Linkage Infrastructure, Equipment And Facilities - Grant ID: LE130100119
Funder
Australian Research Council
Funding Amount
$420,000.00
Summary
Materials characterisation facility for a sustainable future. Sustainable development will require access to large-scale carbon-neutral energy production. The tools provided through this project will enable the development of new knowledge and novel materials and processes technologies that will deliver this carbon-neutral energy.
Green cool wine: solar powered solid adsorption refrigeration system with ice storage to provide cooling capability for wine industry. The project is to develop a solar thermal powered refrigeration system that is able to build up an ice bank (as a storage) through daily intermittent cycle, from late Australian spring. The ice bank will used in the vintage season in a winery for cooling purposes. The system is able to reduce the carbon foot print of the wineries significantly.
Improved models of nanoporous carbons for greater fundamental insight and better sustainable technology. Storage of hydrogen and energy from intermittent sources like solar and wind, and 'carbon capture' from coal-fired power stations are essential requirements for a sustainable future. A state-of-the-art computer model will be developed and demonstrated to help deliver these and other technologies for a safe and sustainable future.
Novel CO2-stable oxygen transporting membranes for oxyfuel-based CO2 capture and utilization. Industrial carbon dioxide (CO2) emission is considered the main contribution to global warming. This project aims to develop a new class of oxygen transporting membrane (OTM) for CO2 capture and utilisation. To achieve this objective, the formation process and the unique characteristic of the membrane, as well as the oxygen transportation mechanism through the membrane will be investigated, experimental ....Novel CO2-stable oxygen transporting membranes for oxyfuel-based CO2 capture and utilization. Industrial carbon dioxide (CO2) emission is considered the main contribution to global warming. This project aims to develop a new class of oxygen transporting membrane (OTM) for CO2 capture and utilisation. To achieve this objective, the formation process and the unique characteristic of the membrane, as well as the oxygen transportation mechanism through the membrane will be investigated, experimentally and theoretically. This will advance the membrane technology in economically viable and efficient, clean energy applications.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE120102052
Funder
Australian Research Council
Funding Amount
$375,000.00
Summary
Resolving flame stabilisation mechanisms in the transition to moderate or intense low oxygen dilution (MILD) combustion. Next-generation combustion technologies are required in the transition to more efficient, and less polluting, energy production. This project will address the important issue of understanding flame stabilisation on a fundamental level to facilitate the design and development of more efficient and sustainable combustion systems.
High power density, low cogging torque and low-cost micro-scale wind turbine generator system utilising soft magnetic composite materials. This project will develop a low-cost, high-performance and high-ef?ciency micro-scale wind turbine generator using a new magnetic material consisting of iron powder, which can be easily pressed into any desirable shape. This allows considerably simpli?ed manufacturing, greater design ?exibility and ease of scaling to higher output powers.
Better predictions of spray flames. This project aims to predict spray flames using experimental and computational modelling of the combustion near burning droplets in spray flames. Spray flames are the dominant source of energy for the transportation sector, and are expected to remain so well into the future. Limited understanding of combustion processes surrounding the burning of the droplets restricts further technological development. This project is expected to enable progress in design too ....Better predictions of spray flames. This project aims to predict spray flames using experimental and computational modelling of the combustion near burning droplets in spray flames. Spray flames are the dominant source of energy for the transportation sector, and are expected to remain so well into the future. Limited understanding of combustion processes surrounding the burning of the droplets restricts further technological development. This project is expected to enable progress in design tools for spray flame combustors operating on liquid fuels, including bio-fuels. The result will be lower pollutant emissions and lower the cost of design of new engines.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE120101161
Funder
Australian Research Council
Funding Amount
$375,000.00
Summary
Compressive sensing based probabilistic graphical models (PGM). The aim of the project is to develop fast, large scale probabilistic graphical models (PGM) learning and inference methods. The resulting system will be able to process large scale PGMs on a standard PC, and will be easily extendable to computer clustering for larger scale PGMs requiring higher precision.
Efficient causal discovery from observational data. Discovering cause-effect relationships is the ultimate goal for many applications. Randomised control trial is the gold standard for discovering causal relationships. However, conducting such trials is impossible in many cases due to cost and/or ethical concerns. In contrast, a large amount of data has been accumulated in all areas. It is desirable to infer causal relationships from data directly and automatically. This project aims to develop ....Efficient causal discovery from observational data. Discovering cause-effect relationships is the ultimate goal for many applications. Randomised control trial is the gold standard for discovering causal relationships. However, conducting such trials is impossible in many cases due to cost and/or ethical concerns. In contrast, a large amount of data has been accumulated in all areas. It is desirable to infer causal relationships from data directly and automatically. This project aims to develop fast and scalable data mining methods for identifying causal relationships from large and/or high dimensional data sets. The developed methods will mainly be evaluated in real world biological applications. The research outcomes will be useful in many areas for causal reasoning and decision making.Read moreRead less
Developing novel data mining methods to reveal complex group relationships from heterogeneous data. This project aims to develop novel and effective data mining methods that will enable us to unravel the relationships between multiple, rather than individual, components of complex systems (such as genes, gene regulators and cancer), which is crucial to understanding how such systems work. Potential applications for such methods are extensive.