In-situ based reanalysis of the global ocean temperature and salinity with ISAS: variability of the heat content and steric height

The In Situ Analysis System (ISAS) was developed to produce gridded fields of temperature and salinity that preserve as much as possible the time and space sampling capabilities of the Argo network of profiling floats. Since the first global re-analysis performed in 2009, the system has evolved and a careful delayed mode processing of the 2002-2012 dataset has been carried out using version 6 of ISAS and updating the statistics to produce the ISAS13 analysis. This last version is now implemented as the operational analysis tool at the Coriolis data centre. The robustness of the results with respect to the system evolution is explored through global quantities of climatological interest: the Ocean Heat Content and the Steric Height. Estimates of errors consistent with the methodology are computed. This study shows that building reliable statistics on the fields is fundamental to improve the monthly estimates and to determine the absolute error bars. The new mean fields and variances deduced from the ISAS13 re-analysis and dataset show significant changes relative to the previous ISAS estimates, in particular in the southern ocean, justifying the iterative procedure. During the decade covered by Argo, the intermediate waters appear warmer and saltier in the North Atlantic and fresher in the Southern Ocean than in WOA05 long term mean. At inter-annual scale, the impact of ENSO on the Ocean Heat Content and Steric Height is observed during the 2006-2007 and 2009-2010 events captured by the network.

Catalytic CO2 hydrogenation: study at the molecular scale using field emission techniques

An accurate characterization of the rhodium specimen was obtained via FIM experiments. Reaction behaviors between H2 and CO2 were observed in FEM mode at 700 K. At this temperature, CO desorption occurs, preventing CO+H2 reaction. Surface is mainly recovered by oxygen; reaction with hydrogen occurs. Finally, we can identify the reaction as the Reverse Water Gas Shift.

Implementation of an intelligent sensor for measurement and prediction of solar radiation and atmospheric temperature

Dissertação de mestrado, Engenharia Electrónica e Telecomunicações, Faculdade de Ciências e Tecnologia, Universidade do Algarve, 2011

Síntese e caracterização de catalisadores de LaNiO3 não suportados e suportados em Al2O3 e ZrO2 para a reforma a vapor do metano

Ionic oxides with ABO3 structure, where A represents a rare earth element or an alkaline metal and B is a transition metal from group VIII of the periodic table are potential catalysts for oxidation and good candidates for steam reforming reaction. Different methods have been considered for the synthesis of the oxide materials with perovskite structure to produce a high homogeneous material with low amount of impurities and low calcination temperatures. In the current work, oxides with the LaNiO3 formula had been synthesized using the method of the polymeric precursors. The thermal treatment of the materials took place at 300 ºC for 2h. The material supported in alumina and/or zirconia was calcined at 800 ºC temperature for 4h. The samples had been characterized by the following techniques: thermogravimetry; infrared spectroscopy; X-ray diffraction; specific surface area; distribution of particle size; scanning electron microscopy and thermo-programmed reduction. The steam reforming reaction was carried out in a pilot plant using reducing atmosphere in the reactor with a mixture of 10% H2-Argon, a mass about 5g of catalyst, flowing at 50 mL.min-1. The temperature range used was 50 - 1000 oC with a heating rate of 10 oC.min-1. A thermal conductivity detector was used to analyze the gas after the water trapping, in order to permit to quantify the consumption of hydrogen for the lanthanum nickelates (LaNiO3). The results showed that lanthanum nickelate were more efficient when supported in alumina than when supported in zirconia. It was observed that the methane conversion was approximately 100% and the selectivity to hydrogen was about 70%. In all cases were verified low selectivity to CO and CO2

Development of measurement and modeling techniques to quantify atmospheric deposition of persistent, bioaccumulative and toxic chemicals in the Great Lakes

Measurement and modeling techniques were developed to improve over-water gaseous air-water exchange measurements for persistent bioaccumulative and toxic chemicals (PBTs). Analytical methods were applied to atmospheric measurements of hexachlorobenzene (HCB), polychlorinated biphenyls (PCBs), and polybrominated diphenyl ethers (PBDEs). Additionally, the sampling and analytical methods are well suited to study semivolatile organic compounds (SOCs) in air with applications related to secondary organic aerosol formation, urban, and indoor air quality. A novel gas-phase cleanup method is described for use with thermal desorption methods for analysis of atmospheric SOCs using multicapillary denuders. The cleanup selectively removed hydrogen-bonding chemicals from samples, including much of the background matrix of oxidized organic compounds in ambient air, and thereby improved precision and method detection limits for nonpolar analytes. A model is presented that predicts gas collection efficiency and particle collection artifact for SOCs in multicapillary denuders using polydimethylsiloxane (PDMS) sorbent. An approach is presented to estimate the equilibrium PDMS-gas partition coefficient (Kpdms) from an Abraham solvation parameter model for any SOC. A high flow rate (300 L min-1) multicapillary denuder was designed for measurement of trace atmospheric SOCs. Overall method precision and detection limits were determined using field duplicates and compared to the conventional high-volume sampler method. The high-flow denuder is an alternative to high-volume or passive samplers when separation of gas and particle-associated SOCs upstream of a filter and short sample collection time are advantageous. A Lagrangian internal boundary layer transport exchange (IBLTE) Model is described. The model predicts the near-surface variation in several quantities with fetch in coastal, offshore flow: 1) modification in potential temperature and gas mixing ratio, 2) surface fluxes of sensible heat, water vapor, and trace gases using the NOAA COARE Bulk Algorithm and Gas Transfer Model, 3) vertical gradients in potential temperature and mixing ratio. The model was applied to interpret micrometeorological measurements of air-water exchange flux of HCB and several PCB congeners in Lake Superior. The IBLTE Model can be applied to any scalar, including water vapor, carbon dioxide, dimethyl sulfide, and other scalar quantities of interest with respect to hydrology, climate, and ecosystem science.

Leakage Temperature Dependency Aware Real-Time Scheduling for Power and Thermal Optimization

Catering to society’s demand for high performance computing, billions of transistors are now integrated on IC chips to deliver unprecedented performances. With increasing transistor density, the power consumption/density is growing exponentially. The increasing power consumption directly translates to the high chip temperature, which not only raises the packaging/cooling costs, but also degrades the performance/reliability and life span of the computing systems. Moreover, high chip temperature also greatly increases the leakage power consumption, which is becoming more and more significant with the continuous scaling of the transistor size. As the semiconductor industry continues to evolve, power and thermal challenges have become the most critical challenges in the design of new generations of computing systems. In this dissertation, we addressed the power/thermal issues from the system-level perspective. Specifically, we sought to employ real-time scheduling methods to optimize the power/thermal efficiency of the real-time computing systems, with leakage/ temperature dependency taken into consideration. In our research, we first explored the fundamental principles on how to employ dynamic voltage scaling (DVS) techniques to reduce the peak operating temperature when running a real-time application on a single core platform. We further proposed a novel real-time scheduling method, “M-Oscillations” to reduce the peak temperature when scheduling a hard real-time periodic task set. We also developed three checking methods to guarantee the feasibility of a periodic real-time schedule under peak temperature constraint. We further extended our research from single core platform to multi-core platform. We investigated the energy estimation problem on the multi-core platforms and developed a light weight and accurate method to calculate the energy consumption for a given voltage schedule on a multi-core platform. Finally, we concluded the dissertation with elaborated discussions of future extensions of our research.

High Pressure and Low Temperature Study of Ammonia Borane and Lithium Amidoborane

Hydrogen has been considered as a potentially efficient and environmentally friendly alternative energy solution. However, one of the most important scientific and technical challenges that the “hydrogen economy” faces is the development of safe and economically viable on-board hydrogen storage for fuel cell applications, especially to the transportation sector. Ammonia borane (BH3NH3), a solid state hydrogen storage material, possesses exceptionally high hydrogen content (19.6 wt%).However, a fairly high temperature is required to release all the hydrogen atoms, along with the emission of toxic borazine. Recently research interests are focusing on the improvement of H2 discharge from ammonia borane (AB) including lowering the dehydrogenation temperature and enhancing hydrogen release rate using different techniques. Till now the detailed information about the bonding characteristics of AB is not sufficient to understand details about its phases and structures. Elemental substitution of ammonia borane produces metal amidoboranes. Introduction of metal atoms to the ammonia borane structure may alter the bonding characteristics. Lithium amidoborane is synthesized by ball milling of ammonia borane and lithium hydride. High pressure study of molecular crystal provides unique insight into the intermolecular bonding forces and phase stability. During this dissertation, Raman spectroscopic study of lithium amidoborane has been carried out at high pressure in a diamond anvil cell. It has been identified that there is no dihydrogen bond in the lithium amidoborane structure, whereas dihydrogen bond is the characteristic bond of the parent compound ammonia borane. It has also been identified that the B-H bond becomes weaker, whereas B-N and N-H bonds become stronger than those in the parent compound ammonia borane. At high pressure up to 15 GPa, Raman spectroscopic study indicates two phase transformations of lithium amidoborane, whereas synchrotron X-ray diffraction data indicates only one phase transformation of this material. Pressure and temperature has a significant effect on the structural stability of ammonia borane. This dissertation explored the phase transformation behavior of ammonia borane at high pressure and low temperature using in situ Raman spectroscopy. The P-T phase boundary between the tetragonal (I4mm) and orthorhombic (Pmn21) phases of ammonia borane has been determined. The transition has a positive Clapeyron slope which indicates the transition is of exothermic in nature. Influence of nanoconfinemment on the I4mm to Pmn21 phase transition of ammonia borane was also investigated. Mesoporus silica scaffolds SBA-15 with pore size of ~8 nm and MCM-41 with pore size of 2.1-2.7 nm, were used to nanoconfine ammonia borane. During cooling down, the I4mm to Pmn21 phase transition was not observed in MCM-41 nanoconfined ammonia borane, whereas the SBA-15 nanocondfined ammonia borane shows the phase transition at ~195 K. Four new phases of ammonia borane were also identified at high pressure up to 15 GPa and low temperature down to 90 K.

An investigation of two real time machine learning techniques that could enhance the adaptability of game AI agents

Developers strive to create innovative Artificial Intelligence (AI) behaviour in their games as a key selling point. Machine Learning is an area of AI that looks at how applications and agents can be programmed to learn their own behaviour without the need to manually design and implement each aspect of it. Machine learning methods have been utilised infrequently within games and are usually trained to learn offline before the game is released to the players. In order to investigate new ways AI could be applied innovatively to games it is wise to explore how machine learning methods could be utilised in real-time as the game is played, so as to allow AI agents to learn directly from the player or their environment. Two machine learning methods were implemented into a simple 2D Fighter test game to allow the agents to fully showcase their learned behaviour as the game is played. The methods chosen were: Q-Learning and an NGram based system. It was found that N-Grams and QLearning could significantly benefit game developers as they facilitate fast, realistic learning at run-time.

An application of two techniques for the analysis of short, multivariate non-stationary time-series of Mauritanian trawl survey data

Min/max autocorrelation factor analysis (MAFA) and dynamic factor analysis (DFA) are complementary techniques for analysing short (> 15-25 y), non-stationary, multivariate data sets. We illustrate the two techniques using catch rate (cpue) time-series (1982-2001) for 17 species caught during trawl surveys off Mauritania, with the NAO index, an upwelling index, sea surface temperature, and an index of fishing effort as explanatory variables. Both techniques gave coherent results, the most important common trend being a decrease in cpue during the latter half of the time-series, and the next important being an increase during the first half. A DFA model with SST and UPW as explanatory variables and two common trends gave good fits to most of the cpue time-series. (c) 2004 International Council for the Exploration of the Sea. Published by Elsevier Ltd. All rights reserved.

A new low temperature synthesis route of fraipontite (Zn,Al)3(Si,Al)2O5(OH)4

A low temperature synthesis method based on the decomposition of urea at 90°C in water has been developed to synthesise fraipontite. This material is characterised by a basal reflection 001 at 7.44 Å. The trioctahedral nature of the fraipontite is shown by the presence of a 06l band around 1.54 Å, while a minor band around 1.51 Å indicates some cation ordering between Zn and Al resulting in Al-rich areas with a more dioctahedral nature. TEM and IR indicate that no separate kaolinite phase is present. An increase in the Al content however, did result in the formation of some SiO2 in the form of quartz. Minor impurities of carbonate salts were observed during the synthesis caused by to the formation of CO32- during the decomposition of urea.

Cross-correlations between weather variables in Australia

The information on climate variations is essential for the research of many subjects, such as the performance of buildings and agricultural production. However, recorded meteorological data are often incomplete. There may be a limited number of locations recorded, while the number of recorded climatic variables and the time intervals can also be inadequate. Therefore, the hourly data of key weather parameters as required by many building simulation programmes are typically not readily available. To overcome this gap in measured information, several empirical methods and weather data generators have been developed. They generally employ statistical analysis techniques to model the variations of individual climatic variables, while the possible interactions between different weather parameters are largely ignored. Based on a statistical analysis of 10 years historical hourly climatic data over all capital cities in Australia, this paper reports on the finding of strong correlations between several specific weather variables. It is found that there are strong linear correlations between the hourly variations of global solar irradiation (GSI) and dry bulb temperature (DBT), and between the hourly variations of DBT and relative humidity (RH). With an increase in GSI, DBT would generally increase, while the RH tends to decrease. However, no such a clear correlation can be found between the DBT and atmospheric pressure (P), and between the DBT and wind speed. These findings will be useful for the research and practice in building performance simulation.