Australian Laureate Fellowships - Grant ID: FL110100281
Funder
Australian Research Council
Funding Amount
$2,777,066.00
Summary
Large-scale statistical machine learning. This research program aims to develop the science behind statistical decision problems as varied as web retrieval, genomic data analysis and financial portfolio optimisation. Advances will have a very significant practical impact in the many areas of science and technology that need to make sense of large, complex data streams.
Optical tweezers as a micro-rheological probe of soft surfaces. Biomembranes are more than soft containers - their dynamic flexibility plays an important role in cell function, but measurements of mechanical properties of soft surfaces are non-existent. This project develops and applies a new optical tweezers method to measure the flexibility of membranes and its effects upon the friction of nearby particles.
Promoting new reaction pathways with nonequilibrium flow. This project aims to develop a fundamental molecular level understanding of flow-induced physical and chemical reactions in liquids. Nonequilibrium molecular dynamics simulations will be used to gain insight into the mechanisms that promote reactions under shear, and how these are related to molecular structure and fluid composition. New relationships for determination of rate constants of reactions in nonequilibrium systems will also be ....Promoting new reaction pathways with nonequilibrium flow. This project aims to develop a fundamental molecular level understanding of flow-induced physical and chemical reactions in liquids. Nonequilibrium molecular dynamics simulations will be used to gain insight into the mechanisms that promote reactions under shear, and how these are related to molecular structure and fluid composition. New relationships for determination of rate constants of reactions in nonequilibrium systems will also be developed and tested. It is expected that this knowledge will enhance the capacity to control and promote reactions. This is significant for advancement of many technologies, from development of new synthetic pathways and products, to design of lubricants that can withstand extreme strain rates.Read moreRead less
X-ray imaging and magnetic resonance approach for enhanced oil recovery. This project aims to develop an efficient multi-scale modelling capability to quantify the effect of two-phase fluid flow within porous material by modelling rock wettability heterogeneity and alteration on two-phase flow performance for heterogeneous rock. Super-resolution methods combined with a deep learning approach will be used to determine a digital representation of reservoir rock, achieving an unprecedented combinat ....X-ray imaging and magnetic resonance approach for enhanced oil recovery. This project aims to develop an efficient multi-scale modelling capability to quantify the effect of two-phase fluid flow within porous material by modelling rock wettability heterogeneity and alteration on two-phase flow performance for heterogeneous rock. Super-resolution methods combined with a deep learning approach will be used to determine a digital representation of reservoir rock, achieving an unprecedented combination of resolution necessary to resolve small-scale fluid connectivity and field of view required to capture heterogeneity. The project expects to develop a workflow to populate a high-resolution model with wettability parameters by combining micro-CT imaging with nuclear magnetic resonance measurements. This improved understanding should provide significant benefits by enhancing our capability to optimise enhanced oil and gas recovery programs.Read moreRead less
Collision Avoidance in Shipping Lanes via Intelligent Sensor Data Fusion . This project aims to develop an online maritime traffic monitoring system for reliable collision/contact avoidance that exploits complementary data from high-resolution airborne sensors and surface vessel sensors. Our approach is based on optimal scheduling and fusion of the sensor data and possibly other sources of data to construct a comprehensive dynamic picture of maritime traffic, in real-time. Moreover, the proposed ....Collision Avoidance in Shipping Lanes via Intelligent Sensor Data Fusion . This project aims to develop an online maritime traffic monitoring system for reliable collision/contact avoidance that exploits complementary data from high-resolution airborne sensors and surface vessel sensors. Our approach is based on optimal scheduling and fusion of the sensor data and possibly other sources of data to construct a comprehensive dynamic picture of maritime traffic, in real-time. Moreover, the proposed methodology enables quantification of confidence in the predictions. This will provide ship owners, directly to their vessels and/or at the fleet management centres, information such as weather reports, reliable collision/no-collision warnings and avoidance strategies, on-the-fly. Read moreRead less
A coupled finite volume method for viscoelastic flow problems on highly-skewed unstructured meshes: a computational rheology revolution. Commercial tools are unavailable for 21st century industry to analyse complex flow processes involving viscoelastic materials. Using fabrication of microstructured polymer optical fibre as a key case study, a coupled finite volume methodology holds the key for the next generation of computational rheology simulators.
Maximum entropy modelling and Bayesian inference in turbulent fluid mechanics. Fluid turbulence, characterised by fluctuating properties such as velocity and density, remains one of the great unsolved problems of science, due to the difficulty of calculating the Reynolds stresses created by the turbulence. This project will bring a new technique, the maximum entropy method of Jaynes, to this challenge, for the formulation and closure of theoretical and reduced-order numerical models of turbulent ....Maximum entropy modelling and Bayesian inference in turbulent fluid mechanics. Fluid turbulence, characterised by fluctuating properties such as velocity and density, remains one of the great unsolved problems of science, due to the difficulty of calculating the Reynolds stresses created by the turbulence. This project will bring a new technique, the maximum entropy method of Jaynes, to this challenge, for the formulation and closure of theoretical and reduced-order numerical models of turbulent flows. Several well-characterised case study flows, of importance to human society, will be examined. Turbulent flow models will also be constructed by maximum-entropy and Bayesian methods directly from experimental data. The project will substantially enhance our ability to predict the behaviour of turbulent flows.Read moreRead less
Implementation of cognitive radar techniques in resource limited radar systems. Cognitive radar technology enables a multiple functional radar system to be built on a single chip, to be of high efficiency and low cost. Waveform design and scheduling play a key role in such a system. This project will investigate and design waveforms and scheduling methods for building a real cognitive radar system in the extremely high frequency band.
A Bayesian Approach to Distributed Estimation for Multi-Object Systems. This project aims to develop new signal processing techniques that facilitate autonomous technologies for environmental perception, with the ability to efficiently process large data volumes from multiple sensing modalities. Rapid advances in sensors and networks have led to a digital data deluge, from which extracting useful information presents new technological challenges and opportunities. To address this development, th ....A Bayesian Approach to Distributed Estimation for Multi-Object Systems. This project aims to develop new signal processing techniques that facilitate autonomous technologies for environmental perception, with the ability to efficiently process large data volumes from multiple sensing modalities. Rapid advances in sensors and networks have led to a digital data deluge, from which extracting useful information presents new technological challenges and opportunities. To address this development, this project seeks to develop new distributed solutions for statistical estimation, which are specifically designed for dynamic systems with multiple object states, and are inherently scalable and robust. The potential benefits include new technologies for smart cities, autonomous infrastructure, and digital productivity.Read moreRead less
A spatio-temporal partitioning approach to colloidal flows in porous media. This project aims to develop an efficient multi-scale laboratory-based modelling framework for colloidal suspensions flow in porous media by utilizing recent advances in 3D/4D image-based geometrical/topological analysis. Regional partitioning techniques based on local structural measures are used to observe the penetration/retention of colloids into identified zones. Zone-dependent colloid interaction probabilities for ....A spatio-temporal partitioning approach to colloidal flows in porous media. This project aims to develop an efficient multi-scale laboratory-based modelling framework for colloidal suspensions flow in porous media by utilizing recent advances in 3D/4D image-based geometrical/topological analysis. Regional partitioning techniques based on local structural measures are used to observe the penetration/retention of colloids into identified zones. Zone-dependent colloid interaction probabilities for computational modelling are derived from fundamental relationships. Expected outcomes of this project include a full-scale modelling capability for heterogeneous samples validated by experiment and the extraction of robust model coefficients for newly developed theory for colloid-suspension transport through porous media.Read moreRead less