ORCID Profile
0000-0003-1558-7222
Current Organisation
University of Sydney
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In Research Link Australia (RLA), "Research Topics" refer to ANZSRC FOR and SEO codes. These topics are either sourced from ANZSRC FOR and SEO codes listed in researchers' related grants or generated by a large language model (LLM) based on their publications.
Turbulent Flows | Interdisciplinary Engineering | Fluid mechanics and thermal engineering | Statistics | Knowledge Representation and Machine Learning | Applied Statistics | Turbulent flows | Energy Generation, Conversion and Storage Engineering | Computational Fluid Dynamics | Mechanical Engineering | Fluidisation and Fluid Mechanics | Natural Resource Management |
Expanding Knowledge in the Physical Sciences | Expanding Knowledge in the Chemical Sciences | Environmental Management Systems | Emerging Defence Technologies | Hydrogen-based Energy Systems (incl. Internal Hydrogen Combustion Engines) | Biofuel (Biomass) Energy | Industrial Energy Conservation and Efficiency | Solar-Thermal Energy | Energy Conservation and Efficiency in Transport | Energy not elsewhere classified | Expanding Knowledge in Engineering | Precious (Noble) Metal Ore Exploration | Mining Land and Water Management | Space Transport
Publisher: Elsevier BV
Date: 2017
Publisher: Elsevier BV
Date: 06-2017
Publisher: MDPI AG
Date: 27-03-2014
DOI: 10.3390/EN7041899
Publisher: Elsevier BV
Date: 08-2019
Publisher: Elsevier BV
Date: 04-2022
Publisher: SAE International
Date: 05-04-2016
DOI: 10.4271/2016-01-0859
Publisher: Elsevier BV
Date: 03-2021
Publisher: Elsevier BV
Date: 04-2022
Publisher: Cambridge University Press (CUP)
Date: 17-10-2017
DOI: 10.1017/JFM.2017.653
Abstract: This paper presents a probability density function (PDF) form of the population balance equation (PBE) for polysized and polyshaped droplets and solid particles in turbulent flows. A key contribution of this paper lies in the inclusion of an explicit consideration of the inertial effects and the shape of particles in the PDF-PBE formulation. The number density is taken as a function of droplet or particle size (volume) and shape as well as space and time. Potentially, other particle properties could also be included in the formulation. Inertial effects are quantified through the Stokes number, leading to accurate modelling of the different trajectories that are followed by droplets and/or particles with different sizes and shapes. To treat these effects, a new affordable approach is proposed and referred to as the method of Stokes binning. Here, the inertial dispersed elements are accelerated due to fluid dynamic forces associated with an averaged Stokes number in each bin. The model is validated against two data sets. The first data set includes a series of numerical test cases involving the injection of polyshaped droplets ranging in size from 1 to 50 $\\unicode[STIX]{x03BC}\\text{m}$ into a turbulent jet resulting in inlet Stokes numbers ranging from 0.03 to 75.2. The second data set consists of an experimental case focusing on the dispersion of 60 and 90 $\\unicode[STIX]{x03BC}\\text{m}$ spherical droplets in a turbulent round jet, resulting in inlet Stokes numbers of 53 and 122, respectively. The results confirm the ability of the approach to accurately model the polysized and polyshaped droplet dispersion using as few as eight Stokes bins. This approach has the potential to greatly reduce the computational cost of modelling the evolution of inertial droplets and particles in turbulent flows.
Publisher: Elsevier BV
Date: 06-2020
Publisher: Bentham Science Publishers Ltd.
Date: 06-07-2010
Publisher: Elsevier BV
Date: 11-2019
Publisher: Elsevier BV
Date: 07-2018
Publisher: Informa UK Limited
Date: 20-07-2021
Publisher: Springer Science and Business Media LLC
Date: 07-10-2010
Publisher: Elsevier BV
Date: 2013
Publisher: Elsevier BV
Date: 03-2014
Publisher: Elsevier BV
Date: 2015
Publisher: Informa UK Limited
Date: 11-09-2019
Publisher: Elsevier BV
Date: 05-2012
DOI: 10.1016/J.MEDENGPHY.2011.12.025
Abstract: Pulmonary drug delivery is the focus of much research and development because of its great potential to produce maximum therapeutic benefit. Among the available options the dry powder inhaler (DPI) is the preferred device for the treatment of an increasingly erse number of diseases. However, as drug delivery from a DPI involves a complicated set of physical processes and the integration of drug formulations, device design and patient usage, the engineering development of this medical technology is proving to be a great challenge. Currently there is large range of devices that are either available on the market or under development, however, none exhibit superior clinical efficacy. A major concern is the inter- and intra-patient variability of the drug dosage delivered to the deep lungs. The extent of variability depends on the drug formulation, the device design and the patient's inhalation profile. This article reviews recent advances in DPI technology and presents the key factors which motivate and constrain the successful engineering of a universal, patient-independent DPI that is capable of efficient, reliable and repeatable drug delivery. A strong emphasis is placed on the physical processes of drug powder aerosolisation, deagglomeration, and dispersion and on the engineering of formulations and inhalers that can optimise these processes.
Publisher: Informa UK Limited
Date: 17-01-2023
Publisher: Springer Science and Business Media LLC
Date: 17-02-2012
Publisher: AIP Publishing
Date: 10-2020
DOI: 10.1063/5.0026654
Abstract: The Multiple Mapping Conditioning/Large Eddy Simulation (MMC-LES) model is extended for the first time to high-speed, compressible flow conditions and validated against non-reacting and reacting experimental data from a model supersonic combustor. The MMC-LES method solves the subgrid joint composition filtered density function through a Monte Carlo approach, and it permits a low-cost numerical implementation using a sparse distribution of stochastic Lagrangian particles. The sensitivity of results to the particle resolution is examined, and similar to past low-speed applications of MMC-LES, that sensitivity is found to be low. In comparison to the model equations for subsonic turbulent combustion conditions, the pressure work and viscous heating effects have been incorporated here to account for the effects of compressibility. As expected, the viscous heating effects are small for this flow case and can be ignored, while the pressure work is not negligible and makes a significant contribution at expansion fans and shock fronts where the magnitude of the pressure derivative term in non-reacting/reacting cases is as much as 23.8%/24.5% and 19.2%/18.6% of the stochastic particle standardized enthalpy, respectively. The MMC-LES predictions show good quantitative agreement with the available experimental data for the mean and root-mean-square of axial velocity, mean temperature, and wall pressure. Good qualitative comparison to the data is also observed for major flow characteristics, including location and size of shocks, expansion fans, and recirculation zone, and combustion characteristics such as flame lift-off distance. Although the effects of the pressure work on the mean flame lift-off distance are negligible, they have a significant influence on the predicted spatial fluctuations of the flame base.
Publisher: AIP Publishing
Date: 06-2021
DOI: 10.1063/5.0053452
Abstract: A novel volume of fluid (VoF) model called explicit volume diffusion (EVD) is developed for the simulation of interfacial flows, including those with turbulence and primary spray atomization. It is derived by volume averaging the VoF equations over an explicit, physically-defined length scale. Fluctuations at the sub-volume scales, which can arise due to interface dynamics or turbulence, are attenuated by the volume averaging process and their effects appear in unclosed terms representing sub-volume flux, sub-volume stress, and volume averaged surface tension force. Models are proposed for each of these. The sub-volume flux is closed by a gradient diffusion model and involves an explicit volume diffusion coefficient, that is, linked to the explicit length scale. The sub-volume stress closure introduces an explicit volume viscosity that can be augmented by a turbulent viscosity in turbulent flows. The volume averaged surface tension force closure is based on a fractal representation of the wrinkled sub-volume interface. To test the closures in idually and the EVD model overall, a series of two-dimensional laminar and three-dimensional turbulent interfacial shear flows and a laboratory-scale airblast spray jet are considered. Numerical convergence is demonstrated by keeping the explicit volume length scale constant while refining the numerical grid such that the numerical diffusion diminishes and becomes overwhelmed by the explicit volume diffusion. Sensitivity analyses are also conducted for variations in the explicit length scale which may be linked to the boundary layer thickness on the light fluid side of the interface.
Publisher: Elsevier BV
Date: 2021
Publisher: Springer Netherlands
Date: 2011
Publisher: Elsevier BV
Date: 10-2021
Publisher: Elsevier BV
Date: 2021
Publisher: Informa UK Limited
Date: 22-06-2007
Publisher: Elsevier BV
Date: 10-2023
Publisher: Informa UK Limited
Date: 07-06-2016
Publisher: Springer Science and Business Media LLC
Date: 29-06-2020
DOI: 10.1007/S10494-020-00188-0
Abstract: A novel multiple mapping conditioning (MMC) approach has been developed for the modelling of turbulent premixed flames including mixture inhomogeneities due to mixture stratification or mixing with the cold surroundings. MMC requires conditioning of a mixing operator on characteristic quantities (reference variables) to ensure localness of mixing in composition space. Previous MMC used the LES-filtered reaction progress variable as reference field. Here, the reference variable space is extended by adding the LES-filtered mixture fraction effectively leading to a double conditioning of the mixing operator. The model is used to predict a turbulent stratified flame and is validated by comparison with experimental data. The introduction of the second reference variable also requires modification of the mixing time scale. Two different mixing time scale models are compared in this work. A novel anisotropic model for stratified combustion leads to somewhat higher levels of fluctuations for the passive scalar when compared with the original model but differences remain small within the flame front. The results show that both models predict flame position and flame structure with good accuracy.
Publisher: Elsevier BV
Date: 10-2019
Publisher: Springer Science and Business Media LLC
Date: 12-05-2020
DOI: 10.1007/S10494-020-00140-2
Abstract: This paper presents a comparison of experimental and numerical results for a series of turbulent reacting jets where silica nanoparticles are formed and grow due to surface growth and agglomeration. We use large-eddy simulation coupled with a multiple mapping conditioning approach for the solution of the transport equation for the joint probability density function of scalar composition and particulate size distribution. The model considers inception based on finite-rate chemistry, volumetric surface growth and agglomeration. The sub-models adopted for these particulate processes are the standard ones used by the community. Validation follows the “paradigm shift” approach where elastic light scattering signals (that depend on particulate number and size), OH- and SiO-LIF signals are computed from the simulation results and compared with “raw signals” from laser diagnostics. The sensitivity towards variable boundary conditions such as co-flow temperature, Reynolds number and precursor doping of the jet is investigated. Agreement between simulation and experiments is very good for a reference case which is used to calibrate the signals. While keeping the model parameters constant, the sensitivity of the particulate size distribution on co-flow temperature is predicted satisfactorily upstream although quantitative differences with the data exist downstream for the lowest coflow temperature case that is considered. When the precursor concentration is varied, the model predicts the correct direction of the change in signal but notable qualitative and quantitative differences with the data are observed. In particular, the measured signals show a highly non-linear variation while the predictions exhibit a square dependence on precursor doping at best. So, while the results for the reference case appear to be very good, shortcomings in the standard submodels are revealed through variation of the boundary conditions. This demonstrates the importance of testing complex nanoparticle synthesis models on a flame series to ensure that the physical trends are correctly accounted for.
Publisher: Elsevier BV
Date: 2009
Publisher: Elsevier BV
Date: 2017
Publisher: Elsevier BV
Date: 03-2018
Publisher: Elsevier BV
Date: 2021
Publisher: Elsevier BV
Date: 2021
Publisher: Springer Science and Business Media LLC
Date: 19-06-2009
Publisher: Elsevier BV
Date: 2019
Publisher: Elsevier BV
Date: 2017
Publisher: Elsevier BV
Date: 2019
Publisher: Elsevier BV
Date: 08-2017
Publisher: Elsevier BV
Date: 2011
Publisher: AIP Publishing
Date: 02-2014
DOI: 10.1063/1.4864101
Abstract: This work introduces modeling of differential diffusion within the multiple mapping conditioning (MMC) turbulent mixing and combustion framework. The effect of differential diffusion on scalar variance decay is analyzed and, following a number of publications, is found to scale as Re−1/2. The ability to model the differential decay rates is the most important aim of practical differential diffusion models, and here this is achieved in MMC by introducing what is called the side-stepping method. The approach is practical and, as it does not involve an increase in the number of MMC reference variables, economical. In addition we also investigate the modeling of a more refined and difficult to reproduce differential diffusion effect – the loss of correlation between the different scalars. For this we develop an alternative MMC model with two reference variables but which also makes use of the side-stepping method. The new models are successfully validated against DNS results available in literature for homogenous, isotropic two scalar mixing.
Publisher: Elsevier BV
Date: 05-2017
Publisher: Elsevier BV
Date: 07-2022
Publisher: Springer Singapore
Date: 13-12-2017
Publisher: AIP Publishing
Date: 04-2021
DOI: 10.1063/5.0045020
Abstract: This paper presents three volume of fluid (VoF)-based methods for large eddy simulations of atomizing sprays with different treatments of the unresolved interface. The turbulent filtered VoF model uses conventional turbulent viscosity models to close the combined interfacial and turbulent sub-grid fluctuations. The hybrid turbulence filtering and artificial compression model includes an additional artificial compression term that is applied along regions where the liquid–air interface is continuous, while conventional turbulence filtering is activated in regions with discrete liquid objects. The new explicit volume diffusion model (EVD) is based on the concept of averaging the VoF equations over explicitly defined physical volumes that are independent of the numerical grid. Closure models of the sub-volume flux and stress terms introduce explicit volume diffusion and explicit volume viscosity that are physically defined and linked to the volume size. Numerical convergence is achieved by reducing the grid size while keeping the explicit volume size constant. The models are tested for two experimental atomizing spray cases with different Weber numbers. The superior numerical convergence of the EVD model is demonstrated by analysis of the mean and rms of the volume fraction and velocity fields. Two models for the surface tension force are investigated for the EVD simulations. Compared with the simple surface tension model which neglects sub-grid fluctuations, an improved volume-averaged model based on fractal properties of wrinkled sub-volume interfaces gives better predictions of the mean volume fraction relative to the experimental data but requires selection of a model constant.
Publisher: AIP Publishing
Date: 11-2011
DOI: 10.1063/1.3657085
Publisher: Elsevier BV
Date: 2019
Publisher: Elsevier BV
Date: 08-2021
Publisher: Bentham Science Publishers Ltd.
Date: 06-07-2010
Publisher: MDPI AG
Date: 31-03-2014
DOI: 10.3390/EN7042051
Publisher: Elsevier BV
Date: 12-2005
Publisher: Elsevier BV
Date: 08-2018
Publisher: Elsevier BV
Date: 07-2017
Publisher: Elsevier BV
Date: 07-2017
Location: United Kingdom of Great Britain and Northern Ireland
Start Date: 2023
End Date: 2026
Funder: Australian Research Council
View Funded ActivityStart Date: 2016
End Date: 2019
Funder: Australian Research Council
View Funded ActivityStart Date: 2018
End Date: 2022
Funder: Australian Research Council
View Funded ActivityStart Date: 2021
End Date: 2021
Funder: Defence Innovation Hub
View Funded ActivityStart Date: 2021
End Date: 2021
Funder: Mitsubishi Heavy Industries
View Funded ActivityStart Date: 2022
End Date: 2025
Funder: Australian Research Council
View Funded ActivityStart Date: 2019
End Date: 2023
Funder: Australian Research Council
View Funded ActivityStart Date: 2018
End Date: 2018
Funder: Australian Research Council
View Funded ActivityStart Date: 2012
End Date: 02-2016
Amount: $310,000.00
Funder: Australian Research Council
View Funded ActivityStart Date: 2013
End Date: 12-2015
Amount: $400,000.00
Funder: Australian Research Council
View Funded ActivityStart Date: 02-2016
End Date: 12-2019
Amount: $465,000.00
Funder: Australian Research Council
View Funded ActivityStart Date: 08-2020
End Date: 08-2025
Amount: $3,973,202.00
Funder: Australian Research Council
View Funded ActivityStart Date: 03-2013
End Date: 12-2016
Amount: $290,000.00
Funder: Australian Research Council
View Funded ActivityStart Date: 06-2023
End Date: 06-2026
Amount: $530,000.00
Funder: Australian Research Council
View Funded ActivityStart Date: 04-2018
End Date: 03-2022
Amount: $498,606.00
Funder: Australian Research Council
View Funded ActivityStart Date: 09-2022
End Date: 09-2025
Amount: $500,000.00
Funder: Australian Research Council
View Funded ActivityStart Date: 2018
End Date: 12-2019
Amount: $956,700.00
Funder: Australian Research Council
View Funded Activity