Hydrogen combustion, high-fidelity CFD and the future of aircraft propulsion are the focus of this conversation with Dr. Daniel Mira, Head of the Propulsion Technologies Group at the Barcelona Supercomputing Center. Neil and Dani discuss why reacting flows are so difficult to simulate, how hydrogen changes combustion and aircraft design, the limits of RANS, LES and DNS, GPU-native solvers, coding agents and AI surrogate models.
Full episode, corrected transcript and resources:
https://neilashton.co.uk/podcasts/s4-e6-daniel-mira-on-hydrogen-combustion-modelling-and-future-propulsion/
Topics
Why reacting flows are so computationally difficult
Hydrogen versus hydrocarbon combustion
When hydrogen could reach commercial aviation
How engines and aircraft must be redesigned
Industrial trust in high-fidelity combustion CFD
RANS, LES and DNS for reacting flows
Chemistry, load balancing and computational cost
Wall modelling in combustion LES
GPU acceleration and solver redesign
Coding agents for scientific software
AI surrogate models and digital engineering workflows
Selected resources
Daniel Mira and the Propulsion Technologies Group
https://ptg.bsc.es/?p=44
Propulsion Technologies Group — research lines
https://ptg.bsc.es/research-lines/
BSC — Combustion research
https://www.bsc.es/research-development/research-areas/engineering-simulations/combustion
Center of Excellence in Combustion (CoEC)
https://coec-project.eu/
High-fidelity simulations of the mixing and combustion of a technically premixed hydrogen flame
https://upcommons.upc.edu/entities/publication/08a27c10-cb13-4357-a3ab-8e9ec1d706cc
Chapters
00:00 Podcast intro
00:39 Introducing Daniel Mira
03:00 Conversation begins
04:55 Why combustion CFD is so hard
10:23 Daniel’s path into hydrogen and jet-engine combustion
12:48 Hydrogen versus hydrocarbon combustion
17:58 Industrial adoption of hydrogen
20:54 Gas turbines, aviation and fuel infrastructure
25:35 How jet engines must change
30:43 Redesigning the whole aircraft
34:46 What will trigger commercial adoption?
37:27 Why aerospace projects take a decade
42:14 RANS, LES and DNS for reacting flows
44:31 Replacing expensive tests with high-fidelity CFD
46:01 The biggest accuracy gaps in combustion LES
49:26 Where the computational cost goes
52:06 Chemistry, species and source-term bottlenecks
55:35 Wall modelling in combustion LES
59:49 GPUs, algorithms and solver redesign
01:08:52 Can coding agents accelerate combustion CFD?
01:12:27 AI surrogate models for combustion
01:24:20 Closing thoughts
