David Stewart
DPhil Student · Aerothermal Engineering · Oxford Thermofluids Institute

I build physics-grounded machine learning models for aerospace thermal-fluid systems.

Final-year DPhil student at the University of Oxford, working with Rolls-Royce to make liquid hydrogen jet engines thermally viable. My research combines hands-on aerothermal engineering with applied machine learning, spanning wind tunnel testing, CFD validation, and surrogate-model-driven design optimisation.

Key Achievements

A CFD-validated, ML-optimised heat exchanger predicting over 200% performance improvement

Conceptualised a Novel Heat Exchanger for Liquid Hydrogen Jet Engines Experimentally Tested a Reference Geometry Validated CFD Against Experimental Data Optimised the Novel Design via ML-Driven Methods Now Quantifying System-Level Engine Impact
Core Skills

Physical rigour, computational leverage

CFD (ANSYS Fluent) Experimental Rig Design & Testing Bayesian Optimisation Gaussian Process Surrogate Modelling Multi-Objective Optimisation (NSGA-II) Design of Experiments (DoE) Sensitivity Analysis & Uncertainty Quantification MATLAB & Python Low-Order Thermal Modelling CAD (Autodesk Inventor) Instrumentation & Control (LabVIEW)
DPhil Research Journey

Investigation of Surface Heat Exchangers (HX) for Jet Engines

A six-stage research programme sponsored by Rolls-Royce (industrial supervisor: Prof. Marko Bacic) and supervised by Prof. John Coull & Prof. Peter Ireland — click any stage to explore it in full.

See the full research journey, methodology & publications →