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Status : Active
Research Topic : systems models
Australian State/Territory : TAS
Australian State/Territory : ACT
Australian State/Territory : SA
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  • Active Funded Activity

    Linkage Projects - Grant ID: LP200100406

    Funder
    Australian Research Council
    Funding Amount
    $1,161,512.00
    Summary
    Building Australia's next-generation ocean-sea ice model. Ocean and sea ice models are used for predicting future ocean and climate states, and for climate process research. This project aims to bring the next generation of ocean-sea ice models to Australia and configure the models for our local priorities. The ultimate goal is to create a new coupled ocean-sea ice model for Australia that includes surface waves and biogeochemistry. The model will be optimised and evaluated on Australian facilit .... Building Australia's next-generation ocean-sea ice model. Ocean and sea ice models are used for predicting future ocean and climate states, and for climate process research. This project aims to bring the next generation of ocean-sea ice models to Australia and configure the models for our local priorities. The ultimate goal is to create a new coupled ocean-sea ice model for Australia that includes surface waves and biogeochemistry. The model will be optimised and evaluated on Australian facilities, and released for community use. These developments underpin future ocean state forecasts, sea ice forecasts, wave forecasts, decadal climate prediction and climate process studies. The project will benefit search and rescue, Defence and shipping operations, and will enhance future climate projections.
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    Active Funded Activity

    Linkage Projects - Grant ID: LP220200912

    Funder
    Australian Research Council
    Funding Amount
    $612,689.00
    Summary
    Hydrogen generation by subsurface iron mineral transformations. Aim The aim of this project is to elucidate key factors responsible for natural hydrogen generation in Australian subsurface environments. Significance Large amounts of this valuable resource are produced naturally with estimates of production rates of this “gold” hydrogen at least 100 times the annual demand for this critical resource. Expected Outcomes Based on improved understanding of the source of natural hydrogen, predictive .... Hydrogen generation by subsurface iron mineral transformations. Aim The aim of this project is to elucidate key factors responsible for natural hydrogen generation in Australian subsurface environments. Significance Large amounts of this valuable resource are produced naturally with estimates of production rates of this “gold” hydrogen at least 100 times the annual demand for this critical resource. Expected Outcomes Based on improved understanding of the source of natural hydrogen, predictive tools will be developed that will assist in assessing the viability in Australia of hydrogen exploration and engineered retrieval. Benefits Ready access to naturally produced hydrogen could enable Australia to replace hydrogen that is currently generated via the use of unabated hydrocarbons.
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    Active Funded Activity

    Industrial Transformation Research Hubs - Grant ID: IH180100002

    Funder
    Australian Research Council
    Funding Amount
    $5,000,000.00
    Summary
    ARC Research Hub for Driving Farming Productivity and Disease Prevention. The ARC Research Hub for Driving Farming Productivity and Disease Prevention aims to increase farm production and disease prevention through advancing and transferring new artificial intelligence technologies into industrial deployment. The Hub will combine machine vision, machine learning, software quality control, engineering, biology, and farming industries to develop technologies to build more intelligent systems. Thes .... ARC Research Hub for Driving Farming Productivity and Disease Prevention. The ARC Research Hub for Driving Farming Productivity and Disease Prevention aims to increase farm production and disease prevention through advancing and transferring new artificial intelligence technologies into industrial deployment. The Hub will combine machine vision, machine learning, software quality control, engineering, biology, and farming industries to develop technologies to build more intelligent systems. These dynamic systems will help determine what goal to achieve and the most efficient plan to achieve it. This Hub is expected to contribute to higher farming efficiency, lower production costs and fewer disease risks, giving the Australian industry new business opportunities and an international competitive advantage.
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