Dr Jun Xia
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Summary

Dr Jun Xia obtained his BEng and MSc degrees from Zhejiang University, China. He then earned his PhD from the University of Southampton, focusing on direct and dynamic large-eddy simulations of flame suppression by water mists and sprays, followed by postdoctoral research on diluted combustion at the same institution. His research not only advanced the understanding of interactions between inert, dispersing and evaporating droplets and diffusion flames using high-fidelity simulations, but also explored the fundamental differences in modeling frameworks between these burning systems and fuel-spray combustion. He subsequently joined the Centre for Advanced Powertrain and Fuels at Brunel University as an academic faculty member.

Dr Xia is a certified software engineer. He aims to better understand multi-physics engineering flow dynamics and transport phenomena in energy storage and multiphase systems. To achieve this, he utilises high-fidelity simulations supported by high-performance computing and physics-guided machine learning, which aid in the development of physics-based subgrid models.

One of his primary research interests lies in fuel droplet and spray dynamics, including flow and combustion. Interface-capturing numerical techniques, combining sharp-interface-retaining level-sets with mass-conserving volume-of-fluid methods, have been further developed to better understand the puffing and microexplosion dynamics of emulsion droplets and droplet groups, as well as their effects on fuel-air mixing and burning under convective heating. Recently, the capability of the code has been extended to handle multicomponent droplets by incorporating realistic non-ideal liquid evaporation models that account for liquid-component activities. This development enables the modeling of complex spray processes involving disruptive secondary breakup and atomisation, such as microexplosion. His other major efforts include developing an integrated simulation tool for dense, transitional and dilute spray regimes to minimise the impact of upstream boundary condition uncertainties on spray modeling, a crucial step for accurately predicting spray combustion dynamics and emissions, particularly minor species at ppm levels. Additionally, his team has utilised graphics processing units (GPUs) to accelerate computing in spray solvers and advanced Lattice Boltzmann methods to simulate low-Reynolds-number, inside-injector cavitating flows with realistic gas-liquid density ratios interacting with idealised moving needle valves.

Beyond gas-liquid two-phase flows, Dr Xia investigates gas-solid two-phase reacting flows. In collaboration with leading international research groups, high-fidelity simulation techniques have been further developed to investigate solid-fuel (coal and biomass) combustion and alkali-metal minor-species emissions, incorporating essential radiation and pyrolysis models. Chemistry tabulation methods have been developed to predict alkali-metal emissions from turbulent pulverised coal flames, quantitatively characterised via turbulence-resolving simulations. To bridge a critical knowledge gap regarding alkali-species emissions from particles during burning, his group has advanced Lattice Boltzmann methods to simulate a burning porous char particle. This work aims to clarify emissions from subgrid point-source fuel particles within macroscopic high-fidelity simulations of turbulent solid-fuel combustion.

Supported by the EPSRC, Dr Xia also utilises microscopic molecular dynamics simulations to investigate underground CO2 storage in depleted oil reservoirs, specifically examining the properties of three-phase dodecane droplets and the impacts of CO2 and H2O on droplets and films during oil recovery. Furthermore, his research has quantified the transport and thermodynamic properties of CO2/H2 mixtures across various compositions under subsurface conditions, addressing both H2 impurities in deposited CO2 and CO2 as a cushion gas in H2 storage within porous aquifers. Using molecular dynamics, his work clearly identified the anisotropic diffusion of supercritical species under these conditions, and a recurrent neural network was subsequently developed to predict the transition between anomalous and normal self-diffusion. With these fundamental knowledge gaps addressed, his group is ready for developing macroscopic models of geological flows to guide large-scale underground CO2 and H2 storage.

Responsibility

Course Director: MSc Advanced Mechanical Engineering

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