## Biography

Daniel graduated from The University of Melbourne with a BE (Mechatronics) / BCS in 2003, and then went on to complete his PhD (Aeronautics) at the California Institute of Technology in 2009. Then he was a postdoctoral scholar researching climate physics at the NASA Jet Propulsion Laboratory before joining the department as Lecturer in 2012.## Research interests

Daniel is interested in interdisciplinary research centred around fluid mechanics, including large-eddy simulation and modelling, direct numerical simulation, wall-bounded turbulence (especially over roughness), turbulent convection and planetary boundary layers. Daniel's research is fundamental in nature, aimed at developing and improving the predictive tools used in engineering and meteorology.Turbulence is prevalent in engineering and nature but despite a century of research, the prediction of turbulent flows remains a challenge. Advances at the fundamental level are therefore necessary and, towards this end, Daniel's research distils the complexity of turbulent flows into simple yet essential configurations that, aided by simulation and modelling, yield insights into the inner workings of turbulence.

## Open positions

## Postdocs

- Amirreza Rouhi (2017–; PhD Queen's University)

Spatially varying roughness.

- Davide Modesti (2018–; PhD University of Rome "La Sapienza")

Riblets. - Michael MacDonald (2017; PhD University of Melbourne)

Riblets.

## Graduate students

- Sebastian Endrikat (2017–)

Riblets. - Melissa Kozul (2014–)

The turbulent boundary layer studied using novel numerical frameworks.

Present: Norwegian University of Science and Technology (NTNU)

- M. MacDonald (2017)

Numerical simulation of turbulent flows over rough surfaces.

PhD thesis University of Melbourne

- C. S. Ng (2017)

Boundary layer and bulk dynamics in vertical natural convection.

PhD thesis University of Melbourne

C. S. Ng (2013)

Direct numerical simulation of turbulent natural convection bounded by differentially heated vertical walls.

MPhil thesis University of Melbourne

Present: University of Twente

## Refereed journal articles

- D. I. Pullin, N. Hutchins & D. Chung (2017)

Turbulent flow over a long flat plate with uniform roughness.

Phys. Rev. Fluids 2:082601(R) DOI:10.1103/PhysRevFluids.2.082601 - I. Gat, G. Matheou, D. Chung & P. E. Dimotakis (2017)

Incompressible variable-density turbulence in an external acceleration field.

J. Fluid Mech. 827:506–535 DOI:10.1017/jfm.2017.490 - C. S. Ng, A. Ooi, D. Lohse & D. Chung (2017)

Changes in the boundary-layer structure at the edge of the ultimate regime in vertical natural convection.

J. Fluid Mech. 825:550–572 DOI:10.1017/jfm.2017.387 - M. MacDonald, D. Chung, N. Hutchins, L. Chan, A. Ooi & R. García-Mayoral (2017)

The minimal-span channel for rough-wall turbulent flows.

J. Fluid Mech. 816:5–42 DOI:10.1017/jfm.2017.69 - D. Krug, D. Chung, J. Philip & I. Marusic (2017)

Global and local aspects of entrainment in temporal plumes.

J. Fluid Mech. 812:222–250 DOI:10.1017/jfm.2016.786 - M. MacDonald, L. Chan, D. Chung, N. Hutchins & A. Ooi (2016)

Turbulent flow over transitionally rough surfaces with varying roughness densities.

J. Fluid Mech. 804:130–161 DOI:10.1017/jfm.2016.459 - M. Kozul, D. Chung & J. P. Monty (2016)

Direct numerical simulation of the incompressible temporally developing turbulent boundary layer.

J. Fluid Mech. 796:437–472 DOI:10.1017/jfm.2016.207 - K. Owen, R. A. Dunlop, J. P. Monty, D. Chung, M. J. Noad, D. Donnelly, A. W. Goldizen & T. Mackenzie (2016)

Detecting surface-feeding behavior by rorqual whales in accelerometer data.

Mar. Mammal Sci. 32:327–348 DOI:10.1111/mms.12271 - D. Chung, I. Marusic, J. P. Monty, M. Vallikivi & A. J. Smits (2015)

On the universality of inertial energy in the log layer of turbulent boundary layer and pipe flows.

Exp. Fluids 56:141 DOI:10.1007/s00348-015-1994-7 - D. Chung, L. Chan, M. MacDonald, N. Hutchins & A. Ooi (2015)

A fast direct numerical simulation method for characterising hydraulic roughness.

J. Fluid Mech. 773:418–431 DOI:10.1017/jfm.2015.230 - L. Chan, M. MacDonald, D. Chung, N. Hutchins & A. Ooi (2015)

A systematic investigation of roughness height and wavelength in turbulent pipe flow in the transitionally rough regime.

J. Fluid Mech. 771:743–777 DOI:10.1017/jfm.2015.172 - C. S. Ng, A. Ooi, D. Lohse & D. Chung (2015)

Vertical natural convection: application of the unifying theory of thermal convection.

J. Fluid Mech. 764:349–361 DOI:10.1017/jfm.2014.712 - E. K. W. Poon, A. S. H. Ooi, M. Giacobello, G. Iaccarino & D. Chung (2014)

Flow past a transversely rotating sphere at Reynolds numbers above the laminar regime.

J. Fluid Mech. 759:751–781 DOI:10.1017/jfm.2014.570 - G. Matheou & D. Chung (2014)

Large-eddy simulation of stratified turbulence. Part II: application of the stretched-vortex model to the atmospheric boundary layer.

J. Atmos. Sci. 71:4439–4460 DOI:10.1175/JAS-D-13-0306.1 - D. Chung & G. Matheou (2014)

Large-eddy simulation of stratified turbulence. Part I: a vortex-based subgrid-scale model.

J. Atmos. Sci. 71:1863–1879 DOI:10.1175/JAS-D-13-0126.1 - D. Chung, J. P. Monty & A. Ooi (2014)

An idealised assessment of Townsend's outer-layer similarity hypothesis for wall turbulence.

J. Fluid Mech. 742:R3 DOI:10.1017/jfm.2014.17 - C. S. Ng, D. Chung & A. Ooi (2013)

Turbulent natural convection scaling in a vertical channel.

Int. J. Heat Fluid Flow 44:554–562 DOI:10.1016/j.ijheatfluidflow.2013.08.011 - K. Sušelj, J. Teixeira & D. Chung (2013)

A unified model for moist convective boundary layers based on a stochastic eddy-diffusivity/mass-flux parameterization.

J. Atmos. Sci. 70:1929–1953 DOI:10.1175/JAS-D-12-0106.1 - B. Ganapathisubramani, N. Hutchins, J. P. Monty, D. Chung & I. Marusic (2012)

Amplitude and frequency modulation in wall turbulence.

J. Fluid Mech. 712:61–91 DOI:10.1017/jfm.2012.398 - D. Chung, G. Matheou & J. Teixeira (2012)

Steady-state large-eddy simulations to study the stratocumulus to shallow-cumulus cloud transition.

J. Atmos. Sci. 69:3264–3276 DOI:10.1175/JAS-D-11-0256.1 - D. Chung & G. Matheou (2012)

Direct numerical simulation of stationary homogeneous stratified sheared turbulence.

J. Fluid Mech. 696:434–467 DOI:10.1017/jfm.2012.59 - D. Chung & J. Teixeira (2012)

A simple model for stratocumulus to shallow-cumulus cloud transitions.

J. Climate 25:2547–2554 DOI:10.1175/JCLI-D-11-00105.1 - G. Matheou, D. Chung, L. Nuijens, B. Stevens & J. Teixeira (2011)

On the fidelity of large-eddy simulation of shallow precipitating cumulus convection.

Mon. Wea. Rev. 139:2918–2939 DOI:10.1175/2011MWR3599.1 - D. Chung & B. J. McKeon (2010)

Large-eddy simulation of large-scale structures in long channel flow.

J. Fluid Mech. 661:341–364 DOI:10.1017/S0022112010002995 - D. Chung & D. I. Pullin (2010)

Direct numerical simulation and large-eddy simulation of stationary buoyancy-driven turbulence.

J. Fluid Mech. 643:279–308 DOI:10.1017/S0022112009992801 - D. Chung & D. I. Pullin (2009)

Large-eddy simulation and wall modelling of turbulent channel flow.

J. Fluid Mech. 631:281–309 DOI:10.1017/S0022112009006867

## Refereed conference papers

- M. MacDonald, A. Ooi, N. Hutchins & D. Chung (2017)

Direct numerical simulation of high aspect ratio spanwise-aligned bars.

Proceedings of the 10th International Symposium on Turbulence and Shear Flow Phenomena, 9A-5, Chicago PDF - L. Chan, M. MacDonald, D. Chung, N. Hutchins & A. Ooi (2017)

Analysis of the coherent and turbulent stresses of a numerically simulated rough wall pipe.

J. Phys.: Conf. Ser. 822:012011 DOI:10.1088/1742-6596/822/1/012011 - N. George, A. Ooi, K. Moinuddin, G. Thorpe, I. Marusic & D. Chung (2016)

Direct numerical simulation of a turbulent line plume in a confined region.

Proceedings of the 20th Australasian Fluid Mechanics Conference, 668, Perth PDF - C. S. Ng, A. Ooi & D. Chung (2016)

Potential energy in vertical natural convection.

Proceedings of the 20th Australasian Fluid Mechanics Conference, 727, Perth PDF - M. MacDonald, D. Chung, N. Hutchins, L. Chan, A. Ooi & R. García-Mayoral (2016)

The minimal channel: a fast and direct method for characterising roughness.

J. Phys.: Conf. Ser. 708:012010 DOI:10.1088/1742-6596/708/1/012010 - D. Chung, M. MacDonald, L. Chan, N. Hutchins & A. Ooi (2015)

A fast and direct method for characterizing hydraulic roughness.

Proceedings of the 9th International Symposium on Turbulence and Shear Flow Phenomena, 2A-1, Melbourne PDF - L. Chan, M. MacDonald, D. Chung, N. Hutchins & A. Ooi (2015)

Investigation of a turbulent flow from the transitionally rough regime to the fully rough regime.

Proceedings of the 9th International Symposium on Turbulence and Shear Flow Phenomena, 2A-3, Melbourne PDF - J. Philip, I. Bermejo-Moreno, D. Chung & I. Marusic (2015)

Characteristics of the entrainment velocity in a developing wake.

Proceedings of the 9th International Symposium on Turbulence and Shear Flow Phenomena, 9C-5, Melbourne PDF - L. Chan, M. MacDonald, D. Chung, N. Hutchins & A. Ooi (2014)

Numerical simulation of a rough-wall pipe from the transitionally rough regime to the fully rough regime.

Proceedings of the 19th Australasian Fluid Mechanics Conference, 319, Melbourne PDF - M. Kozul & D. Chung (2014)

Direct numerical simulation of the incompressible temporally developing turbulent boundary layer.

Proceedings of the 19th Australasian Fluid Mechanics Conference, 332, Melbourne PDF - M. MacDonald, D. Chung, N. Hutchins, L. Chan, A. Ooi, G. I. Park & B. Pierce (2014)

A comprehensive DNS database to investigate measures of roughness and LES wall models.

Proceedings of the 15th Center for Turbulence Research Summer Program, 445–455, Stanford University PDF - C. S. Ng, D. Chung & A. Ooi (2012)

Direct numerical simulation of natural convection in a vertical channel.

Proceedings of the 18th Australasian Fluid Mechanics Conference, 222, Launceston PDF

## Other

- G. Matheou, D. Chung & J. Teixeira (2017)

Large-eddy simulation of a stratocumulus cloud.

Phys. Rev. Fluids 2:090509 DOI:10.1103/PhysRevFluids.2.090509 - G. Matheou & D. Chung (2012)

Direct numerical simulation of stratified turbulence.

Phys. Fluids 24:091106 DOI:10.1063/1.4747156

## Awards

- Gallery of Fluid Motion Award (2016)

Visual aesthetic and technical insight of poster (co-authors: G. Matheou, J. Teixeira)

American Physical Society Division of Fluid Dynamics Meeting, Portland - Milton Van Dyke Award (2011)

Visual aesthetic and technical insight of poster (co-author: G. Matheou)

American Physical Society Division of Fluid Dynamics Meeting, Baltimore - W. F. Ballhaus Prize (2009)

Outstanding aeronautics doctoral dissertation

Graduate Aerospace Laboratories California Institute of Technology - R. B. Chapman Memorial Award (2009)

Distinguished hydrodynamics research

Division of Engineering and Applied Science California Institute of Technology

## Open positions

Potential PhD students who are interested in the projects below can email Daniel at daniel.chung@unimelb.edu.au with a cover letter, CV and two academic references. The PhD student will have an undergraduate degree in areas related to fluid mechanics such as aeronautical engineering, mechanical engineering, mathematics or physics. The PhD student will have a strong interest in fundamental fluid mechanics."The purpose of computing is insight, not numbers." R. W. Hamming

### Tailoring aircraft surface textures to minimise drag (PhD student)

Turbulent skin friction drag, occurring whenever a fluid such as air or water flows over a surface at high speeds, dictates the energy expenditure of many important engineering systems. For example, over 50% of the drag on a commercial aircraft is due to skin friction. This means that a large proportion of the fuel expended and emissions produced in this case are directly attributable to skin friction drag.Riblets (figure 1), a microscale surface texture modelled after shark skin, are an emergent technology designed to reduce the skin friction drag of aircraft. Efforts to optimise riblets have, to date, been stymied by the prohibitive cost and long turnaround times of experiments and simulations. The situation is compounded by an incomplete understanding of the underlying flow physics.

Recently, however, researchers at the University of Melbourne and their collaborators have made major breakthroughs in overcoming these obstacles. In particular, a novel simulation method for determining the drag of complex surface topologies with fast turnaround times has been developed and the flow mechanism that currently limits the drag-reducing capacity of riblets has been identified.

This project will build on these breakthroughs to develop, for the first time, a physics-informed virtual rapid prototyping framework that places the problem of riblet optimisation firmly within grasp. The outcomes of this project will be 1) the discovery of new optimised riblet geometries and 2) the advancement of turbulent drag-reduction physics.

### Turbulent flow over surfaces with spatially varying roughness (PhD student)

Rough-wall turbulent boundary layers are formed where fluid flows over non-smooth surfaces. These flows profoundly influence our daily lives. Examples include: wind blowing over the Earth's surface, water flowing through a fouled pipe, and aircraft, ships and submarines in motion through the atmosphere or ocean. Though engineers, meteorologists, climate modellers and atmospheric scientists have developed models for evenly distributed roughness, such an idealised configuration is rarely encountered in practise. Rather, the large majority of boundary layer flows are characterised by abrupt changes in roughness, for example at the edges of forests or wind-farms, crop boundaries, land-water interfaces, coral reefs, localised patches of bio-fouling on a ship's hull or at rivets on aircraft (figure 2). In these cases, the common set of empirically based rules that are applied so widely for evenly distributed roughness offer little insight into the complex physics of heterogeneous roughness.The current socio-economic climate demands that these flows be predicted with ever increasing accuracy: climate and meteorological models should faithfully predict the influence of changes in surface topology; drag and fuel consumption predictions for aircraft should accurately take into account the influence of roughness heterogeneity; and gas turbine and wind turbine operators must be capable of predicting the performance degradation in the presence of turbine fouling.

This project will adopt a unified approach that exploits a combination of unique state-of-the-art experimental facilities, high fidelity measurement techniques and novel high resolution numerical simulations to study the influence of heterogeneous roughness on wall bounded turbulent flows. The computational component of this project will provide an unprecedented access not only to the mean velocity but also to the turbulent statistics in the vicinity of roughness transitions. This information will improve our understanding and predictive capability of turbulent flows over heterogeneous roughness.