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Research Topic : Flight Dynamics
Socio-Economic Objective : Wind Energy
Australian State/Territory : NSW
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  • Active Funded Activity

    Discovery Projects - Grant ID: DP210100990

    Funder
    Australian Research Council
    Funding Amount
    $360,000.00
    Summary
    Machine Learning and Shape Optimisation of Fluid-Structure Interactions. This project aims to address vibrations of solid structures by utilising a combination of advanced experimental and computational methods. This project expects to generate new knowledge in the area of flow-induced vibrations utilising the new techniques of machine learning and evolutionary shape optimisation. Expected outcomes of this project include greatly accelerated discovery of mechanisms leading to structural vibratio .... Machine Learning and Shape Optimisation of Fluid-Structure Interactions. This project aims to address vibrations of solid structures by utilising a combination of advanced experimental and computational methods. This project expects to generate new knowledge in the area of flow-induced vibrations utilising the new techniques of machine learning and evolutionary shape optimisation. Expected outcomes of this project include greatly accelerated discovery of mechanisms leading to structural vibrations and optimising structure geometries to either enhance or suppress the vibrations. This should provide significant benefits, such as the design strategies for improved energy harvesters, such as current oscillators, or more stable structures, such as platforms for offshore wind turbines.
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    Active Funded Activity

    Linkage Projects - Grant ID: LP230100456

    Funder
    Australian Research Council
    Funding Amount
    $371,499.00
    Summary
    Dynamic model assisted fault diagnostics of wind turbine gearbox. This project aims to develop novel condition monitoring methodologies for the gearbox of large horizontal-axis wind turbines which are widely installed in wind farms for generating renewable energy. This project expects to generate a new diagnostic framework by integrating dynamic model assisted simulations and digital twin-based approaches. Expected outcomes of this project include new vibration-based methods for fault diagnostic .... Dynamic model assisted fault diagnostics of wind turbine gearbox. This project aims to develop novel condition monitoring methodologies for the gearbox of large horizontal-axis wind turbines which are widely installed in wind farms for generating renewable energy. This project expects to generate a new diagnostic framework by integrating dynamic model assisted simulations and digital twin-based approaches. Expected outcomes of this project include new vibration-based methods for fault diagnostics and predictions of the remaining useful life of turbine gearboxes. This should provide significant benefits to the Australian Wind Industry by ensuring reliable operation of wind turbines, reducing turbine downtime and reducing operation and maintenance costs; ultimately lowering the cost of energy from wind.
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    Funded Activity

    Discovery Projects - Grant ID: DP160103501

    Funder
    Australian Research Council
    Funding Amount
    $385,000.00
    Summary
    A new role for vibration analysis in gear wear modelling and prediction. This project aims to improve prediction of the remaining useful life of gears. Gears are widely used in industry and transport. This project aims to integrate the two main methods of gear condition monitoring, vibration and oil analysis, and perform model-based wear prediction with the tribology and dynamic models continually updated on the basis of measured wear debris and vibration. New signal processing tools should allo .... A new role for vibration analysis in gear wear modelling and prediction. This project aims to improve prediction of the remaining useful life of gears. Gears are widely used in industry and transport. This project aims to integrate the two main methods of gear condition monitoring, vibration and oil analysis, and perform model-based wear prediction with the tribology and dynamic models continually updated on the basis of measured wear debris and vibration. New signal processing tools should allow estimation of relatively weak friction forces, previously neglected, as an important prognostic tool. This would allow detailed root cause analysis and prediction of remaining useful life. Improvements in gear prognosis would have safety and economic benefits by eliminating unforeseen catastrophic failures and optimising maintenance schedules.
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    Active Funded Activity

    Discovery Projects - Grant ID: DP190103231

    Funder
    Australian Research Council
    Funding Amount
    $309,934.00
    Summary
    Cepstral methods of operational modal analysis to separate multiple sources. This project aims to develop new methods of operational modal analysis in situations with multiple complex sources, such as rotating machines. The project will obtain scaled mode shapes as well as separated scaled sources. One of the main applications will be to improve the prognostics of machines by having separated scaled estimates of the forcing functions to make it easier to find fault parameters which trend monoton .... Cepstral methods of operational modal analysis to separate multiple sources. This project aims to develop new methods of operational modal analysis in situations with multiple complex sources, such as rotating machines. The project will obtain scaled mode shapes as well as separated scaled sources. One of the main applications will be to improve the prognostics of machines by having separated scaled estimates of the forcing functions to make it easier to find fault parameters which trend monotonically towards failure, and thus greatly improve the estimates of remaining useful equipment life. An additional benefit of the application will be the ability to predict overall noise radiation from a machine or object if both the sources and modal models are scaled.
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