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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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.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE200101650
Funder
Australian Research Council
Funding Amount
$412,700.00
Summary
Intelligent active control of flow-induced vibration. This project aims to develop advanced and effective control methods using an innovative interdisciplinary approach for flow-induced vibration for a wide range of generic elements of engineering structures. This project expects to generate new scientific knowledge of fluid-structure interaction that is essential for the prediction and control of flow-induced vibration. The expected outcomes of this project are artificial intelligence based act ....Intelligent active control of flow-induced vibration. This project aims to develop advanced and effective control methods using an innovative interdisciplinary approach for flow-induced vibration for a wide range of generic elements of engineering structures. This project expects to generate new scientific knowledge of fluid-structure interaction that is essential for the prediction and control of flow-induced vibration. The expected outcomes of this project are artificial intelligence based active control methods for flow-induced vibration. Ultimately, this project should provide significant benefits, such as advances in scientific knowledge and improved technologies for the areas of energy, transport, buildings and infrastructure.Read moreRead less
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.Read moreRead less
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.Read moreRead less
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.Read moreRead less
Linkage Infrastructure, Equipment And Facilities - Grant ID: LE120100067
Funder
Australian Research Council
Funding Amount
$210,000.00
Summary
Wind profiler network for planetary boundary layer research. Understanding winds in the lower atmosphere is of great fundamental and practical importance. This new wind monitoring network will help Australian scientists to better predict propagation of tropical cyclones, to improve the efficiency of wind energy production, and to better understand atmosphere-ocean interactions affecting weather and climate.