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Research Topic : Productive/Technical Efficiency
Socio-Economic Objective : Transport Energy Efficiency
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  • Active Funded Activity

    Discovery Projects - Grant ID: DP240103015

    Funder
    Australian Research Council
    Funding Amount
    $574,488.00
    Summary
    Impact of roughness on adverse pressure gradient turbulent boundary layers. This project aims to develop a novel technique for measuring time-resolved fluid velocity vector fields in high-speed flows to investigate rough wall turbulence in adverse pressure gradient environments in unprecedented detail. By using this innovative instrument to study these widespread but poorly understood turbulent flows in power generation and transport, the project seeks to generate new knowledge. Expected outcome .... Impact of roughness on adverse pressure gradient turbulent boundary layers. This project aims to develop a novel technique for measuring time-resolved fluid velocity vector fields in high-speed flows to investigate rough wall turbulence in adverse pressure gradient environments in unprecedented detail. By using this innovative instrument to study these widespread but poorly understood turbulent flows in power generation and transport, the project seeks to generate new knowledge. Expected outcomes include the development of a new instrument and fundamental knowledge leading to improved designs with higher efficiencies in power generation and transport, resulting in significant benefits such as increased energy security, reduced greenhouse gas emissions, and improved quality of life for individuals and society.
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    Active Funded Activity

    Discovery Projects - Grant ID: DP230100555

    Funder
    Australian Research Council
    Funding Amount
    $432,307.00
    Summary
    Gas-enriched slippery surfaces. This project will exploit novel experimental and simulations approaches to investigate gas enrichment at liquid-liquid interfaces, and its effect on interfacial slip. The outcomes of the project will be a deeper understanding of oil-water interfaces capturing the presence of interfacial gas layers, slippery surfaces with superior drag reducing and fouling reducing properties, and control over nanobubble formation under flow. The new surfaces will have potential ap .... Gas-enriched slippery surfaces. This project will exploit novel experimental and simulations approaches to investigate gas enrichment at liquid-liquid interfaces, and its effect on interfacial slip. The outcomes of the project will be a deeper understanding of oil-water interfaces capturing the presence of interfacial gas layers, slippery surfaces with superior drag reducing and fouling reducing properties, and control over nanobubble formation under flow. The new surfaces will have potential application in improving the energy efficiency of microfluidic and multiphase flow. Benefits are expected in terms of reduced emissions, fuel cost and pollution related to transport of goods by sea, and extraction of oil from rocks.
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    Active Funded Activity

    Discovery Projects - Grant ID: DP240101743

    Funder
    Australian Research Council
    Funding Amount
    $457,359.00
    Summary
    An adaptive surface for improved modelling of rough wall bounded turbulence. This project aims to improve the prediction of drag where fluid flows over rough surfaces. This is a significant problem, with the uncertainty in drag penalty prediction for shipping alone exceeding ten billion dollars annually. The societal importance of these flows demands action, yet novel approaches must be sought to efficiently explore the wide range of roughness types encountered in practice. An adaptive surface i .... An adaptive surface for improved modelling of rough wall bounded turbulence. This project aims to improve the prediction of drag where fluid flows over rough surfaces. This is a significant problem, with the uncertainty in drag penalty prediction for shipping alone exceeding ten billion dollars annually. The societal importance of these flows demands action, yet novel approaches must be sought to efficiently explore the wide range of roughness types encountered in practice. An adaptive surface is proposed, where a roughness configuration can be dialled in at the press of a button, to rapidly converge on improved models. A key outcome of this project will be improved predictive models of drag for rough wall flows. Benefits will include improved efficiencies and reduced emissions across a wide range of industries.
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    Active Funded Activity

    Discovery Early Career Researcher Award - Grant ID: DE230100754

    Funder
    Australian Research Council
    Funding Amount
    $426,154.00
    Summary
    Drag Prediction over Rough Surfaces using Hardware-Accelerated Simulations. This project aims to uncover the relationship between roughness topography and drag by utilising high-performance and efficient hardware acceleration. This project expects to generate new knowledge in the area of rough-wall turbulent boundary layer by using state-of-the-art hardware accelerated high fidelity simulations and machine learning techniques to identify important roughness parameters. Expected outcomes of this .... Drag Prediction over Rough Surfaces using Hardware-Accelerated Simulations. This project aims to uncover the relationship between roughness topography and drag by utilising high-performance and efficient hardware acceleration. This project expects to generate new knowledge in the area of rough-wall turbulent boundary layer by using state-of-the-art hardware accelerated high fidelity simulations and machine learning techniques to identify important roughness parameters. Expected outcomes of this project include the development of a novel, more accurate, and robust model to predict drag. This would lead to improved data-driven policies for more sustainable and profitable airline and maritime industries.
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    Active Funded Activity

    Discovery Projects - Grant ID: DP230100081

    Funder
    Australian Research Council
    Funding Amount
    $350,000.00
    Summary
    Driving Towards Greener and Safer Roads using Big Spatiotemporal Data. This project aims to design novel techniques for using big spatiotemporal data to reduce the impact of road transport on the environment and improve road safety. This project expects to address key challenges and lay scientific foundations of using the big data for developing a next-generation eco-friendly navigation system and increasing situational awareness for road transport safety. Expected outcomes of this project inclu .... Driving Towards Greener and Safer Roads using Big Spatiotemporal Data. This project aims to design novel techniques for using big spatiotemporal data to reduce the impact of road transport on the environment and improve road safety. This project expects to address key challenges and lay scientific foundations of using the big data for developing a next-generation eco-friendly navigation system and increasing situational awareness for road transport safety. Expected outcomes of this project include novel big data management and analytics techniques, and new edge computing models for vehicular networks. The success of this project should bring several key benefits including reducing greenhouse gas emissions on roads, facilitating urban planning, and improving road safety.
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    Active Funded Activity

    Discovery Projects - Grant ID: DP240102189

    Funder
    Australian Research Council
    Funding Amount
    $687,578.00
    Summary
    Safe and efficient eco-driving using connected and automated vehicles. This project aims to solve the paradox of trading off liveability for mobility by simultaneously reducing traffic congestion, vehicle energy consumption, and emission. This project is expected to generate fundamental knowledge and powerful tools on utilising connected and automated vehicles to help individuals become green drivers. Expected outcomes include ground-breaking models capable of holistically optimising traffic ef .... Safe and efficient eco-driving using connected and automated vehicles. This project aims to solve the paradox of trading off liveability for mobility by simultaneously reducing traffic congestion, vehicle energy consumption, and emission. This project is expected to generate fundamental knowledge and powerful tools on utilising connected and automated vehicles to help individuals become green drivers. Expected outcomes include ground-breaking models capable of holistically optimising traffic efficiency, energy consumption and emission, and innovative control strategies and policies that focus on energy efficiency and environment protection. This research will bring a wide range of substantial national benefits related to mobility, public health, environmental protection, and energy security.
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    Showing 1-6 of 6 Funded Activites

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