Boiling heat transfer modelling
Assessing boiling closures with XBOIL and developing mixture-of-engineering-models approaches for heat flux, bubble departure, and critical heat flux prediction.
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I am a researcher in the Thermofluids group at the School of Mechanical, Aerospace and Civil Engineering, UoS. My research focuses on computational modelling of turbulent flows and boiling heat transfer phenomena with particular applications for aerodynamics and power generation with nuclear reactors.
Interests
- Computational fluid dynamics
- Data assimilation
- Machine learning
- Engineering design optimisation
- Aerodynamics
- Nuclear thermal hydraulics
Education
PhD Aerospace Engineering
Univeristy of Sheffield
MEng Aerospace Engineering
University of Sheffield
Global Engineering Education Exchange programme
University of Michigan
Research Areas
My research develops computational modelling methods for complex fluid dynamics, with a focus on turbulence, boiling heat transfer, and data-driven model improvement. I combine physics-based simulation, experimental data, uncertainty quantification, and machine learning to make engineering predictions more accurate, interpretable, and useful.
Data assimilation for turbulent flows
Using uncertainty-informed sensor placement and multi-sensor data assimilation to reconstruct separated turbulent flows from sparse measurements.
Read more →Data-driven aerodynamic shape optimisation
Using high-fidelity-informed RANS corrections to improve optimisation reliability while retaining RANS-level cost and verifying designs with IDDES.
Read more →Also on this site
- Talks Recent and upcoming presentations.
- Codes Research software and code contributions.
- Student projects Past supervised undergraduate projects.
Latest Publications
(2026).
Towards data-driven boiling models using a mixture-of-experts approach.
18th International Heat Transfer Conference.
(2026).
Evaluations of boiling heat flux partitioning and bubble parameter models using a 0-D framework.
International Journal of Heat and Mass Transfer, Elsevier.
(2025).
Assessment of Heat Flux Partitioning Approaches for the Prediction of Subcooled Flow Boiling.
Proceedings of 21st International Topical Meeting on Nuclear Reactor Thermal Hydraulics.
(2024).
A Hybrid RANS-LES Dataset for Data-Driven Turbulent Mean Flow Reconstruction.
CUFS, 2024.
(2024).
PhD Thesis: Data-driven Augmentation of Turbulence Models for Complex Fluid Flows.
University of Sheffield.
Recent Talks
2026 Annual Technical Meeting of CCP/SIG Nuclear Thermal Hydraulics
Talk title: Data-driven boiling predictions: introducing the neural-gated mixture of engineering models approach
2025 Annual Technical Meeting of CCP-NTH
Talk title: Comprehensive Assessments of Boiling Models through Combinatorial Variations of Heat Flux Partitioning and Bubble Parameter Closures
2024 DAFoam Workshop
Talk title: Data-driven turbulence model augmentation using DAFoam: sparse sensor placement, and aerodynamic shape optimisation