274 - SPyro model for predicting fire growth with Jonathan Hodges and Jason Floyd

Modeling the consequences of a fire is what most of us do day to day. Modeling how a fire actually grows across real materials, under changing heat flux and ventilation, is the part we tend to avoid, hiding behind a prescribed design fire. That gap is exactly what we tackle with Jason Floyd and Jonathan Hodges from UL Research Institutes, the developers of S-PYRO, a new engineering pyrolysis model already included in Fire Dynamics Simulator (FDS).

We walk through the origin story in railcar fire growth modeling, where “traveling fire” assumptions and fixed HRR curves can fall apart once the compartment becomes ventilation-limited. S-PYRO takes a more practical route to predictive fire modeling: it uses cone calorimeter data (multiple heat flux levels, and optionally multiple thicknesses) and scales burning rate and time so mass consumption stays consistent while the exposure conditions come from your CFD simulation. That means the local incident heat flux in FDS drives which cone response the surface should follow, instead of guessing the fuel output up front.

The hardest detail is also the most important: flame feedback. A burning sample sees extra heat flux from its own flame, and the cone heater flux can be partially absorbed by soot and hot gases. Jason and Jonathan explain how they built a “digital twin” cone using high-resolution FDS runs to generate lookup tables for flame heat flux and cone absorption across different fuel properties, plus how validation against large public cone databases reveals where the self-similarity assumption holds and where it breaks (dripping polymers, delamination, collapse, long-duration char oxidation, and complex assemblies).

If you’ve ever wanted fire growth modeling that’s more realistic than a scripted curve but more approachable than full kinetics, this is the roadmap. Subscribe for more fire science, share this episode with a colleague who uses FDS, and leave a review if it helps. What material or scenario would you try S-PYRO on first?

Resources:

• Jack Bono Award winning paper "An Approach for Flux and Thickness Scaling of Cone Calorimeter Data for Predicting the Pyrolysis of Materials" (improved SPyro)
• An engineering model for the pyrolysis of materials
• FDS github with all the resources

Cover image from a paper: J. Hodges et al., MODELING THE FIRE GROWTH IN A RAILCAR, Interflam 2025

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