing various levels of imbalance. In this research the theoretical and empirical reasons are explored and discussed.

This research synthesizes existing research findings that social presence (sense of connection with others) and physical presence (sense of being there) increase learning outcomes in Virtual Learning Environments (VLEs) with findings that traditional motion tracking of participants wearing head mounted displays in virtual reality increases both physical and social presence. This information suggests that motion tracking in mixed reality VLEs has a positive impact on social presence and on physical presence. For this study, the affordance of free movement among virtual objects is enabled by Microsoft Kinect tracking of the user's position that is translated to movement of the virtual camera to simulate user movement and proximity to elements of the virtual environment. This study used a mixed method, multimodal approach including qualitative, subjective, objective, and physiological data to measure social and physical presence. The testbed for this research was TLE TeachLivE™, a mixed reality classroom populated with virtual students. The subjective measures are 1) modified Witmer and Singer Questionnaire and 2) Social Presence Instrument (Bailenson, 2002b). The objective measure is a literature based Social Presence Behavioral Coding sheet used to record frequency of occurrences of factors of social presence. Finally, the physiological measure is heart rate as recorded by the MIO Alpha. The primary contribution of this study was that the hypotheses that the affordance of movement in a mixed reality classroom has a positive impact on user perception and experience of a) physical presence and b) social presence in a VLE were supported. This hypothesis was supported in all three measures. The secondary contribution of this research is the literature based Social Presence Behavioral Coding. The final contribution of this research is a research framework that integrates subjective, objective, and physiological measures of social presence in one study. This approach can be applied to various user experience research studies of various VLEs. Finally, in addition to general alignment of the physiological, objective, and subjective measures, there were anecdotal instances of factors of social presence occurring simultaneously with increased heart rate.

Parallel distributed discrete event simulation (PDDES) is the execution of a discrete event simulation on a tightly or loosely coupled computer system with multiple processors. The discrete-event simulation model is decomposed into several logical processors (LPs) or simulation objects that can be executed concurrently using partitioning types such as spatial and temporal. PDDES is exceedingly important for the reduction of the simulation time, increase of model size, intellectual property issue mitigation in multi-enterprise simulations, and the sharing of resources. One of the problems with PDDES is the time management to provide flow control over event processing, the process flow, and the coordination of different logical processors to take advantage of parallelism. Time Warp (TW), Breathing Time Buckets (BTB), and Breathing Time Warp (BTW) are three time management schemes studied by this research. For a particular PDDES problem, unfortunately, there is no clear methodology to decide a priori a time management scheme to achieve higher system and simulation performance. This dissertation shows a new approach for selecting the optimal time synchronization technique class that corresponds to a particular parallel distributed and discrete simulation with different levels of simulation logic complexity. Simulation complexities such as branching, parallelism, function calls, concurrency, iterations, mathematical computations, messaging frequency, event processing, and number of simulation objects interactions were given a weighted parameter value based on the cognitive weight approach. Deep belief neural networks were then used to perform deep learning from the simulation complexity parameters and their corresponding optimal time synchronization scheme value as measured by speedup performance.

Traditional techniques used for verification, validation, and accreditation (VV&A) of Synthetic Natural Environments for military applications are time consuming, subjective, and often costly. Due to varying levels of common visual factors, Synthetic Natural Environments (SNE) vary widely in appearance and use case. Early identification of these factors in the SNE life cycle may improve its Visual Aesthetic Quality (VAQ) while reducing VV&A issues downstream and informing future development. This research explores supplementing existing VV&A techniques with the Delphi Method during the conceptualization phase of an interoperable SNE development in order to identify the level of importance of SNE VAQ factors for distributed, dissimilar simulations earlier in the life cycle. Delphi Method findings on VAQ factors drove the development of four different SNEs for a selected urban city center. The importance of VAQ factors within the SNEs were derived through Conjoint Analysis of data from a survey in which end user participants evaluated each SNE using a design that incorporated fractional factorial screening and Graeco-Latin Squares. Research findings suggest: (1) using an online Delphi Method enables early identification of a correlated set of expertly accepted primary VAQ factors that affect overall realism and training utility in the virtual domain; (2) Conjoint analysis improves the understanding of the significance and power of identified factors and preferences; (3) VAQ importance rankings differed across the Delphi Method and Conjoint Analysis, nor did the Delphi Method successfully predict the two-factor interactions discovered through Conjoint Analysis of the screening design; and (4) Data mining of historical SNE issue reports did not identify the same level of importance of VAQ factors as users reviewing SNE representations through a Conjoint Analysis and Delphi panel expert forecasts. Limitations with the proposed technique, as well as recommendations for additional research are provided to further refine the parameters associated with these subjective factors to increase the efficiency and application of the proposed approach.