Dylan Rubini
Postdoctoral Researcher in Multiphysics Modelling and Machine Learning at the University of Oxford and Junior Research Fellow in Engineering at St Anne's College.

About Me
Multiphysics Modelling Engineer specialising in AI-accelerated computational modelling. Passionate about developing advanced numerical solutions to solve high-impact, complex, and multidisciplinary engineering challenges in the energy transition.
Selected Research Projects
I have worked on a variety of projects, including, but not limited to:
- Agentic LLMs for Science: Automating tasks in computational science using agentic LLMs.
- Accelerating Chemically Reacting Flow Simulations: Using machine learning to elegantly speed up simulations of chemically reacting flows by three orders of magnitude.
- Developing a 3D Viscous Unstructured Turbomachinery Flow Solver: Creating an unstructured mesh flow solver designed for both multiple GPUs and CPUs.
- Chemical Kinetic Solvers with Embedded Multi-Objective Optimizers: Developing solvers that incorporate multi-objective optimization techniques for improved reaction performance.
- High-Fidelity Computational Fluid Dynamics (CFD): Using high-fidelity CFD to investigate complex aerothermal, supersonic, highly turbulent interactions in a novel turbomachinery concept aimed at decarbonizing over 40 high-temperature processes.
Selected Publications
- ASME JTA Novel Axial Energy-Imparting Turbomachine for High-Enthalpy Gas Heating: Robustness of the Aerodynamic Design (**Best Paper Award**)ASME Journal of Turbomachinery, Nov 2023
- GPPSDecarbonisation of High-Temperature Endothermic Chemical Reaction Processes using a Novel Turbomachine: Robustness of the Concept to Feed Variability (**Best Paper Award**)Journal of the Global Power and Propulsion Society, May 2024