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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Harnessing the Power of Wind: Revolutionising Wind Farm Optimisation. This project aims to develop a rigorous, efficient and accurate framework for optimisation of control policies for complete wind farms. It expects to generate new knowledge in data-driven physics informed transient aerodynamic and structural modelling of entire wind farms, generation of low order yet sufficiently accurate models using machine learning, and game-theoretic and model predictive control techniques for operation of ....Harnessing the Power of Wind: Revolutionising Wind Farm Optimisation. This project aims to develop a rigorous, efficient and accurate framework for optimisation of control policies for complete wind farms. It expects to generate new knowledge in data-driven physics informed transient aerodynamic and structural modelling of entire wind farms, generation of low order yet sufficiently accurate models using machine learning, and game-theoretic and model predictive control techniques for operation of an entire wind farm. Expected outcomes are engineering tools to tackle wind farm inefficiencies totalling $700m/year in Australia alone, contributing to energy stability, security and lowered emissions aligned to the National Science and Research Priority ‘Energy’.Read moreRead less