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Field of Research : Computational Fluid Dynamics
Research Topic : Wind
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Computational Fluid Dynamics (2)
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  • Funded Activity

    Linkage Projects - Grant ID: LP100100746

    Funder
    Australian Research Council
    Funding Amount
    $160,014.00
    Summary
    Investigation into flow over complex topography and escarpments for wind turbine siting using experimental and computational methods. This project will improve national capability to optimise power production from wind turbine farms in complex terrain by improving the understanding of the flow regime. By better understanding separated regions and the turbulent structures within these regions power production can be optimised and fatigue risks associated with turbine positioning in complex sites .... Investigation into flow over complex topography and escarpments for wind turbine siting using experimental and computational methods. This project will improve national capability to optimise power production from wind turbine farms in complex terrain by improving the understanding of the flow regime. By better understanding separated regions and the turbulent structures within these regions power production can be optimised and fatigue risks associated with turbine positioning in complex sites can be reduced. This will improve confidence in wind farm site assessment techniques and consequently reduce economic risks associated with current wind farm viability assessments. By increasing national capacity to generate clean energy stationary energy emissions can be reduced. This project will also deliver high calibre graduates that will be potential future industry leaders.
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    Funded Activity

    Discovery Projects - Grant ID: DP180100898

    Funder
    Australian Research Council
    Funding Amount
    $403,306.00
    Summary
    High-fidelity simulations for new models that reduce noise pollution. This project aims to develop a method for accurate and affordable prediction and mitigation of flow-induced noise. The innovative approach, based on recent developments in simulation and data-driven modelling, expects to reduce environmental noise pollution, improve public health and ease the impact of urbanisation. To date methodological limitations have hampered our ability to predict noise reliably and hence control it. Thi .... High-fidelity simulations for new models that reduce noise pollution. This project aims to develop a method for accurate and affordable prediction and mitigation of flow-induced noise. The innovative approach, based on recent developments in simulation and data-driven modelling, expects to reduce environmental noise pollution, improve public health and ease the impact of urbanisation. To date methodological limitations have hampered our ability to predict noise reliably and hence control it. This project, exploiting proven high-fidelity simulation and machine-learning techniques to overcome limitations to produce the scientific knowledge required for practical noise mitigation. Benefits include quieter aerospace, marine and renewable energy technologies, creating more pleasant communities.
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