Dr Xiaobing Yu

• Lecturer, University of New South Wales (UNSW)
• PhD (UNSW, 2021)

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Dr Yu’s research lies at the intersection of metallurgical engineering, multiphase flow simulation, machine learning, and low-carbon ironmaking technologies. His work focuses on developing physically consistent, industrial-scale numerical models to understand in-reactor flow–reaction–heat transfer mechanisms, particularly in high-temperature metallurgical systems. He studied metallurgy under the supervision of Prof. Zongshu Zou at NEU (2012–2017) for his Master's and PhD degrees (discontinued due to a change in circumstances) in Metallurgy, then joined the School of Chemical Engineering at UNSW as a PhD candidate (under the supervision of Prof. Yansong Shen) on a full scholarship in 2017.

Over the past decade, he has designed and developed a systematic modelling framework for complex processing and transport systems​​​​​, specifically ironmaking and steelmaking, covering:

• Fluent-based steady and dynamic blast furnace models
• Blast furnace raceway-scale and hearth-scale sub-models
• Heat and mass balance modelling
• Burden layer structure modelling
• Gas residence time analysis
• Central coke and nut coke charging strategies
• Carbon composite briquette charging
• Oxygen enrichment operation
• Hydrogen shaft injection modelling
• Transient-state blast furnace/shaft furnace modelling
• Evaluation of geometric and operational parameters (e.g., shaft angle effects)

More recently, his research has extended to:

• Hydrogen-assisted ironmaking and in-furnace dynamic behaviours
• Gas residence time modelling and reducing gas utilisation efficiency under shaft hydrogen injection
• Raceway modelling of coal gasifiers, revealing reacting flow features
• CFD-based industrial-scale Basic Oxygen Furnace (BOF) modelling incorporating flow, thermal and chemical interactions
• AI-assisted mathematical model coupled with traditional CFD

Dr Yu supervises PhD and Master’s students in metallurgical process modelling and decarbonisation technologies (6 PhD students and 5 Master's students, up to 2025). His pioneering work and a suite of original computational models developed at UNSW—including the CFD raceway model, burden layer structure model for blast furnaces, hydrogen ironmaking model, transient-state blast furnace model, and AI-aided metallurgical mathematical model—laid a foundation for research conducted by PhD students and postdoctoral researchers in the laboratory. His broader research vision is to establish a mechanistically grounded modelling framework and develop predictive tools to support the transition towards low-carbon smelting systems.
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·Google Scholar: https://scholar.google.com.au/citations?user=sZYJdl8AAAAJ&hl=en
·PhD Thesis (Aug-2020): https://unsworks.unsw.edu.au/entities/publication/7ebabed1-7c22-40e9-8aee-077d00555f57
·Email: xiaobing.yu@unsw.edu.au